1
|
Tincani M, Gilroy SP, Dowdy A. Extensions of open science for applied behavior analysis: Preregistration for single-case experimental designs. J Appl Behav Anal 2024. [PMID: 39140415 DOI: 10.1002/jaba.2909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 07/23/2024] [Indexed: 08/15/2024]
Abstract
Open science practices are designed to enhance the utility, integrity, and credibility of scientific research. This article highlights how preregistration in open science practice can be leveraged to enhance the rigor and transparency of single-case experimental designs within an applied behavior analysis framework. We provide an overview of the benefits of preregistration including increased transparency, reduced risk of researcher bias, and improved replicability, and we review the specific contexts under which these practices most benefit the proposed framework. We discuss potential concerns with and unique considerations for preregistering experiments that use single-case designs, with practical guidance for researchers who are seeking to preregister their studies. We present a checklist as a tool for researchers in applied behavior analysis to use for preregistration and provide recommendations for our field to strengthen the contingencies for open science practices that include preregistration.
Collapse
Affiliation(s)
- Matt Tincani
- Department of Teaching and Learning, Temple University, Philadelphia, PA, USA
| | - Shawn P Gilroy
- Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
| | - Art Dowdy
- Department of Teaching and Learning, Temple University, Philadelphia, PA, USA
| |
Collapse
|
2
|
Kocak B, Akinci D'Antonoli T, Ates Kus E, Keles A, Kala A, Kose F, Kadioglu M, Solak S, Sunman S, Temiz ZH. Self-reported checklists and quality scoring tools in radiomics: a meta-research. Eur Radiol 2024; 34:5028-5040. [PMID: 38180530 DOI: 10.1007/s00330-023-10487-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 11/11/2023] [Accepted: 11/24/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVE To evaluate the use of reporting checklists and quality scoring tools for self-reporting purposes in radiomics literature. METHODS Literature search was conducted in PubMed (date, April 23, 2023). The radiomics literature was sampled at random after a sample size calculation with a priori power analysis. A systematic assessment for self-reporting, including the use of documentation such as completed checklists or quality scoring tools, was conducted in original research papers. These eligible papers underwent independent evaluation by a panel of nine readers, with three readers assigned to each paper. Automatic annotation was used to assist in this process. Then, a detailed item-by-item confirmation analysis was carried out on papers with checklist documentation, with independent evaluation of two readers. RESULTS The sample size calculation yielded 117 papers. Most of the included papers were retrospective (94%; 110/117), single-center (68%; 80/117), based on their private data (89%; 104/117), and lacked external validation (79%; 93/117). Only seven papers (6%) had at least one self-reported document (Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), or Checklist for Artificial Intelligence in Medical Imaging (CLAIM)), with a statistically significant binomial test (p < 0.001). Median rate of confirmed items for all three documents was 81% (interquartile range, 6). For quality scoring tools, documented scores were higher than suggested scores, with a mean difference of - 7.2 (standard deviation, 6.8). CONCLUSION Radiomic publications often lack self-reported checklists or quality scoring tools. Even when such documents are provided, it is essential to be cautious, as the accuracy of the reported items or scores may be questionable. CLINICAL RELEVANCE STATEMENT Current state of radiomic literature reveals a notable absence of self-reporting with documentation and inaccurate reporting practices. This critical observation may serve as a catalyst for motivating the radiomics community to adopt and utilize such tools appropriately, thereby fostering rigor, transparency, and reproducibility of their research, moving the field forward. KEY POINTS • In radiomics literature, there has been a notable absence of self-reporting with documentation. • Even if such documents are provided, it is critical to exercise caution because the accuracy of the reported items or scores may be questionable. • Radiomics community needs to be motivated to adopt and appropriately utilize the reporting checklists and quality scoring tools.
Collapse
Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey.
| | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland
| | - Ece Ates Kus
- Department of Neuroradiology, Klinikum Lippe, Lemgo, Germany
| | - Ali Keles
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Ahmet Kala
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Fadime Kose
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Mehmet Kadioglu
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Sila Solak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Seyma Sunman
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| | - Zisan Hayriye Temiz
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey
| |
Collapse
|
3
|
Lan T, Kuang S, Liang P, Ning C, Li Q, Wang L, Wang Y, Lin Z, Hu H, Yang L, Li J, Liu J, Li Y, Wu F, Chai H, Song X, Huang Y, Duan X, Zeng D, Li J, Cao H. MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study. Int J Surg 2024; 110:4648-4659. [PMID: 38729119 PMCID: PMC11325978 DOI: 10.1097/js9.0000000000001578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/25/2024] [Indexed: 05/12/2024]
Abstract
INTRODUCTION The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions. AIM To construct and evaluate a preoperative diagnostic method to predict OCLNM in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features. METHODS A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA), and survival analysis. RESULTS Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927), and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1, and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC. CONCLUSION The proposed MRI-based Resnet50 DL model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC.
Collapse
Affiliation(s)
- Tianjun Lan
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Shijia Kuang
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Peisheng Liang
- Guanghua School of Stomatology, Hospital of Stomatology, Guangdong Province Key Laboratory of Stomatology, Sun Yat-Sen University, Guangzhou
| | - Chenglin Ning
- School of Biomedical Engineering, Southern Medical University, Guangzhou
| | - Qunxing Li
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Liansheng Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Youyuan Wang
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Zhaoyu Lin
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Huijun Hu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Lingjie Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Jintao Li
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Jingkang Liu
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Yanyan Li
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Fan Wu
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Hua Chai
- School of Mathematics and Big Data, Foshan University, Foshan, Guangdong
| | - Xinpeng Song
- School of Mathematics and Big Data, Foshan University, Foshan, Guangdong
| | - Yiqian Huang
- School of Mathematics and Big Data, Foshan University, Foshan, Guangdong
| | - Xiaohui Duan
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China
| | - Jinsong Li
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| | - Haotian Cao
- Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Guangzhou
| |
Collapse
|
4
|
Zhu L, Dong H, Sun J, Wang L, Xing Y, Hu Y, Lu J, Yang J, Chu J, Yan C, Yuan F, Zhong J. Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study. Eur Radiol 2024:10.1007/s00330-024-10976-1. [PMID: 39048741 DOI: 10.1007/s00330-024-10976-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 06/18/2024] [Accepted: 07/05/2024] [Indexed: 07/27/2024]
Abstract
OBJECTIVES To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems. METHODS A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). RESULTS The test-retest repeatability analysis presented that 97.1% of features were repeatable between scan-rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems. CONCLUSION The radiomics features were non-reproducible with significant variability in values among different CT techniques. CLINICAL RELEVANCE STATEMENT Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine. KEY POINTS CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.
Collapse
Affiliation(s)
- Lan Zhu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Haipeng Dong
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jing Sun
- Department of General Surgery, Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Junjie Lu
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Jiarui Yang
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Jingshen Chu
- Department of Science and Technology Development, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Chao Yan
- Department of Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Fei Yuan
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
| |
Collapse
|
5
|
Trusova V, Karnaukhov I, Zelinsky A, Borts B, Ushakov I, Sidenko L, Gorbenko G. Radiolabeling of bionanomaterials with technetium 99m: current state and future prospects. Nanomedicine (Lond) 2024; 19:1569-1580. [PMID: 39011593 PMCID: PMC11321418 DOI: 10.1080/17435889.2024.2368454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 06/12/2024] [Indexed: 07/17/2024] Open
Abstract
Radiolabeling of bionanomaterials with technetium-99m (99mTc) has become a promising approach in combining the benefits of nanotechnology and nuclear medicine for diagnostic and therapeutic purposes. This review is intended to provide a comprehensive overview of the state-of-the-art of radiolabeling of bionanomaterials with 99mTc, highlighting the synthesis methods, labeling mechanisms, biological evaluation, physicochemical characterization and clinical applications of 99mTc-labeled bionanomaterials. Various types of nanomaterials are considered in the review, including lipid- and protein-based nanosystems, dendrimers and polymeric nanomaterials. Moreover, the review assesses the challenges presented by this emerging field, such as stability of the radiolabel, potential toxicity of the nanomaterials and regulatory aspects. Finally, promising future perspectives and areas of research development in 99mTc-labeled bionanomaterials are discussed.
Collapse
Affiliation(s)
- Valeriya Trusova
- Department of Medical Physics & Biomedical Nanotechnologies, V.N. Karazin Kharkiv National University, 4 Svobody Sq., Kharkiv61022, Ukraine
| | - Ivan Karnaukhov
- National Science Center “Kharkov Institute of Physics and Technology”, Kharkiv, Ukraine
| | - Andrey Zelinsky
- National Science Center “Kharkov Institute of Physics and Technology”, Kharkiv, Ukraine
| | - Borys Borts
- National Science Center “Kharkov Institute of Physics and Technology”, Kharkiv, Ukraine
| | - Igor Ushakov
- National Science Center “Kharkov Institute of Physics and Technology”, Kharkiv, Ukraine
| | - Larysa Sidenko
- National Science Center “Kharkov Institute of Physics and Technology”, Kharkiv, Ukraine
| | - Galyna Gorbenko
- Department of Medical Physics & Biomedical Nanotechnologies, V.N. Karazin Kharkiv National University, 4 Svobody Sq., Kharkiv61022, Ukraine
| |
Collapse
|
6
|
Hosseini SA, Shiri I, Ghaffarian P, Hajianfar G, Avval AH, Seyfi M, Servaes S, Rosa-Neto P, Zaidi H, Ay MR. The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods. Ann Nucl Med 2024; 38:493-507. [PMID: 38575814 PMCID: PMC11217131 DOI: 10.1007/s12149-024-01923-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/07/2024] [Indexed: 04/06/2024]
Abstract
PURPOSE This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC). METHODS We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with 'n_splits' set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome. RESULTS From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity. CONCLUSION Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.
Collapse
Affiliation(s)
- Seyyed Ali Hosseini
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Pardis Ghaffarian
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
- PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | | | - Milad Seyfi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, 500, Odense, Denmark.
- University Research and Innovation Center, Óbudabuda University, Budapest, Hungary.
| | - Mohammad Reza Ay
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
7
|
Oliveira C, Oliveira F, Constantino C, Alves C, Brito MJ, Cardoso F, Costa DC. Baseline [ 18F]FDG PET/CT and MRI first-order breast tumor features do not improve pathological complete response prediction to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06815-6. [PMID: 38922396 DOI: 10.1007/s00259-024-06815-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 06/17/2024] [Indexed: 06/27/2024]
Abstract
PURPOSE To verify the ability of pretreatment [18F]FDG PET/CT and T1-weighed dynamic contrast-enhanced MRI to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. METHODS This retrospective study includes patients with BC of no special type submitted to baseline [18F]FDG PET/CT, NAC and surgery. [18F]FDG PET-based features reflecting intensity and heterogeneity of tracer uptake were extracted from the primary BC and suspicious axillary lymph nodes (ALN), for comparative analysis related to NAC response (pCR vs. non-pCR). Multivariate logistic regression was performed for response prediction combining the breast tumor-extracted PET-based features and clinicopathological features. A subanalysis was performed in a patients' subsample by adding breast tumor-extracted first-order MRI-based features to the multivariate logistic regression. RESULTS A total of 170 tumors from 168 patients were included. pCR was observed in 60/170 tumors (20/107 luminal B-like, 25/45 triple-negative and 15/18 HER2-enriched surrogate molecular subtypes). Higher intensity and higher heterogeneity of [18F]FDG uptake in the primary BC were associated with NAC response in HER2-negative tumors (immunohistochemistry score 0, 1 + or 2 + non-amplified by in situ hybridization). Also, higher intensity of tracer uptake was observed in ALN in the pCR group among HER2-negative tumors. No [18F]FDG PET-based features were associated with pCR in the other subgroup analyses. A subsample of 103 tumors was also submitted to extraction of MRI-based features. When combined with clinicopathological features, neither [18F]FDG PET nor MRI-based features had additional value for pCR prediction. The only significant predictors were estrogen receptor status, HER2 expression and grade. CONCLUSION Pretreatment [18F]FDG PET-based features from primary BC and ALN are not associated with response to NAC, except in HER2-negative tumors. As compared with pathological features, no breast tumor-extracted PET or MRI-based feature improved response prediction.
Collapse
Affiliation(s)
- Carla Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal.
| | - Francisco Oliveira
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Cláudia Constantino
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Celeste Alves
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Maria José Brito
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
- Pathology Department, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Fátima Cardoso
- Breast Unit, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| | - Durval C Costa
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Centre/Champalimaud Foundation, Lisbon, Portugal
| |
Collapse
|
8
|
Horng H, Scott C, Winham S, Jensen M, Pantalone L, Mankowski W, Kerlikowske K, Vachon CM, Kontos D, Shinohara RT. Multivariate testing and effect size measures for batch effect evaluation in radiomic features. Sci Rep 2024; 14:13923. [PMID: 38886407 PMCID: PMC11183083 DOI: 10.1038/s41598-024-64208-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 06/06/2024] [Indexed: 06/20/2024] Open
Abstract
While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause "batch effects" that reduce reproducibility and affect downstream predictive analyses. Harmonization methods such as ComBat have been developed to correct these effects, but evaluation methods for quantifying batch effects are inconsistent. In this study, we propose the use of the multivariate statistical test PERMANOVA and the Robust Effect Size Index (RESI) to better quantify and characterize batch effects in radiomics data. We evaluate these methods in both simulated and real radiomics features extracted from full-field digital mammography (FFDM) data. PERMANOVA demonstrated higher power than standard univariate statistical testing, and RESI was able to interpretably quantify the effect size of site at extremely large sample sizes. These methods show promise as more powerful and interpretable methods for the detection and quantification of batch effects in radiomics studies.
Collapse
Affiliation(s)
- Hannah Horng
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | | | | | | | - Lauren Pantalone
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Walter Mankowski
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | | | - Despina Kontos
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), Columbia University, New York, NY, 10027, USA
| | - Russell T Shinohara
- Department of Radiology, Center for Biomedical Image Computing and Analysis (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn Statistics in Imaging Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA
| |
Collapse
|
9
|
Hinzpeter R, Mirshahvalad SA, Kulanthaivelu R, Kohan A, Ortega C, Metser U, Liu A, Farag A, Elimova E, Wong RKS, Yeung J, Jang RWJ, Veit-Haibach P. Gastro-Esophageal Cancer: Can Radiomic Parameters from Baseline 18F-FDG-PET/CT Predict the Development of Distant Metastatic Disease? Diagnostics (Basel) 2024; 14:1205. [PMID: 38893731 PMCID: PMC11171817 DOI: 10.3390/diagnostics14111205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/30/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024] Open
Abstract
We aimed to determine if clinical parameters and radiomics combined with sarcopenia status derived from baseline 18F-FDG-PET/CT could predict developing metastatic disease and overall survival (OS) in gastroesophageal cancer (GEC). Patients referred for primary staging who underwent 18F-FDG-PET/CT from 2008 to 2019 were evaluated retrospectively. Overall, 243 GEC patients (mean age = 64) were enrolled. Clinical, histopathology, and sarcopenia data were obtained, and primary tumor radiomics features were extracted. For classification (early-stage vs. advanced disease), the association of the studied parameters was evaluated. Various clinical and radiomics models were developed and assessed. Accuracy and area under the curve (AUC) were calculated. For OS prediction, univariable and multivariable Cox analyses were performed. The best model included PET/CT radiomics features, clinical data, and sarcopenia score (accuracy = 80%; AUC = 88%). For OS prediction, various clinical, CT, and PET features entered the multivariable analysis. Three clinical factors (advanced disease, age ≥ 70 and ECOG ≥ 2), along with one CT-derived and one PET-derived radiomics feature, retained their significance. Overall, 18F-FDG PET/CT radiomics seems to have a potential added value in identifying GEC patients with advanced disease and may enhance the performance of baseline clinical parameters. These features may also have a prognostic value for OS, improving the decision-making for GEC patients.
Collapse
Affiliation(s)
- Ricarda Hinzpeter
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Seyed Ali Mirshahvalad
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Roshini Kulanthaivelu
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Andres Kohan
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Claudia Ortega
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Ur Metser
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Amy Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 1X6, Canada;
| | - Adam Farag
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| | - Elena Elimova
- Department of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada;
| | - Rebecca K. S. Wong
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (R.K.S.W.); (R.W.-J.J.)
| | - Jonathan Yeung
- Division of Thoracic Surgery, Department of Surgery, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada;
| | - Raymond Woo-Jun Jang
- Department of Radiation Oncology, Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, ON M5G 2C4, Canada; (R.K.S.W.); (R.W.-J.J.)
| | - Patrick Veit-Haibach
- University Medical Imaging Toronto, Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, University of Toronto, Toronto, ON M5G 2N2, Canada; (R.H.); (R.K.); (A.K.); (C.O.); (U.M.); (A.F.); (P.V.-H.)
| |
Collapse
|
10
|
Zhuang M, Li X, Qiu Z, Guan J. Does consensus contour improve robustness and accuracy in 18F-FDG PET radiomic features? EJNMMI Phys 2024; 11:48. [PMID: 38839641 PMCID: PMC11153434 DOI: 10.1186/s40658-024-00652-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024] Open
Abstract
PURPOSE The purpose of our study is to validate the robustness and accuracy of consensus contour in 2-deoxy-2-[18 F]fluoro-D-glucose (18 F-FDG) PET radiomic features. METHODS 225 nasopharyngeal carcinoma (NPC) and 13 extended cardio-torso (XCAT) simulated data were enrolled. All segmentation were performed with four segmentation methods under two different initial masks, respectively. Consensus contour (ConSeg) was then developed using the majority vote rule. 107 radiomic features were extracted by Pyradiomics based on segmentation and the intraclass correlation coefficient (ICC) was calculated for each feature between masks or among segmentation, respectively. In XCAT ICC between segmentation and simulated ground truth were also calculated to access the accuracy. RESULTS ICC varied with the dataset, segmentation method, initial mask and feature type. ConSeg presented higher ICC for radiomic features in robustness tests and similar ICC in accuracy tests, compared with the average of four segmentation results. Higher ICC were also generally observed in irregular initial masks compared with rectangular masks in both robustness and accuracy tests. Furthermore, 19 features (17.76%) had ICC ≥ 0.75 in both robustness and accuracy tests for any of the segmentation methods or initial masks. The dataset was observed to have a large impact on the correlation relationships between radiomic features, but not the segmentation method or initial mask. CONCLUSIONS The consensus contour combined with irregular initial mask could improve the robustness and accuracy in radiomic analysis to some extent. The correlation relationships between radiomic features and feature clusters largely depended on the dataset, but not segmentation method or initial mask.
Collapse
Affiliation(s)
- Mingzan Zhuang
- Department of Nuclear Medicine, Meizhou People's Hospital, Meizhou, China.
- Guangdong Engineering Technological Research Center of Clinical Molecular Diagnosis and Antibody Drugs, Meizhou People's Hospital, Meizhou, China.
| | - Xianru Li
- Department of Nuclear Medicine, Meizhou People's Hospital, Meizhou, China
| | - Zhifen Qiu
- Department of Nuclear Medicine, Meizhou People's Hospital, Meizhou, China
| | - Jitian Guan
- Department of Radiology, The Second Affiliated Hospital of Shantou University Medical College, Shantou, China
| |
Collapse
|
11
|
Floca R, Bohn J, Haux C, Wiestler B, Zöllner FG, Reinke A, Weiß J, Nolden M, Albert S, Persigehl T, Norajitra T, Baeßler B, Dewey M, Braren R, Büchert M, Fallenberg EM, Galldiks N, Gerken A, Götz M, Hahn HK, Haubold J, Haueise T, Große Hokamp N, Ingrisch M, Iuga AI, Janoschke M, Jung M, Kiefer LS, Lohmann P, Machann J, Moltz JH, Nattenmüller J, Nonnenmacher T, Oerther B, Othman AE, Peisen F, Schick F, Umutlu L, Wichtmann BD, Zhao W, Caspers S, Schlemmer HP, Schlett CL, Maier-Hein K, Bamberg F. Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics. Insights Imaging 2024; 15:124. [PMID: 38825600 PMCID: PMC11144687 DOI: 10.1186/s13244-024-01704-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/20/2024] [Indexed: 06/04/2024] Open
Abstract
OBJECTIVES Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. MATERIALS AND METHODS The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. RESULTS Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows. CONCLUSION To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. CRITICAL RELEVANCE STATEMENT Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. KEY POINTS Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.
Collapse
Affiliation(s)
- Ralf Floca
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany.
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
- National Center for Radiation Research in Oncology NCRO, Heidelberg Institute for Radiation Oncology HIRO, Heidelberg, Germany.
| | - Jonas Bohn
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Faculty of Bioscience, University of Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
- Translational Lung Research Center (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Christian Haux
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, TU Munich University Hospital, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, TU Munich, Munich, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Annika Reinke
- Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Weiß
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Marco Nolden
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Steffen Albert
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Tobias Norajitra
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Marc Dewey
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin Institute of Health, DZHK (German Centre for Cardiovascular Research), and DKTK (German Cancer Consortium), both partner sites Berlin, Berlin, Germany
| | - Rickmer Braren
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine & Health, Ismaninger Str. 22, 81675, München, Germany
- Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany
| | - Martin Büchert
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Eva Maria Fallenberg
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine & Health, Ismaninger Str. 22, 81675, München, Germany
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3), Research Center Juelich (FZJ), Juelich, Germany
- Center of Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Bonn, Cologne & Duesseldorf, Germany
| | - Annika Gerken
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Michael Götz
- Division of Experimental Radiology, Department for Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Horst K Hahn
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- Faculty 3, Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Tobias Haueise
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Nils Große Hokamp
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Andra-Iza Iuga
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Marco Janoschke
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Matthias Jung
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Lena Sophie Kiefer
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich (FZJ), Juelich, Germany
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | - Jürgen Machann
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | | | - Johanna Nattenmüller
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Tobias Nonnenmacher
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Benedict Oerther
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Ahmed E Othman
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Felix Peisen
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Fritz Schick
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Barbara D Wichtmann
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Wenzhao Zhao
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Heinz-Peter Schlemmer
- German Cancer Research Center (DKFZ) Heidelberg, Division of Radiology, Heidelberg, Germany
| | - Christopher L Schlett
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| |
Collapse
|
12
|
Hindocha S, Hunter B, Linton-Reid K, George Charlton T, Chen M, Logan A, Ahmed M, Locke I, Sharma B, Doran S, Orton M, Bunce C, Power D, Ahmad S, Chan K, Ng P, Toshner R, Yasar B, Conibear J, Murphy R, Newsom-Davis T, Goodley P, Evison M, Yousaf N, Bitar G, McDonald F, Blackledge M, Aboagye E, Lee R. Validated machine learning tools to distinguish immune checkpoint inhibitor, radiotherapy, COVID-19 and other infective pneumonitis. Radiother Oncol 2024; 195:110266. [PMID: 38582181 DOI: 10.1016/j.radonc.2024.110266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 03/27/2024] [Accepted: 03/31/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Pneumonitis is a well-described, potentially disabling, or fatal adverse effect associated with both immune checkpoint inhibitors (ICI) and thoracic radiotherapy. Accurate differentiation between checkpoint inhibitor pneumonitis (CIP) radiation pneumonitis (RP), and infective pneumonitis (IP) is crucial for swift, appropriate, and tailored management to achieve optimal patient outcomes. However, correct diagnosis is often challenging, owing to overlapping clinical presentations and radiological patterns. METHODS In this multi-centre study of 455 patients, we used machine learning with radiomic features extracted from chest CT imaging to develop and validate five models to distinguish CIP and RP from COVID-19, non-COVID-19 infective pneumonitis, and each other. Model performance was compared to that of two radiologists. RESULTS Models to distinguish RP from COVID-19, CIP from COVID-19 and CIP from non-COVID-19 IP out-performed radiologists (test set AUCs of 0.92 vs 0.8 and 0.8; 0.68 vs 0.43 and 0.4; 0.71 vs 0.55 and 0.63 respectively). Models to distinguish RP from non-COVID-19 IP and CIP from RP were not superior to radiologists but demonstrated modest performance, with test set AUCs of 0.81 and 0.8 respectively. The CIP vs RP model performed less well on patients with prior exposure to both ICI and radiotherapy (AUC 0.54), though the radiologists also had difficulty distinguishing this test cohort (AUC values 0.6 and 0.6). CONCLUSION Our results demonstrate the potential utility of such tools as a second or concurrent reader to support oncologists, radiologists, and chest physicians in cases of diagnostic uncertainty. Further research is required for patients with exposure to both ICI and thoracic radiotherapy.
Collapse
Affiliation(s)
- Sumeet Hindocha
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK; Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK.
| | - Benjamin Hunter
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Kristofer Linton-Reid
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Thomas George Charlton
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Mitchell Chen
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Andrew Logan
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Merina Ahmed
- Lung Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Imogen Locke
- Lung Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Bhupinder Sharma
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Simon Doran
- Institute of Cancer Research NIHR Biomedical Research Centre, London, UK
| | - Matthew Orton
- Artificial Intelligence Imaging Hub, Royal Marsden NHS Foundation Trust, Downs Road, Sutton SM25PT, UK
| | - Catey Bunce
- Institute of Cancer Research NIHR Biomedical Research Centre, London, UK
| | - Danielle Power
- Department of Clinical Oncology, Imperial College Healthcare NHS Trust, Fulham Palace Road, London W6 8RF, UK
| | - Shahreen Ahmad
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Karen Chan
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Peng Ng
- Guy's Cancer Centre, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE19RT, UK
| | - Richard Toshner
- Interstitial lung disease unit, St Bartholomews' Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Binnaz Yasar
- Department of Clinical Oncology, St Batholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - John Conibear
- Department of Clinical Oncology, St Batholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK
| | - Ravindhi Murphy
- Chelsea and Westminster Hospital, Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London SW10 9NH, UK
| | - Tom Newsom-Davis
- Chelsea and Westminster Hospital, Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London SW10 9NH, UK
| | - Patrick Goodley
- Lung Cancer & Thoracic Surgery Directorate, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Greater Manchester, UK; Division of Immunology, Immunity to Infection & Respiratory Medicine, University of Manchester, Manchester, UK
| | - Matthew Evison
- Lung Cancer & Thoracic Surgery Directorate, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Greater Manchester, UK
| | - Nadia Yousaf
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - George Bitar
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Fiona McDonald
- Lung Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| | - Matthew Blackledge
- Radiotherapy and Imaging, Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Eric Aboagye
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Richard Lee
- Early Diagnosis and Detection Centre, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW36JJ, UK
| |
Collapse
|
13
|
Demircioğlu A. Applying oversampling before cross-validation will lead to high bias in radiomics. Sci Rep 2024; 14:11563. [PMID: 38773233 PMCID: PMC11109211 DOI: 10.1038/s41598-024-62585-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 05/20/2024] [Indexed: 05/23/2024] Open
Abstract
Class imbalance is often unavoidable for radiomic data collected from clinical routine. It can create problems during classifier training since the majority class could dominate the minority class. Consequently, resampling methods like oversampling or undersampling are applied to the data to class-balance the data. However, the resampling must not be applied upfront to all data because it would lead to data leakage and, therefore, to erroneous results. This study aims to measure the extent of this bias. Five-fold cross-validation with 30 repeats was performed using a set of 15 radiomic datasets to train predictive models. The training involved two scenarios: first, the models were trained correctly by applying the resampling methods during the cross-validation. Second, the models were trained incorrectly by performing the resampling on all the data before cross-validation. The bias was defined empirically as the difference between the best-performing models in both scenarios in terms of area under the receiver operating characteristic curve (AUC), sensitivity, specificity, balanced accuracy, and the Brier score. In addition, a simulation study was performed on a randomly generated dataset for verification. The results demonstrated that incorrectly applying the oversampling methods to all data resulted in a large positive bias (up to 0.34 in AUC, 0.33 in sensitivity, 0.31 in specificity, and 0.37 in balanced accuracy). The bias depended on the data balance, and approximately an increase of 0.10 in the AUC was observed for each increase in imbalance. The models also showed a bias in calibration measured using the Brier score, which differed by up to -0.18 between the correctly and incorrectly trained models. The undersampling methods were not affected significantly by bias. These results emphasize that any resampling method should be applied correctly only to the training data to avoid data leakage and, subsequently, biased model performance and calibration.
Collapse
Affiliation(s)
- Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Hufelandstraße 55, 45147, Essen, Germany.
| |
Collapse
|
14
|
Haider SP, Zeevi T, Sharaf K, Gross M, Mahajan A, Kann BH, Judson BL, Prasad ML, Burtness B, Aboian M, Canis M, Reichel CA, Baumeister P, Payabvash S. Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers. J Nucl Med 2024; 65:803-809. [PMID: 38514087 DOI: 10.2967/jnumed.123.266637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/13/2024] [Indexed: 03/23/2024] Open
Abstract
We aimed to investigate the effects of 18F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. Methods: We extracted 1,037 18F-FDG PET radiomic features quantifying the shape, intensity, and texture of 430 OPSCC primary tumors. The reproducibility of individual features across 3 intensity-normalized images (body-weight SUV, reference tissue activity ratio to lentiform nucleus of brain and cerebellum) and the raw PET data was assessed using an intraclass correlation coefficient (ICC). We investigated the effects of intensity normalization on the features' utility in predicting the human papillomavirus (HPV) status of OPSCCs in univariate logistic regression, receiver-operating-characteristic analysis, and extreme-gradient-boosting (XGBoost) machine-learning classifiers. Results: Of 1,037 features, a high (ICC ≥ 0.90), medium (0.90 > ICC ≥ 0.75), and low (ICC < 0.75) degree of reproducibility across normalization methods was attained in 356 (34.3%), 608 (58.6%), and 73 (7%) features, respectively. In univariate analysis, features from the PET normalized to the lentiform nucleus had the strongest association with HPV status, with 865 of 1,037 (83.4%) significant features after multiple testing corrections and a median area under the receiver-operating-characteristic curve (AUC) of 0.65 (interquartile range, 0.62-0.68). Similar tendencies were observed in XGBoost models, with the lentiform nucleus-normalized model achieving the numerically highest average AUC of 0.72 (SD, 0.07) in the cross validation within the training cohort. The model generalized well to the validation cohorts, attaining an AUC of 0.73 (95% CI, 0.60-0.85) in independent validation and 0.76 (95% CI, 0.58-0.95) in external validation. The AUCs of the XGBoost models were not significantly different. Conclusion: Only one third of the features demonstrated a high degree of reproducibility across intensity-normalization techniques, making uniform normalization a prerequisite for interindividual comparability of radiomic markers. The choice of normalization technique may affect the radiomic features' predictive value with respect to HPV. Our results show trends that normalization to the lentiform nucleus may improve model performance, although more evidence is needed to draw a firm conclusion.
Collapse
Affiliation(s)
- Stefan P Haider
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany;
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Tal Zeevi
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Kariem Sharaf
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
| | - Moritz Gross
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
- Charité Center for Diagnostic and Interventional Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Amit Mahajan
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Benjamin H Kann
- Department of Radiation Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Benjamin L Judson
- Division of Otolaryngology, Yale School of Medicine, New Haven, Connecticut
| | - Manju L Prasad
- Department of Pathology, Yale School of Medicine, New Haven, Connecticut; and
| | - Barbara Burtness
- Section of Medical Oncology, Yale School of Medicine, New Haven, Connecticut
| | - Mariam Aboian
- Section of Neuroradiology, Yale School of Medicine, New Haven, Connecticut
| | - Martin Canis
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
| | - Christoph A Reichel
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
| | - Philipp Baumeister
- Department of Otorhinolaryngology, LMU Clinic of Ludwig Maximilians University of Munich, Munich, Germany
| | | |
Collapse
|
15
|
Wang W, Dai J, Li J, Du X. Predicting postoperative rehemorrhage in hypertensive intracerebral hemorrhage using noncontrast CT radiomics and clinical data with an interpretable machine learning approach. Sci Rep 2024; 14:9717. [PMID: 38678066 PMCID: PMC11055901 DOI: 10.1038/s41598-024-60463-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/23/2024] [Indexed: 04/29/2024] Open
Abstract
In hypertensive intracerebral hemorrhage (HICH) patients, while emergency surgeries effectively reduce intracranial pressure and hematoma volume, their significant risk of causing postoperative rehemorrhage necessitates early detection and management to improve patient prognosis. This study sought to develop and validate machine learning (ML) models leveraging clinical data and noncontrast CT radiomics to pinpoint patients at risk of postoperative rehemorrhage, equipping clinicians with an early detection tool for prompt intervention. The study conducted a retrospective analysis on 609 HICH patients, dividing them into training and external verification cohorts. These patients were categorized into groups with and without postoperative rehemorrhage. Radiomics features from noncontrast CT images were extracted, standardized, and employed to create several ML models. These models underwent internal validation using both radiomics and clinical data, with the best model's feature significance assessed via the Shapley additive explanations (SHAP) method, then externally validated. In the study of 609 patients, postoperative rehemorrhage rates were similar in the training (18.8%, 80/426) and external verification (17.5%, 32/183) cohorts. Six significant noncontrast CT radiomics features were identified, with the support vector machine (SVM) model outperforming others in both internal and external validations. SHAP analysis highlighted five critical predictors of postoperative rehemorrhage risk, encompassing three radiomics features from noncontrast CT and two clinical data indicators. This study highlights the effectiveness of an SVM model combining radiomics features from noncontrast CT and clinical parameters in predicting postoperative rehemorrhage among HICH patients. This approach enables timely and effective interventions, thereby improving patient outcomes.
Collapse
Affiliation(s)
- Weigong Wang
- Department of Neurosurgery, Lu'an Hospital of Traditional Chinese Medicine, No. 76 Renmin Road, Jin'an District, Lu'an, 237000, Anhui, China
| | - Jinlong Dai
- Department of Neurosurgery, Lu'an Hospital of Traditional Chinese Medicine, No. 76 Renmin Road, Jin'an District, Lu'an, 237000, Anhui, China
| | - Jibo Li
- Department of Neurosurgery, Lu'an Hospital of Traditional Chinese Medicine, No. 76 Renmin Road, Jin'an District, Lu'an, 237000, Anhui, China
| | - Xiangyang Du
- Department of Neurosurgery, Lu'an Hospital of Traditional Chinese Medicine, No. 76 Renmin Road, Jin'an District, Lu'an, 237000, Anhui, China.
| |
Collapse
|
16
|
Wang K, Karalis JD, Elamir A, Bifolco A, Wachsmann M, Capretti G, Spaggiari P, Enrico S, Balasubramanian K, Fatimah N, Pontecorvi G, Nebbia M, Yopp A, Kaza R, Pedrosa I, Zeh H, Polanco P, Zerbi A, Wang J, Aguilera T, Ligorio M. Delta Radiomic Features Predict Resection Margin Status and Overall Survival in Neoadjuvant-Treated Pancreatic Cancer Patients. Ann Surg Oncol 2024; 31:2608-2620. [PMID: 38151623 PMCID: PMC10908610 DOI: 10.1245/s10434-023-14805-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 12/06/2023] [Indexed: 12/29/2023]
Abstract
BACKGROUND Neoadjuvant therapy (NAT) emerged as the standard of care for patients with pancreatic ductal adenocarcinoma (PDAC) who undergo surgery; however, surgery is morbid, and tools to predict resection margin status (RMS) and prognosis in the preoperative setting are needed. Radiomic models, specifically delta radiomic features (DRFs), may provide insight into treatment dynamics to improve preoperative predictions. METHODS We retrospectively collected clinical, pathological, and surgical data (patients with resectable, borderline, locally advanced, and metastatic disease), and pre/post-NAT contrast-enhanced computed tomography (CT) scans from PDAC patients at the University of Texas Southwestern Medical Center (UTSW; discovery) and Humanitas Hospital (validation cohort). Gross tumor volume was contoured from CT scans, and 257 radiomics features were extracted. DRFs were calculated by direct subtraction of pre/post-NAT radiomic features. Cox proportional models and binary prediction models, including/excluding clinical variables, were constructed to predict overall survival (OS), disease-free survival (DFS), and RMS. RESULTS The discovery and validation cohorts comprised 58 and 31 patients, respectively. Both cohorts had similar clinical characteristics, apart from differences in NAT (FOLFIRINOX vs. gemcitabine/nab-paclitaxel; p < 0.05) and type of surgery resections (pancreatoduodenectomy, distal or total pancreatectomy; p < 0.05). The model that combined clinical variables (pre-NAT carbohydrate antigen (CA) 19-9, the change in CA19-9 after NAT (∆CA19-9), and resectability status) and DRFs outperformed the clinical feature-based models and other radiomics feature-based models in predicting OS (UTSW: 0.73; Humanitas: 0.66), DFS (UTSW: 0.75; Humanitas: 0.64), and RMS (UTSW 0.73; Humanitas: 0.69). CONCLUSIONS Our externally validated, predictive/prognostic delta-radiomics models, which incorporate clinical variables, show promise in predicting the risk of predicting RMS in NAT-treated PDAC patients and their OS or DFS.
Collapse
Affiliation(s)
- Kai Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - John D Karalis
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ahmed Elamir
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alessandro Bifolco
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Megan Wachsmann
- Department of Pathology, Veterans Affairs North Texas Health Care System, Dallas, TX, USA
| | - Giovanni Capretti
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Paola Spaggiari
- Department of Pathology, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Sebastian Enrico
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Nafeesah Fatimah
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Giada Pontecorvi
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Martina Nebbia
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
| | - Adam Yopp
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ravi Kaza
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Herbert Zeh
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Patricio Polanco
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Alessandro Zerbi
- Pancreatic Surgery Unit, IRCCS Humanitas Research Hospital, Rozzano, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Todd Aguilera
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Matteo Ligorio
- Department of Surgery, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| |
Collapse
|
17
|
Khalighi S, Reddy K, Midya A, Pandav KB, Madabhushi A, Abedalthagafi M. Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. NPJ Precis Oncol 2024; 8:80. [PMID: 38553633 PMCID: PMC10980741 DOI: 10.1038/s41698-024-00575-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/13/2024] [Indexed: 04/02/2024] Open
Abstract
This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent a significant global health issue. AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, and treatment planning. Assessing its influence across all facets of malignant brain tumor management- diagnosis, prognosis, and therapy- AI models outperform human evaluations in terms of accuracy and specificity. Their ability to discern molecular aspects from imaging may reduce reliance on invasive diagnostics and may accelerate the time to molecular diagnoses. The review covers AI techniques, from classical machine learning to deep learning, highlighting current applications and challenges. Promising directions for future research include multimodal data integration, generative AI, large medical language models, precise tumor delineation and characterization, and addressing racial and gender disparities. Adaptive personalized treatment strategies are also emphasized for optimizing clinical outcomes. Ethical, legal, and social implications are discussed, advocating for transparency and fairness in AI integration for neuro-oncology and providing a holistic understanding of its transformative impact on patient care.
Collapse
Affiliation(s)
- Sirvan Khalighi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Kartik Reddy
- Department of Radiology, Emory University, Atlanta, GA, USA
| | - Abhishek Midya
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Krunal Balvantbhai Pandav
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.
| | - Malak Abedalthagafi
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA.
- The Cell and Molecular Biology Program, Winship Cancer Institute, Atlanta, GA, USA.
| |
Collapse
|
18
|
Sheikh TS, Cho M. Segmentation of Variants of Nuclei on Whole Slide Images by Using Radiomic Features. Bioengineering (Basel) 2024; 11:252. [PMID: 38534526 DOI: 10.3390/bioengineering11030252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/10/2024] [Accepted: 02/26/2024] [Indexed: 03/28/2024] Open
Abstract
The histopathological segmentation of nuclear types is a challenging task because nuclei exhibit distinct morphologies, textures, and staining characteristics. Accurate segmentation is critical because it affects the diagnostic workflow for patient assessment. In this study, a framework was proposed for segmenting various types of nuclei from different organs of the body. The proposed framework improved the segmentation performance for each nuclear type using radiomics. First, we used distinct radiomic features to extract and analyze quantitative information about each type of nucleus and subsequently trained various classifiers based on the best input sub-features of each radiomic feature selected by a LASSO operator. Second, we inputted the outputs of the best classifier to various segmentation models to learn the variants of nuclei. Using the MoNuSAC2020 dataset, we achieved state-of-the-art segmentation performance for each category of nuclei type despite the complexity, overlapping, and obscure regions. The generalized adaptability of the proposed framework was verified by the consistent performance obtained in whole slide images of different organs of the body and radiomic features.
Collapse
Affiliation(s)
- Taimoor Shakeel Sheikh
- AIMI-Artificial Intelligence and Medical Imaging Laboratory, Department of Computer & Media Engineering, Tongmyong University, Busan 48520, Republic of Korea
| | - Migyung Cho
- AIMI-Artificial Intelligence and Medical Imaging Laboratory, Department of Computer & Media Engineering, Tongmyong University, Busan 48520, Republic of Korea
| |
Collapse
|
19
|
Cheng T, Li H. Prediction of Gleason score in prostate cancer patients based on radiomic features of transrectal ultrasound images. Br J Radiol 2024; 97:415-421. [PMID: 38308030 DOI: 10.1093/bjr/tqad036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/20/2023] [Accepted: 11/20/2023] [Indexed: 02/04/2024] Open
Abstract
OBJECTIVES The aim of this study was to develop a model for predicting the Gleason score of patients with prostate cancer based on ultrasound images. METHODS Transrectal ultrasound images of 838 prostate cancer patients from The Cancer Imaging Archive database were included in this cross-section study. Data were randomly divided into the training set and testing set (ratio 7:3). A total of 103 radiomic features were extracted from the ultrasound image. Lasso regression was used to select radiomic features. Random forest and broad learning system (BLS) methods were utilized to develop the model. The area under the curve (AUC) was calculated to evaluate the model performance. RESULTS After the screening, 10 radiomic features were selected. The AUC and accuracy of the radiomic feature variables random forest model in the testing set were 0.727 (95% CI, 0.694-0.760) and 0.646 (95% CI, 0.620-0.673), respectively. When PSA and radiomic feature variables were included in the random forest model, the AUC and accuracy of the model were 0.770 (95% CI, 0.740-0.800) and 0.713 (95% CI, 0.688-0.738), respectively. While the BLS method was utilized to construct the model, the AUC and accuracy of the model were 0.726 (95% CI, 0.693-0.759) and 0.698 (95% CI, 0.673-0.723), respectively. In predictions for different Gleason grades, the highest AUC of 0.847 (95% CI, 0.749-0.945) was found to predict Gleason grade 5 (Gleason score ≥9). CONCLUSIONS A model based on transrectal ultrasound image features showed a good ability to predict Gleason scores in prostate cancer patients. ADVANCES IN KNOWLEDGE This study used ultrasound-based radiomics to predict the Gleason score of patients with prostate cancer.
Collapse
Affiliation(s)
- Tao Cheng
- Department of Ultrasound, Changzhou Tumor Hospital, Changzhou 213000, China
| | - Huiming Li
- Department of Ultrasound, Changzhou Tumor Hospital, Changzhou 213000, China
| |
Collapse
|
20
|
Huisman M, Akinci D'Antonoli T. What a Radiologist Needs to Know About Radiomics, Standardization, and Reproducibility. Radiology 2024; 310:e232459. [PMID: 38319170 DOI: 10.1148/radiol.232459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Affiliation(s)
- Merel Huisman
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (M.H.); and Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland (T.A.D.)
| | - Tugba Akinci D'Antonoli
- From the Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (M.H.); and Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland (T.A.D.)
| |
Collapse
|
21
|
Zhong J, Wu Z, Wang L, Chen Y, Xia Y, Wang L, Li J, Lu W, Shi X, Feng J, Dong H, Zhang H, Yao W. Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:123-133. [PMID: 38343265 DOI: 10.1007/s10278-023-00901-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 03/02/2024]
Abstract
This study aims to investigate the influence of adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) on CT radiomics feature robustness. A standardized phantom was scanned under single-energy CT (SECT) and dual-energy CT (DECT) modes at standard and low (20 and 10 mGy) dose levels. Images of SECT 120 kVp and corresponding DECT 120 kVp-like virtual monochromatic images were generated with filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) blending levels, and DLIR algorithm at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. Ninety-four features were extracted via Pyradiomics. Reproducibility of features was calculated between standard and low dose levels, between reconstruction algorithms in reference to FBP images, and within scan mode, using intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The average percentage of features with ICC > 0.90 and CCC > 0.90 between the two dose levels was 21.28% and 20.75% in AV-40 images, and 39.90% and 35.11% in AV-100 images, respectively, and increased from 15.43 to 45.22% and from 15.43 to 44.15% with an increasing strength level of DLIR. The average percentage of features with ICC > 0.90 and CCC > 0.90 in reference to FBP images was 26.07% and 25.80% in AV-40 images, and 18.88% and 18.62% in AV-100 images, respectively, and decreased from 27.93 to 17.82% and from 27.66 to 17.29% with an increasing strength level of DLIR. DLIR and ASIR-V algorithms showed low reproducibility in reference to FBP images, while the high-strength DLIR algorithm provides an opportunity for minimizing radiomics variability due to dose reduction.
Collapse
Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Zhiyuan Wu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yihan Xia
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lan Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK
| | - Jianxing Feng
- Haohua Technology Co., Ltd., Shanghai, 201100, China
| | - Haipeng Dong
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
| |
Collapse
|
22
|
Alves VM, dos Santos Cardoso J, Gama J. Classification of Pulmonary Nodules in 2-[ 18F]FDG PET/CT Images with a 3D Convolutional Neural Network. Nucl Med Mol Imaging 2024; 58:9-24. [PMID: 38261899 PMCID: PMC10796312 DOI: 10.1007/s13139-023-00821-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/17/2023] [Accepted: 08/08/2023] [Indexed: 01/25/2024] Open
Abstract
Purpose 2-[18F]FDG PET/CT plays an important role in the management of pulmonary nodules. Convolutional neural networks (CNNs) automatically learn features from images and have the potential to improve the discrimination between malignant and benign pulmonary nodules. The purpose of this study was to develop and validate a CNN model for classification of pulmonary nodules from 2-[18F]FDG PET images. Methods One hundred thirteen participants were retrospectively selected. One nodule per participant. The 2-[18F]FDG PET images were preprocessed and annotated with the reference standard. The deep learning experiment entailed random data splitting in five sets. A test set was held out for evaluation of the final model. Four-fold cross-validation was performed from the remaining sets for training and evaluating a set of candidate models and for selecting the final model. Models of three types of 3D CNNs architectures were trained from random weight initialization (Stacked 3D CNN, VGG-like and Inception-v2-like models) both in original and augmented datasets. Transfer learning, from ImageNet with ResNet-50, was also used. Results The final model (Stacked 3D CNN model) obtained an area under the ROC curve of 0.8385 (95% CI: 0.6455-1.0000) in the test set. The model had a sensibility of 80.00%, a specificity of 69.23% and an accuracy of 73.91%, in the test set, for an optimised decision threshold that assigns a higher cost to false negatives. Conclusion A 3D CNN model was effective at distinguishing benign from malignant pulmonary nodules in 2-[18F]FDG PET images. Supplementary Information The online version contains supplementary material available at 10.1007/s13139-023-00821-6.
Collapse
Affiliation(s)
- Victor Manuel Alves
- Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-464 Porto, Portugal
- Department of Nuclear Medicine, University Hospital Center of São João, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal
| | - Jaime dos Santos Cardoso
- Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - João Gama
- Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, Porto, 4200-464 Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| |
Collapse
|
23
|
Kocak B, Akinci D'Antonoli T, Mercaldo N, Alberich-Bayarri A, Baessler B, Ambrosini I, Andreychenko AE, Bakas S, Beets-Tan RGH, Bressem K, Buvat I, Cannella R, Cappellini LA, Cavallo AU, Chepelev LL, Chu LCH, Demircioglu A, deSouza NM, Dietzel M, Fanni SC, Fedorov A, Fournier LS, Giannini V, Girometti R, Groot Lipman KBW, Kalarakis G, Kelly BS, Klontzas ME, Koh DM, Kotter E, Lee HY, Maas M, Marti-Bonmati L, Müller H, Obuchowski N, Orlhac F, Papanikolaou N, Petrash E, Pfaehler E, Pinto Dos Santos D, Ponsiglione A, Sabater S, Sardanelli F, Seeböck P, Sijtsema NM, Stanzione A, Traverso A, Ugga L, Vallières M, van Dijk LV, van Griethuysen JJM, van Hamersvelt RW, van Ooijen P, Vernuccio F, Wang A, Williams S, Witowski J, Zhang Z, Zwanenburg A, Cuocolo R. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging 2024; 15:8. [PMID: 38228979 PMCID: PMC10792137 DOI: 10.1186/s13244-023-01572-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 11/20/2023] [Indexed: 01/18/2024] Open
Abstract
PURPOSE To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. METHODS We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. RESULT In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. CONCLUSION In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. CRITICAL RELEVANCE STATEMENT A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. KEY POINTS • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).
Collapse
Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, Turkey
| | - Tugba Akinci D'Antonoli
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Baselland, Liestal, Switzerland.
| | - Nathaniel Mercaldo
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Bettina Baessler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Ilaria Ambrosini
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Anna E Andreychenko
- Laboratory for Digital Public Health Technologies, ITMO University, St. Petersburg, Russian Federation
| | - Spyridon Bakas
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
- Center for Federated Learning in Precision Medicine, Indiana University, Indianapolis, IN, USA
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
| | - Keno Bressem
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Irene Buvat
- Institut Curie, Inserm, PSL University, Laboratory of Translational Imaging in Oncology, Orsay, France
| | - Roberto Cannella
- Section of Radiology - Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy
| | | | - Armando Ugo Cavallo
- Division of Radiology, Istituto Dermopatico dell'Immacolata (IDI) IRCCS, Rome, Italy
| | - Leonid L Chepelev
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada
| | - Linda Chi Hang Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Aydin Demircioglu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital , Essen, Germany
| | - Nandita M deSouza
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, UK
- Department of Imaging, The Royal Marsden National Health Service (NHS) Foundation Trust, London, UK
| | - Matthias Dietzel
- Department of Radiology, University Hospital Erlangen, Erlangen, Germany
| | | | - Andrey Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Laure S Fournier
- Department of Radiology, Université Paris Cité, AP-HP, Hôpital Européen Georges Pompidou, PARCC UMRS 970, INSERM, Paris, France
| | | | - Rossano Girometti
- Institute of Radiology, Department of Medicine, University of Udine, University Hospital S. Maria della Misericordia, Udine, Italy
| | - Kevin B W Groot Lipman
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
- Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Georgios Kalarakis
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
- Department of Clinical Science, Division of Radiology, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
- Department of Radiology, Medical School, University of Crete, Heraklion, Greece
| | - Brendan S Kelly
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
- Insight Centre for Data Analytics, UCD, Dublin, Ireland
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece
- Department of Radiology, School of Medicine, University of Crete, Heraklion, Crete, Greece
- Computational Biomedicine Laboratory, Institute of Computer Science, FORTH, Heraklion, Crete, Greece
| | - Dow-Mu Koh
- Department of Radiology, Royal Marsden Hospital, Sutton, UK
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea
| | - Mario Maas
- Department of Radiology & Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands
| | - Luis Marti-Bonmati
- Medical Imaging Department and Biomedical Imaging Research Group, Hospital Universitario y Politécnico La Fe and Health Research Institute, Valencia, Spain
| | - Henning Müller
- University of Applied Sciences of Western Switzerland (HES-SO Valais), Sierra, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UniGe), Geneva, Switzerland
| | - Nancy Obuchowski
- Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Fanny Orlhac
- Institut Curie, Inserm, PSL University, Laboratory of Translational Imaging in Oncology, Orsay, France
| | - Nikolaos Papanikolaou
- Computational Clinical Imaging Group, Centre for the Unknown, Champalimaud Foundation, Lisbon, Portugal
- Department of Radiology, Royal Marsden Hospital and The Institute of Cancer Research, London, UK
| | - Ekaterina Petrash
- Radiology department, Research Institute of Pediatric Oncology and Hematology n. a. L.A. Durnov, National Medical Research Center of Oncology n. a. N.N. Blokhin Ministry of Health of Russian Federation, Moscow, Russia
- Medical Department IRA-Labs, Moscow, Russia
| | - Elisabeth Pfaehler
- Institute for advanced simulation (IAS-8): Machine learning and data analytics, Forschungszentrum Jülich, Jülich, Germany
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Institute for Diagnostic and Interventional Radiology, Goethe-University Frankfurt Am Main, Frankfurt, Germany
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Sebastià Sabater
- Department of Radiation Oncology, Complejo Hospitalario Universitario de Albacete, Albacete, Spain
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
| | - Philipp Seeböck
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Nanna M Sijtsema
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Alberto Traverso
- Department of Radiotherapy, Maastro Clinic, Maastricht, the Netherlands
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Canada
- Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Sherbrooke, Canada
| | - Lisanne V van Dijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Robbert W van Hamersvelt
- Department of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Peter van Ooijen
- Department of Radiotherapy, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Federica Vernuccio
- Section of Radiology, Department of Biomedicine, Neuroscience and Advanced Diagnosis (Bi.N.D), University of Palermo, Palermo, 90127, Italy
| | - Alan Wang
- Centre for Medical Imaging & Centre for Brain Research, Faculty of Medical and Health Sciences, Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Stuart Williams
- Department of Radiology, Norfolk & Norwich University Hospital, Colney Lane, Norwich, Norfolk, UK
| | - Jan Witowski
- Department of Radiology, New York University Grossman School of Medicine, New York, USA
| | - Zhongyi Zhang
- School of Information and Communication Technology, Griffith University, Nathan, Brisbane, Australia
| | - Alex Zwanenburg
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| |
Collapse
|
24
|
Pasini G, Russo G, Mantarro C, Bini F, Richiusa S, Morgante L, Comelli A, Russo GI, Sabini MG, Cosentino S, Marinozzi F, Ippolito M, Stefano A. A Critical Analysis of the Robustness of Radiomics to Variations in Segmentation Methods in 18F-PSMA-1007 PET Images of Patients Affected by Prostate Cancer. Diagnostics (Basel) 2023; 13:3640. [PMID: 38132224 PMCID: PMC10743045 DOI: 10.3390/diagnostics13243640] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 11/29/2023] [Accepted: 12/08/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Radiomics shows promising results in supporting the clinical decision process, and much effort has been put into its standardization, thus leading to the Imaging Biomarker Standardization Initiative (IBSI), that established how radiomics features should be computed. However, radiomics still lacks standardization and many factors, such as segmentation methods, limit study reproducibility and robustness. AIM We investigated the impact that three different segmentation methods (manual, thresholding and region growing) have on radiomics features extracted from 18F-PSMA-1007 Positron Emission Tomography (PET) images of 78 patients (43 Low Risk, 35 High Risk). Segmentation was repeated for each patient, thus leading to three datasets of segmentations. Then, feature extraction was performed for each dataset, and 1781 features (107 original, 930 Laplacian of Gaussian (LoG) features, 744 wavelet features) were extracted. Feature robustness and reproducibility were assessed through the intra class correlation coefficient (ICC) to measure agreement between the three segmentation methods. To assess the impact that the three methods had on machine learning models, feature selection was performed through a hybrid descriptive-inferential method, and selected features were given as input to three classifiers, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost and Neural Networks (NN), whose performance in discriminating between low-risk and high-risk patients have been validated through 30 times repeated five-fold cross validation. CONCLUSIONS Our study showed that segmentation methods influence radiomics features and that Shape features were the least reproducible (average ICC: 0.27), while GLCM features the most reproducible. Moreover, feature reproducibility changed depending on segmentation type, resulting in 51.18% of LoG features exhibiting excellent reproducibility (range average ICC: 0.68-0.87) and 47.85% of wavelet features exhibiting poor reproducibility that varied between wavelet sub-bands (range average ICC: 0.34-0.80) and resulted in the LLL band showing the highest average ICC (0.80). Finally, model performance showed that region growing led to the highest accuracy (74.49%), improved sensitivity (84.38%) and AUC (79.20%) in contrast with manual segmentation.
Collapse
Affiliation(s)
- Giovanni Pasini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
- National Laboratory of South, National Institute for Nuclear Physics (LNS-INFN), 95125 Catania, Italy
| | - Cristina Mantarro
- Nuclear Medicine Department, Cannizzaro Hospital, 95125 Catania, Italy; (C.M.); (S.C.); (M.I.)
| | - Fabiano Bini
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
| | - Selene Richiusa
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
| | - Lucrezia Morgante
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
| | - Albert Comelli
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
- Ri.MED Foundation, Via Bandiera 11, 90133 Palermo, Italy
| | - Giorgio Ivan Russo
- Department of Surgery, Urology Section, University of Catania, 95125 Catania, Italy;
| | | | - Sebastiano Cosentino
- Nuclear Medicine Department, Cannizzaro Hospital, 95125 Catania, Italy; (C.M.); (S.C.); (M.I.)
| | - Franco Marinozzi
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Eudossiana 18, 00184 Rome, Italy; (G.P.); (L.M.); (F.M.)
| | - Massimo Ippolito
- Nuclear Medicine Department, Cannizzaro Hospital, 95125 Catania, Italy; (C.M.); (S.C.); (M.I.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Contrada, Pietrapollastra-Pisciotto, 90015 Cefalù, Italy; (G.R.); (S.R.); (A.C.); (A.S.)
- National Laboratory of South, National Institute for Nuclear Physics (LNS-INFN), 95125 Catania, Italy
| |
Collapse
|
25
|
Ren C, Zhang F, Zhang J, Song S, Sun Y, Cheng J. Clinico-biological-radiomics (CBR) based machine learning for improving the diagnostic accuracy of FDG-PET false-positive lymph nodes in lung cancer. Eur J Med Res 2023; 28:554. [PMID: 38042812 PMCID: PMC10693151 DOI: 10.1186/s40001-023-01497-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/02/2023] [Indexed: 12/04/2023] Open
Abstract
BACKGROUND The main problem of positron emission tomography/computed tomography (PET/CT) for lymph node (LN) staging is the high false positive rate (FPR). Thus, we aimed to explore a clinico-biological-radiomics (CBR) model via machine learning (ML) to reduce FPR and improve the accuracy for predicting the hypermetabolic mediastinal-hilar LNs status in lung cancer than conventional PET/CT. METHODS A total of 260 lung cancer patients with hypermetabolic mediastinal-hilar LNs (SUVmax ≥ 2.5) were retrospectively reviewed. Patients were treated with surgery with systematic LN resection and pathologically divided into the LN negative (LN-) and positive (LN +) groups, and randomly assigned into the training (n = 182) and test (n = 78) sets. Preoperative CBR dataset containing 1738 multi-scale features was constructed for all patients. Prediction models for hypermetabolic LNs status were developed using the features selected by the supervised ML algorithms, and evaluated using the classical diagnostic indicators. Then, a nomogram was developed based on the model with the highest area under the curve (AUC) and the lowest FPR, and validated by the calibration plots. RESULTS In total, 109 LN- and 151 LN + patients were enrolled in this study. 6 independent prediction models were developed to differentiate LN- from LN + patients using the selected features from clinico-biological-image dataset, radiomics dataset, and their combined CBR dataset, respectively. The DeLong test showed that the CBR Model containing all-scale features held the highest predictive efficiency and the lowest FPR among all of established models (p < 0.05) in both the training and test sets (AUCs of 0.90 and 0.89, FPRs of 12.82% and 6.45%, respectively) (p < 0.05). The quantitative nomogram based on CBR Model was validated to have a good consistency with actual observations. CONCLUSION This study presents an integrated CBR nomogram that can further reduce the FPR and improve the accuracy of hypermetabolic mediastinal-hilar LNs evaluation than conventional PET/CT in lung cancer, thereby greatly reducing the risk of overestimation and assisting for precision treatment.
Collapse
Affiliation(s)
- Caiyue Ren
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Fuquan Zhang
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Jiangang Zhang
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
| | - Shaoli Song
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, 201315, China
- Center for Biomedical Imaging, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai, China
| | - Yun Sun
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai, 201315, China.
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China.
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.
| | - Jingyi Cheng
- Shanghai Key Laboratory of Radiation Oncology (20dz2261000), Shanghai, China.
- Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai, China.
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai, 201315, China.
| |
Collapse
|
26
|
Chen Y, Kan K, Liu S, Lin H, Lue K. Impact of respiratory motion on 18 F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner. J Appl Clin Med Phys 2023; 24:e14200. [PMID: 37937706 PMCID: PMC10691638 DOI: 10.1002/acm2.14200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/13/2023] [Accepted: 10/30/2023] [Indexed: 11/09/2023] Open
Abstract
PURPOSE 18 F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18 F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18 F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner. MATERIALS AND METHODS A total of 101 patients who underwent oncological 18 F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. 18 F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability. RESULTS In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most 18 F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features. CONCLUSION Respiratory motion has a significant impact on 18 F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.
Collapse
Affiliation(s)
- Yu‐Hung Chen
- Department of Nuclear MedicineHualien Tzu Chi HospitalBuddhist Tzu Chi Medical FoundationHualienTaiwan
- School of MedicineCollege of MedicineTzu Chi UniversityHualienTaiwan
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
| | - Kuo‐Yi Kan
- Department of Nuclear MedicineFu Jen Catholic University HospitalNew Taipei CityTaiwan
| | - Shu‐Hsin Liu
- Department of Nuclear MedicineHualien Tzu Chi HospitalBuddhist Tzu Chi Medical FoundationHualienTaiwan
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
| | - Hsin‐Hon Lin
- Department of Medical Imaging and Radiological SciencesCollege of MedicineChang Gung UniversityTaoyuanTaiwan
- Department of Nuclear MedicineChang Gung Memorial HospitalLinkouTaiwan
| | - Kun‐Han Lue
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
| |
Collapse
|
27
|
Fuchs T, Kaiser L, Müller D, Papp L, Fischer R, Tran-Gia J. Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data. Nuklearmedizin 2023; 62:389-398. [PMID: 37907246 PMCID: PMC10689089 DOI: 10.1055/a-2187-5701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 09/21/2023] [Indexed: 11/02/2023]
Abstract
Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions.
Collapse
Affiliation(s)
- Timo Fuchs
- Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany
- Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
| | - Lena Kaiser
- Department of Nuclear Medicine, LMU University Hospital, LMU, Munich, Germany
| | - Dominik Müller
- IT-Infrastructure for Translational Medical Research, University of Augsburg, Augsburg, Germany
- Medical Data Integration Center, University Hospital Augsburg, Augsburg, Germany
| | - Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Wien, Austria
| | - Regina Fischer
- Medical Data Integration Center (MEDIZUKR), University Hospital Regensburg, Regensburg, Germany
- Partner Site Regensburg, Bavarian Center for Cancer Research (BZKF), Regensburg, Germany
| | - Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Wurzburg, Germany
| |
Collapse
|
28
|
Huang W, Tao Z, Younis MH, Cai W, Kang L. Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives. VIEW 2023; 4:20230032. [PMID: 38179181 PMCID: PMC10766416 DOI: 10.1002/viw.20230032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 07/20/2023] [Indexed: 01/06/2024] Open
Abstract
Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.
Collapse
Affiliation(s)
- Wenpeng Huang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Zihao Tao
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| | - Muhsin H. Younis
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Weibo Cai
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lei Kang
- Department of Nuclear Medicine, Peking University First Hospital, Beijing, China
| |
Collapse
|
29
|
Lohmann P, Bundschuh RA, Miederer I, Mottaghy FM, Langen KJ, Galldiks N. Clinical Applications of Radiomics in Nuclear Medicine. Nuklearmedizin 2023; 62:354-360. [PMID: 37935406 DOI: 10.1055/a-2191-3271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2023]
Abstract
Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine. This review article examines the current state of radiomics in nuclear medicine and shows its potential to improve patient care. Selected clinical applications for diseases such as cancer, neurodegenerative diseases, cardiovascular problems and thyroid diseases are examined. The article concludes with a brief classification in terms of future perspectives and strategies for linking research findings to clinical practice.
Collapse
Affiliation(s)
- Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-3/-4), Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Isabelle Miederer
- Department of Nuclear Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Felix M Mottaghy
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
- Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Karl Josef Langen
- Institute of Neuroscience and Medicine (INM-3/-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Norbert Galldiks
- Faculty of Medicine and University Hospital Cologne, Department of Neurology, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3/-4), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| |
Collapse
|
30
|
Shang Y, Chen W, Li G, Huang Y, Wang Y, Kui X, Li M, Zheng H, Zhao W, Liu J. Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma. LA RADIOLOGIA MEDICA 2023; 128:1483-1496. [PMID: 37749461 PMCID: PMC10700425 DOI: 10.1007/s11547-023-01722-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 09/04/2023] [Indexed: 09/27/2023]
Abstract
OBJECTIVE To investigate the value of Computed Tomography (CT) radiomics derived from different peritumoral volumes of interest (VOIs) in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients. MATERIALS AND METHODS A retrospective cohort of 779 patients who had pathologically confirmed lung adenocarcinoma were enrolled. 640 patients were randomly divided into a training set, a validation set, and an internal testing set (3:1:1), and the remaining 139 patients were defined as an external testing set. The intratumoral VOI (VOI_I) was manually delineated on the thin-slice CT images, and seven peritumoral VOIs (VOI_P) were automatically generated with 1, 2, 3, 4, 5, 10, and 15 mm expansion along the VOI_I. 1454 radiomic features were extracted from each VOI. The t-test, the least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) algorithm were used for feature selection, followed by the construction of radiomics models (VOI_I model, VOI_P model and combined model). The performance of the models were evaluated by the area under the curve (AUC). RESULTS 399 patients were classified as EGFR mutant (EGFR+), while 380 were wild-type (EGFR-). In the training and validation sets, internal and external testing sets, VOI4 (intratumoral and peritumoral 4 mm) model achieved the best predictive performance, with AUCs of 0.877, 0.727, and 0.701, respectively, outperforming the VOI_I model (AUCs of 0.728, 0.698, and 0.653, respectively). CONCLUSIONS Radiomics extracted from peritumoral region can add extra value in predicting EGFR mutation status of lung adenocarcinoma patients, with the optimal peritumoral range of 4 mm.
Collapse
Affiliation(s)
- Youlan Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
| | - Weidao Chen
- Infervision, Chaoyang District, Beijing, 100025, China
| | - Ge Li
- Department of Radiology, Xiangya Hospital, Central South University, No. 87 Xiangya Rd, Changsha, 410008, Hunan, People's Republic of China
| | - Yijie Huang
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
| | - Yisong Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
| | - Xiaoyan Kui
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China
| | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, People's Republic of China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Wei Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China.
- Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China.
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan Province, People's Republic of China.
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China.
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan Province, People's Republic of China.
| |
Collapse
|
31
|
Schurink NW, van Kranen SR, van Griethuysen JJM, Roberti S, Snaebjornsson P, Bakers FCH, de Bie SH, Bosma GPT, Cappendijk VC, Geenen RWF, Neijenhuis PA, Peterson GM, Veeken CJ, Vliegen RFA, Peters FP, Bogveradze N, El Khababi N, Lahaye MJ, Maas M, Beets GL, Beets-Tan RGH, Lambregts DMJ. Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer. Eur Radiol 2023; 33:8889-8898. [PMID: 37452176 PMCID: PMC10667134 DOI: 10.1007/s00330-023-09920-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
OBJECTIVES To develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset. METHODS Baseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1-2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups: (1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval (2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation (3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length (4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC) Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97). RESULTS After external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48-0.72) to predict complete response and 0.65 (95%CI=0.53-0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables. CONCLUSIONS Overall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset). CLINICAL RELEVANCE STATEMENT Predicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization. KEY POINTS This multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer. Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates. No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.
Collapse
Affiliation(s)
- Niels W Schurink
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Simon R van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joost J M van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Sander Roberti
- Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Petur Snaebjornsson
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Frans C H Bakers
- Department of Radiology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Shira H de Bie
- Department of Radiology, Deventer Ziekenhuis, Schalkhaar, The Netherlands
| | - Gerlof P T Bosma
- Department of Interventional Radiology, Elisabeth Tweesteden Hospital, Tilburg, The Netherlands
| | - Vincent C Cappendijk
- Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - Remy W F Geenen
- Department of Radiology, Northwest Clinics, Alkmaar, The Netherlands
| | | | | | - Cornelis J Veeken
- Department of Radiology, IJsselland Hospital, Capelle aan den IJssel, The Netherlands
| | - Roy F A Vliegen
- Department of Radiology, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Femke P Peters
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Radiology, Acad. F. Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia
| | - Najim El Khababi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Max J Lahaye
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
| | - Geerard L Beets
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Department of Surgery, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands
- Institute of Regional Health Research, University of Southern Denmark, Vejle, Denmark
| | - Doenja M J Lambregts
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- GROW School for Oncology & Developmental Biology, University of Maastricht, Maastricht, The Netherlands.
| |
Collapse
|
32
|
Luining WI, Oprea-Lager DE, Vis AN, van Moorselaar RJA, Knol RJJ, Wondergem M, Boellaard R, Cysouw MCF. Optimization and validation of 18F-DCFPyL PET radiomics-based machine learning models in intermediate- to high-risk primary prostate cancer. PLoS One 2023; 18:e0293672. [PMID: 37943772 PMCID: PMC10635444 DOI: 10.1371/journal.pone.0293672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
INTRODUCTION Radiomics extracted from prostate-specific membrane antigen (PSMA)-PET modeled with machine learning (ML) may be used for prediction of disease risk. However, validation of previously proposed approaches is lacking. We aimed to optimize and validate ML models based on 18F-DCFPyL-PET radiomics for the prediction of lymph-node involvement (LNI), extracapsular extension (ECE), and postoperative Gleason score (GS) in primary prostate cancer (PCa) patients. METHODS Patients with intermediate- to high-risk PCa who underwent 18F-DCFPyL-PET/CT before radical prostatectomy with pelvic lymph-node dissection were evaluated. The training dataset included 72 patients, the internal validation dataset 24 patients, and the external validation dataset 27 patients. PSMA-avid intra-prostatic lesions were delineated semi-automatically on PET and 480 radiomics features were extracted. Conventional PET-metrics were derived for comparative analysis. Segmentation, preprocessing, and ML methods were optimized in repeated 5-fold cross-validation (CV) on the training dataset. The trained models were tested on the combined validation dataset. Combat harmonization was applied to external radiomics data. Model performance was assessed using the receiver-operating-characteristics curve (AUC). RESULTS The CV-AUCs in the training dataset were 0.88, 0.79 and 0.84 for LNI, ECE, and GS, respectively. In the combined validation dataset, the ML models could significantly predict GS with an AUC of 0.78 (p<0.05). However, validation AUCs for LNI and ECE prediction were not significant (0.57 and 0.63, respectively). Conventional PET metrics-based models had comparable AUCs for LNI (0.59, p>0.05) and ECE (0.66, p>0.05), but a lower AUC for GS (0.73, p<0.05). In general, Combat harmonization improved external validation AUCs (-0.03 to +0.18). CONCLUSION In internal and external validation, 18F-DCFPyL-PET radiomics-based ML models predicted high postoperative GS but not LNI or ECE in intermediate- to high-risk PCa. Therefore, the clinical benefit seems to be limited. These results underline the need for external and/or multicenter validation of PET radiomics-based ML model analyses to assess their generalizability.
Collapse
Affiliation(s)
- Wietske I. Luining
- Department of Urology, Amsterdam University Medical Centers, Prostate Cancer Network Netherlands, Amsterdam, The Netherlands
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Daniela E. Oprea-Lager
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - André N. Vis
- Department of Urology, Amsterdam University Medical Centers, Prostate Cancer Network Netherlands, Amsterdam, The Netherlands
| | - Reindert J. A. van Moorselaar
- Department of Urology, Amsterdam University Medical Centers, Prostate Cancer Network Netherlands, Amsterdam, The Netherlands
| | - Remco J. J. Knol
- Department of Nuclear Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | - Maurits Wondergem
- Department of Nuclear Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Matthijs C. F. Cysouw
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Centers, Cancer Center Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
33
|
Kocak B, Chepelev LL, Chu LC, Cuocolo R, Kelly BS, Seeböck P, Thian YL, van Hamersvelt RW, Wang A, Williams S, Witowski J, Zhang Z, Pinto Dos Santos D. Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology. Eur Radiol 2023; 33:7556-7560. [PMID: 37358612 DOI: 10.1007/s00330-023-09768-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 03/24/2023] [Accepted: 04/14/2023] [Indexed: 06/27/2023]
Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey.
| | - Leonid L Chepelev
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Linda C Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy
| | - Brendan S Kelly
- Department of Radiology, St Vincent's University Hospital, Dublin, Ireland
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
| | - Philipp Seeböck
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Yee Liang Thian
- Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore
| | - Robbert W van Hamersvelt
- Department of Radiology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Alan Wang
- Centre for Medical Imaging & Centre for Brain Research, Faculty of Medical and Health Sciences, Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Stuart Williams
- Department of Radiology, Norfolk & Norwich University Hospital, Norwich, UK
| | - Jan Witowski
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | | | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Institute for Diagnostic and Interventional Radiology, Goethe-University Frankfurt am Main, Frankfurt, Germany
| |
Collapse
|
34
|
Xu H, Lv W, Zhang H, Yuan Q, Wang Q, Wu Y, Lu L. Multimodality radiomics analysis based on [ 18F]FDG PET/CT imaging and multisequence MRI: application to nasopharyngeal carcinoma prognosis. Eur Radiol 2023; 33:6677-6688. [PMID: 37060444 DOI: 10.1007/s00330-023-09606-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 01/02/2023] [Accepted: 02/13/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVES To determine whether radiomics models developed from 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT combined with multisequence MRI could contribute to predicting the progression-free survival (PFS) of nasopharyngeal carcinoma (NPC) patients. METHODS One hundred thirty-two NPC patients who underwent both PET/CT and MRI scanning were retrospectively enrolled (88 vs. 44 for training vs. testing). For each modality/sequence (i.e., PET, CT, T1, T1C, and T2), 1906 radiomics features were extracted from the primary tumor volume. Univariate Cox model and correlation analysis were used for feature selection. A multivariate Cox model was used to establish radiomics signature. Prognostic performances of 5 individual modality models and 12 multimodality models (3 integrations × 4 fusion strategies) were assessed by the concordance index (C-index) and log-rank test. A clinical-radiomics nomogram was built to explore the clinical utilities of radiomics signature, which was evaluated by discrimination, calibration curve, and decision curve analysis (DCA). RESULTS The radiomics signatures of individual modalities showed limited prognostic efficacy with a C-index of 0.539-0.664 in the testing cohort. Different fusion strategies exhibited a slight difference in predictive performance. The PET/CT and MRI integrated model achieved the best performance with a C-index of 0.745 (95% CI, 0.619-0.865) in the testing cohort (log-rank test, p < 0.05). Clinical-radiomics nomogram further improved the prognosis, which also showed satisfactory discrimination, calibration, and net benefit. CONCLUSIONS Multimodality radiomics analysis by combining PET/CT with multisequence MRI could potentially improve the efficacy of PFS prediction for NPC patients. KEY POINTS • Individual modality radiomics models showed limited performance in prognosis evaluation for NPC patients. • Combined PET, CT and multisequence MRI radiomics signature could improve the prognostic efficacy. • Multilevel fusion strategies exhibit comparable performance but feature-level fusion deserves more attention.
Collapse
Affiliation(s)
- Hui Xu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Provincial Key Laboratory of Medial Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Pazhou Lab, Guangzhou, 510330, China
| | - Wenbing Lv
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Provincial Key Laboratory of Medial Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Pazhou Lab, Guangzhou, 510330, China
| | - Hao Zhang
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Qingyu Yuan
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Quanshi Wang
- Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Yuankui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Lijun Lu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- Guangdong Provincial Key Laboratory of Medial Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- Pazhou Lab, Guangzhou, 510330, China.
| |
Collapse
|
35
|
Zhao F, Huang X, He J, Li J, Li Q, Wei F, Chen H, Su J. A nomogram for distinguishing benign and malignant parotid gland tumors using clinical data and preoperative blood markers: development and validation. J Cancer Res Clin Oncol 2023; 149:11719-11733. [PMID: 37402966 DOI: 10.1007/s00432-023-05032-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 06/27/2023] [Indexed: 07/06/2023]
Abstract
PURPOSE This study aimed to construct and validate a nomogram that incorporated clinical data and preoperative blood markers to differentiate BPGTs from MPGTs more efficiently and at low cost. METHODS We retrospectively analyzed patients who underwent parotidectomy and histopathological diagnosis at the First Affiliated Hospital of Guangxi Medical University from January 2013 to June 2022. Subjects were randomly divided into training and validation sets with a 7:3 ratio. In the training set, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select the most relevant features from 19 variables and built a nomogram using logistic regression. We evaluated the model's performance using receiver-operating characteristic (ROC) curves, calibration curves, clinical decision curve analysis (DCA), and clinical impact curve analysis (CICA). RESULTS The final sample consisted of 644 patients, of whom 108 (16.77%) had MPGTs. The nomogram included four features: current smoking status, pain/tenderness, peripheral facial paralysis, and lymphocyte-to-monocyte ratio (LMR). The optimal cut-off value for the nomogram was 0.17. The areas under the ROC curves (AUCs) of the nomogram were 0.748 (95% confidence interval [CI] = 0.689-0.807) and 0.754 (95% CI = 0.636-0.872) in the training and validation sets, respectively. The nomogram also showed good calibration, high accuracy, moderate sensitivity, and acceptable specificity in both sets. The DCA and CICA demonstrated that the nomogram had significant net benefits for a wide range of threshold probabilities (0.06-0.88 for the training set; 0.06-0.57 and 0.73-0.95 for the validation set). CONCLUSION The nomogram based on clinical characteristics and preoperative blood markers was a reliable tool for discriminating BPGTs from MPGTs preoperatively.
Collapse
Affiliation(s)
- Feng Zhao
- Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xiaoying Huang
- Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Department of Otolaryngology, The Guangxi International Zhuang Medicine Hospital, Nanning, China
| | - Junkun He
- Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jiangmiao Li
- Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Qiyun Li
- Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Fangyu Wei
- Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Huiying Chen
- Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jiping Su
- Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
| |
Collapse
|
36
|
Zhou Y, Zhang B, Han J, Dai N, Jia T, Huang H, Deng S, Sang S. Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells. J Cancer Res Clin Oncol 2023; 149:11549-11560. [PMID: 37395846 DOI: 10.1007/s00432-023-05038-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/28/2023] [Indexed: 07/04/2023]
Abstract
BACKGROUND In our current work, an 18F-FDG PET/CT radiomics-based model was developed to assess the progression-free survival (PFS) and overall survival (OS) of patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who received chimeric antigen receptor (CAR)-T cell therapy. METHODS A total of 61 DLBCL cases receiving 18F-FDG PET/CT before CAR-T cell infusion were included in the current analysis, and these patients were randomly assigned to a training cohort (n = 42) and a validation cohort (n = 19). Radiomic features from PET and CT images were obtained using LIFEx software, and radiomics signatures (R-signatures) were then constructed by choosing the optimal parameters according to their PFS and OS. Subsequently, the radiomics model and clinical model were constructed and validated. RESULTS The radiomics model that integrated R-signatures and clinical risk factors showed superior prognostic performance compared with the clinical models in terms of both PFS (C-index: 0.710 vs. 0.716; AUC: 0.776 vs. 0.712) and OS (C-index: 0.780 vs. 0.762; AUC: 0.828 vs. 0.728). For validation, the C-index of the two approaches was 0.640 vs. 0.619 and 0.676 vs. 0.699 for predicting PFS and OS, respectively. Moreover, the AUC was 0.886 vs. 0.635 and 0.778 vs. 0.705, respectively. The calibration curves indicated good agreement, and the decision curve analysis suggested that the net benefit of radiomics models was higher than that of clinical models. CONCLUSIONS PET/CT-derived R-signature could be a potential prognostic biomarker for R/R DLBCL patients undergoing CAR-T cell therapy. Moreover, the risk stratification could be further enhanced when the PET/CT-derived R-signature was combined with clinical factors.
Collapse
Affiliation(s)
- Yeye Zhou
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Bin Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Jiangqin Han
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Na Dai
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Tongtong Jia
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Haiwen Huang
- Institute of Blood and Marrow Transplantation, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
| | - Shengming Deng
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, 215123, China.
| | - Shibiao Sang
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
| |
Collapse
|
37
|
Marturano F, Guglielmo P, Bettinelli A, Zattoni F, Novara G, Zorz A, Sepulcri M, Gregianin M, Paiusco M, Evangelista L. Role of radiomic analysis of [ 18F]fluoromethylcholine PET/CT in predicting biochemical recurrence in a cohort of intermediate and high risk prostate cancer patients at initial staging. Eur Radiol 2023; 33:7199-7208. [PMID: 37079030 PMCID: PMC10511374 DOI: 10.1007/s00330-023-09642-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 04/21/2023]
Abstract
AIM To study the feasibility of radiomic analysis of baseline [18F]fluoromethylcholine positron emission tomography/computed tomography (PET/CT) for the prediction of biochemical recurrence (BCR) in a cohort of intermediate and high-risk prostate cancer (PCa) patients. MATERIAL AND METHODS Seventy-four patients were prospectively collected. We analyzed three prostate gland (PG) segmentations (i.e., PGwhole: whole PG; PG41%: prostate having standardized uptake value - SUV > 0.41*SUVmax; PG2.5: prostate having SUV > 2.5) together with three SUV discretization steps (i.e., 0.2, 0.4, and 0.6). For each segmentation/discretization step, we trained a logistic regression model to predict BCR using radiomic and/or clinical features. RESULTS The median baseline prostate-specific antigen was 11 ng/mL, the Gleason score was > 7 for 54% of patients, and the clinical stage was T1/T2 for 89% and T3 for 9% of patients. The baseline clinical model achieved an area under the receiver operating characteristic curve (AUC) of 0.73. Performances improved when clinical data were combined with radiomic features, in particular for PG2.5 and 0.4 discretization, for which the median test AUC was 0.78. CONCLUSION Radiomics reinforces clinical parameters in predicting BCR in intermediate and high-risk PCa patients. These first data strongly encourage further investigations on the use of radiomic analysis to identify patients at risk of BCR. CLINICAL RELEVANCE STATEMENT The application of AI combined with radiomic analysis of [18F]fluoromethylcholine PET/CT images has proven to be a promising tool to stratify patients with intermediate or high-risk PCa in order to predict biochemical recurrence and tailor the best treatment options. KEY POINTS • Stratification of patients with intermediate and high-risk prostate cancer at risk of biochemical recurrence before initial treatment would help determine the optimal curative strategy. • Artificial intelligence combined with radiomic analysis of [18F]fluorocholine PET/CT images allows prediction of biochemical recurrence, especially when radiomic features are complemented with patients' clinical information (highest median AUC of 0.78). • Radiomics reinforces the information of conventional clinical parameters (i.e., Gleason score and initial prostate-specific antigen level) in predicting biochemical recurrence.
Collapse
Affiliation(s)
- Francesca Marturano
- Department of Medical Physics, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Priscilla Guglielmo
- Nuclear Medicine Unit, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Andrea Bettinelli
- Department of Medical Physics, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy.
- Department of Information Engineering, University of Padua, Padua, Italy.
| | - Fabio Zattoni
- Department of Surgical Oncological & Gastroenterological Sciences (DiSCOG), University of Padua, Padua, Italy
- Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy
| | - Giacomo Novara
- Department of Surgical Oncological & Gastroenterological Sciences (DiSCOG), University of Padua, Padua, Italy
- Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy
| | - Alessandra Zorz
- Department of Medical Physics, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Matteo Sepulcri
- Radiotherapy Unit, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Michele Gregianin
- Nuclear Medicine Unit, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Marta Paiusco
- Department of Medical Physics, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy
| | - Laura Evangelista
- Nuclear Medicine Unit, Department of Medicine DIMED, University of Padua, Padua, Italy
| |
Collapse
|
38
|
Huynh BN, Groendahl AR, Tomic O, Liland KH, Knudtsen IS, Hoebers F, van Elmpt W, Malinen E, Dale E, Futsaether CM. Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics. Front Med (Lausanne) 2023; 10:1217037. [PMID: 37711738 PMCID: PMC10498924 DOI: 10.3389/fmed.2023.1217037] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/07/2023] [Indexed: 09/16/2023] Open
Abstract
Background Radiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI. Purpose The purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment 18F-fluorodeoxuglucose positron emission tomography (FDG PET) and computed tomography (CT) images. Materials and methods FDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS; n = 139) and Maastricht University Medical Center (MAASTRO; n = 99) were collected retrospectively. OUS data was used for model training and initial evaluation. MAASTRO data was used for external testing to assess cross-institutional generalizability. Models trained on clinical and/or conventional radiomics features, with or without feature selection, were compared to CNNs trained on PET/CT images without or with the gross tumor volume (GTV) included. Model performance was measured using accuracy, area under the receiver operating characteristic curve (AUC), Matthew's correlation coefficient (MCC), and the F1 score calculated for both classes separately. Results CNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours. Conclusion High performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.
Collapse
Affiliation(s)
- Bao Ngoc Huynh
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | | | - Oliver Tomic
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Kristian Hovde Liland
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Ingerid Skjei Knudtsen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands
| | - Eirik Malinen
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Einar Dale
- Department of Oncology, Oslo University Hospital, Oslo, Norway
| | | |
Collapse
|
39
|
Lu D, Yan Y, Jiang M, Sun S, Jiang H, Lu Y, Zhang W, Zhou X. Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis. Front Oncol 2023; 13:1173090. [PMID: 37664048 PMCID: PMC10469000 DOI: 10.3389/fonc.2023.1173090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
Purpose This study summarized the previously-published studies regarding the use of radiomics-based predictive models for the identification of breast cancer-associated prognostic factors, which can help clinical decision-making and follow-up strategy. Materials and methods This study has been pre-registered on PROSPERO. PubMed, Embase, Cochrane Library, and Web of Science were searched, from inception to April 23, 2022, for studies that used radiomics for prognostic prediction of breast cancer patients. Then the search was updated on July 18, 2023. Quality assessment was conducted using the Radiomics Quality Score, and meta-analysis was performed using R software. Results A total of 975 articles were retrieved, and 13 studies were included, involving 5014 participants and 35 prognostic models. Among the models, 20 models were radiomics-based and the other 15 were based on clinical or pathological information. The primary outcome was Disease-free Survival (DFS). The retrieved studies were screened using LASSO, and Cox Regression was applied for modeling. The mean RQS was 18. The c-index of radiomics-based models for DFS prediction was 0.763 (95%CI 0.718-0.810) in the training set and 0.702 (95%CI 0.637-0.774) in the validation set. The c-index of combination models was 0.807 (95%CI0.736-0.885) in the training set and 0.840 (95%CI 0.794-0.888) in the validation set. There was no significant change in the c-index of DFS at 1, 2, 3, and over 5 years of follow-up. Conclusion This study has proved that radiomics-based prognostic models are of great predictive performance for the prognosis of breast cancer patients. combination model shows significantly enhanced predictive performance. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022332392.
Collapse
Affiliation(s)
- Dongmei Lu
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yuke Yan
- The Second Department of General Surgery, Gansu Provincial Hospital, Lanzhou, China
| | - Min Jiang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Shaoqin Sun
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Haifeng Jiang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yashan Lu
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Wenwen Zhang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Xing Zhou
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| |
Collapse
|
40
|
Song Y, Zhou G, Zhou Y, Xu Y, Zhang J, Zhang K, He P, Chen M, Liu Y, Sun J, Hu C, Li M, Liao M, Zhang Y, Liao W, Zhou Y. Artificial intelligence CT radiomics to predict early recurrence of intrahepatic cholangiocarcinoma: a multicenter study. Hepatol Int 2023; 17:1016-1027. [PMID: 36821045 DOI: 10.1007/s12072-023-10487-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/13/2023] [Indexed: 02/24/2023]
Abstract
OBJECTIVES In this multicenter study, we sought to develop and validate a preoperative model for predicting early recurrence (ER) risk after curative resection of intrahepatic cholangiocarcinoma (ICC) through artificial intelligence (AI)-based CT radiomics approach. MATERIALS AND METHODS A total of 311 patients (Derivation: 160; Internal and two external validations: 36, 74 and 61) from 8 medical centers who underwent curative resection were collected retrospectively. In derivation cohort, radiomics and clinical-radiomics models for ER prediction were constructed by LightGBM (a machine learning algorithm). A clinical model was also developed for comparison. Model performance was validated in internal and two external cohorts by ROC. In addition, we investigated the interpretability of the LightGBM model. RESULTS The combined clinical-radiomics model that included 15 radiomic features and 3 clinical features (CA19-9 > 1000 U/ml, vascular invasion and tumor margin), resulting in the area under the curves (AUCs) of 0.974 (95% CI 0.946-1.000) in the derivation cohort, and 0.871-0.882 (95% CI 0.672-0.962) in the internal and external validation cohorts, respectively, which are higher than the AJCC 8th TNM staging system (AUCs: 0.686-0.717, p all < 0.05). Especially, the sensitivity of this machine learning model could reach 94.6% on average for all the cohorts. CONCLUSIONS This AI-driven combined radiomics model may provide as a useful tool to preoperatively predict ER and improve therapeutic management of ICC patients.
Collapse
Affiliation(s)
- Yangda Song
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Guangyao Zhou
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Yucheng Zhou
- Department of General Surgery, Hospital of Integrated TCM and Western Medicine, Southern Medical University, Guangzhou, 510315, China
| | - Yikai Xu
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Jing Zhang
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Ketao Zhang
- Department of Hepatobiliary Surgery, Shunde Hospital of Southern Medical University, Foshan, 528308, Guangdong, China
| | - Pengyuan He
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong, China
| | - Maowei Chen
- Department of Infectious Diseases, Wuming Hospital of Guangxi Medical University, Nanning, 530199, Guangxi, China
| | - Yanping Liu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Gastroenterology, Second Affiliated Hospital, University of South China, Hengyang, 421001, Hunan, China
| | - Jiarun Sun
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Chengguang Hu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
| | - Meng Li
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Minjun Liao
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | | | - Weijia Liao
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi, China.
| | - Yuanping Zhou
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Ave, Guangzhou, 510515, China.
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| |
Collapse
|
41
|
Kocak B, Yardimci AH, Nazli MA, Yuzkan S, Mutlu S, Guzelbey T, Sam Ozdemir M, Akin M, Yucel S, Bulut E, Bayrak ON, Okumus AA. REliability of consensus-based segMentatIoN in raDiomic feature reproducibility (REMIND): A word of caution. Eur J Radiol 2023; 165:110893. [PMID: 37285646 DOI: 10.1016/j.ejrad.2023.110893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/01/2023] [Accepted: 05/23/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVE To evaluate the reliability of consensus-based segmentation in terms of reproducibility of radiomic features. METHODS In this retrospective study, three tumor data sets were investigated: breast cancer (n = 30), renal cell carcinoma (n = 30), and pituitary macroadenoma (n = 30). MRI was utilized for breast and pituitary data sets, while CT was used for renal data set. 12 readers participated in the segmentation process. Consensus segmentation was created by making corrections on a previous region or volume of interest. Four experiments were designed to evaluate the reproducibility of radiomic features. Reliability was assessed with intraclass correlation coefficient (ICC) with two cut-off values: 0.75 and 0.9. RESULTS Considering the lower bound of the 95% confidence interval and the ICC threshold of 0.90, at least 61% of the radiomic features were not reproducible in the inter-consensus analysis. In the susceptibility experiment, at least half (54%) became non-reproducible when the first reader is replaced with a different reader. In the intra-consensus analysis, at least about one-third (32%) were non-reproducible when the same second reader segmented the image over the same first reader two weeks later. Compared to inter-reader analysis based on independent single readers, the inter-consensus analysis did not statistically significantly improve the rates of reproducible features in all data sets and analyses. CONCLUSIONS Despite the positive connotation of the word "consensus", it is essential to REMIND that consensus-based segmentation has significant reproducibility issues. Therefore, the usage of consensus-based segmentation alone should be avoided unless a reliability analysis is performed, even if it is not practical in clinical settings.
Collapse
Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey.
| | - Aytul Hande Yardimci
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Mehmet Ali Nazli
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Sabahattin Yuzkan
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Samet Mutlu
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Tevfik Guzelbey
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Merve Sam Ozdemir
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Meliha Akin
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Serap Yucel
- Department of Radiology, Baskent University, Istanbul Hospital, Istanbul, Turkey
| | - Elif Bulut
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Osman Nuri Bayrak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| | - Ahmet Arda Okumus
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey
| |
Collapse
|
42
|
Balma M, Laudicella R, Gallio E, Gusella S, Lorenzon L, Peano S, Costa RP, Rampado O, Farsad M, Evangelista L, Deandreis D, Papaleo A, Liberini V. Applications of Artificial Intelligence and Radiomics in Molecular Hybrid Imaging and Theragnostics for Neuro-Endocrine Neoplasms (NENs). Life (Basel) 2023; 13:1647. [PMID: 37629503 PMCID: PMC10455722 DOI: 10.3390/life13081647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/12/2023] [Accepted: 07/25/2023] [Indexed: 08/27/2023] Open
Abstract
Nuclear medicine has acquired a crucial role in the management of patients with neuroendocrine neoplasms (NENs) by improving the accuracy of diagnosis and staging as well as their risk stratification and personalized therapies, including radioligand therapies (RLT). Artificial intelligence (AI) and radiomics can enable physicians to further improve the overall efficiency and accuracy of the use of these tools in both diagnostic and therapeutic settings by improving the prediction of the tumor grade, differential diagnosis from other malignancies, assessment of tumor behavior and aggressiveness, and prediction of treatment response. This systematic review aims to describe the state-of-the-art AI and radiomics applications in the molecular imaging of NENs.
Collapse
Affiliation(s)
- Michele Balma
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100 Cuneo, Italy; (S.P.); (A.P.); (V.L.)
| | - Riccardo Laudicella
- Unit of Nuclear Medicine, Biomedical Department of Internal and Specialist Medicine, University of Palermo, 90133 Palermo, Italy; (R.L.); (R.P.C.)
| | - Elena Gallio
- Medical Physics Unit, A.O.U. Città Della Salute E Della Scienza Di Torino, Corso Bramante 88/90, 10126 Torino, Italy; (E.G.); (O.R.)
| | - Sara Gusella
- Nuclear Medicine, Central Hospital Bolzano, 39100 Bolzano, Italy; (S.G.); (M.F.)
| | - Leda Lorenzon
- Medical Physics Department, Central Bolzano Hospital, 39100 Bolzano, Italy;
| | - Simona Peano
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100 Cuneo, Italy; (S.P.); (A.P.); (V.L.)
| | - Renato P. Costa
- Unit of Nuclear Medicine, Biomedical Department of Internal and Specialist Medicine, University of Palermo, 90133 Palermo, Italy; (R.L.); (R.P.C.)
| | - Osvaldo Rampado
- Medical Physics Unit, A.O.U. Città Della Salute E Della Scienza Di Torino, Corso Bramante 88/90, 10126 Torino, Italy; (E.G.); (O.R.)
| | - Mohsen Farsad
- Nuclear Medicine, Central Hospital Bolzano, 39100 Bolzano, Italy; (S.G.); (M.F.)
| | - Laura Evangelista
- Department of Biomedical Sciences, Humanitas University, 20089 Milan, Italy;
| | - Desiree Deandreis
- Department of Nuclear Medicine and Endocrine Oncology, Gustave Roussy and Université Paris Saclay, 94805 Villejuif, France;
| | - Alberto Papaleo
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100 Cuneo, Italy; (S.P.); (A.P.); (V.L.)
| | - Virginia Liberini
- Nuclear Medicine Department, S. Croce e Carle Hospital, 12100 Cuneo, Italy; (S.P.); (A.P.); (V.L.)
| |
Collapse
|
43
|
Jia T, Lv Q, Cai X, Ge S, Sang S, Zhang B, Yu C, Deng S. Radiomic signatures based on pretreatment 18F-FDG PET/CT, combined with clinicopathological characteristics, as early prognostic biomarkers among patients with invasive breast cancer. Front Oncol 2023; 13:1210125. [PMID: 37576897 PMCID: PMC10415070 DOI: 10.3389/fonc.2023.1210125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/06/2023] [Indexed: 08/15/2023] Open
Abstract
Purpose The aim of this study was to investigate the predictive role of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in the prognostic risk stratification of patients with invasive breast cancer (IBC). To achieve this, we developed a clinicopathologic-radiomic-based model (C-R model) and established a nomogram that could be utilized in clinical practice. Methods We retrospectively enrolled a total of 91 patients who underwent preoperative 18F-FDG PET/CT and randomly divided them into training (n=63) and testing cohorts (n=28). Radiomic signatures (RSs) were identified using the least absolute shrinkage and selection operator (LASSO) regression algorithm and used to compute the radiomic score (Rad-score). Patients were assigned to high- and low-risk groups based on the optimal cut-off value of the receiver operating characteristic (ROC) curve analysis for both Rad-score and clinicopathological risk factors. Univariate and multivariate Cox regression analyses were performed to determine the association between these variables and progression-free survival (PFS) or overall survival (OS). We then plotted a nomogram integrating all these factors to validate the predictive performance of survival status. Results The Rad-score, age, clinical M stage, and minimum standardized uptake value (SUVmin) were identified as independent prognostic factors for predicting PFS, while only Rad-score, age, and clinical M stage were found to be prognostic factors for OS in the training cohort. In the testing cohort, the C-R model showed superior performance compared to single clinical or radiomic models. The concordance index (C-index) values for the C-R model, clinical model, and radiomic model were 0.816, 0.772, and 0.647 for predicting PFS, and 0.882, 0.824, and 0.754 for OS, respectively. Furthermore, decision curve analysis (DCA) and calibration curves demonstrated that the C-R model had a good ability for both clinical net benefit and application. Conclusion The combination of clinicopathological risks and baseline PET/CT-derived Rad-score could be used to evaluate the prognosis in patients with IBC. The predictive nomogram based on the C-R model further enhanced individualized estimation and allowed for more accurate prediction of patient outcomes.
Collapse
Affiliation(s)
- Tongtong Jia
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qingfu Lv
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaowei Cai
- Department of Nuclear Medicine, The Affiliated Suqian First People’s Hospital of Nanjing Medical University, Suqian, China
| | - Shushan Ge
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Shibiao Sang
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Bin Zhang
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chunjing Yu
- Department of Nuclear Medicine, Affiliated Hospital of Jiangnan University, Wuxi, China
| | - Shengming Deng
- Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China
| |
Collapse
|
44
|
Jia T, Lv Q, Zhang B, Yu C, Sang S, Deng S. Assessment of androgen receptor expression in breast cancer patients using 18 F-FDG PET/CT radiomics and clinicopathological characteristics. BMC Med Imaging 2023; 23:93. [PMID: 37460990 PMCID: PMC10353086 DOI: 10.1186/s12880-023-01052-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/30/2023] [Indexed: 07/20/2023] Open
Abstract
OBJECTIVE In the present study, we mainly aimed to predict the expression of androgen receptor (AR) in breast cancer (BC) patients by combing radiomic features and clinicopathological factors in a non-invasive machine learning way. MATERIALS AND METHODS A total of 48 BC patients, who were initially diagnosed by 18F-FDG PET/CT, were retrospectively enrolled in this study. LIFEx software was used to extract radiomic features based on PET and CT data. The most useful predictive features were selected by the LASSO (least absolute shrinkage and selection operator) regression and t-test. Radiomic signatures and clinicopathologic characteristics were incorporated to develop a prediction model using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (H-L) test, and decision curve analysis (DCA) were conducted to assess the predictive efficiency of the model. RESULTS In the univariate analysis, the metabolic tumor volume (MTV) was significantly correlated with the expression of AR in BC patients (p < 0.05). However, there only existed feeble correlations between estrogen receptor (ER), progesterone receptor (PR), and AR status (p = 0.127, p = 0.061, respectively). Based on the binary logistic regression method, MTV, SHAPE_SphericityCT (CT Sphericity from SHAPE), and GLCM_ContrastCT (CT Contrast from grey-level co-occurrence matrix) were included in the prediction model for AR expression. Among them, GLCM_ContrastCT was an independent predictor of AR status (OR = 9.00, p = 0.018). The area under the curve (AUC) of ROC in this model was 0.832. The p-value of the H-L test was beyond 0.05. CONCLUSIONS A prediction model combining radiomic features and clinicopathological characteristics could be a promising approach to predict the expression of AR and noninvasively screen the BC patients who could benefit from anti-AR regimens.
Collapse
Affiliation(s)
- Tongtong Jia
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Qingfu Lv
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Bin Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Chunjing Yu
- Department of Nuclear Medicine, Affiliated Hospital of Jiangnan University, Wuxi, 214122, China.
| | - Shibiao Sang
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
| | - Shengming Deng
- Department of Nuclear Medicine, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
| |
Collapse
|
45
|
Wang P, Chen K, Han Y, Zhao M, Abiyasi N, Peng H, Yan S, Shang J, Shang N, Meng W. Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in gastric cancer patients. Future Oncol 2023; 19:1613-1626. [PMID: 37377070 DOI: 10.2217/fon-2022-1025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023] Open
Abstract
Background: We explored whether a model based on contrast-enhanced computed tomography radiomics features and clinicopathological factors can evaluate preoperative lymphovascular invasion (LVI) in patients with gastric cancer (GC) with Lauren classification. Methods: Based on clinical and radiomic characteristics, we established three models: Clinical + Arterial phase_Radcore, Clinical + Venous phase_Radcore and a combined model. The relationship between Lauren classification and LVI was analyzed using a histogram. Results: We retrospectively analyzed 495 patients with GC. The areas under the curve of the combined model were 0.8629 and 0.8343 in the training and testing datasets, respectively. The combined model showed a superior performance to the other models. Conclusion: CECT-based radiomics models can effectively predict preoperative LVI in GC patients with Lauren classification.
Collapse
Affiliation(s)
- Ping Wang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Kaige Chen
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Ying Han
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Min Zhao
- Pharmaceutical Diagnostics, GE Healthcare, Beijing, China, 1#Tongji South Road, Daxing District, Beijing, 100176, China
| | - Nanding Abiyasi
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Haiyong Peng
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Shaolei Yan
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Jiming Shang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Naijian Shang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Wei Meng
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| |
Collapse
|
46
|
Guglielmo P, Marturano F, Bettinelli A, Sepulcri M, Pasello G, Gregianin M, Paiusco M, Evangelista L. Additional Value of PET and CT Image-Based Features in the Detection of Occult Lymph Node Metastases in Lung Cancer: A Systematic Review of the Literature. Diagnostics (Basel) 2023; 13:2153. [PMID: 37443547 DOI: 10.3390/diagnostics13132153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/05/2023] [Accepted: 06/17/2023] [Indexed: 07/15/2023] Open
Abstract
Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([18F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [18F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs.
Collapse
Affiliation(s)
- Priscilla Guglielmo
- Nuclear Medicine Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Francesca Marturano
- Medical Physics Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Andrea Bettinelli
- Medical Physics Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Matteo Sepulcri
- Radiotherapy, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Giulia Pasello
- Department of Surgery, Oncology and Gastroenterology, University of Padua, 35128 Padua, Italy
- Medical Oncology 2, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Michele Gregianin
- Nuclear Medicine Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Marta Paiusco
- Medical Physics Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Laura Evangelista
- Nuclear Medicine Unit, Department of Medicine DIMED, University of Padua, 35128 Padua, Italy
| |
Collapse
|
47
|
Zhong J, Xing Y, Zhang G, Hu Y, Ding D, Ge X, Pan Z, Yin Q, Zhang H, Yang Q, Zhang H, Yao W. A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers. J Orthop Surg Res 2023; 18:414. [PMID: 37287036 DOI: 10.1186/s13018-023-03863-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 05/16/2023] [Indexed: 06/09/2023] Open
Abstract
PURPOSE To systematically assess the quality of radiomics research in giant cell tumor of bone (GCTB) and to test the feasibility of analysis at the level of radiomics feature. METHODS We searched PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data to identify articles of GCTB radiomics until 31 July 2022. The studies were assessed by radiomics quality score (RQS), transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement, checklist for artificial intelligence in medical imaging (CLAIM), and modified quality assessment of diagnostic accuracy studies (QUADAS-2) tool. The radiomic features selected for model development were documented. RESULTS Nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 26%, 56%, and 57%, respectively. The risk of bias and applicability concerns were mainly related to the index test. The shortness in external validation and open science were repeatedly emphasized. In GCTB radiomics models, the gray level co-occurrence matrix features (40%), first order features (28%), and gray-level run-length matrix features (18%) were most selected features out of all reported features. However, none of the individual feature has appeared repeatably in multiple studies. It is not possible to meta-analyze radiomics features at present. CONCLUSION The quality of GCTB radiomics studies is suboptimal. The reporting of individual radiomics feature data is encouraged. The analysis at the level of radiomics feature has potential to generate more practicable evidence for translating radiomics into clinical application.
Collapse
Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Guangcheng Zhang
- Department of Sports Medicine, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Zhen Pan
- Department of Orthopedics, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Qian Yin
- Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Huizhen Zhang
- Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Qingcheng Yang
- Department of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
| |
Collapse
|
48
|
Zhong J, Pan Z, Chen Y, Wang L, Xia Y, Wang L, Li J, Lu W, Shi X, Feng J, Yan F, Zhang H, Yao W. Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability. Insights Imaging 2023; 14:79. [PMID: 37166511 PMCID: PMC10175529 DOI: 10.1186/s13244-023-01426-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 04/05/2023] [Indexed: 05/12/2023] Open
Abstract
OBJECTIVES To evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms. METHODS A phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI70keV). Test-retest repeatability was assessed by Bland-Altman analysis. Inter-platform reproducibility of VUE images and VMI70keV was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD) among platforms, and by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) between platform pairs. The correlation between variability of CT number radiomics reproducibility was estimated. RESULTS 92.02% and 92.87% of features were repeatable between scan-rescans for VUE images and VMI70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI70keV were with CV < 10% and QCD < 10%. The average percentages of radiomics features with ICC > 0.90 and CCC > 0.90 between platform pairs were 10.00% and 9.86% in VUE images and 11.23% and 11.23% in VMI70keV. The CT number inter-platform reproducibility using CV and QCD showed negative correlations with percentage of the first-order radiomics features with CV < 10% and QCD < 10%, in both VUE images and VMI70keV (r2 0.3870-0.6178, all p < 0.001). CONCLUSIONS The majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation. Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.
Collapse
Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Zilai Pan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yihan Xia
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Lan Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Jianying Li
- Computed Tomography Research Center, GE Healthcare, Beijing, 100176, China
| | - Wei Lu
- Computed Tomography Research Center, GE Healthcare, Shanghai, 201203, China
| | - Xiaomeng Shi
- Department of Materials, Imperial College London, London, SW7 2AZ, UK
| | - Jianxing Feng
- Haohua Technology Co., Ltd., Shanghai, 201100, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
| |
Collapse
|
49
|
Kocak B, Baessler B, Bakas S, Cuocolo R, Fedorov A, Maier-Hein L, Mercaldo N, Müller H, Orlhac F, Pinto Dos Santos D, Stanzione A, Ugga L, Zwanenburg A. CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 2023; 14:75. [PMID: 37142815 PMCID: PMC10160267 DOI: 10.1186/s13244-023-01415-8] [Citation(s) in RCA: 100] [Impact Index Per Article: 100.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/24/2023] [Indexed: 05/06/2023] Open
Abstract
Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine clinical practice. The workflow of radiomics is complex due to several methodological steps and nuances, which often leads to inadequate reporting and evaluation, and poor reproducibility. Available reporting guidelines and checklists for artificial intelligence and predictive modeling include relevant good practices, but they are not tailored to radiomic research. There is a clear need for a complete radiomics checklist for study planning, manuscript writing, and evaluation during the review process to facilitate the repeatability and reproducibility of studies. We here present a documentation standard for radiomic research that can guide authors and reviewers. Our motivation is to improve the quality and reliability and, in turn, the reproducibility of radiomic research. We name the checklist CLEAR (CheckList for EvaluAtion of Radiomics research), to convey the idea of being more transparent. With its 58 items, the CLEAR checklist should be considered a standardization tool providing the minimum requirements for presenting clinical radiomics research. In addition to a dynamic online version of the checklist, a public repository has also been set up to allow the radiomics community to comment on the checklist items and adapt the checklist for future versions. Prepared and revised by an international group of experts using a modified Delphi method, we hope the CLEAR checklist will serve well as a single and complete scientific documentation tool for authors and reviewers to improve the radiomics literature.
Collapse
Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey.
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Spyridon Bakas
- Center for Artificial Intelligence for Integrated Diagnostics (AI2D) & Center for Biomedical Image Computing & Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy
| | - Andrey Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lena Maier-Hein
- Division of Intelligent Medical Systems, German Cancer Research Center, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Nathaniel Mercaldo
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Henning Müller
- University of Applied Sciences of Western Switzerland (HES-SO Valais), Valais, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva (UniGe), Geneva, Switzerland
| | - Fanny Orlhac
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO)-U1288, Institut Curie, Inserm, Université PSL, Orsay, France
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Institute for Diagnostic and Interventional Radiology, Goethe-University Frankfurt Am Main, Frankfurt, Germany
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - Alex Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| |
Collapse
|
50
|
Werner RA, Habacha B, Lütje S, Bundschuh L, Kosmala A, Essler M, Derlin T, Higuchi T, Lapa C, Buck AK, Pienta KJ, Lodge MA, Eisenberger MA, Markowski MC, Pomper MG, Gorin MA, Frey EC, Rowe SP, Bundschuh RA. Lack of repeatability of radiomic features derived from PET scans: Results from a 18 F-DCFPyL test-retest cohort. Prostate 2023; 83:547-554. [PMID: 36632656 DOI: 10.1002/pros.24483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/06/2022] [Accepted: 12/28/2022] [Indexed: 01/13/2023]
Abstract
OBJECTIVES PET-based radiomic metrics are increasingly utilized as predictive image biomarkers. However, the repeatability of radiomic features on PET has not been assessed in a test-retest setting. The prostate-specific membrane antigen-targeted compound 18 F-DCFPyL is a high-affinity, high-contrast PET agent that we utilized in a test-retest cohort of men with metastatic prostate cancer (PC). METHODS Data of 21 patients enrolled in a prospective clinical trial with histologically proven PC underwent two 18 F-DCFPyL PET scans within 7 days, using identical acquisition and reconstruction parameters. Sites of disease were segmented and a set of 29 different radiomic parameters were assessed on both scans. We determined repeatability of quantification by using Pearson's correlations, within-subject coefficient of variation (wCOV), and Bland-Altman analysis. RESULTS In total, 230 lesions (177 bone, 38 lymph nodes, 15 others) were assessed on both scans. For all investigated radiomic features, a broad range of inter-scan correlation was found (r, 0.07-0.95), with acceptable reproducibility for entropy and homogeneity (wCOV, 16.0% and 12.7%, respectively). On Bland-Altman analysis, no systematic increase or decrease between the scans was observed for either parameter (±1.96 SD: 1.07/-1.30, 0.23/-0.18, respectively). The remaining 27 tested radiomic metrics, however, achieved unacceptable high wCOV (≥21.7%). CONCLUSION Many common radiomic features derived from a test-retest PET study had poor repeatability. Only Entropy and homogeneity achieved good repeatability, supporting the notion that those image biomarkers may be incorporated in future clinical trials. Those radiomic features based on high frequency aspects of images appear to lack the repeatability on PET to justify further study.
Collapse
Affiliation(s)
- Rudolf A Werner
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Bilêl Habacha
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
| | - Susanne Lütje
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
| | - Lena Bundschuh
- Nuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Alekandser Kosmala
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Markus Essler
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
| | - Thorsten Derlin
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Takahiro Higuchi
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Constantin Lapa
- Nuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Andreas K Buck
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Kenneth J Pienta
- Department of Urology, The James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Martin A Lodge
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mario A Eisenberger
- Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Mark C Markowski
- Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Martin G Pomper
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Urology, The James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Michael A Gorin
- Milton and Carroll Petrie Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eric C Frey
- Radiopharmaceutical Imaging and Dosimetry, LLC, Baltimore, Maryland, USA
| | - Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Urology, The James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
| | - Ralph A Bundschuh
- Department of Nuclear Medicine, University Hospital Bonn, Bonn, Germany
- Nuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany
| |
Collapse
|