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Avasthi KK, Choi J, Glushko T, Manley BJ, Yu A, Pow-Sang J, Gatenby R, Wang L, Balagurunathan Y. Extracellular microvesicle microRNAs, along with imaging metrics, improve detection of aggressive prostate cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.23.24312491. [PMID: 39228742 PMCID: PMC11370497 DOI: 10.1101/2024.08.23.24312491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Prostate cancer is the most commonly diagnosed cancer in men worldwide. Early diagnosis of the disease provides better treatment options for these patients. Magnetic resonance imaging (MRI) provides an overall assessment of prostate disease. Quantitative metrics (radiomics) from the MRI provide a better evaluation of the tumor and have been shown to improve disease detection. Recent studies have demonstrated that plasma extracellular vesicle microRNAs (miRNAs) are functionally linked to cancer progression, metastasis, and aggressiveness. In our study, we analyzed a matched cohort with baseline blood plasma and MRI to access tumor morphology using imaging-based radiomics and cellular characteristics using miRNAs-based transcriptomics. Our findings indicate that the univariate feature-based model with the highest Youden's index achieved average areas under the receiver operating characteristic curve (AUC) of 0.76, 0.82, and 0.84 for miRNA, MR-T2W, and MR-ADC features, respectively, in identifying clinically aggressive (Gleason grade) disease. The multivariable feature-based model demonstrated an average AUC of 0.88 and 0.95 using combinations of miRNA markers with imaging features in MR-ADC and MR-T2W, respectively. Our study demonstrates combining miRNA markers with MRI-based radiomics improves predictability of clinically aggressive prostate cancer.
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Affiliation(s)
- Kapil K Avasthi
- Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center, Tampa, FL
| | - Jung Choi
- Diagnostic & Interventional Radiology, H Lee Moffitt Cancer Center, Tampa, FL
| | - Tetiana Glushko
- Diagnostic & Interventional Radiology, H Lee Moffitt Cancer Center, Tampa, FL
| | | | - Alice Yu
- Genitourinary Oncology, H Lee Moffitt Cancer Center, Tampa, FL
| | - Julio Pow-Sang
- Genitourinary Oncology, H Lee Moffitt Cancer Center, Tampa, FL
| | - Robert Gatenby
- Diagnostic & Interventional Radiology, H Lee Moffitt Cancer Center, Tampa, FL
| | - Liang Wang
- Tumor Microenvironment and Metastasis, H Lee Moffitt Cancer Center, Tampa, FL
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Pistel M, Brock L, Laun FB, Erber R, Weiland E, Uder M, Wenkel E, Ohlmeyer S, Bickelhaupt S. Stability of Radiomic Features against Variations in Lesion Segmentations Computed on Apparent Diffusion Coefficient Maps of Breast Lesions. Diagnostics (Basel) 2024; 14:1427. [PMID: 39001317 PMCID: PMC11241112 DOI: 10.3390/diagnostics14131427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm2). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.
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Affiliation(s)
- Mona Pistel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
- Siemens Healthineers AG, 91052 Erlangen, Germany
| | - Luise Brock
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Ramona Erber
- Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthineers AG, 91052 Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Evelyn Wenkel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
- Radiologie München, 80331 München, Germany
| | - Sabine Ohlmeyer
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
| | - Sebastian Bickelhaupt
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany
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Wiedeman C, Lorraine P, Wang G, Do R, Simpson A, Peoples J, De Man B. Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction. Vis Comput Ind Biomed Art 2024; 7:13. [PMID: 38861067 PMCID: PMC11166620 DOI: 10.1186/s42492-024-00161-y] [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: 12/02/2023] [Accepted: 04/14/2024] [Indexed: 06/12/2024] Open
Abstract
Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes, especially as the disease progresses into liver metastases. Computed tomography (CT) is a frontline tool for this task; however, the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction algorithm. We hypothesized that image reconstruction with a high-frequency kernel could result in a better characterization of liver metastases features via deep neural networks. This kernel produces images that appear noisier but preserve more sinogram information. A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver metastases. This pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases, and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using CatSim. Datasets of 10,000 liver metastases were generated, scanned, and reconstructed using either standard or high-frequency kernels. These data were used to train and validate deep neural networks to recover crafted metastases characteristics, such as internal heterogeneity, edge sharpness, and edge fractal dimension. In the absence of noise, models scored, on average, 12.2% ( α = 0.012 ) and 7.5% ( α = 0.049 ) lower squared error for characterizing edge sharpness and fractal dimension, respectively, when using high-frequency reconstructions compared to standard. However, the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical scan. Our results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization, provided that noise is limited. Future work should investigate the information-preserving kernels in datasets with clinical labels.
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Affiliation(s)
- Christopher Wiedeman
- Department of Electrical and Computer Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | | | - Ge Wang
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - Richard Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Amber Simpson
- Biomedical Computing and Informatics, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Jacob Peoples
- Biomedical Computing and Informatics, Queen's University, Kingston, ON, K7L 3N6, Canada
| | - Bruno De Man
- GE Research - Healthcare, Niskayuna, NY, 12309, USA.
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Lococo F, Ghaly G, Chiappetta M, Flamini S, Evangelista J, Bria E, Stefani A, Vita E, Martino A, Boldrini L, Sassorossi C, Campanella A, Margaritora S, Mohammed A. Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review. Cancers (Basel) 2024; 16:1832. [PMID: 38791910 PMCID: PMC11119930 DOI: 10.3390/cancers16101832] [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: 03/26/2024] [Revised: 05/02/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.
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Affiliation(s)
- Filippo Lococo
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Galal Ghaly
- Faculty of Medicine and Surgery, Thoracic Surgery Unit, Cairo University, Giza 12613, Egypt; (G.G.); (A.M.)
| | - Marco Chiappetta
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Sara Flamini
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Jessica Evangelista
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Emilio Bria
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Alessio Stefani
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Emanuele Vita
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Medical Oncology, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Antonella Martino
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Radiotherapy Unit, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Luca Boldrini
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Radiotherapy Unit, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy
| | - Carolina Sassorossi
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Annalisa Campanella
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Stefano Margaritora
- Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (M.C.); (J.E.); (E.B.); (A.S.); (E.V.); (A.M.); (L.B.); (C.S.); (S.M.)
- Thoracic Surgery, A. Gemelli University Hospital Foundation IRCCS, 00168 Rome, Italy; (S.F.); (A.C.)
| | - Abdelrahman Mohammed
- Faculty of Medicine and Surgery, Thoracic Surgery Unit, Cairo University, Giza 12613, Egypt; (G.G.); (A.M.)
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Thomas HMT, Wang HYC, Varghese AJ, Donovan EM, South CP, Saxby H, Nisbet A, Prakash V, Sasidharan BK, Pavamani SP, Devadhas D, Mathew M, Isiah RG, Evans PM. Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets. APPLIED SCIENCES (BASEL, SWITZERLAND) 2024; 166:s00701-024-05977-4. [PMID: 38725869 PMCID: PMC7615943 DOI: 10.3390/app13127291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/12/2024]
Abstract
Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman's rank coefficient, rs, calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset, and vice versa. A total of 29 out of the 43 reported stable features were found to be highly reproducible between MATLAB and Pyradiomics implementations, having a consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 (rs > 0.8). 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets.
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Affiliation(s)
- Hannah Mary T. Thomas
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Helen Y. C. Wang
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK
- Department of Medical Physics, Royal Surrey NHS Foundation Trust, Guildford GU2 7XX, UK
| | - Amal Joseph Varghese
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Ellen M. Donovan
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK
| | - Chris P. South
- Department of Medical Physics, Royal Surrey NHS Foundation Trust, Guildford GU2 7XX, UK
| | - Helen Saxby
- St Luke’s Cancer Centre, Royal Surrey NHS Foundation Trust, Guildford GU2 7XX, UK
| | - Andrew Nisbet
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Vineet Prakash
- St Luke’s Cancer Centre, Royal Surrey NHS Foundation Trust, Guildford GU2 7XX, UK
| | - Balu Krishna Sasidharan
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Simon Pradeep Pavamani
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Devakumar Devadhas
- Department of Nuclear Medicine, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Manu Mathew
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Rajesh Gunasingam Isiah
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Philip M. Evans
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK
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Cherezov D, Viswanathan VS, Fu P, Gupta A, Madabhushi A. Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107990. [PMID: 38194767 PMCID: PMC10872259 DOI: 10.1016/j.cmpb.2023.107990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/12/2023] [Accepted: 12/19/2023] [Indexed: 01/11/2024]
Abstract
BACKGROUND Radiomics is a method within medical image analysis that involves the extraction of quantitative data from radiologic scans, often in conjunction with machine learning algorithms to phenotype disease appearance, prognosticate disease outcome, and predict treatment response. However, variance in CT scanner acquisition parameters, such as convolution kernels or pixel spacing, can impact radiomics texture feature values. PURPOSE The extent to which the parameters influence radiomics features continues to be an active area of investigation. In this study, we describe a novel approach, Acquisition Impact on Radiomics Estimation (AcquIRE), to rank the impact of CT acquisition parameters on radiomic texture features. METHODS In this work, we used three chest CT imaging datasets (n = 749 patients) from nine sites comprising: i) lung granulomas and adenocarcinomas (D1) (10 and 52 patients, respectively); ii) minimal and frank invasive adenocarcinoma (D2) (74 and 145 patients); and iii) early-stage NSCLC patients (D3) (315 patients). Datasets D2 and D3 were collected from four sites each, and D1 from a single site. For each patient, 744 texture features and nine acquisition parameters were extracted and utilized to evaluate which parameters impact radiomic features the most. The AcquIRE method establishes a relative assessment between acquisition parameters and radiomic texture featuresa through the creation of a classification model, which is then utilized to assess the rank of the acquisition parameters. RESULTS Across the use cases, CT software version and convolution kernel parameters were found to have the most variance. In D1, it was observed that the Haralick texture feature family was the least affected by variations in acquisition parameters, while the Gabor feature family was the most impacted. However, in datasets D2 and D3, the Gabor features were found to be the least affected. Our findings suggest that the impact on radiomic parameters is as much a function of the problem in question as it is acquisition parameters. CONCLUSIONS The software version and convolution kernel parameters impacted the radiomics feature the most.
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Affiliation(s)
- Dmitry Cherezov
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA
| | | | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Radiology, Case Western Reserve University, Cleveland, OH, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University, Atlanta, GA, USA; Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.
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Fusco R, Granata V, Simonetti I, Setola SV, Iasevoli MAD, Tovecci F, Lamanna CMP, Izzo F, Pecori B, Petrillo A. An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies. Curr Oncol 2024; 31:403-424. [PMID: 38248112 PMCID: PMC10814313 DOI: 10.3390/curroncol31010027] [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: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy;
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Maria Assunta Daniela Iasevoli
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Filippo Tovecci
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Ciro Michele Paolo Lamanna
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Biagio Pecori
- Division of Radiation Protection and Innovative Technology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
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8
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Fu C, Zhang B, Guo T, Li J. Imaging Evaluation of Peritoneal Metastasis: Current and Promising Techniques. Korean J Radiol 2024; 25:86-102. [PMID: 38184772 PMCID: PMC10788608 DOI: 10.3348/kjr.2023.0840] [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: 06/22/2023] [Revised: 09/28/2023] [Accepted: 10/08/2023] [Indexed: 01/08/2024] Open
Abstract
Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.
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Affiliation(s)
- Chen Fu
- The First School of Clinical Medical, Gansu University of Chinese Medicine, Lanzhou, Gansu, China
| | - Bangxing Zhang
- School of Clinical Medicine, Ningxia Medical University, Yinchuan, Ningxia, China
| | - Tiankang Guo
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou, Gansu, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Gansu, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, Gansu, China
| | - Junliang Li
- The First School of Clinical Medical, Gansu University of Chinese Medicine, Lanzhou, Gansu, China
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou, Gansu, China
- Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Gansu, China
- NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital, Lanzhou, Gansu, China.
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Salmanpour MR, Hosseinzadeh M, Rezaeijo SM, Rahmim A. Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107714. [PMID: 37473589 DOI: 10.1016/j.cmpb.2023.107714] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 05/19/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Numerous features are commonly generated in radiomics applications as applied to medical imaging, and identification of robust radiomics features (RFs) can be an important step to derivation of reliable, reproducible solutions. In this work, we utilize a tensor radiomics (TR) framework, where numerous fusions are explored, to generate different flavours of RFs, and we aimed to identify RFs that are robust to fusion techniques in head and neck cancer. Overall, we aimed to predict progression-free survival (PFS) using Hybrid Machine Learning Systems (HMLS) and reproducible RFs. METHODS The study was performed on 408 patients with head and neck cancer from The Cancer Imaging Archive. After image preprocessing, 15 fusion techniques were employed to combine Positron Emission Tomography (PET) and Computed Tomography (CT) images. Subsequently, 215 RFs were extracted through a standardized radiomics software, with 17 'flavours' generated using PET-only, CT-only, and 15 fused PET&CT images. The variability of RFs across flavours was studied using the Intraclass Correlation Coefficient (ICC). Furthermore, the features were categorized into seven reliability groups, 106 reproducible RFs with ICC>0.75 were selected, highly correlated flavours were removed, Principal Component Analysis was used to convert 17 flavours to 1 attribute, the polynomial function was utilized to increase RFs, and Analysis of variance (ANOVA) was used to select the relevant attributes. Finally, 3 classifiers including Random Forest (RFC), Logistic regression (LR), and Multi-layer perceptron were applied to the preselected relevant attributes to predict binary PFS. In 5-fold cross-validation, 80% of 4 divisions were utilized to train the model, and the remaining 20% was utilized to evaluate the model. Further, the remaining fold was used for external nested testing. RESULTS Reliability analysis indicated that most morphological features belong to the high-reliability category. By contrast, local intensity and statistical features extracted from images belong to the low-reliability category. In the tensor framework, the highest 5-fold cross-validation accuracy of 76.7%±3.3% with an external nested testing of 70.6%±6.7% resulted from the reproducible TR+polynomial function+ANOVA+LR algorithm while the accuracy of 70.0%±4.2% with the external nested testing of 67.7%±4.9% was achieved through the PCA fusion+RFC (non-tensor paradigm). CONCLUSIONS This study demonstrated that using reproducible RFs as utilized within a tensor fusion radiomics framework, linked with ANOVA and LR, added value to prediction of progression-free survival outcome in head and neck cancer patients.
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Affiliation(s)
- Mohammad R Salmanpour
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC, Canada.
| | - Mahdi Hosseinzadeh
- Technological Virtual Collaboration (TECVICO Corp.), Vancouver, BC, Canada; Department of Electrical & Computer Engineering, University of Tarbiat Modares, Tehran, Iran
| | - Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
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10
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Gong J, Yin R, Sun L, Gao N, Wang X, Zhang L, Zhang Z. CT-based radiomics model to predict spread through air space in resectable lung cancer. Cancer Med 2023; 12:18755-18766. [PMID: 37676092 PMCID: PMC10557899 DOI: 10.1002/cam4.6496] [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: 05/09/2023] [Revised: 08/21/2023] [Accepted: 08/23/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND Spread through air space (STAS) has been identified as a pathological pattern associated with lung cancer progression. Patients with STAS were related to a worse prognosis compared with patients without STAS. The objective of this study was to establish a radiomics model capable of forecasting STAS before surgery, which can assist surgeons in selecting the most appropriate operation type for patients with STAS. METHOD There were 537 eligible patients retrospectively included in this study. ROI segmentation was performed manually on all CT images to identify the region of interest. From each segmented lesion, a total of 1688 features were extracted. The tumor size, maximum tumor diameters, and tumor type were also recorded. Using Spearman's correlation coefficient to calculate the correlation and redundancy of elements, and redundant features less than 0.80 were removed. In order to reduce the level of overfitting and avoid statistical biases, a dimension reduction process of the dataset was conducted to decrease the number of features. Finally, a radiomics model included 44 features was established to predict STAS. To evaluate the performance of the model, the receiver operating characteristic (ROC) curve was used, and the area under the curve (AUC) was calculated, and the accuracy of the model was verified by 10-fold cross-validation. RESULTS The incidence of STAS was 38.2% (205/537). The tumor type, maximum tumor diameters, and consolidation tumor ratio were significantly different between STAS group and non-STAS group. The training group included 430 patients, while the test group was consisted with 107. The training group achieved an AUC of 0.825 (sensitivity, 0.875; specificity, 0.621; and accuracy, 0.749) and the test group had an AUC of 0.802 (sensitivity, 0.797; specificity,0.688; and accuracy, 0.748). The 10-fold cross-validation had an AUC of 0.834. CONCLUSION CT-based radiomic model can predict STAS effectively, which is of great importance to guide the selection of operation types before surgery.
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Affiliation(s)
- Jialin Gong
- Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Rui Yin
- School of Biomedical Engineering & TechnologyTianjin Medical UniversityTianjinChina
| | - Leina Sun
- Department of Pathology, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Na Gao
- Department of Pathology, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Xiaofei Wang
- Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Lianmin Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Zhenfa Zhang
- Department of Lung Cancer, Tianjin Lung Cancer Center, Tianjin Medical University Cancer Institute and HospitalNational Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for CancerTianjinChina
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11
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Libling WA, Korn R, Weiss GJ. Review of the use of radiomics to assess the risk of recurrence in early-stage non-small cell lung cancer. Transl Lung Cancer Res 2023; 12:1575-1589. [PMID: 37577298 PMCID: PMC10413018 DOI: 10.21037/tlcr-23-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 06/13/2023] [Indexed: 08/15/2023]
Abstract
Background and Objective Radiomics is an emerging field of advanced image analysis that has shown promise as a non-invasive, companion diagnostic in predicting clinical outcomes and response assessments in solid tumors. Radiomics aims to extract high-content information from medical images not visible to the naked eye, especially in early-stage non-small cell lung cancer (NSCLC) patients. Although these patients are being identified by early detection programs, it remains unclear which patients would benefit from adjuvant treatment versus active surveillance. Having a radiomic signature(s) that could predict early recurrence would be beneficial. In this review, an overview of the basic radiomic approaches used to evaluate solid tumors on radiologic scans, including NSCLC is provided followed by a review of relevant literature that supports the use of radiomics to help predict tumor recurrence in early-stage NSCLC patients. Methods A review of the radiomic literature from 1985 to present focusing on the prediction of disease recurrence in early-stage NSCLC was conducted. PubMed database was searched using key terms for radiomics and NSCLC. A total of 41 articles were identified and 13 studies were considered suitable for inclusion based upon study population, patient number (n>50), use of well described radiomic methodologies, suitable model building features, and well-defined testing/training and validation where feasible. Key Content and Findings Examples of using radiomics in early-stage NSCLC patients will be presented, where disease free survival is a primary consideration. A summary of the findings demonstrates the importance of both the intratumor and peritumoral radiomic signals as a marker of outcomes. Conclusions The value of radiomic information for predicting disease recurrence in early-stage NSCLC patients is accumulating. However, overcoming several challenges along with the lack of prospective trials, has inhibited it use as a clinical decision-making support tool in early-stage NSCLC.
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Affiliation(s)
- William Adam Libling
- Arizona College of Osteopathic Medicine, Midwestern University, Glendale, AZ, USA
| | - Ronald Korn
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA
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12
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Thulasi Seetha S, Garanzini E, Tenconi C, Marenghi C, Avuzzi B, Catanzaro M, Stagni S, Villa S, Chiorda BN, Badenchini F, Bertocchi E, Sanduleanu S, Pignoli E, Procopio G, Valdagni R, Rancati T, Nicolai N, Messina A. Stability of Multi-Parametric Prostate MRI Radiomic Features to Variations in Segmentation. J Pers Med 2023; 13:1172. [PMID: 37511785 PMCID: PMC10381192 DOI: 10.3390/jpm13071172] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Stability analysis remains a fundamental step in developing a successful imaging biomarker to personalize oncological strategies. This study proposes an in silico contour generation method for simulating segmentation variations to identify stable radiomic features. Ground-truth annotation provided for the whole prostate gland on the multi-parametric MRI sequences (T2w, ADC, and SUB-DCE) were perturbed to mimic segmentation differences observed among human annotators. In total, we generated 15 synthetic contours for a given image-segmentation pair. One thousand two hundred twenty-four unfiltered/filtered radiomic features were extracted applying Pyradiomics, followed by stability assessment using ICC(1,1). Stable features identified in the internal population were then compared with an external population to discover and report robust features. Finally, we also investigated the impact of a wide range of filtering strategies on the stability of features. The percentage of unfiltered (filtered) features that remained robust subjected to segmentation variations were T2w-36% (81%), ADC-36% (94%), and SUB-43% (93%). Our findings suggest that segmentation variations can significantly impact radiomic feature stability but can be mitigated by including pre-filtering strategies as part of the feature extraction pipeline.
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Affiliation(s)
- Sithin Thulasi Seetha
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.T.S.); (R.V.)
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6211 LK Maastricht, The Netherlands
| | - Enrico Garanzini
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (E.G.); (A.M.)
| | - Chiara Tenconi
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy;
- Department of Oncology and Hematooncology, Università degli Studi di Milano, 20133 Milan, Italy
| | - Cristina Marenghi
- Unit of Genito-Urinary Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (C.M.); (F.B.); (E.B.); (G.P.)
| | - Barbara Avuzzi
- Department of Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (B.A.); (S.V.); (B.N.C.)
| | - Mario Catanzaro
- Department of Urology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (M.C.); (S.S.); (N.N.)
| | - Silvia Stagni
- Department of Urology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (M.C.); (S.S.); (N.N.)
| | - Sergio Villa
- Department of Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (B.A.); (S.V.); (B.N.C.)
| | - Barbara Noris Chiorda
- Department of Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (B.A.); (S.V.); (B.N.C.)
| | - Fabio Badenchini
- Unit of Genito-Urinary Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (C.M.); (F.B.); (E.B.); (G.P.)
| | - Elena Bertocchi
- Unit of Genito-Urinary Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (C.M.); (F.B.); (E.B.); (G.P.)
| | - Sebastian Sanduleanu
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6211 LK Maastricht, The Netherlands
| | - Emanuele Pignoli
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy;
| | - Giuseppe Procopio
- Unit of Genito-Urinary Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (C.M.); (F.B.); (E.B.); (G.P.)
| | - Riccardo Valdagni
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (S.T.S.); (R.V.)
- Department of Oncology and Hematooncology, Università degli Studi di Milano, 20133 Milan, Italy
| | - Tiziana Rancati
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Nicola Nicolai
- Department of Urology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (M.C.); (S.S.); (N.N.)
| | - Antonella Messina
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy; (E.G.); (A.M.)
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13
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Midya A, Chakraborty J, Srouji R, Narayan RR, Boerner T, Zheng J, Pak LM, Creasy JM, Escobar LA, Harrington KA, Gonen M, D'Angelica MI, Kingham TP, Do RKG, Jarnagin WR, Simpson AL. Computerized Diagnosis of Liver Tumors From CT Scans Using a Deep Neural Network Approach. IEEE J Biomed Health Inform 2023; 27:2456-2464. [PMID: 37027632 PMCID: PMC10245221 DOI: 10.1109/jbhi.2023.3248489] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
The liver is a frequent site of benign and malignant, primary and metastatic tumors. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are the most common primary liver cancers, and colorectal liver metastasis (CRLM) is the most common secondary liver cancer. Although the imaging characteristic of these tumors is central to optimal clinical management, it relies on imaging features that are often non-specific, overlap, and are subject to inter-observer variability. Thus, in this study, we aimed to categorize liver tumors automatically from CT scans using a deep learning approach that objectively extracts discriminating features not visible to the naked eye. Specifically, we used a modified Inception v3 network-based classification model to classify HCC, ICC, CRLM, and benign tumors from pretreatment portal venous phase computed tomography (CT) scans. Using a multi-institutional dataset of 814 patients, this method achieved an overall accuracy rate of 96%, with sensitivity rates of 96%, 94%, 99%, and 86% for HCC, ICC, CRLM, and benign tumors, respectively, using an independent dataset. These results demonstrate the feasibility of the proposed computer-assisted system as a novel non-invasive diagnostic tool to classify the most common liver tumors objectively.
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14
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Wang X, Pennello G, deSouza NM, Huang EP, Buckler AJ, Barnhart HX, Delfino JG, Raunig DL, Wang L, Guimaraes AR, Hall TJ, Obuchowski NA. Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics. Acad Radiol 2023; 30:215-229. [PMID: 36411153 PMCID: PMC9825652 DOI: 10.1016/j.acra.2022.10.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 09/21/2022] [Accepted: 10/01/2022] [Indexed: 11/19/2022]
Abstract
This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM. This paper aims to set guidelines on how to build machine learning models using DIMs in radiomics and to apply and report them appropriately. We provide a list of recommendations, named RANDAM (an abbreviation of "Radiomic ANalysis and DAta Modeling"), for analysis, modeling, and reporting in a radiomic study to make machine learning analyses in radiomics more reproducible. RANDAM contains five main components to use in reporting radiomics studies: design, data preparation, data analysis and modeling, reporting, and material availability. Real case studies in lung cancer research are presented along with simulation studies to compare different feature selection methods and several validation strategies.
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Affiliation(s)
- Xiaofeng Wang
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave/JJN3, Cleveland, OH 44195.
| | - Gene Pennello
- Center for Devices and Radiological Health, US Food and Drug Administration Division of Imaging, Diagnostic and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - Nandita M deSouza
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden Hospital, London, United Kingdom; European Imaging Biomarkers Alliance, European Society of Radiology, London, UK
| | - Erich P Huang
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | | | - Huiman X Barnhart
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Jana G Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - David L Raunig
- Data Science Institute, Statistical and Quantitative Sciences, Takeda Pharmaceuticals America Inc, Lexington, Massachusetts
| | - Lu Wang
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave/JJN3, Cleveland, OH 44195
| | - Alexander R Guimaraes
- Department of Diagnostic Radiology, Oregon Health & Sciences University, Portland, Oregon
| | - Timothy J Hall
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Ave/JJN3, Cleveland, OH 44195
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15
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Ye N, Yang Q, Liu P, Chen Z, Li X. A comprehensive machine-learning model applied to MRI to classify germinomas of the pineal region. Comput Biol Med 2023; 152:106366. [PMID: 36470145 DOI: 10.1016/j.compbiomed.2022.106366] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 10/20/2022] [Accepted: 11/25/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Pineal region tumors (PRTs) are highly histologically heterogeneous. Germinoma is the most common PRT and is treatable with radiotherapy and chemotherapy. A non-invasive system that helps identify germinoma in the pineal region could reduce lab exams and traumatic therapies. METHODS In this retrospective study, 122 patients with histologically confirmed PRTs and pre-operative multi-modal MR images were included. Radiomics features were extracted from different ROIs and image sequences separately. A computational framework that combines a few classification and feature selection algorithms were used to predict histology with radiomics features and demographics. We systemically benchmarked performance of models with feature matrices from all possible combinations of ROIs and image sequences. The Area under the ROC Curve (AUC) was then used to evaluate model performance. RESULTS Models with demographics and radiomics features outperform radiomics-only or demographics-only models. The best demographical-radiomics model reached the highest AUC of 0.88 (CI95%: 0.81-0.96). Through the comprehensive evaluation of possible sequence combinations in the differential diagnosis of pineal tumor, T1 and T2 emerged as the most informative sequences for the task. There is imbalanced usage of feature classes as we analyze their proportion in all models. CONCLUSIONS The demographical-radiomics model can accurately and efficiently identify germinomas in the pineal region. The preference for MRI sequences, radiomics feature classes, features selection and classification algorithms provide a valuable reference for future attempts at developing classifiers on medical images.
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Affiliation(s)
- Ningrong Ye
- Department of Neurosurgery, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan, 410008, PR China; Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan, 410008, PR China
| | - Qi Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan, 410008, PR China; Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan, 410008, PR China
| | - Peikun Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan, 410008, PR China; Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan, 410008, PR China
| | - Ziyan Chen
- Department of Neurosurgery, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan, 410008, PR China; Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan, 410008, PR China
| | - Xuejun Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan, 410008, PR China; Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, No. 87, Xiangya Road, Changsha, Hunan, 410008, PR China.
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16
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Kasinathan P, Prabha R, Sabeenian RS, Baskar K, Ramkumar A, Alemayehu S. Development of Deep Learning Technique of Features for the Analysis of Clinical Images Integrated with CANN. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2742274. [PMID: 36277892 PMCID: PMC9586784 DOI: 10.1155/2022/2742274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/30/2022] [Indexed: 11/23/2022]
Abstract
Computer tomography is an extensively used method for the detection of the disease in the subjects. Basically, computer-aided tomography depending on the artificial intelligence reveals its significance in smart health care monitoring system. Owing to its security and the private issue, analyzing the computed tomography dataset has become a tedious process. This study puts forward the convolutional autoencrypted deep learning neural network to assist unsupervised learning technique. By carrying out various experiments, our proposed method produces better results comparative to other traditional methods, which efficaciously solves the issues related to the artificial image description. Hence, the convolutional autoencoder is widely used in measuring the lumps in the bronchi. With the unsupervised machine learning, the extracted features are used for various applications.
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Affiliation(s)
- Prabakaran Kasinathan
- Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600062 Tamil Nadu, India
| | - R. Prabha
- Department of Electronics and Communication Engineering, Sri Sai Ram Institute of Technology, Chennai, 600044 Tamil Nadu, India
| | - R. S. Sabeenian
- Department of Electronics and Communication Engineering, Sona College of Technology, Salem, 636005 Tamil Nadu, India
| | - K. Baskar
- Department of Artificial Intelligence and Data Science, Kongunadu College of Engineering and Technology, Trichy, 621215 Tamil Nadu, India
| | - A. Ramkumar
- Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India
| | - Samson Alemayehu
- Department of Electrical and Computer Engineering, Faculty of Electrical and Biomedical Engineering, Institute of Technology, Hawassa University, Ethiopia
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17
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Robustness of radiomics to variations in segmentation methods in multimodal brain MRI. Sci Rep 2022; 12:16712. [PMID: 36202934 PMCID: PMC9537186 DOI: 10.1038/s41598-022-20703-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 09/16/2022] [Indexed: 11/09/2022] Open
Abstract
Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77–0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3–0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods.
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Ge G, Zhang J. Uniqueness of radiomic features in non-small cell lung cancer. J Appl Clin Med Phys 2022; 23:e13787. [PMID: 36173022 PMCID: PMC9797180 DOI: 10.1002/acm2.13787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 07/26/2022] [Accepted: 08/24/2022] [Indexed: 01/01/2023] Open
Abstract
PURPOSE The uniqueness of radiomic features, combined with their reproducibility, determines the reliability of radiomic studies. This study is to test the hypothesis that radiomic features extracted from a defined region of interest (ROI) are unique to the underlying structure (e.g., tumor). APPROACH Two cohorts of non-small cell lung cancer (NSCLC) patients were retrospectively retrieved from a GE and a Siemens CT scanner. The lung nodules (ROI) were delineated manually and radiomic features were extracted using IBEX. The same ROI was then translocated randomly to four other tissue regions of the same set of images: adipose, heart, lung beyond nodule, and muscle for radiomic feature extraction. Coefficient of variation (CV) within different ROIs and concordance correlation coefficient (CCC) between lung nodule and a given tissue region were calculated to test to determine feature uniqueness. The radiomic features were considered nonunique when (1) the CV < 10% and CCC > 0.85 for over 50% of patients; and (2) the CCC > 0.85 appeared in ≥2 tissue regions beyond the defined region. RESULTS In total, 14 patients from GE and 18 patients from Siemens are analyzed. The results show that 12 features fall below the 10% CV threshold for over 50% of patients in the GE cohort and 29 features in the Siemens cohort. According to CCC, 18 radiomic features in GE and 16 features in Siemens are identified as nonunique, with 11 overlapping features. Combining CV and CCC, 9 of 123 calculated features (7.3%) are identified as nonunique to a defined ROI. CONCLUSIONS Radiomic feature uniqueness should be considered to improve the reliability of radiomics study.
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Affiliation(s)
- Gary Ge
- Department of RadiologyUniversity of KentuckyLexingtonKentucky40536USA
| | - Jie Zhang
- Department of RadiologyUniversity of KentuckyLexingtonKentucky40536USA
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Bhuiyan MR, Abdullah J. Detection on Cell Cancer Using the Deep Transfer Learning and Histogram Based Image Focus Quality Assessment. SENSORS (BASEL, SWITZERLAND) 2022; 22:7007. [PMID: 36146356 PMCID: PMC9504738 DOI: 10.3390/s22187007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/15/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
In recent years, the number of studies using whole-slide imaging (WSIs) of histopathology slides has expanded significantly. For the development and validation of artificial intelligence (AI) systems, glass slides from retrospective cohorts including patient follow-up data have been digitized. It has become crucial to determine that the quality of such resources meets the minimum requirements for the development of AI in the future. The need for automated quality control is one of the obstacles preventing the clinical implementation of digital pathology work processes. As a consequence of the inaccuracy of scanners in determining the focus of the image, the resulting visual blur can render the scanned slide useless. Moreover, when scanned at a resolution of 20× or higher, the resulting picture size of a scanned slide is often enormous. Therefore, for digital pathology to be clinically relevant, computational algorithms must be used to rapidly and reliably measure the picture's focus quality and decide if an image requires re-scanning. We propose a metric for evaluating the quality of digital pathology images that uses a sum of even-derivative filter bases to generate a human visual-system-like kernel, which is described as the inverse of the lens' point spread function. This kernel is then used for a digital pathology image to change high-frequency image data degraded by the scanner's optics and assess the patch-level focus quality. Through several studies, we demonstrate that our technique correlates with ground-truth z-level data better than previous methods, and is computationally efficient. Using deep learning techniques, our suggested system is able to identify positive and negative cancer cells in images. We further expand our technique to create a local slide-level focus quality heatmap, which can be utilized for automated slide quality control, and we illustrate our method's value in clinical scan quality control by comparing it to subjective slide quality ratings. The proposed method, GoogleNet, VGGNet, and ResNet had accuracy values of 98.5%, 94.5%, 94.00%, and 95.00% respectively.
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Jensen LJ, Kim D, Elgeti T, Steffen IG, Schaafs LA, Hamm B, Nagel SN. Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study. Eur Radiol Exp 2022; 6:43. [PMID: 36104519 PMCID: PMC9474978 DOI: 10.1186/s41747-022-00297-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In radiomics studies, differences in the volume of interest (VOI) are often inevitable and may confound the extracted features. We aimed to correct this confounding effect of VOI variability by applying parametric maps with a fixed voxel size. METHODS Ten scans of a cup filled with sodium chloride solution were scanned using a multislice computed tomography (CT) unit. Sphere-shaped VOIs with different diameters (4, 8, or 16 mm) were drawn centrally into the phantom. A total of 93 features were extracted conventionally from the original images using PyRadiomics. Using a self-designed and pretested software tool, parametric maps for the same 93 features with a fixed voxel size of 4 mm3 were created. To retrieve the feature values from the maps, VOIs were copied from the original images to preserve the position. Differences in feature quantities between the VOI sizes were tested with the Mann-Whitney U-test and agreement with overall concordance correlation coefficients (OCCC). RESULTS Fifty-five conventionally extracted features were significantly different between the VOI sizes, and none of the features showed excellent agreement in terms of OCCCs. When read from the parametric maps, only 8 features showed significant differences, and 3 features showed an excellent OCCC (≥ 0.85). The OCCCs for 89 features substantially increased using the parametric maps. CONCLUSIONS This phantom study shows that converting CT images into parametric maps resolves the confounding effect of VOI variability and increases feature reproducibility across VOI sizes.
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Affiliation(s)
- Laura J Jensen
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Damon Kim
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Thomas Elgeti
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Ingo G Steffen
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Lars-Arne Schaafs
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Bernd Hamm
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Sebastian N Nagel
- Klinik für Radiologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
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Yoon HJ, Choi J, Kim E, Um SW, Kang N, Kim W, Kim G, Park H, Lee HY. Deep learning analysis to predict EGFR mutation status in lung adenocarcinoma manifesting as pure ground-glass opacity nodules on CT. Front Oncol 2022; 12:951575. [PMID: 36119545 PMCID: PMC9478848 DOI: 10.3389/fonc.2022.951575] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/15/2022] [Indexed: 11/28/2022] Open
Abstract
Background Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) showed potency as a non-invasive therapeutic approach in pure ground-glass opacity nodule (pGGN) lung adenocarcinoma. However, optimal methods of extracting information about EGFR mutation from pGGN lung adenocarcinoma images remain uncertain. We aimed to develop, validate, and evaluate the clinical utility of a deep learning model for predicting EGFR mutation status in lung adenocarcinoma manifesting as pGGN on computed tomography (CT). Methods We included 185 resected pGGN lung adenocarcinomas in the primary cohort. The patients were divided into training (n = 125), validation (n = 23), and test sets (n = 37). A preoperative CT-based deep learning model with clinical factors as well as clinical and radiomics models was constructed and applied to the test set. We evaluated the clinical utility of the deep learning model by applying it to 83 GGNs that received EGFR-TKI from an independent cohort (clinical validation set), and treatment response was regarded as the reference standard. Results The prediction efficiencies of each model were compared in terms of area under the curve (AUC). Among the 185 pGGN lung adenocarcinomas, 122 (65.9%) were EGFR-mutant and 63 (34.1%) were EGFR-wild type. The AUC of the clinical, radiomics, and deep learning with clinical models to predict EGFR mutations were 0.50, 0.64, and 0.85, respectively, for the test set. The AUC of deep learning with the clinical model in the validation set was 0.72. Conclusions Deep learning approach of CT images combined with clinical factors can predict EGFR mutations in patients with lung adenocarcinomas manifesting as pGGN, and its clinical utility was demonstrated in a real-world sample.
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Affiliation(s)
- Hyun Jung Yoon
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Radiology, Veterans Health Service Medical Center, Seoul, South Korea
| | - Jieun Choi
- Department of Artificial Intelligence, Sungkyunkwan University, Suwon, South Korea
| | - Eunjin Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Sang-Won Um
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Department of Health Science and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea
| | - Noeul Kang
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
- Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Wook Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Geena Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, South Korea
- *Correspondence: Ho Yun Lee, ; Hyunjin Park,
| | - 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 Science and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea
- *Correspondence: Ho Yun Lee, ; Hyunjin Park,
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Kendrick J, Francis RJ, Hassan GM, Rowshanfarzad P, Ong JSL, Ebert MA. Fully automatic prognostic biomarker extraction from metastatic prostate lesion segmentations in whole-body [ 68Ga]Ga-PSMA-11 PET/CT images. Eur J Nucl Med Mol Imaging 2022; 50:67-79. [PMID: 35976392 DOI: 10.1007/s00259-022-05927-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/01/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE This study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [68Ga]Ga-PSMA-11 PET/CT images for the purpose of extracting patient-level prognostic biomarkers. METHODS Three hundred thirty-seven [68Ga]Ga-PSMA-11 PET/CT images were retrieved from a cohort of biochemically recurrent PCa patients. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework, and was trained on a subset of these scans, with an independent test set reserved for model evaluation. Voxel-level segmentation results were assessed using the dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity. Sensitivity and PPV were calculated to assess lesion level detection; patient-level classification results were assessed by the accuracy, PPV, and sensitivity. Whole-body biomarkers total lesional volume (TLVauto) and total lesional uptake (TLUauto) were calculated from the automated segmentations, and Kaplan-Meier analysis was used to assess biomarker relationship with patient overall survival. RESULTS At the patient level, the accuracy, sensitivity, and PPV were all > 90%, with the best metric being the PPV (97.2%). PPV and sensitivity at the lesion level were 88.2% and 73.0%, respectively. DSC and PPV measured at the voxel level performed within measured inter-observer variability (DSC, median = 50.7% vs. second observer = 32%, p = 0.012; PPV, median = 64.9% vs. second observer = 25.7%, p < 0.005). Kaplan-Meier analysis of TLVauto and TLUauto showed they were significantly associated with patient overall survival (both p < 0.005). CONCLUSION The fully automated assessment of whole-body [68Ga]Ga-PSMA-11 PET/CT images using deep learning shows significant promise, yielding accurate scan classification, voxel-level segmentations within inter-observer variability, and potentially clinically useful prognostic biomarkers associated with patient overall survival. TRIAL REGISTRATION This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.
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Affiliation(s)
- Jake Kendrick
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia.
| | - Roslyn J Francis
- Medical School, University of Western Australia, Crawley, WA, Australia.,Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Jeremy S L Ong
- Department of Nuclear Medicine, Fiona Stanley Hospital, Murdoch, WA, Australia
| | - Martin A Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia.,Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, WA, Australia.,5D Clinics, Claremont, WA, Australia
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Cui Y, Yin FF. Impact of image quality on radiomics applications. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7fd7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/08/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.
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Verma R, Hill VB, Statsevych V, Bera K, Correa R, Leo P, Ahluwalia M, Madabhushi A, Tiwari P. Stable and Discriminatory Radiomic Features from the Tumor and Its Habitat Associated with Progression-Free Survival in Glioblastoma: A Multi-Institutional Study. AJNR Am J Neuroradiol 2022; 43:1115-1123. [PMID: 36920774 PMCID: PMC9575418 DOI: 10.3174/ajnr.a7591] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 06/13/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Glioblastoma is an aggressive brain tumor, with no validated prognostic biomarkers for survival before surgical resection. Although recent approaches have demonstrated the prognostic ability of tumor habitat (constituting necrotic core, enhancing lesion, T2/FLAIR hyperintensity subcompartments) derived radiomic features for glioblastoma survival on treatment-naive MR imaging scans, radiomic features are known to be sensitive to MR imaging acquisitions across sites and scanners. In this study, we sought to identify the radiomic features that are both stable across sites and discriminatory of poor and improved progression-free survival in glioblastoma tumors. MATERIALS AND METHODS We used 150 treatment-naive glioblastoma MR imaging scans (Gadolinium-T1w, T2w, FLAIR) obtained from 5 sites. For every tumor subcompartment (enhancing tumor, peritumoral FLAIR-hyperintensities, necrosis), a total of 316 three-dimensional radiomic features were extracted. The training cohort constituted studies from 4 sites (n = 93) to select the most stable and discriminatory radiomic features for every tumor subcompartment. These features were used on a hold-out cohort (n = 57) to evaluate their ability to discriminate patients with poor survival from those with improved survival. RESULTS Incorporating the most stable and discriminatory features within a linear discriminant analysis classifier yielded areas under the curve of 0.71, 0.73, and 0.76 on the test set for distinguishing poor and improved survival compared with discriminatory features alone (areas under the curve of 0.65, 0.54, 0.62) from the necrotic core, enhancing tumor, and peritumoral T2/FLAIR hyperintensity, respectively. CONCLUSIONS Incorporating stable and discriminatory radiomic features extracted from tumors and associated habitats across multisite MR imaging sequences may yield robust prognostic classifiers of patient survival in glioblastoma tumors.
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Affiliation(s)
- R Verma
- From the Department of Biomedical Engineering (R.V., K.B., R.C., P.L.), Case Western Reserve University, Cleveland, Ohio .,Alberta Machine Intelligence Institute (R.V.), Edmonton, Alberta
| | - V B Hill
- Department of Neuroradiology (V.B.H.), Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - V Statsevych
- Brain Tumor and Neuro-Oncology Center (V.S.), Cleveland Clinic, Cleveland, Ohio
| | - K Bera
- From the Department of Biomedical Engineering (R.V., K.B., R.C., P.L.), Case Western Reserve University, Cleveland, Ohio
| | - R Correa
- From the Department of Biomedical Engineering (R.V., K.B., R.C., P.L.), Case Western Reserve University, Cleveland, Ohio
| | - P Leo
- From the Department of Biomedical Engineering (R.V., K.B., R.C., P.L.), Case Western Reserve University, Cleveland, Ohio
| | - M Ahluwalia
- Miami Cancer Institute (M.A.), Miami, FL and Herbert Wertheim College of Medicine, Florida International University, Florida
| | - A Madabhushi
- Department of Biomedical Engineering (A.M.), Emory University, Atlanta Veterans Administration Medical Center
| | - P Tiwari
- Departments of Radiology and Biomedical Engineering (P.T.), University of Wisconsin Madison, Wisconsin
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Zhang JZ, Ganesh H, Raslau FD, Nair R, Escott E, Wang C, Wang G, Zhang J. Deep learning versus iterative reconstruction on image quality and dose reduction in abdominal CT: a live animal study. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 06/16/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. While simulated low-dose CT images and phantom studies cannot fully approximate subjective and objective effects of deep learning (DL) denoising on image quality, live animal models may afford this assessment. This study is to investigate the potential of DL in CT dose reduction on image quality compared to iterative reconstruction (IR). Approach. The upper abdomen of a live 4 year old sheep was scanned on a CT scanner at different exposure levels. Images were reconstructed using FBP and ADMIRE with 5 strengths. A modularized DL network with 5 modules was used for image reconstruction via progressive denoising. Radiomic features were extracted from a region over the liver. Concordance correlation coefficient (CCC) was applied to quantify agreement between any two sets of radiomic features. Coefficient of variation was calculated to measure variation in a radiomic feature series. Structural similarity index (SSIM) was used to measure the similarity between any two images. Diagnostic quality, low-contrast detectability, and image texture were qualitatively evaluated by two radiologists. Pearson correlation coefficient was computed across all dose-reconstruction/denoising combinations. Results. A total of 66 image sets, with 405 radiomic features extracted from each, are analyzed. IR and DL can improve diagnostic quality and low-contrast detectability and similarly modulate image texture features. In terms of SSIM, DL has higher potential in preserving image structure. There is strong correlation between SSIM and radiologists’ evaluations for diagnostic quality (0.559) and low-contrast detectability (0.635) but moderate correlation for texture (0.313). There is moderate correlation between CCC of radiomic features and radiologists’ evaluation for diagnostic quality (0.397), low-contrast detectability (0.417), and texture (0.326), implying that improvement of image features may not relate to improvement of diagnostic quality. Conclusion. DL shows potential to further reduce radiation dose while preserving structural similarity, while IR is favored by radiologists and more predictably alters radiomic features.
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Radiomics model for preoperative prediction of 3-year survival-based CT image biomarkers in esophageal cancer. Eur Arch Otorhinolaryngol 2022; 279:5433-5443. [PMID: 35857100 DOI: 10.1007/s00405-022-07510-8] [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: 11/08/2021] [Accepted: 02/04/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE This work aimed to develop a radiomics nomogram to predict 3-year overall survival of esophageal cancer patients after chemoradiotherapy. METHODS A total of 109 esophageal cancer patients, diagnosed from November 2012 to February 2015, were enrolled in this retrospective study. They were randomly divided into training set (77 cases) and verification set (32 cases). Image standardization was performed prior to feature extraction. And then, about 1670 radiomics features were extracted from the pretreatment diagnostic computed tomography image. A radiomics signature was constructed with the lasso algorithm; then, a radiomics score was calculated to reflect survival probability using the radiomics signature for each patient. A radiomics nomogram was developed by incorporating the radiomics score and clinical factors. A clinical model was constructed using clinical factors only. The performance of the nomogram was assessed with respect to its calibration and discrimination. Kaplan-Meier survival analysis was performed. RESULTS Sixteen radiomics features were selected to build the radiomics signature. The radiomics nomogram showed better calibration and classification capacity than the clinical model with AUC 0.96 vs. 0.72 for the training cohort, and 0.87 vs. 0.67 for the validation cohort. The model showed good discrimination with a Harrell's Concordance Index of 0.76 in the training cohort and 0.81 in the validation cohort. Decision curve analysis demonstrated the clinical usefulness of the radiomics nomogram. A significant difference (p value < 0.05; log-rank test) was observed between the survival curves of the nomogram-predicted survival and non-survival groups. CONCLUSIONS The present study proposed a radiomics-based nomogram involving the radiomics signature and clinical factors. It can be potentially applied in the individual preoperative prediction of 3-year survival in esophageal cancer patients.
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Chen Q, Li Y, Cheng Q, Van Valkenburgh J, Sun X, Zheng C, Zhang R, Yuan R. EGFR Mutation Status and Subtypes Predicted by CT-Based 3D Radiomic Features in Lung Adenocarcinoma. Onco Targets Ther 2022; 15:597-608. [PMID: 35669165 PMCID: PMC9165655 DOI: 10.2147/ott.s352619] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 05/06/2022] [Indexed: 11/23/2022] Open
Abstract
Objective In this study, we aim to establish a non-invasive tool to predict epidermal growth factor receptor (EGFR) mutation status and subtypes based on radiomic features of computed tomography (CT). Methods A total of 233 lung adenocarcinoma patients were investigated and randomly divided into the training and test cohorts. In this study, 2300 radiomic features were extracted from original and filtered (Exponential, Laplacian of Gaussian, Logarithm, Gabor, Wavelet) CT images. The radiomic features were divided into four categories, including histogram, volumetric, morphologic, and texture features. An RF-BFE algorithm was developed to select the features for building the prediction models. Clinicopathological features (including age, gender, smoking status, TNM staging, maximum diameter, location, and growth pattern) were combined to establish an integrated model with radiomic features. ROC curve and AUC quantified the effectiveness of the predictor of EGFR mutation status and subtypes. Results A set of 10 features were selected to predict EGFR mutation status between EGFR mutant and wild type, while 9 selected features were used to predict mutation subtypes between exon 19 deletion and exon 21 L858R mutation. To predict the EGFR mutation status, the AUC of the training cohort was 0.778 and the AUC of the test cohort was 0.765. To predict the EGFR mutation subtypes, the AUC of training cohort was 0.725 and the AUC of test cohort was 0.657. The integrated model showed the most optimal predictive performance with EGFR mutation status (AUC = 0.870 and 0.759) and subtypes (AUC = 0.797 and 0.554) in the training and test cohorts. Conclusion CT-based radiomic features can extract information on tumor heterogeneity in lung adenocarcinoma. In addition, we have established a radiomic model and an integrated model to non-invasively predict the EGFR mutation status and subtypes of lung adenocarcinoma, which is conducive to saving clinical costs and guiding targeted therapy.
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Affiliation(s)
- Quan Chen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, People’s Republic of China
| | - Yan Li
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Qiguang Cheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, People’s Republic of China
| | - Juno Van Valkenburgh
- Department of Radiology, Molecular Imaging Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xiaotian Sun
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, People’s Republic of China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, People’s Republic of China
| | - Ruiguang Zhang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- Correspondence: Ruiguang Zhang, Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China, Email
| | - Rong Yuan
- Department of Radiology, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen, People’s Republic of China
- Rong Yuan, Department of Radiology, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen, People’s Republic of China, Email
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Assessing radiomics feature stability with simulated CT acquisitions. Sci Rep 2022; 12:4732. [PMID: 35304508 PMCID: PMC8933485 DOI: 10.1038/s41598-022-08301-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 03/03/2022] [Indexed: 11/29/2022] Open
Abstract
Medical imaging quantitative features had once disputable usefulness in clinical studies. Nowadays, advancements in analysis techniques, for instance through machine learning, have enabled quantitative features to be progressively useful in diagnosis and research. Tissue characterisation is improved via the “radiomics” features, whose extraction can be automated. Despite the advances, stability of quantitative features remains an important open problem. As features can be highly sensitive to variations of acquisition details, it is not trivial to quantify stability and efficiently select stable features. In this work, we develop and validate a Computed Tomography (CT) simulator environment based on the publicly available ASTRA toolbox (www.astra-toolbox.com). We show that the variability, stability and discriminative power of the radiomics features extracted from the virtual phantom images generated by the simulator are similar to those observed in a tandem phantom study. Additionally, we show that the variability is matched between a multi-center phantom study and simulated results. Consequently, we demonstrate that the simulator can be utilised to assess radiomics features’ stability and discriminative power.
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Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging. Diagnostics (Basel) 2022; 12:diagnostics12020550. [PMID: 35204639 PMCID: PMC8870954 DOI: 10.3390/diagnostics12020550] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/09/2022] [Accepted: 02/17/2022] [Indexed: 02/05/2023] Open
Abstract
Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning.
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Kim J, Lee JH. A novel graphical evaluation of agreement. BMC Med Res Methodol 2022; 22:51. [PMID: 35184729 PMCID: PMC8858529 DOI: 10.1186/s12874-022-01532-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 01/28/2022] [Indexed: 11/19/2022] Open
Abstract
Background The Bland-Altman plot with the limits of agreement has been widely used as an absolute index for assessing test-retest reliability or reproducibility between two measurements. We have observed that in the settings where the relative index such as concordance correlation coefficient (CCC) or intraclass correlation coefficient is employed, the limits of agreement approach may be inconsistent with the scaled index. Particularly, the broad width of the limits of agreement may indicate a lack of agreement when the two measurements are highly concordant but an acceptable difference is not known and the common variance of the data is large. This research aims to create a novel, CCC-based guidance for graphical evaluation of reproducibility or reliability. Methods The concordance correlation coefficient is used to create a 100(1-α)% reference band from two measurements. Simulation studies and real examples, including the peak expiratory flow rate data in Bland and Altman’s paper and the test-retest reproducibility data of the Radiomics study, are implemented to assess the use of the reference band. Results In the absence of an acceptable difference between measurements, we found that the limits of agreement may not be consistent with the concordance correlation coefficient. Our simulation study results and real data application show that the proposed method can provide practitioners with a novel graphical evaluation that is consistent with results from the concordance correlation coefficient. Conclusions Our proposed novel scaled index-based guidance can be used for the graphical evaluation of reproducibility or reliability and may have advantages over the limits of agreement in settings where the concordance correlation coefficient is employed. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01532-w.
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Ma JW, Li M. Molecular typing of lung adenocarcinoma with computed tomography and CT image-based radiomics: a narrative review of research progress and prospects. Transl Cancer Res 2022; 10:4217-4231. [PMID: 35116717 PMCID: PMC8797562 DOI: 10.21037/tcr-21-1037] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 09/03/2021] [Indexed: 12/21/2022]
Abstract
Objective The purpose of this paper was to perform a narrative review of current research evidence on conventional computed tomography (CT) imaging features and CT image-based radiomic features for predicting gene mutations in lung adenocarcinoma and discuss how to translate the research findings to guide future practice. Background Lung cancer, especially lung adenocarcinoma, is the leading cause of cancer-related deaths. With advances in the diagnosis and treatment of lung adenocarcinoma with the emergence of molecular testing, the prediction of oncogenes and even drug resistance gene mutations have become key to individualized and precise clinical treatment in order to prolong survival and improve quality of life. The progress of imageological examination includes the development of CT and radiomics are promising quantitative methods for predicting different gene mutations in lung adenocarcinoma, especially common mutations, such as epidermal growth factor receptor (EGFR) mutation, anaplastic lymphoma kinase (ALK) mutation and Kirsten rat sarcoma viral oncogene (KRAS) mutation. Methods The PubMed electronic database was searched along with a set of terms specific to lung adenocarcinoma, radiomics (including texture analysis), CT, computed tomography, EGFR, ALK, KRAS, rearranging transfection (RET) rearrangement and c-ros oncogene 1 (ROS-1), v-raf murine sarcoma viral oncogene homolog B1 (BRAF), and human epidermal growth factor receptor 2 (HER2) mutations et al. This review has been reported in compliance with the Narrative Review checklist guidelines. From each full-text article, information was extracted regarding a set of terms above. Conclusions Research on the application of conventional CT features and CT image-based radiomic features for predicting the gene mutation status of lung adenocarcinoma is still in a preliminary stage. Noninvasively determination of mutation status in lung adenocarcinoma before targeted therapy with conventional CT features and CT image-based radiomic features remains both hopes and challenges. Before radiomics could be applied in clinical practice, more work needs to be done.
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Affiliation(s)
- Jing-Wen Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Wu S, Zhang N, Wu Z, Ren J, E L. Can Peritumoral Radiomics Improve the Prediction of Malignancy of Solid Pulmonary Nodule Smaller Than 2 cm? Acad Radiol 2022; 29 Suppl 2:S47-S52. [PMID: 33189549 DOI: 10.1016/j.acra.2020.10.029] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/21/2020] [Accepted: 10/31/2020] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVES To compare the ability of radiomics models including the perinodular parenchyma and standard nodular radiomics model in lung cancer diagnosis of solid pulmonary nodules smaller than 2 cm. MATERIALS AND METHODS In this retrospective study, the computed tomography (CT) scans of 206 patients with a lung nodule from a single institution in 2012-2019 were collected. For each nodule, four volumes of interest were defined using the gross tumor volume (GTV) and peritumoral volumes (PTVs) of 5, 10, and 15 mm around the tumor. RESULTS Radiomics models created from GTV, GTV plus 5 mm of PTV, GTV plus 10 mm of PTV, and GTV plus 15 mm of PTV achieved AUCs of 0.89, 0.81, 0.81, and 0.73, respectively, in the validation cohort for the diagnostic classification of benign and malignant pulmonary nodules. The performance of the models gradually decreased as the PTV increased. Wavelet features were the primary features identified in optimal radiomics signatures (2/3 in R, 4/5 in GTV plus 5 mm PTV, 3/4 in GTV plus 10 mm PTV, 2/3 in GTV plus 15 mm PTV). CONCLUSION Our study indicated that the radiomics signatures of GTV had a good prediction ability in distinguishing benign and malignant solid pulmonary nodules smaller than 2 cm on CT. However, the radiomics feature of the surrounding parenchyma of the nodule did not enhance the effectiveness of the diagnostic model.
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Affiliation(s)
- Shan Wu
- Department of Radiology, Shanxi Bethune Hospital, 99 Longcheng Street, Taiyuan, Shanxi 030032, China
| | - Na Zhang
- Department of Radiology, Shanxi Bethune Hospital, 99 Longcheng Street, Taiyuan, Shanxi 030032, China
| | - Zhifeng Wu
- Department of Radiology, Shanxi Bethune Hospital, 99 Longcheng Street, Taiyuan, Shanxi 030032, China
| | | | - Linning E
- Department of Radiology, Shanxi Bethune Hospital, 99 Longcheng Street, Taiyuan, Shanxi 030032, China.
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Mireștean CC, Volovăț C, Iancu RI, Iancu DPT. Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease. J Clin Med 2022; 11:jcm11030616. [PMID: 35160069 PMCID: PMC8836903 DOI: 10.3390/jcm11030616] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/23/2022] [Accepted: 01/24/2022] [Indexed: 12/17/2022] Open
Abstract
In the last decade, the analysis of the medical images has evolved significantly, applications and tools capable to extract quantitative characteristics of the images beyond the discrimination capacity of the investigator's eye being developed. The applications of this new research field, called radiomics, presented an exponential growth with direct implications in the diagnosis and prediction of response to therapy. Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype with a severe prognosis, despite the aggressive multimodal treatments applied according to the guidelines. Radiomics has already proven the ability to differentiate TNBC from fibroadenoma. Radiomics features extracted from digital mammography may also distinguish between TNBC and non-TNBC. Recent research has identified three distinct subtypes of TNBC using IRM breast images voxel-level radiomics features (size/shape related features, texture features, sharpness). The correlation of these TNBC subtypes with the clinical response to neoadjuvant therapy may lead to the identification of biomarkers in order to guide the clinical decision. Furthermore, the variation of some radiomics features in the neoadjuvant settings provides a tool for the rapid evaluation of treatment efficacy. The association of radiomics features with already identified biomarkers can generate complex predictive and prognostic models. Standardization of image acquisition and also of radiomics feature extraction is required to validate this method in clinical practice.
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Affiliation(s)
- Camil Ciprian Mireștean
- Department of Oncology and Radiotherapy, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
- Department of Surgery, Railways Clinical Hospital, 700506 Iasi, Romania
| | - Constantin Volovăț
- Department of Medical Oncology-Radiotherapy, Faculty of Medicine, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (C.V.); (D.P.T.I.)
- Euroclinic Oncological Hospital, 700110 Iasi, Romania
| | - Roxana Irina Iancu
- Department of Oral Pathology, Faculty of Dentistry, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
- Clinical Laboratory Department, “St. Spiridon” Emergency Hospital, 700111 Iasi, Romania
- Correspondence: ; Tel.: +40-232-301-603
| | - Dragoș Petru Teodor Iancu
- Department of Medical Oncology-Radiotherapy, Faculty of Medicine, “Gr. T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (C.V.); (D.P.T.I.)
- Department of Radiotherapy, Regional Institute of Oncology, 700483 Iasi, Romania
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Yi J, Lei X, Zhang L, Zheng Q, Jin J, Xie C, Jin X, Ai Y. The Influence of Different Ultrasonic Machines on Radiomics Models in Prediction Lymph Node Metastasis for Patients with Cervical Cancer. Technol Cancer Res Treat 2022; 21:15330338221118412. [PMID: 35971568 PMCID: PMC9386859 DOI: 10.1177/15330338221118412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Objective To investigate the effects of different ultrasonic machines on the performance of radiomics models using ultrasound (US) images in the prediction of lymph node metastasis (LNM) for patients with cervical cancer (CC) preoperatively. Methods A total of 536 CC patients with confirmed histological characteristics and lymph node status after radical hysterectomy and pelvic lymphadenectomy were enrolled. Radiomics features were extracted and selected with US images acquired with ATL HDI5000, Voluson E8, MyLab classC, ACUSON S2000, and HI VISION Preirus to build radiomics models for LNM prediction using support vector machine (SVM) and logistic regression, respectively. Results There were 148 patients (training vs validation: 102:46) scanned in machine HDI5000, 75 patients (53:22) in machine Voluson E8, 100 patients (69:31) in machine MyLab classC, 110 patients (76:34) in machine ACUSON S2000, and 103 patients (73:30) in machine HI VISION Preirus, respectively. Few radiomics features were reproducible among different machines. The area under the curves (AUCs) ranged from 0.75 to 0.86, 0.73 to 0.86 in the training cohorts, and from 0.71 to 0.82, 0.70 to 0.80 in the validation cohorts for SVM and logistic regression models, respectively. The highest difference in AUCs for different machines reaches 17.8% and 15.5% in the training and validation cohorts, respectively. Conclusions The performance of radiomics model is dependent on the type of scanner. The problem of scanner dependency on radiomics features should be considered, and their effects should be minimized in future studies for US images.
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Affiliation(s)
- Jinling Yi
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiyao Lei
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lei Zhang
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qiao Zheng
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Juebin Jin
- Department of Medical Engineering, 89657The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Congying Xie
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,Department of Radiation and Medical Oncology, 26452The 2nd Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiance Jin
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.,School of Basic Medical Science, 26453Wenzhou Medical University, Wenzhou, China
| | - Yao Ai
- Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Sellami S, Bourbonne V, Hatt M, Tixier F, Bouzid D, Lucia F, Pradier O, Goasduff G, Visvikis D, Schick U. Predicting response to radiotherapy of head and neck squamous cell carcinoma using radiomics from cone-beam CT images. Acta Oncol 2022; 61:73-80. [PMID: 34632924 DOI: 10.1080/0284186x.2021.1983207] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. MATERIAL AND METHODS A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 × SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. RESULTS Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p = .006), 0.85 (p < .001), and 0.99 (p < .001) for clinical, CBCT4-radiomics (Coarseness) and clinical + radiomics based models respectively. The mean AUC of this last model on a 5-fold cross-validation was 0.80 (±0.09). On the testing cohort, the best prediction was given by the combined model (balanced accuracy [BAcc] 0.67 , p < .001). CONCLUSIONS We described a feature selection methodology for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clinical-based prediction models.
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Affiliation(s)
- S. Sellami
- Radiation Oncology Department, University Hospital, Brest, France
| | - V. Bourbonne
- Radiation Oncology Department, University Hospital, Brest, France
| | - M. Hatt
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - F. Tixier
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - D. Bouzid
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - F. Lucia
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - O. Pradier
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - G. Goasduff
- Radiation Oncology Department, University Hospital, Brest, France
| | - D. Visvikis
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - U. Schick
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
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Wu X, Yu P, Jia C, Mao N, Che K, Li G, Zhang H, Mou Y, Song X. Radiomics Analysis of Computed Tomography for Prediction of Thyroid Capsule Invasion in Papillary Thyroid Carcinoma: A Multi-Classifier and Two-Center Study. Front Endocrinol (Lausanne) 2022; 13:849065. [PMID: 35692398 PMCID: PMC9174423 DOI: 10.3389/fendo.2022.849065] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/20/2022] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE To investigate the application of computed tomography (CT)-based radiomics model for prediction of thyroid capsule invasion (TCI) in papillary thyroid carcinoma (PTC). METHODS This retrospective study recruited 412 consecutive PTC patients from two independent institutions and randomly assigned to training (n=265), internal test (n=114) and external test (n=33) cohorts. Radiomics features were extracted from non-contrast (NC) and artery phase (AP) CT scans. We also calculated delta radiomics features, which are defined as the absolute differences between the extracted radiomics features. One-way analysis of variance and least absolute shrinkage and selection operator were used to select optimal radiomics features. Then, six supervised machine learning radiomics models (k-nearest neighbor, logistic regression, decision tree, linear support vector machine [L-SVM], Gaussian-SVM, and polynomial-SVM) were constructed. Univariate was used to select clinicoradiological risk factors. Combined models including optimal radiomics features and clinicoradiological risk factors were constructed by these six classifiers. The prediction performance was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). RESULTS In the internal test cohort, the best combined model (L-SVM, AUC=0.820 [95% CI 0.758-0.888]) performed better than the best radiomics model (L-SVM, AUC = 0.733 [95% CI 0.654-0.812]) and the clinical model (AUC = 0.709 [95% CI 0.649-0.783]). Combined-L-SVM model combines 23 radiomics features and 1 clinicoradiological risk factor (CT-reported TCI). In the external test cohort, the AUC was 0.776 (0.625-0.904) in the combined-L-SVM model, showing that the model is stable. DCA demonstrated that the combined model was clinically useful. CONCLUSIONS Our combined model based on machine learning incorporated with CT radiomics features and the clinicoradiological risk factor shows good predictive ability for TCI in PTC.
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Affiliation(s)
- Xinxin Wu
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
| | - Pengyi Yu
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
| | - Chuanliang Jia
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Guan Li
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Haicheng Zhang
- Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
| | - Yakui Mou
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
- *Correspondence: Xicheng Song, ; Yakui Mou,
| | - Xicheng Song
- Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China
- Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, China
- *Correspondence: Xicheng Song, ; Yakui Mou,
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Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study. Tomography 2021; 7:866-876. [PMID: 34941645 PMCID: PMC8706942 DOI: 10.3390/tomography7040073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 11/20/2021] [Accepted: 11/29/2021] [Indexed: 11/17/2022] Open
Abstract
We aimed to evaluate the stability of radiomic features in the liver of healthy individuals across different three-dimensional regions of interest (3D ROI) sizes in T1-weighted (T1w) and T2-weighted (T2w) images from different MR scanners. We retrospectively included 66 examinations of patients without known diseases or pathological imaging findings acquired on three MRI scanners (3 Tesla I: 25 patients, 3 Tesla II: 19 patients, 1.5 Tesla: 22 patients). 3D ROIs of different diameters (10, 20, 30 mm) were drawn on T1w GRE and T2w TSE images into the liver parenchyma (segment V–VIII). We extracted 93 radiomic features from the different ROIs and tested features for significant differences with the Mann–Whitney-U (MWU)-test. The MWU-test revealed significant differences for most second- and higher-order features, indicating a systematic difference dependent on the ROI size. The features mean, median, root mean squared (RMS), 10th percentile, and 90th percentile were not significantly different. We also assessed feature robustness to ROI size variation with overall concordance correlation coefficients (OCCCs). OCCCs across the different ROI-sizes for mean, median, and RMS were excellent (>0.90) in both sequences on all three scanners. These features, therefore, seem robust to ROI-size variation and suitable for radiomic studies of liver MRI.
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Kendrick J, Francis R, Hassan GM, Rowshanfarzad P, Jeraj R, Kasisi C, Rusanov B, Ebert M. Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies. Front Oncol 2021; 11:771787. [PMID: 34790581 PMCID: PMC8591174 DOI: 10.3389/fonc.2021.771787] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 10/11/2021] [Indexed: 12/21/2022] Open
Abstract
Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.
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Affiliation(s)
- Jake Kendrick
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Roslyn Francis
- Medical School, University of Western Australia, Crawley, WA, Australia
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Robert Jeraj
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Collin Kasisi
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, WA, Australia
| | - Branimir Rusanov
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Martin Ebert
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, WA, Australia
- 5D Clinics, Claremont, WA, Australia
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Chen W, Hou X, Hu Y, Huang G, Ye X, Nie S. A deep learning- and CT image-based prognostic model for the prediction of survival in non-small cell lung cancer. Med Phys 2021; 48:7946-7958. [PMID: 34661294 DOI: 10.1002/mp.15302] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 09/19/2021] [Accepted: 10/10/2021] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE To assist clinicians in arranging personalized treatment, planning follow-up programs and extending survival times for non-small cell lung cancer (NSCLC) patients, a method of deep learning combined with computed tomography (CT) imaging for survival prediction was designed. METHODS Data were collected from 484 patients from four research centers. The data from 344 patients were utilized to build the A_CNN survival prognosis model to classify 2-year overall survival time ranges (730 days cut-off). Data from 140 patients, including independent internal and external test sets, were utilized for model testing. First, a series of preprocessing techniques were used to process the original CT images and generate training and test data sets from the axial, coronal, and sagittal planes. Second, the structure of the A_CNN model was designed based on asymmetric convolution, bottleneck blocks, the uniform cross-entropy (UC) loss function, and other advanced techniques. After that, the A_CNN model was trained, and numerous comparative experiments were designed to obtain the best prognostic survival model. Last, the model performance was evaluated, and the predicted survival curves were analyzed. RESULTS The A_CNN survival prognosis model yielded a high patient-level accuracy of 88.8%, a patch-level accuracy of 82.9%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.932. When tested on an external data set, the maximum patient-level accuracy was 80.0%. CONCLUSIONS The results suggest that using a deep learning method can improve prognosis in patients with NSCLC and has important application value in establishing individualized prognostic models.
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Affiliation(s)
- Wen Chen
- School of Medical Imaging, Shanghai University of Medicine & Health Science, Shanghai, China
| | - Xuewen Hou
- School of Medical Imaging, Shanghai University of Medicine & Health Science, Shanghai, China
| | - Ying Hu
- School of Medical Imaging, Shanghai University of Medicine & Health Science, Shanghai, China
| | - Gang Huang
- Department of Radiology, Shanghai Chest Hospital, Shanghai, China
| | - Xiaodan Ye
- Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Shengdong Nie
- School of Medical Imaging, Shanghai University of Medicine & Health Science, Shanghai, China
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40
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Zhao Y, Feng M, Wang M, Zhang L, Li M, Huang C. CT Radiomics for the Preoperative Prediction of Ki67 Index in Gastrointestinal Stromal Tumors: A Multi-Center Study. Front Oncol 2021; 11:689136. [PMID: 34595107 PMCID: PMC8476965 DOI: 10.3389/fonc.2021.689136] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/30/2021] [Indexed: 12/15/2022] Open
Abstract
Purpose This study established and verified a radiomics model for the preoperative prediction of the Ki67 index of gastrointestinal stromal tumors (GISTs). Materials and Methods A total of 344 patients with GISTs from three hospitals were divided into a training set and an external validation set. The tumor region of interest was delineated based on enhanced computed-tomography (CT) images to extract radiomic features. The Boruta algorithm was used for dimensionality reduction of the features, and the random forest algorithm was used to construct the model for radiomics prediction of the Ki67 index. The receiver operating characteristic (ROC) curve was used to evaluate the model’s performance and generalization ability. Results After dimensionality reduction, a feature subset having 21 radiomics features was generated. The generated radiomics model had an the area under curve (AUC) value of 0.835 (95% confidence interval(CI): 0.761–0.908) in the training set and 0.784 (95% CI: 0.691–0.874) in the external validation cohort. Conclusion The radiomics model of this study had the potential to predict the Ki67 index of GISTs preoperatively.
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Affiliation(s)
- Yilei Zhao
- First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Meibao Feng
- First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Minhong Wang
- First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Liang Zhang
- Zhejiang Cancer Hospital, University of Chinese Academy of Sciences, Hangzhou, China
| | - Meirong Li
- First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chencui Huang
- Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China
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41
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Litvin AA, Burkin DA, Kropinov AA, Paramzin FN. Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovrem Tekhnologii Med 2021; 13:97-104. [PMID: 34513082 PMCID: PMC8353717 DOI: 10.17691/stm2021.13.2.11] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Indexed: 12/12/2022] Open
Abstract
One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of “virtual biopsy” is of particular relevance in oncology. The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered. Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.
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Affiliation(s)
- A A Litvin
- Professor, Department of Surgical Disciplines, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia; Deputy Head Physician for Medical Aspects, Regional Clinical Hospital of the Kaliningrad Region, 74 Klinicheskaya St., Kaliningrad, 236016, Russia
| | - D A Burkin
- PhD Student in Information Science and Computer Engineering, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia
| | - A A Kropinov
- Therapeutist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
| | - F N Paramzin
- Oncologist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
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42
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Alamoudi AO. Radiomics, aptamers and nanobodies: New insights in cancer diagnostics and imaging. Hum Antibodies 2021; 29:1-15. [PMID: 33554897 DOI: 10.3233/hab-200436] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
At present, cancer is a major health issue and the second leading cause of mortality worldwide. Researchers have been working hard on investigating not only improved therapeutics but also on early detection methods, both critical to increasing treatment efficacy and developing methods for disease prevention. Diagnosis of cancers at an early stage can promote timely medical intervention and effective treatment and will result in inhibiting tumor growth and development. Several advances have been made in the diagnostics and imagining technologies for early tumor detection and deciding an effective therapy these include radiomics, nanobodies, and aptamers. Here in this review, we summarize the main applications of radiomics, aptamers, and the use of nanobody-based probes for molecular imaging applications in diagnosis, treatment planning, and evaluations in the field of oncology to develop quantitative and personalized medicine. The preclinical data reported to date are quite promising, and it is predicted that nanobody-based molecular imaging agents will play an important role in the diagnosis and management of different cancer types in near future.
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Mali SA, Ibrahim A, Woodruff HC, Andrearczyk V, Müller H, Primakov S, Salahuddin Z, Chatterjee A, Lambin P. Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods. J Pers Med 2021; 11:842. [PMID: 34575619 PMCID: PMC8472571 DOI: 10.3390/jpm11090842] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/21/2021] [Accepted: 08/24/2021] [Indexed: 12/13/2022] Open
Abstract
Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews.
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Affiliation(s)
- Shruti Atul Mali
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
| | - Abdalla Ibrahim
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
- Department of Medical Physics, Division of Nuclear Medicine and Oncological Imaging, Hospital Center Universitaire de Liege, 4000 Liege, Belgium
- Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, 52074 Aachen, Germany
| | - Henry C. Woodruff
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
| | - Vincent Andrearczyk
- Institute of Information Systems, University of Applied Sciences and Arts Western Switzerland (HES-SO), rue du Technopole 3, 3960 Sierre, Switzerland; (V.A.); (H.M.)
| | - Henning Müller
- Institute of Information Systems, University of Applied Sciences and Arts Western Switzerland (HES-SO), rue du Technopole 3, 3960 Sierre, Switzerland; (V.A.); (H.M.)
| | - Sergey Primakov
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
| | - Zohaib Salahuddin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
| | - Avishek Chatterjee
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW—School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (A.I.); (H.C.W.); (S.P.); (Z.S.); (A.C.); (P.L.)
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
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Sakai K. [2. Radiomics of MRI]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:866-875. [PMID: 34421076 DOI: 10.6009/jjrt.2021_jsrt_77.8.866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Koji Sakai
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
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45
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Tunali I, Gillies RJ, Schabath MB. Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine. Cold Spring Harb Perspect Med 2021; 11:cshperspect.a039537. [PMID: 33431509 PMCID: PMC8288444 DOI: 10.1101/cshperspect.a039537] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Medical imaging is the standard-of-care for early detection, diagnosis, treatment planning, monitoring, and image-guided interventions of lung cancer patients. Most medical images are stored digitally in a standardized Digital Imaging and Communications in Medicine format that can be readily accessed and used for qualitative and quantitative analysis. Over the several last decades, medical images have been shown to contain complementary and interchangeable data orthogonal to other sources such as pathology, hematology, genomics, and/or proteomics. As such, "radiomics" has emerged as a field of research that involves the process of converting standard-of-care images into quantitative image-based data that can be merged with other data sources and subsequently analyzed using conventional biostatistics or artificial intelligence (AI) methods. As radiomic features capture biological and pathophysiological information, these quantitative radiomic features have shown to provide rapid and accurate noninvasive biomarkers for lung cancer risk prediction, diagnostics, prognosis, treatment response monitoring, and tumor biology. In this review, radiomics and emerging AI methods in lung cancer research are highlighted and discussed including advantages, challenges, and pitfalls.
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Affiliation(s)
- Ilke Tunali
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
| | - Robert J Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612, USA
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Dutta K, Roy S, Whitehead TD, Luo J, Jha AK, Li S, Quirk JD, Shoghi KI. Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary. Cancers (Basel) 2021; 13:cancers13153795. [PMID: 34359696 PMCID: PMC8345151 DOI: 10.3390/cancers13153795] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/22/2021] [Accepted: 07/22/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Co-clinical trials are an emerging area of investigation in which a clinical trial is coupled with a corresponding preclinical trial to inform the corresponding clinical trial. The preclinical arm aids in assessing therapeutic efficacy, patient stratification, and designing optimal imaging strategies. There is much interest in harmonizing preclinical and clinical quantitative imaging pipelines. Radiomics is widely explored in clinical imaging to predict response to therapy. In preclinical imaging, high-throughput radiomic analysis is limited by manual delineation of tumor boundaries, which is labor intensive with poor reproducibility. Our proposed deep-learning-based system was trained to automatically segment tumors from multi-contrast MR images and extract radiomic features. The proposed method is highly reproducible with significant correlation in radiomic features. The deployment of this pipeline in the preclinical arm would provide high throughput and reproducible radiomic analysis. Abstract Preclinical magnetic resonance imaging (MRI) is a critical component in a co-clinical research pipeline. Importantly, segmentation of tumors in MRI is a necessary step in tumor phenotyping and assessment of response to therapy. However, manual segmentation is time-intensive and suffers from inter- and intra- observer variability and lack of reproducibility. This study aimed to develop an automated pipeline for accurate localization and delineation of TNBC PDX tumors from preclinical T1w and T2w MR images using a deep learning (DL) algorithm and to assess the sensitivity of radiomic features to tumor boundaries. We tested five network architectures including U-Net, dense U-Net, Res-Net, recurrent residual UNet (R2UNet), and dense R2U-Net (D-R2UNet), which were compared against manual delineation by experts. To mitigate bias among multiple experts, the simultaneous truth and performance level estimation (STAPLE) algorithm was applied to create consensus maps. Performance metrics (F1-Score, recall, precision, and AUC) were used to assess the performance of the networks. Multi-contrast D-R2UNet performed best with F1-score = 0.948; however, all networks scored within 1–3% of each other. Radiomic features extracted from D-R2UNet were highly corelated to STAPLE-derived features with 67.13% of T1w and 53.15% of T2w exhibiting correlation ρ ≥ 0.9 (p ≤ 0.05). D-R2UNet-extracted features exhibited better reproducibility relative to STAPLE with 86.71% of T1w and 69.93% of T2w features found to be highly reproducible (CCC ≥ 0.9, p ≤ 0.05). Finally, 39.16% T1w and 13.9% T2w features were identified as insensitive to tumor boundary perturbations (Spearman correlation (−0.4 ≤ ρ ≤ 0.4). We developed a highly reproducible DL algorithm to circumvent manual segmentation of T1w and T2w MR images and identified sensitivity of radiomic features to tumor boundaries.
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Affiliation(s)
- Kaushik Dutta
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
| | - Sudipta Roy
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
| | - Timothy Daniel Whitehead
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
| | - Jingqin Luo
- Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA;
| | - Abhinav Kumar Jha
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
- Department of Biomedical Engineering McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Shunqiang Li
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA;
| | - James Dennis Quirk
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
| | - Kooresh Isaac Shoghi
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
- Department of Biomedical Engineering McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO 63110, USA
- Correspondence:
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Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix AN, Lambin P, Bottari F, Tsoutzidis N, Miraglio B, Walsh S, Vos W, Hustinx R, Ferreira M, Lovinfosse P, Leijenaar RTH. A review in radiomics: Making personalized medicine a reality via routine imaging. Med Res Rev 2021; 42:426-440. [PMID: 34309893 DOI: 10.1002/med.21846] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 07/05/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022]
Abstract
Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.
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Affiliation(s)
- Julien Guiot
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Akshayaa Vaidyanathan
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Louis Deprez
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Fadila Zerka
- Radiomics (Oncoradiomics SA), Liège, Belgium.,The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | - Denis Danthine
- Department of Radiology, University Hospital of Liège, Liège, Belgium
| | - Anne-Noelle Frix
- Department of Pneumology, University Hospital of Liège, Liège, Belgium
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, Department of Nuclear Medicine, GROW-School for Oncology, Maastricht University, Maastricht, The Netherlands
| | | | | | | | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Roland Hustinx
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium.,GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Marta Ferreira
- GIGA-CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liege, Liege, Belgium
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Permuth JB, Vyas S, Li J, Chen DT, Jeong D, Choi JW. Comparison of Radiomic Features in a Diverse Cohort of Patients With Pancreatic Ductal Adenocarcinomas. Front Oncol 2021; 11:712950. [PMID: 34367997 PMCID: PMC8339963 DOI: 10.3389/fonc.2021.712950] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/05/2021] [Indexed: 12/20/2022] Open
Abstract
Background Significant racial disparities in pancreatic cancer incidence and mortality rates exist, with the highest rates in African Americans compared to Non-Hispanic Whites and Hispanic/Latinx populations. Computer-derived quantitative imaging or “radiomic” features may serve as non-invasive surrogates for underlying biological factors and heterogeneity that characterize pancreatic tumors from African Americans, yet studies are lacking in this area. The objective of this pilot study was to determine if the radiomic tumor profile extracted from pretreatment computed tomography (CT) images differs between African Americans, Non-Hispanic Whites, and Hispanic/Latinx with pancreatic cancer. Methods We evaluated a retrospective cohort of 71 pancreatic cancer cases (23 African American, 33 Non-Hispanic White, and 15 Hispanic/Latinx) who underwent pretreatment CT imaging at Moffitt Cancer Center and Research Institute. Whole lesion semi-automated segmentation was performed on each slice of the lesion on all pretreatment venous phase CT exams using Healthmyne Software (Healthmyne, Madison, WI, USA) to generate a volume of interest. To reduce feature dimensionality, 135 highly relevant non-texture and texture features were extracted from each segmented lesion and analyzed for each volume of interest. Results Thirty features were identified and significantly associated with race/ethnicity based on Kruskal-Wallis test. Ten of the radiomic features were highly associated with race/ethnicity independent of tumor grade, including sphericity, volumetric mean Hounsfield units (HU), minimum HU, coefficient of variation HU, four gray level texture features, and two wavelet texture features. A radiomic signature summarized by the first principal component partially differentiated African American from non-African American tumors (area underneath the curve = 0.80). Poorer survival among African Americans compared to Non-African Americans was observed for tumors with lower volumetric mean CT [HR: 3.90 (95% CI:1.19–12.78), p=0.024], lower GLCM Avg Column Mean [HR:4.75 (95% CI: 1.44,15.37), p=0.010], and higher GLCM Cluster Tendency [HR:3.36 (95% CI: 1.06–10.68), p=0.040], and associations persisted in volumetric mean CT and GLCM Avg Column after adjustment for key clinicopathologic factors. Conclusions This pilot study identified several textural radiomics features associated with poor overall survival among African Americans with PDAC, independent of other prognostic factors such as grade. Our findings suggest that CT radiomic features may serve as surrogates for underlying biological factors and add value in predicting clinical outcomes when integrated with other parameters in ongoing and future studies of cancer health disparities.
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Affiliation(s)
- Jennifer B Permuth
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.,Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Shraddha Vyas
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Jiannong Li
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Dung-Tsa Chen
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Daniel Jeong
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States.,Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
| | - Jung W Choi
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States
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Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study. Diagnostics (Basel) 2021; 11:diagnostics11071224. [PMID: 34359305 PMCID: PMC8304812 DOI: 10.3390/diagnostics11071224] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 06/28/2021] [Accepted: 06/28/2021] [Indexed: 12/12/2022] Open
Abstract
Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be stable enough to changes in the acquisition and extraction settings. Experimenting on 517 lung lesions from a cohort of 207 patients, we assessed the stability of 88 texture features from the following classes: first-order (13 features), Grey-level Co-Occurrence Matrix (24), Grey-level Difference Matrix (14), Grey-level Run-length Matrix (16), Grey-level Size Zone Matrix (16) and Neighbouring Grey-tone Difference Matrix (five). The analysis was based on a public dataset of lung nodules and open-access routines for feature extraction, which makes the study fully reproducible. Our results identified 30 features that had good or excellent stability relative to lesion delineation, 28 to intensity quantisation and 18 to both. We conclude that selecting the right set of imaging features is critical for building clinical predictive models, particularly when changes in lesion delineation and/or intensity quantisation are involved.
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Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians. J Pers Med 2021; 11:jpm11070602. [PMID: 34202096 PMCID: PMC8306026 DOI: 10.3390/jpm11070602] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician's perspective.
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