1
|
Bortolotto C, Pinto A, Brero F, Messana G, Cabini RF, Postuma I, Robustelli Test A, Stella GM, Galli G, Mariani M, Figini S, Lascialfari A, Filippi AR, Bottinelli OM, Preda L. CT and MRI radiomic features of lung cancer (NSCLC): comparison and software consistency. Eur Radiol Exp 2024; 8:71. [PMID: 38880866 PMCID: PMC11180643 DOI: 10.1186/s41747-024-00468-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 04/10/2024] [Indexed: 06/18/2024] Open
Abstract
BACKGROUND Radiomics is a quantitative approach that allows the extraction of mineable data from medical images. Despite the growing clinical interest, radiomics studies are affected by variability stemming from analysis choices. We aimed to investigate the agreement between two open-source radiomics software for both contrast-enhanced computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) of lung cancers and to preliminarily evaluate the existence of radiomic features stable for both techniques. METHODS Contrast-enhanced CT and MRI images of 35 patients affected with non-small cell lung cancer (NSCLC) were manually segmented and preprocessed using three different methods. Sixty-six Image Biomarker Standardisation Initiative-compliant features common to the considered platforms, PyRadiomics and LIFEx, were extracted. The correlation among features with the same mathematical definition was analyzed by comparing PyRadiomics and LIFEx (at fixed imaging technique), and MRI with CT results (for the same software). RESULTS When assessing the agreement between LIFEx and PyRadiomics across the considered resampling, the maximum statistically significant correlations were observed to be 94% for CT features and 95% for MRI ones. When examining the correlation between features extracted from contrast-enhanced CT and MRI using the same software, higher significant correspondences were identified in 11% of features for both software. CONCLUSIONS Considering NSCLC, (i) for both imaging techniques, LIFEx and PyRadiomics agreed on average for 90% of features, with MRI being more affected by resampling and (ii) CT and MRI contained mostly non-redundant information, but there are shape features and, more importantly, texture features that can be singled out by both techniques. RELEVANCE STATEMENT Identifying and selecting features that are stable cross-modalities may be one of the strategies to pave the way for radiomics clinical translation. KEY POINTS • More than 90% of LIFEx and PyRadiomics features contain the same information. • Ten percent of features (shape, texture) are stable among contrast-enhanced CT and MRI. • Software compliance and cross-modalities stability features are impacted by the resampling method.
Collapse
Affiliation(s)
- Chandra Bortolotto
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
| | - Alessandra Pinto
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy.
| | - Francesca Brero
- Department of Physics, University of Pavia, Via Bassi 6, Pavia, 27100, Italy
- Istituto Nazionale Di Fisica Nucleare, Sezione Di Pavia, Pavia, 27100, Italy
| | - Gaia Messana
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
| | - Raffaella Fiamma Cabini
- Istituto Nazionale Di Fisica Nucleare, Sezione Di Pavia, Pavia, 27100, Italy.
- Department of Mathematics, University of Pavia, Via Ferrata 5, Pavia, 27100, Italy.
| | - Ian Postuma
- Istituto Nazionale Di Fisica Nucleare, Sezione Di Pavia, Pavia, 27100, Italy
| | - Agnese Robustelli Test
- Department of Physics, University of Pavia, Via Bassi 6, Pavia, 27100, Italy.
- Istituto Nazionale Di Fisica Nucleare, Sezione Di Pavia, Pavia, 27100, Italy.
| | - Giulia Maria Stella
- Department of Medical Sciences and Infective Diseases, Unit of Respiratory Diseases, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
- Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, 27100, Italy
| | - Giulia Galli
- Department of Medical Sciences and Infective Diseases, Unit of Respiratory Diseases, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
- Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, 27100, Italy
| | - Manuel Mariani
- Department of Physics, University of Pavia, Via Bassi 6, Pavia, 27100, Italy
| | - Silvia Figini
- Department of Political and Social Sciences, University of Pavia, Pavia, 27100, Italy
| | - Alessandro Lascialfari
- Department of Physics, University of Pavia, Via Bassi 6, Pavia, 27100, Italy
- Istituto Nazionale Di Fisica Nucleare, Sezione Di Pavia, Pavia, 27100, Italy
| | - Andrea Riccardo Filippi
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
- Department of Radiation Oncology, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
| | - Olivia Maria Bottinelli
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
| | - Lorenzo Preda
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
| |
Collapse
|
2
|
Huang YS, Chen JLY, Ko WC, Chang YH, Chang CH, Chang YC. Clinical Variables and Radiomics Features for Predicting Pneumothorax in Patients Undergoing CT-guided Transthoracic Core Needle Biopsy. Radiol Cardiothorac Imaging 2024; 6:e230278. [PMID: 38780426 PMCID: PMC11211933 DOI: 10.1148/ryct.230278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 02/02/2024] [Accepted: 03/27/2024] [Indexed: 05/25/2024]
Abstract
Purpose To develop a prediction model combining both clinical and CT texture analysis radiomics features for predicting pneumothorax complications in patients undergoing CT-guided core needle biopsy. Materials and Methods A total of 424 patients (mean age, 65.6 years ± 12.7 [SD]; 232 male, 192 female) who underwent CT-guided core needle biopsy between January 2021 and October 2022 were retrospectively included as the training data set. Clinical and procedure-related characteristics were documented. Texture analysis radiomics features were extracted from the subpleural lung parenchyma traversed by needle. Moderate pneumothorax was defined as a postprocedure air rim of 2 cm or greater. The prediction model was developed using logistic regression with backward elimination, presented by linear fusion of the selected features weighted by their coefficients. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Validation was conducted in an external cohort (n = 45; mean age, 58.2 years ± 12.7; 19 male, 26 female) from a different hospital. Results Moderate pneumothorax occurred in 12.0% (51 of 424) of the training cohort and 8.9% (four of 45) of the external test cohort. Patients with emphysema (P < .001) or a longer needle path length (P = .01) exhibited a higher incidence of moderate pneumothorax in the training cohort. Texture analysis features, including gray-level co-occurrence matrix cluster shade (P < .001), gray-level run-length matrix low gray-level run emphasis (P = .049), gray-level run-length matrix run entropy (P = .003), gray-level size-zone matrix gray-level variance (P < .001), and neighboring gray-tone difference matrix complexity (P < .001), showed higher values in patients with moderate pneumothorax. The combined clinical-radiomics model demonstrated satisfactory performance in both the training (AUC 0.78, accuracy = 71.9%) and external test cohorts (AUC 0.86, accuracy 73.3%). Conclusion The model integrating both clinical and radiomics features offered practical diagnostic performance and accuracy for predicting moderate pneumothorax in patients undergoing CT-guided core needle biopsy. Keywords: Biopsy/Needle Aspiration, Thorax, CT, Pneumothorax, Core Needle Biopsy, Texture Analysis, Radiomics, CT Supplemental material is available for this article. © RSNA, 2024.
Collapse
Affiliation(s)
- Yu-Sen Huang
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
| | - Jenny Ling-Yu Chen
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
| | - Wei-Chun Ko
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
| | - Yu-Han Chang
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
| | - Chin-Hao Chang
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
| | - Yeun-Chung Chang
- From the Department of Medical Imaging (Y.S.H., W.C.K., Y.C.C.) and
Statistical Consulting Unit (Y.H.C., C.H.C.), National Taiwan University
Hospital, No. 7 Chung-Shan S. Rd, Taipei 100, Taiwan; Department of Radiology,
National Taiwan University College of Medicine, Taipei, Taiwan (Y.S.H.,
J.L.Y.C., Y.C.C.); and Department of Radiation Oncology, National Taiwan
University Cancer Center, Taipei, Taiwan (J.L.Y.C.)
| |
Collapse
|
3
|
Li J, Yin H, Zhang H, Wang Y, Ma F, Li L, Gao J, Qu J. Preoperative Risk Stratification for Gastric Cancer: The Establishment of Dual-Energy CT-Based Radiomics Using Prospective Datasets at Two Centers. Acad Radiol 2024:S1076-6332(24)00243-5. [PMID: 38734580 DOI: 10.1016/j.acra.2024.04.034] [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: 03/28/2024] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/13/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the performance of dual-energy CT (DECT)-based radiomics models for identifying high-risk histopathologic phenotypes-serosal invasion (pT4a), lymph node metastasis (LNM), lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer. MATERIAL AND METHODS This prospective bi-center study recruited histologically confirmed gastric adenocarcinoma patients who underwent triple-phase enhanced DECT before gastrectomy between January 2021 and July 2023. Radiomics features were extracted from polychromatic/monochromatic (40 keV, 100 keV)/iodine images at arterial/venous/delay phase, respectively. Predictive features were selected in the training dataset using logistic regression classifier, and trained models were applied to the external validation dataset. Performances of clinical models, conventional contrast enhanced CT (CECT) models and DECT models were evaluated using areas under the receiver operating characteristic curve (AUCs). RESULTS In total, 503 patients were recruited: 396 at training dataset (60.1 ± 10.8 years, 110 females, 286 males) and 107 at validation dataset (61.4 ± 9.5 years, 29 females, 78 males). DECT models dichotomizing pT4a, LNM, LVI, and PNI achieved AUCs of 0.891, 0.817, 0.834, and 0.889, respectively, in the validation dataset, similar with the CECT models. In the training dataset, compared to the CECT model, the DECT model provided increased performance for identifying pT4a, LNM, LVI (all P<0.05), and similar performance for stratifying PNI (P = 0.104). The DECT models was associated with patient disease-free survival (all P<0.05). CONCLUSION DECT radiomics can stratify patients preoperatively according to high-risk histopathologic phenotypes for gastric cancer and are associated with patient disease-free survival in the training dataset.
Collapse
Affiliation(s)
- Jing Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Hongkun Yin
- Infervision Medical Technology Co., Ltd, Beijing 100025, China
| | - Huiling Zhang
- Infervision Medical Technology Co., Ltd, Beijing 100025, China
| | - Yi Wang
- Department of Pathology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Fei Ma
- Department of Gastrointestinal Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Liming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Jinrong Qu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China.
| |
Collapse
|
4
|
Bleker J, Roest C, Yakar D, Huisman H, Kwee TC. The Effect of Image Resampling on the Performance of Radiomics-Based Artificial Intelligence in Multicenter Prostate MRI. J Magn Reson Imaging 2024; 59:1800-1806. [PMID: 37572098 DOI: 10.1002/jmri.28935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 07/19/2023] [Accepted: 07/19/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND Single center MRI radiomics models are sensitive to data heterogeneity, limiting the diagnostic capabilities of current prostate cancer (PCa) radiomics models. PURPOSE To study the impact of image resampling on the diagnostic performance of radiomics in a multicenter prostate MRI setting. STUDY TYPE Retrospective. POPULATION Nine hundred thirty patients (nine centers, two vendors) with 737 eligible PCa lesions, randomly split into training (70%, N = 500), validation (10%, N = 89), and a held-out test set (20%, N = 148). FIELD STRENGTH/SEQUENCE 1.5T and 3T scanners/T2-weighted imaging (T2W), diffusion-weighted imaging (DWI), and apparent diffusion coefficient maps. ASSESSMENT A total of 48 normalized radiomics datasets were created using various resampling methods, including different target resolutions (T2W: 0.35, 0.5, and 0.8 mm; DWI: 1.37, 2, and 2.5 mm), dimensionalities (2D/3D) and interpolation techniques (nearest neighbor, linear, Bspline and Blackman windowed-sinc). Each of the datasets was used to train a radiomics model to detect clinically relevant PCa (International Society of Urological Pathology grade ≥ 2). Baseline models were constructed using 2D and 3D datasets without image resampling. The resampling configurations with highest validation performance were evaluated in the test dataset and compared to the baseline models. STATISTICAL TESTS Area under the curve (AUC), DeLong test. The significance level used was 0.05. RESULTS The best 2D resampling model (T2W: Bspline and 0.5 mm resolution, DWI: nearest neighbor and 2 mm resolution) significantly outperformed the 2D baseline (AUC: 0.77 vs. 0.64). The best 3D resampling model (T2W: linear and 0.8 mm resolution, DWI: nearest neighbor and 2.5 mm resolution) significantly outperformed the 3D baseline (AUC: 0.79 vs. 0.67). DATA CONCLUSION Image resampling has a significant effect on the performance of multicenter radiomics artificial intelligence in prostate MRI. The recommended 2D resampling configuration is isotropic resampling with T2W at 0.5 mm (Bspline interpolation) and DWI at 2 mm (nearest neighbor interpolation). For the 3D radiomics, this work recommends isotropic resampling with T2W at 0.8 mm (linear interpolation) and DWI at 2.5 mm (nearest neighbor interpolation). EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Jeroen Bleker
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Christian Roest
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Derya Yakar
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Radiology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek Hospital (NCI-AVL), Amsterdam, The Netherlands
| | - Henkjan Huisman
- Department of Medical Imaging, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Thomas C Kwee
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| |
Collapse
|
5
|
Delgadillo R, Deana AM, Ford JC, Studenski MT, Padgett KR, Abramowitz MC, Pra AD, Spieler BO, Dogan N. Increasing the efficiency of cone-beam CT based delta-radiomics using automated contours to predict radiotherapy-related toxicities in prostate cancer. Sci Rep 2024; 14:9563. [PMID: 38671043 PMCID: PMC11053114 DOI: 10.1038/s41598-024-60281-6] [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: 07/10/2023] [Accepted: 04/21/2024] [Indexed: 04/28/2024] Open
Abstract
Extracting longitudinal image quantitative data, known as delta-radiomics, has the potential to capture changes in a patient's anatomy throughout the course of radiation treatment for prostate cancer. Some of the major challenges of delta-radiomics studies are contouring the structures for individual fractions and accruing patients' data in an efficient manner. The manual contouring process is often time consuming and would limit the efficiency of accruing larger sample sizes for future studies. The problem is amplified because the contours are often made by highly trained radiation oncologists with limited time to dedicate to research studies of this nature. This work compares the use of automated prostate contours generated using a deformable image-based algorithm to make predictive models of genitourinary and changes in total international prostate symptom score in comparison to manually contours for a cohort of fifty patients. Area under the curve of manual and automated models were compared using the Delong test. This study demonstrated that the delta-radiomics models were similar for both automated and manual delta-radiomics models.
Collapse
Affiliation(s)
- Rodrigo Delgadillo
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Anthony M Deana
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
- Varian Medical Systems, Advanced Oncology Solutions, Avon, IN, USA
| | - John C Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Matthew T Studenski
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Kyle R Padgett
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Matthew C Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Benjamin O Spieler
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA
| | - Nesrin Dogan
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12Th Ave, Miami, FL, 33136, USA.
| |
Collapse
|
6
|
Lai YH, Tsai YS, Su PF, Li CI, Chen HHW. A computed tomography radiomics-based model for predicting osteoporosis after breast cancer treatment. Phys Eng Sci Med 2024; 47:239-248. [PMID: 38190012 DOI: 10.1007/s13246-023-01360-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 11/21/2023] [Indexed: 01/09/2024]
Abstract
Many treatments against breast cancer decrease the level of estrogen in blood, resulting in bone loss, osteoporosis and fragility fractures in breast cancer patients. This retrospective study aimed to evaluate a novel opportunistic screening for cancer treatment-induced bone loss (CTIBL) in breast cancer patients using CT radiomics. Between 2011 and 2021, a total of 412 female breast cancer patients who received treatment and were followed up in our institution, had post-treatment dual-energy X-ray absorptiometry (DXA) examination of the lumbar vertebrae and had post-treatment chest CT scan that encompassed the L1 vertebra, were included in this study. Results indicated that the T-score of L1 vertebra had a strongly positive correlation with the average T-score of L1-L4 vertebrae derived from DXA (r = 0.91, p < 0.05). On multivariable analysis, four clinical variables (age, body weight, menopause status, aromatase inhibitor exposure duration) and three radiomic features extracted from the region of interest of L1 vertebra (original_firstorder_RootMeanSquared, wavelet.HH_glcm_InverseVariance, and wavelet.LL_glcm_MCC) were selected for building predictive models of L1 T-score and bone health. The predictive model combining clinical and radiomic features showed the greatest adjusted R2 value (0.557), sensitivity (83.6%), specificity (74.2%) and total accuracy (79.4%) compared to models that relied solely on clinical data, radiomic features, or Hounsfield units. In conclusion, the clinical-radiomic predictive model may be used as an opportunistic screening tool for early identification of breast cancer survivors at high risk of CTIBL based on non-contrast CT images of the L1 vertebra, thereby facilitating early intervention for osteoporosis.
Collapse
Affiliation(s)
- Yu-Hsuan Lai
- Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Radiation Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 138 Sheng-Li Rd, Tainan, 704302, Taiwan
- Clinical Innovation and Research Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Yi-Shan Tsai
- Clinical Innovation and Research Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Pei-Fang Su
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan
| | - Chung-I Li
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan
| | - Helen H W Chen
- Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
- Department of Radiation Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, No. 138 Sheng-Li Rd, Tainan, 704302, Taiwan.
| |
Collapse
|
7
|
Geng X, Zhang Y, Li Y, Cai Y, Liu J, Geng T, Meng X, Hao F. Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma. Br J Radiol 2024; 97:652-659. [PMID: 38268475 PMCID: PMC11027331 DOI: 10.1093/bjr/tqae009] [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: 07/14/2023] [Revised: 11/10/2023] [Accepted: 12/18/2023] [Indexed: 01/26/2024] Open
Abstract
OBJECTIVES This research aimed to develop a radiomics-clinical nomogram based on enhanced thin-section CT radiomics and clinical features for the purpose of predicting the presence or absence of metastasis in lymph nodes among patients with resectable esophageal squamous cell carcinoma (ESCC). METHODS This study examined the data of 256 patients with ESCC, including 140 cases with lymph node metastasis. Clinical information was gathered for each case, and radiomics features were derived from thin-section contrast-enhanced CT with the help of a 3D slicer. To validate risk factors that are independent of the clinical and radiomics models, least absolute shrinkage and selection operator logistic regression analysis was used. A nomogram pattern was constructed based on the radiomics features and clinical characteristics. The receiver operating characteristic curve and Brier Score were used to evaluate the model's discriminatory ability, the calibration plot to evaluate the model's calibration, and the decision curve analysis to evaluate the model's clinical utility. The confusion matrix was used to evaluate the applicability of the model. To evaluate the efficacy of the model, 1000 rounds of 5-fold cross-validation were conducted. RESULTS The clinical model identified esophageal wall thickness and clinical T (cT) stage as independent risk factors, whereas the radiomics pattern was built based on 4 radiomics features chosen at random. Area under the curve (AUC) values of 0.684 and 0.701 are observed for the radiomics approach and clinical model, respectively. The AUC of nomogram combining radiomics and clinical features was 0.711. The calibration plot showed good agreement between the incidence of lymph node metastasis predicted by the nomogram and the actual probability of occurrence. The nomogram model displayed acceptable levels of performance. After 1000 rounds of 5-fold cross-validation, the AUC and Brier score had median values of 0.702 (IQR: 0.65, 7.49) and 0.21 (IQR: 0.20, 0.23), respectively. High-risk patients (risk point >110) were found to have an increased risk of lymph node metastasis [odds ratio (OR) = 5.15, 95% CI, 2.95-8.99] based on the risk categorization. CONCLUSION A successful preoperative prediction performance for metastasis to the lymph nodes among patients with ESCC was demonstrated by the nomogram that incorporated CT radiomics, wall thickness, and cT stage. ADVANCES IN KNOWLEDGE This study demonstrates a novel radiomics-clinical nomogram for lymph node metastasis prediction in ESCC, which helps physicians determine lymph node status preoperatively.
Collapse
Affiliation(s)
- Xiaotao Geng
- Shandong University Cancer Center, Shandong University, 440 Jiyan Road, Jinan, 250117, China
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yaping Zhang
- Department of Radiology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yang Li
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Yuanyuan Cai
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Jie Liu
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Tianxiang Geng
- Department of Biomaterials, Faculty of Dentistry, University of Oslo, Oslo, 0455, Norway
| | - Xiangdi Meng
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| | - Furong Hao
- Department of Radiation Oncology, Weifang People’s Hospital, 151 Guangwen Street, Weifang, 261000, China
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Tran AT, Zeevi T, Haider SP, Abou Karam G, Berson ER, Tharmaseelan H, Qureshi AI, Sanelli PC, Werring DJ, Malhotra A, Petersen NH, de Havenon A, Falcone GJ, Sheth KN, Payabvash S. Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. NPJ Digit Med 2024; 7:26. [PMID: 38321131 PMCID: PMC10847454 DOI: 10.1038/s41746-024-01007-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024] Open
Abstract
Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.
Collapse
Grants
- U24 NS107136 NINDS NIH HHS
- UL1 TR001863 NCATS NIH HHS
- K76 AG059992 NIA NIH HHS
- P30 AG021342 NIA NIH HHS
- R03 NS112859 NINDS NIH HHS
- U24 NS107215 NINDS NIH HHS
- U01 NS106513 NINDS NIH HHS
- 2020097 Doris Duke Charitable Foundation
- K23 NS118056 NINDS NIH HHS
- R01 NR018335 NINR NIH HHS
- Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- Doris Duke Charitable Foundation (DDCF)
- Doris Duke Charitable Foundation (2020097), American Society of Neuroradiology, and National Institutes of Health (K23NS118056).
- National Institutes of Health (K76AG059992, R03NS112859, and P30AG021342), the American Heart Association (18IDDG34280056), the Yale Pepper Scholar Award, and the Neurocritical Care Society Research Fellowship
- National Institutes of Health (U24NS107136, U24NS107215, R01NR018335, and U01NS106513) and the American Heart Association (18TPA34170180 and 17CSA33550004) and a Hyperfine Research Inc research grant.
Collapse
Affiliation(s)
- Anh T Tran
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stefan P Haider
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany
| | - Gaby Abou Karam
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Elisa R Berson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Hishan Tharmaseelan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Adnan I Qureshi
- Stroke Institute and Department of Neurology, University of Missouri, Columbia, MO, USA
| | - Pina C Sanelli
- Department of Radiology, Northwell Health, Manhasset, NY, USA
| | - David J Werring
- Stroke Research Centre, University College London, Queen Square Institute of Neurology, London, UK
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Nils H Petersen
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
| |
Collapse
|
10
|
Wang TW, Chao HS, Chiu HY, Lu CF, Liao CY, Lee Y, Chen JR, Shiao TH, Chen YM, Wu YT. Radiomics of metastatic brain tumor as a predictive image biomarker of progression-free survival in patients with non-small-cell lung cancer with brain metastasis receiving tyrosine kinase inhibitors. Transl Oncol 2024; 39:101826. [PMID: 37984256 PMCID: PMC10689936 DOI: 10.1016/j.tranon.2023.101826] [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: 07/15/2023] [Revised: 10/18/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Epidermal growth factor receptor (EGFR)-targeted tyrosine kinase inhibitors (TKIs) are the first-line therapy for EGFR-mutant non-small-cell lung cancer (NSCLC). Early prediction of treatment failure in patients with brain metastases treated with EGFR-TKIs may help in making decisions for systemic drug therapy or local brain tumor control. This study examined the predictive power of the radiomics of both brain metastasis tumors and primary lung tumors. We propose a deep learning based CoxCC model based on quantitative brain magnetic resonance imaging (MRI), a prognostic index and clinical data; the model can be used to predict progression-free survival (PFS) after EGFR-TKI therapy in advanced EGFR-mutant NSCLC. METHODS This retrospective single-center study included 271 patients receiving first-line EGFR-TKI targeted therapy in 2018-2019. Among them, 72 patients who had brain metastases before receiving first-line EGFR-TKI treatment. Three radiomic features were extracted from pretreatment brain MRI images. A CoxCC model for the progression risk stratification of EGFR-TKI treatment was proposed on the basis of MRI radiomics, clinical features, and a prognostic index. We performed time-dependent PFS predictions to evaluate the performance of the CoxCC model. RESULTS The CoxCC model based on a prognostic index, clinical features, and radiomic features of brain metastasis exhibited higher performance than clinical features combined with indexes previously proposed for determining the prognosis of brain metastasis, including recursive partitioning analysis, diagnostic-specific graded prognostic assessment, graded prognostic assessment for lung cancer using molecular markers (lung-molGPA), and modified lung-molGPA, with c-index values of 0.75, 0.67, 0.66, 0.65, and 0.65, respectively. The model achieved areas under the curve of 0.88, 0.73, 0.92, and 0.90 for predicting PFS at 3, 6, 9 and 12 months, respectively. PFS significantly differed between the high- and low-risk groups (p < 0.001). CONCLUSIONS For patients with advanced-stage NSCLC with brain metastasis, MRI radiomics of brain metastases may predict PFS. The CoxCC model integrating brain metastasis radiomics, clinical features, and a prognostic index provided reliable multi-time-point PFS predictions for patients with advanced NSCLC and brain metastases receiving EGFR-TKI treatment.
Collapse
Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Hwa-Yen Chiu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yen Lee
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jyun-Ru Chen
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Chest Medicine, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| |
Collapse
|
11
|
Kremer LE, Chapman AB, Armato SG. Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype. J Med Imaging (Bellingham) 2023; 10:064503. [PMID: 38156331 PMCID: PMC10752557 DOI: 10.1117/1.jmi.10.6.064503] [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: 06/22/2023] [Revised: 11/08/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023] Open
Abstract
Purpose Our study aims to investigate the impact of preprocessing on magnetic resonance imaging (MRI) radiomic features extracted from the noncystic kidney parenchyma of patients with autosomal dominant polycystic kidney disease (ADPKD) in the task of classifying PKD1 versus PKD2 genotypes, which differ with regard to cyst burden and disease outcome. Approach The effect of preprocessing on radiomic features was investigated using a single T2-weighted fat saturated (T2W-FS) MRI scan from PKD1 and PKD2 subjects (29 kidneys in total) from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study. Radiomic feature reproducibility using the intraclass correlation coefficient (ICC) was computed across MRI normalizations (z -score, reference-tissue, and original image), gray-level discretization, and upsampling and downsampling pixel schemes. A second dataset for genotype classification from 136 subjects T2W-FS MRI images previously enrolled in the HALT Progression of Polycystic Kidney Disease study was matched for age, gender, and Mayo imaging classification class. Genotype classification was performed using a logistic regression classifier and radiomic features extracted from (1) the noncystic kidney parenchyma and (2) the entire kidney. The area under the receiver operating characteristic curve (AUC) was used to evaluate the classification performance across preprocessing methods. Results Radiomic features extracted from the noncystic kidney parenchyma were sensitive to preprocessing parameters, with varying reproducibility depending on the parameter. The percentage of features with good-to-excellent ICC scores ranged from 14% to 58%. AUC values ranged between 0.47 to 0.68 and 0.56 to 0.73 for the noncystic kidney parenchyma and entire kidney, respectively. Conclusions Reproducibility of radiomic features extracted from the noncystic kidney parenchyma was dependent on the preprocessing parameters used, and the effect on genotype classification was sensitive to preprocessing parameters. The results suggest that texture features may be indicative of genotype expression in ADPKD.
Collapse
Affiliation(s)
- Linnea E. Kremer
- The University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States
| | - Arlene B. Chapman
- The University of Chicago, Department of Medicine, Chicago, Illinois, United States
| | - Samuel G. Armato
- The University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States
| |
Collapse
|
12
|
Wang TW, Chao HS, Chiu HY, Lin YH, Chen HC, Lu CF, Liao CY, Lee Y, Shiao TH, Chen YM, Huang JW, Wu YT. Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients. Cancers (Basel) 2023; 15:5125. [PMID: 37958300 PMCID: PMC10647242 DOI: 10.3390/cancers15215125] [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: 09/19/2023] [Revised: 10/17/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.
Collapse
Affiliation(s)
- Ting-Wei Wang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Hwa-Yen Chiu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Yi-Hui Lin
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Hung-Chun Chen
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yen Lee
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Yuh-Min Chen
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Jing-Wen Huang
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| |
Collapse
|
13
|
Fiscone C, Rundo L, Lugaresi A, Manners DN, Allinson K, Baldin E, Vornetti G, Lodi R, Tonon C, Testa C, Castelli M, Zaccagna F. Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis. Sci Rep 2023; 13:16239. [PMID: 37758804 PMCID: PMC10533494 DOI: 10.1038/s41598-023-42914-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023] Open
Abstract
Multiple Sclerosis (MS) is an autoimmune demyelinating disease characterised by changes in iron and myelin content. These biomarkers are detectable by Quantitative Susceptibility Mapping (QSM), an advanced Magnetic Resonance Imaging technique detecting magnetic properties. When analysed with radiomic techniques that exploit its intrinsic quantitative nature, QSM may furnish biomarkers to facilitate early diagnosis of MS and timely assessment of progression. In this work, we explore the robustness of QSM radiomic features by varying the number of grey levels (GLs) and echo times (TEs), in a sample of healthy controls and patients with MS. We analysed the white matter in total and within six clinically relevant tracts, including the cortico-spinal tract and the optic radiation. After optimising the number of GLs (n = 64), at least 65% of features were robust for each Volume of Interest (VOI), with no difference (p > .05) between left and right hemispheres. Different outcomes in feature robustness among the VOIs depend on their characteristics, such as volume and variance of susceptibility values. This study validated the processing pipeline for robustness analysis and established the reliability of QSM-based radiomics features against GLs and TEs. Our results provide important insights for future radiomics studies using QSM in clinical applications.
Collapse
Affiliation(s)
- Cristiana Fiscone
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy
| | - Alessandra Lugaresi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- UOSI Riabilitazione Sclerosi Multipla, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - David Neil Manners
- Department for Life Quality Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Kieren Allinson
- Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Elisa Baldin
- Epidemiology and Statistics Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Gianfranco Vornetti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Claudia Testa
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy.
| | - Mauro Castelli
- NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal
| | - Fulvio Zaccagna
- Department of Imaging, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Investigative Medicine Division, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
14
|
Jensen LJ, Kim D, Elgeti T, Steffen IG, Schaafs LA, Hamm B, Nagel SN. The role of parametric feature maps to correct different volume of interest sizes: an in vivo liver MRI study. Eur Radiol Exp 2023; 7:48. [PMID: 37670193 PMCID: PMC10480134 DOI: 10.1186/s41747-023-00362-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 06/13/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND Different volume of interest (VOI) sizes influence radiomic features. This study examined if translating images into feature maps before feature sampling could compensate for these effects in liver magnetic resonance imaging (MRI). METHODS T1- and T2-weighted sequences from three different scanners (two 3-T scanners, one 1.5-T scanner) of 66 patients with normal abdominal MRI were included retrospectively. Three differently sized VOIs (10, 20, and 30 mm in diameter) were drawn in the liver parenchyma (right lobe), excluding adjacent structures. Ninety-three features were extracted conventionally using PyRadiomics. All images were also converted to 93 parametric feature maps using a pretested software. Agreement between the three VOI sizes was assessed with overall concordance correlation coefficients (OCCCs), while OCCCs > 0.85 were rated reproducible. OCCCs were calculated twice: for the VOI sizes of 10, 20, and 30 mm and for those of 20 and 30 mm. RESULTS When extracted from original images, only 4 out of the 93 features were reproducible across all VOI sizes in T1- and T2-weighted images. When the smallest VOI was excluded, 5 features (T1-weighted) and 7 features (T2-weighted) were reproducible. Extraction from parametric maps increased the number of reproducible features to 9 (T1- and T2-weighted) across all VOIs. Excluding the 10-mm VOI, reproducibility improved to 16 (T1-weighted) and 55 features (T2-weighted). The stability of all other features also increased in feature maps. CONCLUSIONS Translating images into parametric maps before feature extraction improves reproducibility across different VOI sizes in normal liver MRI. RELEVANCE STATEMENT The size of the segmented VOI influences the feature quantity of radiomics, while software-based conversion of images into parametric feature maps before feature sampling improves reproducibility across different VOI sizes in MRI of normal liver tissue. KEY POINTS • Parametric feature maps can compensate for different VOI sizes. • The effect seems dependent on the VOI sizes and the MRI sequence. • Feature maps can visualize features throughout the entire image stack.
Collapse
Affiliation(s)
- Laura Jacqueline Jensen
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Damon Kim
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Thomas Elgeti
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Ingo Günter Steffen
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Lars-Arne Schaafs
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Bernd Hamm
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Sebastian Niko Nagel
- Charité-Universitätsmedizin Berlin, Department of Radiology, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Hindenburgdamm 30, 12203, Berlin, Germany
| |
Collapse
|
15
|
Zhuo Y, Shen J, Zhan Y, Tian Y, Yu M, Yang S, Ye P, Fan L, Zhang Z, Shan F. Optimization and validation of voxel size-related radiomics variability by combatting batch effect harmonization in pulmonary nodules: a phantom and clinical study. Quant Imaging Med Surg 2023; 13:6139-6151. [PMID: 37711807 PMCID: PMC10498235 DOI: 10.21037/qims-22-992] [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: 09/19/2022] [Accepted: 06/15/2023] [Indexed: 09/16/2023]
Abstract
Background Broad generalization of radiomics-assisted models may be impeded by concerns about variability. This study aimed to evaluate the merit of combatting batch effect (ComBat) harmonization in reducing the variability of voxel size-related radiomics in both phantom and clinical study in comparison with image resampling correction method. Methods A pulmonary phantom with 22 different types of nodules was scanned by computed tomography (CT) with different voxel sizes. The variability of voxel size-related radiomics features was evaluated using concordance correlation coefficient (CCC), dynamic range (DR), and intraclass correlation coefficient (ICC). ComBat and image resampling compensation methods were used to reduce variability of voxel size-related radiomics. The percentage of robust radiomics features was compared before and after optimization. Pathologically differential diagnosis of invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) (AIS-MIA group) was used for clinical validation in 134 patients. Results Before optimization, the number of excellent features in the phantom and clinical data was 26.12% and 32.31%, respectively. The excellent features were increased after image resampling and ComBat correction. For clinical optimization, the effect of the ComBat compensation method was significantly better than that of image resampling, with excellent features reaching 90.96% and poor features only amounting to 4.96%. In addition, the hierarchical clustering analysis showed that the first-order and shape features had better robustness than did texture features. In clinical validation, the area under the curve (AUC) of the testing set was 0.865 after ComBat correction. Conclusions The ComBat harmonization can optimize voxel size-related CT radiomics variability in pulmonary nodules more efficiently than image resampling harmonization.
Collapse
Affiliation(s)
- Yaoyao Zhuo
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Jie Shen
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Yi Zhan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Ye Tian
- Department of Radiology, Beilun Second People’s Hospital, Ningbo, China
| | - Mingfeng Yu
- Department of Thoracic Surgery, Beilun Second People’s Hospital, Ningbo, China
| | - Shuyi Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Peiyan Ye
- Department of Traditional Chinese Medicine, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Zhiyong Zhang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
- Research Institute of Big Data, Fudan University, Shanghai, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
- Research Institute of Big Data, Fudan University, Shanghai, China
| |
Collapse
|
16
|
Kalantar R, Hindocha S, Hunter B, Sharma B, Khan N, Koh DM, Ahmed M, Aboagye EO, Lee RW, Blackledge MD. Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19. Sci Rep 2023; 13:10568. [PMID: 37386097 PMCID: PMC10310777 DOI: 10.1038/s41598-023-36712-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 06/07/2023] [Indexed: 07/01/2023] Open
Abstract
Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model performance. Contrast-homogenous datasets present a potential solution. We developed a 3D patch-based cycle-consistent generative adversarial network (cycle-GAN) to synthesize non-contrast images from contrast CTs, as a data homogenization tool. We used a multi-centre dataset of 2078 scans from 1,650 patients with COVID-19. Few studies have previously evaluated GAN-generated images with handcrafted radiomics, DL and human assessment tasks. We evaluated the performance of our cycle-GAN with these three approaches. In a modified Turing-test, human experts identified synthetic vs acquired images, with a false positive rate of 67% and Fleiss' Kappa 0.06, attesting to the photorealism of the synthetic images. However, on testing performance of machine learning classifiers with radiomic features, performance decreased with use of synthetic images. Marked percentage difference was noted in feature values between pre- and post-GAN non-contrast images. With DL classification, deterioration in performance was observed with synthetic images. Our results show that whilst GANs can produce images sufficient to pass human assessment, caution is advised before GAN-synthesized images are used in medical imaging applications.
Collapse
Affiliation(s)
- Reza Kalantar
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK
| | - Sumeet Hindocha
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK
- AI for Healthcare Centre for Doctoral Training, Imperial College London, Exhibition Road, London, SW7 2BX, UK
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Early Diagnosis and Detection Team, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Benjamin Hunter
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Early Diagnosis and Detection Team, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Bhupinder Sharma
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Nasir Khan
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Dow-Mu Koh
- Department of Radiology, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Merina Ahmed
- Lung Unit, The Royal Marsden NHS Foundation Trust, Sutton, SM2 5PT, UK
| | - Eric O Aboagye
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Richard W Lee
- Early Diagnosis and Detection Team, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Matthew D Blackledge
- Division of Radiotherapy and Imaging, the Institute of Cancer, London, SM2 5NG, UK.
| |
Collapse
|
17
|
Guglielmo P, Marturano F, Bettinelli A, Sepulcri M, Pasello G, Gregianin M, Paiusco M, Evangelista L. Additional Value of PET and CT Image-Based Features in the Detection of Occult Lymph Node Metastases in Lung Cancer: A Systematic Review of the Literature. Diagnostics (Basel) 2023; 13:2153. [PMID: 37443547 DOI: 10.3390/diagnostics13132153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/05/2023] [Accepted: 06/17/2023] [Indexed: 07/15/2023] Open
Abstract
Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([18F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [18F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs.
Collapse
Affiliation(s)
- Priscilla Guglielmo
- Nuclear Medicine Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Francesca Marturano
- Medical Physics Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Andrea Bettinelli
- Medical Physics Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Matteo Sepulcri
- Radiotherapy, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Giulia Pasello
- Department of Surgery, Oncology and Gastroenterology, University of Padua, 35128 Padua, Italy
- Medical Oncology 2, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Michele Gregianin
- Nuclear Medicine Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Marta Paiusco
- Medical Physics Unit, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
| | - Laura Evangelista
- Nuclear Medicine Unit, Department of Medicine DIMED, University of Padua, 35128 Padua, Italy
| |
Collapse
|
18
|
Wang J, Sourlos N, Zheng S, van der Velden N, Pelgrim GJ, Vliegenthart R, van Ooijen P. Preparing CT imaging datasets for deep learning in lung nodule analysis: Insights from four well-known datasets. Heliyon 2023; 9:e17104. [PMID: 37484314 PMCID: PMC10361226 DOI: 10.1016/j.heliyon.2023.e17104] [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: 05/25/2023] [Revised: 06/06/2023] [Accepted: 06/07/2023] [Indexed: 07/25/2023] Open
Abstract
BACKGROUND Deep learning is an important means to realize the automatic detection, segmentation, and classification of pulmonary nodules in computed tomography (CT) images. An entire CT scan cannot directly be used by deep learning models due to image size, image format, image dimensionality, and other factors. Between the acquisition of the CT scan and feeding the data into the deep learning model, there are several steps including data use permission, data access and download, data annotation, and data preprocessing. This paper aims to recommend a complete and detailed guide for researchers who want to engage in interdisciplinary lung nodule research of CT images and Artificial Intelligence (AI) engineering. METHODS The data preparation pipeline used the following four popular large-scale datasets: LIDC-IDRI (Lung Image Database Consortium image collection), LUNA16 (Lung Nodule Analysis 2016), NLST (National Lung Screening Trial) and NELSON (The Dutch-Belgian Randomized Lung Cancer Screening Trial). The dataset preparation is presented in chronological order. FINDINGS The different data preparation steps before deep learning were identified. These include both more generic steps and steps dedicated to lung nodule research. For each of these steps, the required process, necessity, and example code or tools for actual implementation are provided. DISCUSSION AND CONCLUSION Depending on the specific research question, researchers should be aware of the various preparation steps required and carefully select datasets, data annotation methods, and image preprocessing methods. Moreover, it is vital to acknowledge that each auxiliary tool or code has its specific scope of use and limitations. This paper proposes a standardized data preparation process while clearly demonstrating the principles and sequence of different steps. A data preparation pipeline can be quickly realized by following these proposed steps and implementing the suggested example codes and tools.
Collapse
Affiliation(s)
- Jingxuan Wang
- Department of Radiology, University of Groningen, University Medical Center of Groningen, 9713GZ, Groningen, the Netherlands
| | - Nikos Sourlos
- Department of Radiology, University of Groningen, University Medical Center of Groningen, 9713GZ, Groningen, the Netherlands
| | - Sunyi Zheng
- School of Engineering, Westlake University, Xihu District, 310030, Hangzhou, China
| | - Nils van der Velden
- Department of Radiology, University of Groningen, University Medical Center of Groningen, 9713GZ, Groningen, the Netherlands
| | - Gert Jan Pelgrim
- Department of Radiology, University of Groningen, University Medical Center of Groningen, 9713GZ, Groningen, the Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center of Groningen, 9713GZ, Groningen, the Netherlands
- Data Science Center in Health (DASH), University of Groningen, University Medical Center of Groningen, 9713GZ, Groningen, the Netherlands
| | - Peter van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center of Groningen, 9713GZ, Groningen, the Netherlands
- Data Science Center in Health (DASH), University of Groningen, University Medical Center of Groningen, 9713GZ, Groningen, the Netherlands
| |
Collapse
|
19
|
İnce O, Uysal E, Durak G, Önol S, Dönmez Yılmaz B, Ertürk ŞM, Önder H. Prediction of carcinogenic human papillomavirus types in cervical cancer from multiparametric magnetic resonance images with machine learning-based radiomics models. Diagn Interv Radiol 2023; 29:460-468. [PMID: 36994859 PMCID: PMC10679607 DOI: 10.4274/dir.2022.221335] [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: 01/11/2022] [Accepted: 11/16/2022] [Indexed: 01/15/2023]
Abstract
PURPOSE This study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI). METHODS Pre-treatment MRI images of patients with cervical cancer were collected retrospectively. An HPV DNA oncogene analysis was performed based on cervical biopsy specimens. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1) and T2-weighted images (T2WI). A third feature subset was created as a combined group by concatenating the CE-T1 and T2WI subsets. Feature selection was performed using Pearson's correlation coefficient and wrapper- based sequential-feature selection. Two models were built with each feature subset, using support vector machine (SVM) and logistic regression (LR) classifiers. The models were validated using a five-fold cross-validation technique and compared using Wilcoxon's signed rank and Friedman's tests. RESULTS Forty-one patients were enrolled in the study (26 were positive for carcinogenic HPV oncogenes, and 15 were negative). A total of 851 features were extracted from each imaging sequence. After feature selection, 5, 17, and 20 features remained in the CE-T1, T2WI, and combined groups, respectively. The SVM models showed 83%, 95%, and 95% accuracy scores, and the LR models revealed 83%, 81%, and 92.5% accuracy scores in the CE-T1, T2WI, and combined groups, respectively. The SVM algorithm performed better than the LR algorithm in the T2WI feature subset (P = 0.005), and the feature sets in the T2WI and the combined group performed better than CE-T1 in the SVM model (P = 0.033 and 0.006, respectively). The combined group feature subset performed better than T2WI in the LR model (P = 0.023). CONCLUSION Machine learning-based radiomics models based on pre-treatment MRI can detect carcinogenic HPV status with discriminative accuracy.
Collapse
Affiliation(s)
- Okan İnce
- Clinic of Radiology, University of Health Sciences Turkey, Prof. Dr. Cemil Taşcığlu City Hospital, İstanbul, Turkey
| | - Emre Uysal
- Clinic of Radiation Oncology, University of Health Sciences Turkey, Prof. Dr. Cemil Taşcığlu City Hospital, İstanbul, Turkey
| | - Görkem Durak
- Department of Radiology, İstanbul University, İstanbul Faculty of Medicine, İstanbul, Turkey
| | - Suzan Önol
- Clinic of Radiology, University of Health Sciences Turkey, Prof. Dr. Cemil Taşcığlu City Hospital, İstanbul, Turkey
| | - Binnur Dönmez Yılmaz
- Clinic of Radiation Oncology, University of Health Sciences Turkey, Prof. Dr. Cemil Taşcığlu City Hospital, İstanbul, Turkey
| | - Şükrü Mehmet Ertürk
- Department of Radiology, İstanbul University, İstanbul Faculty of Medicine, İstanbul, Turkey
| | - Hakan Önder
- Clinic of Radiology, University of Health Sciences Turkey, Prof. Dr. Cemil Taşcığlu City Hospital, İstanbul, Turkey
| |
Collapse
|
20
|
Zhang YP, Zhang XY, Cheng YT, Li B, Teng XZ, Zhang J, Lam S, Zhou T, Ma ZR, Sheng JB, Tam VCW, Lee SWY, Ge H, Cai J. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res 2023; 10:22. [PMID: 37189155 DOI: 10.1186/s40779-023-00458-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/04/2023] [Indexed: 05/17/2023] Open
Abstract
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients' anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.
Collapse
Affiliation(s)
- Yuan-Peng Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China
| | - Xin-Yun Zhang
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Yu-Ting Cheng
- Department of Medical Informatics, Nantong University, Nantong, 226001, Jiangsu, China
| | - Bing Li
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Xin-Zhi Teng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Saikit Lam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Ta Zhou
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Zong-Rui Ma
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Jia-Bao Sheng
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Victor C W Tam
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Shara W Y Lee
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China
| | - Hong Ge
- Department of Radiation Oncology, the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
| | - Jing Cai
- Department of Health Technology and Informatics, the Hong Kong Polytechnic University, Hong Kong, 999077, China.
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, Guangdong, China.
| |
Collapse
|
21
|
Brown KH, Illyuk J, Ghita M, Walls GM, McGarry CK, Butterworth KT. Assessment of Variabilities in Lung-Contouring Methods on CBCT Preclinical Radiomics Outputs. Cancers (Basel) 2023; 15:2677. [PMCID: PMC10216427 DOI: 10.3390/cancers15102677] [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: 03/24/2023] [Revised: 04/28/2023] [Accepted: 05/08/2023] [Indexed: 06/01/2023] Open
Abstract
Simple Summary This study is the first to evaluate the impact of contouring differences on radiomics analysis in preclinical CBCT scans. We found that the variation in quantitative image readouts was greater between segmentation tools than between observers. Abstract Radiomics image analysis has the potential to uncover disease characteristics for the development of predictive signatures and personalised radiotherapy treatment. Inter-observer and inter-software delineation variabilities are known to have downstream effects on radiomics features, reducing the reliability of the analysis. The purpose of this study was to investigate the impact of these variabilities on radiomics outputs from preclinical cone-beam computed tomography (CBCT) scans. Inter-observer variabilities were assessed using manual and semi-automated contours of mouse lungs (n = 16). Inter-software variabilities were determined between two tools (3D Slicer and ITK-SNAP). The contours were compared using Dice similarity coefficient (DSC) scores and the 95th percentile of the Hausdorff distance (HD95p) metrics. The good reliability of the radiomics outputs was defined using intraclass correlation coefficients (ICC) and their 95% confidence intervals. The median DSC scores were high (0.82–0.94), and the HD95p metrics were within the submillimetre range for all comparisons. the shape and NGTDM features were impacted the most. Manual contours had the most reliable features (73%), followed by semi-automated (66%) and inter-software (51%) variabilities. From a total of 842 features, 314 robust features overlapped across all contouring methodologies. In addition, our results have a 70% overlap with features identified from clinical inter-observer studies.
Collapse
Affiliation(s)
- Kathryn H. Brown
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
| | - Jacob Illyuk
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
| | - Mihaela Ghita
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
| | - Gerard M. Walls
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Belfast BT9 7JL, UK
| | - Conor K. McGarry
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Belfast BT9 7JL, UK
| | - Karl T. Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
| |
Collapse
|
22
|
Liu Y, Wei X, Feng X, Liu Y, Feng G, Du Y. Repeatability of radiomics studies in colorectal cancer: a systematic review. BMC Gastroenterol 2023; 23:125. [PMID: 37059990 PMCID: PMC10105401 DOI: 10.1186/s12876-023-02743-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 03/22/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Recently, radiomics has been widely used in colorectal cancer, but many variable factors affect the repeatability of radiomics research. This review aims to analyze the repeatability of radiomics studies in colorectal cancer and to evaluate the current status of radiomics in the field of colorectal cancer. METHODS The included studies in this review by searching from the PubMed and Embase databases. Then each study in our review was evaluated using the Radiomics Quality Score (RQS). We analyzed the factors that may affect the repeatability in the radiomics workflow and discussed the repeatability of the included studies. RESULTS A total of 188 studies was included in this review, of which only two (2/188, 1.06%) studies controlled the influence of individual factors. In addition, the median score of RQS was 11 (out of 36), range-1 to 27. CONCLUSIONS The RQS score was moderately low, and most studies did not consider the repeatability of radiomics features, especially in terms of Intra-individual, scanners, and scanning parameters. To improve the generalization of the radiomics model, it is necessary to further control the variable factors of repeatability.
Collapse
Affiliation(s)
- Ying Liu
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | - Xiaoqin Wei
- School of Medical Imaging, North Sichuan Medical College, Sichuan Province, Nanchong City, 637000, China
| | | | - Yan Liu
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Guiling Feng
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China
| | - Yong Du
- Department of Radiology, the Affiliated Hospital of North Sichuan Medical College, 1 Maoyuannan Road, Sichuan Province, 637000, Nanchong City, China.
| |
Collapse
|
23
|
Brown KH, Payan N, Osman S, Ghita M, Walls GM, Patallo IS, Schettino G, Prise KM, McGarry CK, Butterworth KT. Development and optimisation of a preclinical cone beam computed tomography-based radiomics workflow for radiation oncology research. Phys Imaging Radiat Oncol 2023; 26:100446. [PMID: 37252250 PMCID: PMC10213103 DOI: 10.1016/j.phro.2023.100446] [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: 02/24/2023] [Revised: 05/03/2023] [Accepted: 05/08/2023] [Indexed: 05/31/2023] Open
Abstract
Background and purpose Radiomics features derived from medical images have the potential to act as imaging biomarkers to improve diagnosis and predict treatment response in oncology. However, the complex relationships between radiomics features and the biological characteristics of tumours are yet to be fully determined. In this study, we developed a preclinical cone beam computed tomography (CBCT) radiomics workflow with the aim to use in vivo models to further develop radiomics signatures. Materials and methods CBCT scans of a mouse phantom were acquired using onboard imaging from a small animal radiotherapy research platform (SARRP, Xstrahl). The repeatability and reproducibility of radiomics outputs were compared across different imaging protocols, segmentation sizes, pre-processing parameters and materials. Robust features were identified and used to compare scans of two xenograft mouse tumour models (A549 and H460). Results Changes to the radiomics workflow significantly impact feature robustness. Preclinical CBCT radiomics analysis is feasible with 119 stable features identified from scans imaged at 60 kV, 25 bin width and 0.26 mm slice thickness. Large variation in segmentation volumes reduced the number of reliable radiomics features for analysis. Standardization in imaging and analysis parameters is essential in preclinical radiomics analysis to improve accuracy of outputs, leading to more consistent and reproducible findings. Conclusions We present the first optimised workflow for preclinical CBCT radiomics to identify imaging biomarkers. Preclinical radiomics has the potential to maximise the quantity of data captured in in vivo experiments and could provide key information supporting the wider application of radiomics.
Collapse
Affiliation(s)
- Kathryn H. Brown
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Neree Payan
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Sarah Osman
- University College London Hospitals NHS Foundation Trust Department of Radiotherapy, London, UK
| | - Mihaela Ghita
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Gerard M. Walls
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
- Cancer Centre, Belfast Health & Social Care Trust, Lisburn Road, Belfast BT9 7AB, Northern Ireland, UK
| | | | | | - Kevin M. Prise
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| | - Conor K. McGarry
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
- Cancer Centre, Belfast Health & Social Care Trust, Lisburn Road, Belfast BT9 7AB, Northern Ireland, UK
| | - Karl T. Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Northern Ireland, UK
| |
Collapse
|
24
|
Jiang J, Wei J, Zhu Y, Wei L, Wei X, Tian H, Zhang L, Wang T, Cheng Y, Zhao Q, Sun Z, Du H, Huang Y, Liu H, Li Y. Clot-based radiomics model for cardioembolic stroke prediction with CT imaging before recanalization: a multicenter study. Eur Radiol 2023; 33:970-980. [PMID: 36066731 DOI: 10.1007/s00330-022-09116-4] [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: 04/08/2022] [Revised: 07/11/2022] [Accepted: 08/12/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES To develop a clot-based radiomics model using CT imaging radiomic features and machine learning to identify cardioembolic (CE) stroke before mechanical thrombectomy (MTB) in patients with acute ischemic stroke (AIS). MATERIALS AND METHODS This retrospective four-center study consecutively included 403 patients with AIS who sequentially underwent CT and MTB between April 2016 and July 2021. These were grouped into training, testing, and external validation cohorts. Thrombus-extracted radiomic features and basic information were gathered to construct a machine learning model to predict CE stroke. The radiological characteristics and basic information were used to build a routine radiological model. A combined radiomics and radiological features model was also developed. The performances of all models were evaluated and compared in the validation cohort. A histological analysis helped further assess the proposed model in all patients. RESULTS The radiomics model yielded an area under the curve (AUC) of 0.838 (95% confidence interval [CI], 0.771-0.891) for predicting CE stroke in the validation cohort, significantly higher than the radiological model (AUC, 0.713; 95% CI, 0.636-0.781; p = 0.007) but similar to the combined model (AUC, 0.855; 95% CI, 0.791-0.906; p = 0.14). The thrombus radiomic features achieved stronger correlations with red blood cells (|rmax|, 0.74 vs. 0.32) and fibrin and platelet (|rmax|, 0.68 vs. 0.18) than radiological characteristics. CONCLUSION The proposed CT-based radiomics model could reliably predict CE stroke in AIS, performing better than the routine radiological method. KEY POINTS • Admission CT imaging could offer valuable information to identify the acute ischemic stroke source by radiomics analysis. • The proposed CT imaging-based radiomics model yielded a higher area under the curve (0.838) than the routine radiological method (0.713; p = 0.007). • Several radiomic features showed significantly stronger correlations with two main thrombus constituents (red blood cells, |rmax|, 0.74; fibrin and platelet, |rmax|, 0.68) than routine radiological characteristics.
Collapse
Affiliation(s)
- Jingxuan Jiang
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China.,Department of Radiology, Affiliated Hospital of Nantong University, Nantong, 226001, China
| | - Jianyong Wei
- Clinical Research Center, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Yueqi Zhu
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Liming Wei
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Xiaoer Wei
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Hao Tian
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, 226001, China
| | - Lei Zhang
- Department of Radiology, Wuxi Second People's Hospital, Wuxi, 214000, China
| | - Tianle Wang
- Department of Radiology, Affiliated No. 1 People's Hospital of Nantong University, Nantong, 226001, China
| | - Yue Cheng
- Department of Radiology, Wuxi Second People's Hospital, Wuxi, 214000, China
| | - Qianqian Zhao
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Zheng Sun
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Haiyan Du
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Yu Huang
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Hui Liu
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China
| | - Yuehua Li
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600 Yishan Road, Shanghai, 200233, China.
| |
Collapse
|
25
|
Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping. BIOENGINEERING (BASEL, SWITZERLAND) 2023; 10:bioengineering10010080. [PMID: 36671652 PMCID: PMC9854492 DOI: 10.3390/bioengineering10010080] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 12/23/2022] [Indexed: 01/11/2023]
Abstract
Radiomics and artificial intelligence have the potential to become a valuable tool in clinical applications. Frequently, radiomic analyses through machine learning methods present issues caused by high dimensionality and multicollinearity, and redundant radiomic features are usually removed based on correlation analysis. We assessed the effect of preprocessing-in terms of voxel size resampling, discretization, and filtering-on correlation-based dimensionality reduction in radiomic features from cardiac T1 and T2 maps of patients with hypertrophic cardiomyopathy. For different combinations of preprocessing parameters, we performed a dimensionality reduction of radiomic features based on either Pearson's or Spearman's correlation coefficient, followed by the computation of the stability index. With varying resampling voxel size and discretization bin width, for both T1 and T2 maps, Pearson's and Spearman's dimensionality reduction produced a slightly different percentage of remaining radiomic features, with a relatively high stability index. For different filters, the remaining features' stability was instead relatively low. Overall, the percentage of eliminated radiomic features through correlation-based dimensionality reduction was more dependent on resampling voxel size and discretization bin width for textural features than for shape or first-order features. Notably, correlation-based dimensionality reduction was less sensitive to preprocessing when considering radiomic features from T2 compared with T1 maps.
Collapse
|
26
|
Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
Collapse
Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| |
Collapse
|
27
|
Zhang X, Lu B, Yang X, Lan D, Lin S, Zhou Z, Li K, Deng D, Peng P, Zeng Z, Long L. Prognostic analysis and risk stratification of lung adenocarcinoma undergoing EGFR-TKI therapy with time-serial CT-based radiomics signature. Eur Radiol 2023; 33:825-835. [PMID: 36166088 PMCID: PMC9889474 DOI: 10.1007/s00330-022-09123-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 08/05/2022] [Accepted: 08/19/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVES To evaluate the value of time-serial CT radiomics features in predicting progression-free survival (PFS) for lung adenocarcinoma (LUAD) patients after epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) therapy. MATERIALS AND METHODS LUAD patients treated with EGFR-TKIs were retrospectively included from three independent institutes and divided into training and validation cohorts. Intratumoral and peritumoral features were extracted from time-serial non-contrast chest CT (including pre-therapy and first follow-up images); moreover, the percentage variation per unit time (day) was introduced to adjust for the different follow-up periods of each patient. Test-retest was performed to exclude irreproducible features, while the Boruta algorithm was used to select critical radiomics features. Radiomics signatures were constructed with random forest survival models in the training cohort and compared against baseline clinical characteristics through Cox regression and nonparametric testing of concordance indices (C-indices). RESULTS The training cohort included 131 patients (74 women, 56.5%) from one institute and the validation cohort encompassed 41 patients (24 women, 58.5%) from two other institutes. The optimal signature contained 10 features and 7 were unit time feature variations. The comprehensive radiomics model outperformed the pre-therapy clinical characteristics in predicting PFS (training: 0.78, 95% CI: [0.72, 0.84] versus 0.55, 95% CI: [0.49, 0.62], p < 0.001; validation: 0.72, 95% CI: [0.60, 0.84] versus 0.54, 95% CI: [0.42, 0.66], p < 0.001). CONCLUSION Radiomics signature derived from time-serial CT images demonstrated optimal prognostic performance of disease progression. This dynamic imaging biomarker holds the promise of monitoring treatment response and achieving personalized management. KEY POINTS • The intrinsic tumor heterogeneity can be highly dynamic under the therapeutic effect of EGFR-TKI treatment, and the inevitable development of drug resistance may disrupt the duration of clinical benefit. Decision-making remained challenging in practice to detect the emergence of acquired resistance during the early response phase. • Time-serial CT-based radiomics signature integrating intra- and peritumoral features offered the potential to predict progression-free survival for LUAD patients treated with EGFR-TKIs. • The dynamic imaging signature allowed for prognostic risk stratification.
Collapse
Affiliation(s)
- Xiaobo Zhang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Bingfeng Lu
- Department of Radiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | - Xinguan Yang
- Department of Radiology, Guilin People’s Hospital, Guilin, Guangxi China
| | - Dong Lan
- Department of Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi China
| | | | - Zhipeng Zhou
- Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi China
| | - Kai Li
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Dong Deng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Peng Peng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Zisan Zeng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021 Guangxi China
| |
Collapse
|
28
|
A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5905230. [PMID: 36569180 PMCID: PMC9788902 DOI: 10.1155/2022/5905230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.
Collapse
|
29
|
Liu X, Zhang J, Zhou J, He Y, Xu Y, Zhang Z, Cao G, Miao H, Chen Z, Zhao Y, Jin X, Wang M. Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification. Front Oncol 2022; 12:992509. [PMID: 36531052 PMCID: PMC9755840 DOI: 10.3389/fonc.2022.992509] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/11/2022] [Indexed: 10/25/2023] Open
Abstract
OBJECTIVE To develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions. MATERIAL AND METHODS In this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and radiomics signature building. Multivariable logistic regression was used to develop a radiomics nomogram incorporating radiomics signature and clinical factors. The performance of the radiomics nomogram was evaluated by its discrimination, calibration, and clinical usefulness and was compared with BI-RADS classification evaluated by a senior breast radiologist. RESULTS The All-Combination radiomics signature derived from the combination of DCE-MRI, BMUS and SE images showed better diagnostic performance than signatures derived from single modality alone, with area under the curves (AUCs) of 0.953 and 0.941 in training and testing datasets, respectively. The multi-modality radiomics nomogram incorporating the All-Combination radiomics signature and age showed excellent discrimination with the highest AUCs of 0.964 and 0.951 in two datasets, respectively, which outperformed all single modality radiomics signatures and BI-RADS classification. Furthermore, the specificity of radiomics nomogram was significantly higher than BI-RADS classification (both p < 0.04) with the same sensitivity in both datasets. CONCLUSION The proposed multi-modality radiomics nomogram based on DCE-MRI and ultrasound images has the potential to serve as a non-invasive tool for classifying benign and malignant breast lesions and reduce unnecessary biopsy.
Collapse
Affiliation(s)
- Xinmiao Liu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Ji Zhang
- Radiotherapy Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiejie Zhou
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yun He
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yunyu Xu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhenhua Zhang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Guoquan Cao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Haiwei Miao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhongwei Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Youfan Zhao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiance Jin
- Radiotherapy Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- School of Basic Medical Science, Wenzhou Medical University, Wenzhou, China
| | - Meihao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| |
Collapse
|
30
|
Minimising multi-centre radiomics variability through image normalisation: a pilot study. Sci Rep 2022; 12:12532. [PMID: 35869125 PMCID: PMC9307565 DOI: 10.1038/s41598-022-16375-0] [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: 01/11/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification.
Collapse
|
31
|
Delgadillo R, Spieler BO, Deana AM, Ford JC, Kwon D, Yang F, Studenski MT, Padgett KR, Abramowitz MC, Dal Pra A, Stoyanova R, Dogan N. Cone-beam CT delta-radiomics to predict genitourinary toxicities and international prostate symptom of prostate cancer patients: a pilot study. Sci Rep 2022; 12:20136. [PMID: 36418901 PMCID: PMC9684516 DOI: 10.1038/s41598-022-24435-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 11/15/2022] [Indexed: 11/24/2022] Open
Abstract
For prostate cancer (PCa) patients treated with definitive radiotherapy (RT), acute and late RT-related genitourinary (GU) toxicities adversely impact disease-specific quality of life. Early warning of potential RT toxicities can prompt interventions that may prevent or mitigate future adverse events. During intensity modulated RT (IMRT) of PCa, daily cone-beam computed tomography (CBCT) images are used to improve treatment accuracy through image guidance. This work investigated the performance of CBCT-based delta-radiomic features (DRF) models to predict acute and sub-acute International Prostate Symptom Scores (IPSS) and Common Terminology Criteria for Adverse Events (CTCAE) version 5 GU toxicity grades for 50 PCa patients treated with definitive RT. Delta-radiomics models were built using logistic regression, random forest for feature selection, and a 1000 iteration bootstrapping leave one analysis for cross validation. To our knowledge, no prior studies of PCa have used DRF models based on daily CBCT images. AUC of 0.83 for IPSS and greater than 0.7 for CTCAE grades were achieved as early as week 1 of treatment. DRF extracted from CBCT images showed promise for the development of models predictive of RT outcomes. Future studies will include using artificial intelligence and machine learning to expand CBCT sample sizes available for radiomics analysis.
Collapse
Affiliation(s)
- Rodrigo Delgadillo
- grid.26790.3a0000 0004 1936 8606Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL 33136 USA
| | - Benjamin O. Spieler
- grid.26790.3a0000 0004 1936 8606Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL 33136 USA
| | - Anthony M. Deana
- grid.26790.3a0000 0004 1936 8606Department of Biomedical Engineering, University of Miami, Miami, FL USA
| | - John C. Ford
- grid.26790.3a0000 0004 1936 8606Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL 33136 USA
| | - Deukwoo Kwon
- grid.267308.80000 0000 9206 2401Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX USA
| | - Fei Yang
- grid.26790.3a0000 0004 1936 8606Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL 33136 USA
| | - Matthew T. Studenski
- grid.26790.3a0000 0004 1936 8606Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL 33136 USA
| | - Kyle R. Padgett
- grid.26790.3a0000 0004 1936 8606Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL 33136 USA
| | - Matthew C. Abramowitz
- grid.26790.3a0000 0004 1936 8606Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL 33136 USA
| | - Alan Dal Pra
- grid.26790.3a0000 0004 1936 8606Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL 33136 USA
| | - Radka Stoyanova
- grid.26790.3a0000 0004 1936 8606Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL 33136 USA
| | - Nesrin Dogan
- grid.26790.3a0000 0004 1936 8606Department of Radiation Oncology, University of Miami Miller School of Medicine, 1475 NW 12th Ave, Miami, FL 33136 USA
| |
Collapse
|
32
|
Cui Y, Lin Y, Zhao Z, Long H, Zheng L, Lin X. Comprehensive 18F-FDG PET-based radiomics in elevating the pathological response to neoadjuvant immunochemotherapy for resectable stage III non-small-cell lung cancer: A pilot study. Front Immunol 2022; 13:994917. [PMID: 36466929 PMCID: PMC9713843 DOI: 10.3389/fimmu.2022.994917] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 10/27/2022] [Indexed: 07/30/2023] Open
Abstract
PURPOSE To develop a comprehensive PET radiomics model to predict the pathological response after neoadjuvant toripalimab with chemotherapy in resectable stage III non-small-cell lung cancer (NSCLC) patients. METHODS Stage III NSCLC patients who received three cycles of neoadjuvant toripalimab with chemotherapy and underwent 18F-FDG PET/CT were enrolled. Baseline 18F-FDG PET/CT was performed before treatment, and preoperative 18F-FDG PET/CT was performed three weeks after the completion of neoadjuvant treatment. Surgical resection was performed 4-5 weeks after the completion of neoadjuvant treatment. Standardized uptake value (SUV) statistics features and radiomics features were derived from baseline and preoperative PET images. Delta features were derived. The radiologic response and metabolic response were assessed by iRECIST and iPERCIST, respectively. The correlations between PD-L1 expression, driver-gene status, peripheral blood biomarkers, and the pathological responses (complete pathological response [CPR]; major pathological response [MPR]) were assessed. Associations between PET features and pathological responses were evaluated by logistic regression. RESULTS Thirty patients underwent surgery and 29 of them performed preoperative PET/CT. Twenty patients achieved MPR and 16 of them achieved CPR. In univariate analysis, five SUV statistics features and two radiomics features were significantly associated with pathological responses. In multi-variate analysis, SUVmax, SUVpeak, SULpeak, and End-PET-GLDM-LargeDependenceHighGrayLevelEmphasis (End-GLDM-LDHGLE) were independently associated with CPR. SUVpeak and SULpeak performed better than SUVmax and SULmax for MPR prediction. No significant correlation, neither between the radiologic response and the pathological response, nor among PD-L1, driver gene status, and baseline PET features was found. Inflammatory response biomarkers by peripheral blood showed no difference in different treatment responses. CONCLUSION The logistic regression model using comprehensive PET features contributed to predicting the pathological response after neoadjuvant toripalimab with chemotherapy in resectable stage III NSCLC patients.
Collapse
Affiliation(s)
- Yingpu Cui
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yaobin Lin
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Thoracic Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Zerui Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Thoracic Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Hao Long
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Thoracic Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Lie Zheng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xiaoping Lin
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
- Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| |
Collapse
|
33
|
Radiomics Modeling of Catastrophic Proximal Sesamoid Bone Fractures in Thoroughbred Racehorses Using μCT. Animals (Basel) 2022; 12:ani12213033. [DOI: 10.3390/ani12213033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022] Open
Abstract
Proximal sesamoid bone (PSB) fractures are the most common musculoskeletal injury in race-horses. X-ray CT imaging can detect expressed radiological features in horses that experienced catastrophic fractures. Our objective was to assess whether expressed radiomic features in the PSBs of 50 horses can be used to develop machine learning models for predicting PSB fractures. The μCTs of intact contralateral PSBs from 50 horses, 30 of which suffered catastrophic fractures, and 20 controls were studied. From the 129 intact μCT images of PSBs, 102 radiomic features were computed using a variety of voxel resampling dimensions. Decision Trees and Wrapper methods were used to identify the 20 top expressed features, and six machine learning algorithms were developed to model the risk of fracture. The accuracy of all machine learning models ranged from 0.643 to 0.903 with an average of 0.754. On average, Support Vector Machine, Random Forest (RUS Boost), and Log-regression models had higher performance than K-means Nearest Neighbor, Neural Network, and Random Forest (Bagged Trees) models. Model accuracy peaked at 0.5 mm and decreased substantially when the resampling resolution was greater than or equal to 1 mm. We find that, for this in vitro dataset, it is possible to differentiate between unfractured PSBs from case and control horses using μCT images. It may be possible to extend these findings to the assessment of fracture risk in standing horses.
Collapse
|
34
|
18F-FDG PET-Based Combined Baseline and End-Of-Treatment Radiomics Model Improves the Prognosis Prediction in Diffuse Large B Cell Lymphoma After First-Line Therapy. Acad Radiol 2022:S1076-6332(22)00548-7. [DOI: 10.1016/j.acra.2022.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/22/2022] [Accepted: 10/11/2022] [Indexed: 11/27/2022]
|
35
|
Soliman MA, Kelahan LC, Magnetta M, Savas H, Agrawal R, Avery RJ, Aouad P, Liu B, Xue Y, Chae YK, Salem R, Benson AB, Yaghmai V, Velichko YS. A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials. JCO Clin Cancer Inform 2022; 6:e2200023. [PMID: 36332157 PMCID: PMC9668564 DOI: 10.1200/cci.22.00023] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 07/01/2022] [Accepted: 09/21/2022] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Variability in computed tomography images intrinsic to individual scanners limits the application of radiomics in clinical and research settings. The development of reproducible and generalizable radiomics-based models to assess lesions requires harmonization of data. The purpose of this study was to develop, test, and analyze the efficacy of a radiomics data harmonization model. MATERIALS AND METHODS Radiomic features from biopsy-proven untreated hepatic metastasis (N = 380) acquired from 167 unique patients with pancreatic, colon, and breast cancers were analyzed. Radiomic features from volume-match 551 samples of normal liver tissue and 188 hepatic cysts were included as references. A novel linear mixed effect model was used to identify effects associated with lesion size, tissue type, and scanner model. Six separate machine learning models were then used to test the effectiveness of radiomic feature harmonization using multivariate analysis. RESULTS Proposed model identifies and removes scanner-associated effects while preserving cancer-specific functional dependence of radiomic features on the tumor size. Data harmonization improves the performance of classification models by reducing the scanner-associated variability. For example, the multiclass logistic regression model, LogitBoost, demonstrated the improvement in sensitivity in the range from 15% to 40% for each type of liver metastasis, whereas the overall model accuracy and the kappa coefficient increased by 5% and 8% accordingly. CONCLUSION The model removed scanner-associated effects while preserving cancer-specific functional dependence of radiomic features.
Collapse
Affiliation(s)
- Moataz A.S. Soliman
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Linda C. Kelahan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Michael Magnetta
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Hatice Savas
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Rishi Agrawal
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Ryan J. Avery
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Pascale Aouad
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Benjamin Liu
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Yue Xue
- Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Young K. Chae
- Department of Medicine, Division of Hematology/Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL
| | - Riad Salem
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL
| | - Al B. Benson
- Department of Medicine, Division of Hematology/Oncology, Feinberg School of Medicine, Northwestern University, Chicago, IL
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL
| | - Vahid Yaghmai
- Department of Radiological Sciences, University of California, Irvine UCI Health, University of California Irvine, Orange, CA
| | - Yuri S. Velichko
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL
| |
Collapse
|
36
|
Ibrahim A, Lu L, Yang H, Akin O, Schwartz LH, Zhao B. The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model. APPLIED SCIENCES (BASEL, SWITZERLAND) 2022; 12:9824. [PMID: 37091743 PMCID: PMC10121203 DOI: 10.3390/app12199824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field.
Collapse
Affiliation(s)
- Abdalla Ibrahim
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Correspondence:
| | - Lin Lu
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Hao Yang
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Lawrence H. Schwartz
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| |
Collapse
|
37
|
Anne-Leen D, Machaba S, Alex M, Bart DS, Laurence B, Mike S, Hans P, Van de Wiele C. Principal component analysis of texture features derived from FDG PET images of melanoma lesions. EJNMMI Phys 2022; 9:64. [PMID: 36107331 PMCID: PMC9478000 DOI: 10.1186/s40658-022-00491-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 09/01/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The clinical utility of radiomics is hampered by a high correlation between the large number of features analysed which may result in the “bouncing beta” phenomenon which could in part explain why in a similar patient population texture features identified and/or cut-off values of prognostic significance differ from one study to another. Principal component analysis (PCA) is a technique for reducing the dimensionality of large datasets containing highly correlated variables, such as texture feature datasets derived from FDG PET images, increasing data interpretability whilst at the same time minimizing information loss by creating new uncorrelated variables that successively maximize variance. Here, we report on PCA of a texture feature dataset derived from 123 malignant melanoma lesions with a significant range in lesion size using the freely available LIFEx software.
Results
Thirty-eight features were derived from all lesions. All features were standardized. The statistical assumptions for carrying out PCA analysis were met. Seven principal components with an eigenvalue > 1 were identified. Based on the “elbow sign” of the Scree plot, only the first five were retained. The contribution to the total variance of these components derived using Varimax rotation was, respectively, 30.6%, 23.6%, 16.1%, 7.4% and 4.1%. The components provided summarized information on the locoregional FDG distribution with an emphasis on high FDG uptake regions, contrast in FDG uptake values (steepness), tumour volume, locoregional FDG distribution with an emphasis on low FDG uptake regions and on the rapidity of changes in SUV intensity between different regions.
Conclusions
PCA allowed to reduce the dataset of 38 features to a set of 5 uncorrelated new variables explaining approximately 82% of the total variance contained within the dataset. These principal components may prove more useful for multiple regression analysis considering the relatively low numbers of patients usually included in clinical trials on FDG PET texture analysis. Studies assessing the superior differential diagnostic, predictive or prognostic value of principal components derived using PCA as opposed to the initial texture features in clinical relevant settings are warranted.
Collapse
|
38
|
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.
Collapse
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
| |
Collapse
|
39
|
Escudero Sanchez L, Brown E, Rundo L, Ursprung S, Sala E, Bohndiek SE, Partarrieu IX. Photoacoustic imaging radiomics in patient-derived xenografts: a study on feature sensitivity and model discrimination. Sci Rep 2022; 12:15142. [PMID: 36071117 PMCID: PMC9452574 DOI: 10.1038/s41598-022-19084-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 08/24/2022] [Indexed: 11/09/2022] Open
Abstract
Photoacoustic imaging is an increasingly popular method of exploring the tumour microenvironment, which can provide insight into tumour oxygenation status and potentially treatment response assessment. Currently, the measurements most commonly performed on such images are the mean and median of the pixel values of the tumour volumes of interest. We investigated expanding the set of measurements that can be extracted from these images by adding radiomic features. In particular, we found that Skewness was sensitive to differences between basal and luminal patient derived xenograft cancer models with an [Formula: see text] of 0.86, and that it was robust to variations in confounding factors such as reconstruction type and wavelength. We also built discriminant models with radiomic features that were correlated with the underlying tumour model and were independent from each other. We then ranked features by their importance in the model. Skewness was again found to be an important feature, as were 10th Percentile, Root Mean Squared, and several other texture-based features. In summary, this paper proposes a methodology to select radiomic features extracted from photoacoustic images that are robust to changes in acquisition and reconstruction parameters, and discusses features found to have discriminating power between the underlying tumour models in a pre-clinical dataset.
Collapse
Affiliation(s)
- Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK.
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK.
| | - Emma Brown
- Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
- Washington University School of Medicine in St Louis, St. Louis, MO, 63110, USA
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
- Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Fisciano, SA, 84084, Italy
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, CB2 0RE, UK
| | - Sarah E Bohndiek
- Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK
| | | |
Collapse
|
40
|
Varghese B, Cen S, Zahoor H, Siddiqui I, Aron M, Sali A, Rhie S, Lei X, Rivas M, Liu D, Hwang D, Quinn D, Desai M, Vaishampayan U, Gill I, Duddalwar V. Feasibility of using CT radiomic signatures for predicting CD8-T cell infiltration and PD-L1 expression in renal cell carcinoma. Eur J Radiol Open 2022; 9:100440. [PMID: 36090617 PMCID: PMC9460152 DOI: 10.1016/j.ejro.2022.100440] [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: 05/11/2022] [Revised: 08/25/2022] [Accepted: 08/30/2022] [Indexed: 01/26/2023] Open
Abstract
Objectives To identify computed tomography (CT)-based radiomic signatures of cluster of differentiation 8 (CD8)-T cell infiltration and programmed cell death ligand 1 (PD-L1) expression levels in patients with clear-cell renal cell carcinoma (ccRCC). Methods Seventy-eight patients with pathologically confirmed localized ccRCC, preoperative multiphase CT and tumor resection specimens were enrolled in this retrospective study. Regions of interest (ROI) of the ccRCC volume were manually segmented from the CT images and processed using a radiomics panel comprising of 1708 metrics. The extracted metrics were used as inputs to three machine learning classifiers: Random Forest, AdaBoost, and ElasticNet to create radiomic signatures for CD8-T cell infiltration and PD-L1 expression, respectively. Results Using a cut-off of 80 lymphocytes per high power field, 59 % were classified to CD8 highly infiltrated tumors and 41 % were CD8 non highly infiltrated tumors, respectively. An ElasticNet classifier discriminated between these two groups of CD8-T cells with an AUC of 0.68 (95 % CI, 0.55-0.80). In addition, based on tumor proportion score with a cut-off of > 1 % tumor cells expressing PD-L1, 76 % were PD-L1 positive and 24 % were PD-L1 negative. An Adaboost classifier discriminated between PD-L1 positive and PD-L1 negative tumors with an AUC of 0.8 95 % CI: (0.66, 0.95). 3D radiomics metrics of graylevel co-occurrence matrix (GLCM) and graylevel run-length matrix (GLRLM) metrics drove the performance for CD8-Tcell and PD-L1 classification, respectively. Conclusions CT-radiomic signatures can differentiate tumors with high CD8-T cell infiltration with moderate accuracy and positive PD-L1 expression with good accuracy in ccRCC.
Collapse
Affiliation(s)
- Bino Varghese
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA,Correspondence to: Keck Medical Center of USC, University of Southern California, Norris Topping Tower 4417, Los Angeles, CA 90033, USA.
| | - Steven Cen
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Haris Zahoor
- Keck School of Medicine, Department of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Imran Siddiqui
- Keck School of Medicine, Department of Pathology, University of Southern California, Los Angeles, CA, USA
| | - Manju Aron
- Keck School of Medicine, Department of Pathology, University of Southern California, Los Angeles, CA, USA
| | - Akash Sali
- Homi Bhabha Cancer Hospital, Department of Pathology, Sangrur, Punjab, India
| | - Suhn Rhie
- Keck School of Medicine, Department of Molecular Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xiaomeng Lei
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Marielena Rivas
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Derek Liu
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Darryl Hwang
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - David Quinn
- Keck School of Medicine, Department of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Mihir Desai
- Keck School of Medicine, Department of Urology, University of Southern California, Los Angeles, CA, USA
| | - Ulka Vaishampayan
- Rogel Cancer Center, Urologic Oncology Clinic, University of Michigan, Ann Arbor, MI, USA
| | - Inderbir Gill
- Keck School of Medicine, Department of Urology, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- USC Radiomics Laboratory, Keck School of Medicine, Department of Radiology, University of Southern California, Los Angeles, CA, USA
| |
Collapse
|
41
|
Paquier Z, Chao SL, Acquisto A, Fenton C, Guiot T, Dhont J, Levillain H, Gulyban A, Bali MA, Reynaert N. Radiomics software comparison using digital phantom and patient data: IBSI-compliance does not guarantee concordance of feature values. Biomed Phys Eng Express 2022; 8. [PMID: 36049399 DOI: 10.1088/2057-1976/ac8e6f] [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: 05/30/2022] [Accepted: 09/01/2022] [Indexed: 11/12/2022]
Abstract
Introduction Radiomics is a promising imaging-based tool which could enhance clinical observation and identify representative features. To avoid different interpretations, the Image Biomarker Standardisation Initiative (IBSI) imposed conditions for harmonisation. This study evaluates IBSI-compliant radiomics applications against a known benchmark and clinical datasets for agreements. Materials and methods The applications compared were RadiomiX Research Toolbox, LIFEx v7.0.0, and syngo.via Frontier Radiomics v1.2.5 (based on PyRadiomics v2.1). Basic assessment included comparing feature names and their formulas. The IBSI digital phantom was used for evaluation against reference values. For agreement evaluation (including same software but different versions), two clinical datasets were used: 27 contrast-enhanced computed tomography (CECT) of colorectal liver metastases and 39 magnetic resonance imaging (MRI) of breast cancer, including intravoxel incoherent motion and dynamic contrast-enhanced (DCE) MRI. The lower 95% confidence interval of intraclass correlation coefficient was used to define excellent agreement (>0.9). Results The three radiomics applications share 41 (3 shape, 8 intensity, 30 texture) out of 172, 84 and 110 features for RadiomiX, LIFEx and syngo.via, respectively, as well as wavelet filtering. The naming convention is, however, different between them. Syngo.via had excellent agreement with the IBSI benchmark, while LIFEx and RadiomiX showed slightly worse agreement. Excellent reproducibility was achieved for shape features only, while intensity and texture features varied considerably with the imaging type. For intensity, excellent agreement ranged from 46% for the DCE maps to 100% for CECT, while this lowered to 44% and 73% for texture features, respectively. Wavelet features produced the greatest variation between applications, with an excellent agreement for only 3% to 11% features. Conclusion Even with IBSI-compliance, the reproducibility of features between radiomics applications is not guaranteed. To evaluate variation, quality assurance of radiomics applications should be performed and repeated when updating to a new version or adding a new modality.
Collapse
Affiliation(s)
- Zelda Paquier
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Shih-Li Chao
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Anaïs Acquisto
- Radiology, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Chifra Fenton
- Radiology, Erasmus Hospital, Rte de Lennik 808, Bruxelles, 1070, BELGIUM
| | - Thomas Guiot
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Jennifer Dhont
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Hugo Levillain
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | - Akos Gulyban
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| | | | - Nick Reynaert
- Medical Physics, Institut Jules Bordet, Rue Meylemeersch 90, Bruxelles, 1070, BELGIUM
| |
Collapse
|
42
|
Cui R, Yang Z, Liu L. What does radiomics do in PD-L1 blockade therapy of NSCLC patients? Thorac Cancer 2022; 13:2669-2680. [PMID: 36039482 PMCID: PMC9527165 DOI: 10.1111/1759-7714.14620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 08/03/2022] [Accepted: 08/06/2022] [Indexed: 12/19/2022] Open
Abstract
With the in‐depth understanding of programmed cell death 1 ligand 1 (PD‐L1) in non‐small cell lung cancer (NSCLC), PD‐L1 has become a vital immunotherapy target and a significant biomarker. The clinical utility of detecting PD‐L1 by immunohistochemistry or next‐generation sequencing has been written into guidelines. However, the application of these methods is limited in some circumstances where the biopsy size is small or not accessible, or a dynamic monitor is needed. Radiomics can noninvasively, in real‐time, and quantitatively analyze medical images to reflect deeper information about diseases. Since radiomics was proposed in 2012, it has been widely used in disease diagnosis and differential diagnosis, tumor staging and grading, gene and protein phenotype prediction, treatment plan decision‐making, efficacy evaluation, and prognosis prediction. To explore the feasibility of the clinical application of radiomics in predicting PD‐L1 expression, immunotherapy response, and long‐term prognosis, we comprehensively reviewed and summarized recently published works in NSCLC. In conclusion, radiomics is expected to be a companion to the whole immunotherapy process.
Collapse
Affiliation(s)
- Ruichen Cui
- Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Zhenyu Yang
- Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Lunxu Liu
- Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
43
|
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.
Collapse
|
44
|
Bao J, Feng X, Ma Y, Wang Y, Qi J, Qin C, Tan X, Tian Y. The latest application progress of radiomics in prediction and diagnosis of liver diseases. Expert Rev Gastroenterol Hepatol 2022; 16:707-719. [PMID: 35880549 DOI: 10.1080/17474124.2022.2104711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Early detection and individualized treatment of patients with liver disease is the key to survival. Radiomics can extract high-throughput quantitative features by multimode imaging, which has good application prospects for the diagnosis, staging and prognosis of benign and malignant liver diseases. Therefore, this paper summarizes the current research status in the field of liver disease, in order to help these patients achieve personalized and precision medical care. AREAS COVERED This paper uses several keywords on the PubMed database to search the references, and reviews the workflow of traditional radiomics, as well as the characteristics and influencing factors of different imaging modes. At the same time, the references on the application of imaging in different benign and malignant liver diseases were also summarized. EXPERT OPINION For patients with liver disease, the traditional imaging evaluation can only provide limited information. Radiomics exploits the characteristics of high-throughput and high-dimensional extraction, enabling liver imaging capabilities far beyond the scope of traditional visual image analysis. Recent studies have demonstrated the prospect of this technology in personalized diagnosis and treatment decision in various fields of the liver. However, further clinical validation is needed in its application and practice.
Collapse
Affiliation(s)
- Jiaying Bao
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xiao Feng
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Yan Ma
- Department of Ultrasound, Zibo Central Hospital, Zibo, P.R. China
| | - Yanyan Wang
- Departments of Emergency Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Jianni Qi
- Central Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Chengyong Qin
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xu Tan
- Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Yongmei Tian
- Department of Geriatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| |
Collapse
|
45
|
Al-Hayek Y, Spuur K, Davidson R, Hayre C, Zheng X. The Impacts of Vertical Off-Centring, Localiser Direction, Phantom Positioning and Tube Voltage on CT Number Accuracy: An Experimental Study. J Imaging 2022; 8:jimaging8070175. [PMID: 35877619 PMCID: PMC9316438 DOI: 10.3390/jimaging8070175] [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: 04/19/2022] [Revised: 06/17/2022] [Accepted: 06/18/2022] [Indexed: 11/29/2022] Open
Abstract
Background: This study investigates the effects of vertical off-centring, localiser direction, tube voltage, and phantom positioning (supine and prone) on computed tomography (CT) numbers and radiation dose. Methods: An anthropomorphic phantom was scanned using a Discovery CT750 HD—128 slice (GE Healthcare) scanner at different tube voltages (80, 120, and 140 kVp). Images employing 0° and 180° localisers were acquired in supine and prone positions for each vertical off-centring (±100, ±60, and ±30 mm from the iso-centre). CT numbers and displayed volume CT dose index (CTDIvol) were recorded. The relationship between dose variation and CT number was investigated. Results: The maximum changes in CT number between the two phantom positions as a function of vertical-off-centring were for the upper thorax 34 HU (0° localiser, 120 kVp), mid thorax 43 HU (180° localiser, 80 kVp), and for the abdominal section 31 HU (0° localiser, 80 kVp) in the prone position. A strong positive correlation was reported between the variation in dose and CT number (r = 0.969, p < 0.001); 95% CI (0.93, 0.99). Conclusions: Patient positioning demands an approach with a high degree of accuracy, especially in cases where clinical decisions depend on CT number accuracy for tissue lesion characterisation.
Collapse
Affiliation(s)
- Yazan Al-Hayek
- School of Dentistry and Medical Sciences, Faculty of Science and Health, Charles Sturt University, Wagga Wagga, NSW 2650, Australia; (K.S.); (C.H.); (X.Z.)
- Department of Medical Imaging, Faculty of Applied Health Sciences, The Hashemite University, Zarqa 13133, Jordan
- Correspondence:
| | - Kelly Spuur
- School of Dentistry and Medical Sciences, Faculty of Science and Health, Charles Sturt University, Wagga Wagga, NSW 2650, Australia; (K.S.); (C.H.); (X.Z.)
| | - Rob Davidson
- School of Health Sciences, Faculty of Health, University of Canberra, Canberra, ACT 2601, Australia;
| | - Christopher Hayre
- School of Dentistry and Medical Sciences, Faculty of Science and Health, Charles Sturt University, Wagga Wagga, NSW 2650, Australia; (K.S.); (C.H.); (X.Z.)
| | - Xiaoming Zheng
- School of Dentistry and Medical Sciences, Faculty of Science and Health, Charles Sturt University, Wagga Wagga, NSW 2650, Australia; (K.S.); (C.H.); (X.Z.)
| |
Collapse
|
46
|
Marfisi D, Tessa C, Marzi C, Del Meglio J, Linsalata S, Borgheresi R, Lilli A, Lazzarini R, Salvatori L, Vignali C, Barucci A, Mascalchi M, Casolo G, Diciotti S, Traino AC, Giannelli M. Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy. Sci Rep 2022; 12:10186. [PMID: 35715531 PMCID: PMC9205876 DOI: 10.1038/s41598-022-13937-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 03/21/2022] [Indexed: 12/24/2022] Open
Abstract
Radiomics is emerging as a promising and useful tool in cardiac magnetic resonance (CMR) imaging applications. Accordingly, the purpose of this study was to investigate, for the first time, the effect of image resampling/discretization and filtering on radiomic features estimation from quantitative CMR T1 and T2 mapping. Specifically, T1 and T2 maps of 26 patients with hypertrophic cardiomyopathy (HCM) were used to estimate 98 radiomic features for 7 different resampling voxel sizes (at fixed bin width), 9 different bin widths (at fixed resampling voxel size), and 7 different spatial filters (at fixed resampling voxel size/bin width). While we found a remarkable dependence of myocardial radiomic features from T1 and T2 mapping on image filters, many radiomic features showed a limited sensitivity to resampling voxel size/bin width, in terms of intraclass correlation coefficient (> 0.75) and coefficient of variation (< 30%). The estimate of most textural radiomic features showed a linear significant (p < 0.05) correlation with resampling voxel size/bin width. Overall, radiomic features from T2 maps have proven to be less sensitive to image preprocessing than those from T1 maps, especially when varying bin width. Our results might corroborate the potential of radiomics from T1/T2 mapping in HCM and hopefully in other myocardial diseases.
Collapse
Affiliation(s)
- Daniela Marfisi
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Carlo Tessa
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Apuane Hospital, 54100, Massa, Italy
| | - Chiara Marzi
- Institute of Applied Physics "Nello Carrara", Italian National Research Council, 50019, Sesto Fiorentino, Italy
| | - Jacopo Del Meglio
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Stefania Linsalata
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Rita Borgheresi
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Alessio Lilli
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Riccardo Lazzarini
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Luca Salvatori
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Claudio Vignali
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Andrea Barucci
- Institute of Applied Physics "Nello Carrara", Italian National Research Council, 50019, Sesto Fiorentino, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50121, Florence, Italy
| | - Giancarlo Casolo
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041, Lido di Camaiore, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 47522, Cesena, Italy
| | - Antonio Claudio Traino
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Via Roma 67, 56126, Pisa, Italy.
| |
Collapse
|
47
|
Song Y, Li J, Wang H, Liu B, Yuan C, Liu H, Zheng Z, Min F, Li Y. Radiomics Nomogram Based on Contrast-enhanced CT to Predict the Malignant Potential of Gastrointestinal Stromal Tumor: A Two-center Study. Acad Radiol 2022; 29:806-816. [PMID: 34238656 DOI: 10.1016/j.acra.2021.05.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 05/04/2021] [Accepted: 05/05/2021] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES Contrast-enhanced computed tomography (CE-CT) was used to establish radiomics nomogram to evaluate the malignant potential of gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS A total of 500 GIST patients were enrolled in this study and divided into training cohort (n = 346, our center) and validation cohort (n = 154, another center). Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms were used to select the feature subset with the best discriminant features from the three phases image, and five classifiers were used to establish four radiomics signatures. Preoperative radiomics nomogram was constructed by adding the clinical features determined by multivariate logistic regression analysis. The performance of radiomics signatures and nomogram were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC). The calibration of nomogram was appraised by calibration curve. RESULTS A total of 13 radiomic features were extracted from tri-phase combined CE-CT images. Tri-phase combined CE-CT features + Support Vector Machine (SVM) was the best combination at predicting the malignant potential of GIST, with an AUC of 0.895 (95% CI 0.858-0.931) in the training cohort and 0.847 (95% CI 0.778-0.917) in the validation cohort. The nomogram also had good calibration. In the training cohort and the validation cohort, preoperative radiomics nomogram reached AUCs of 0.927 and 0.905, respectively, which were higher than clinical. CONCLUSION The radiomics nomogram had a good predictive effect and generalization on the malignant potential of GIST, which could effectively help guide preoperative clinical decision.
Collapse
Affiliation(s)
- Yancheng Song
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Jie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Bo Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Chentong Yuan
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Hao Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Ziwen Zheng
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong
| | - Fanyi Min
- Department of Radiology, The Affiliated Hospital of Qingdao University, Shandong, Shandong
| | - Yu Li
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, Shandong.
| |
Collapse
|
48
|
Park D, Oh D, Lee M, Lee SY, Shin KM, Jun JS, Hwang D. Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer. Eur Radiol 2022; 32:8716-8725. [PMID: 35639142 DOI: 10.1007/s00330-022-08869-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/09/2022] [Accepted: 05/09/2022] [Indexed: 01/05/2023]
Abstract
OBJECTIVES To analyze whether CT image normalization can improve 3-year recurrence-free survival (RFS) prediction performance in patients with non-small cell lung cancer (NSCLC) relative to the use of unnormalized CT images. METHODS A total of 106 patients with NSCLC were included in the training set. For each patient, 851 radiomic features were extracted from the normalized and the unnormalized CT images, respectively. After the feature selection, random forest models were constructed with selected radiomic features and clinical features. The models were then externally validated in the test set consisting of 79 patients with NSCLC. RESULTS The model using normalized CT images yielded better performance than the model using unnormalized CT images (with an area under the receiver operating characteristic curve of 0.802 vs 0.702, p = 0.01), with the model performing especially well among patients with adenocarcinoma (with an area under the receiver operating characteristic curve of 0.880 vs 0.720, p < 0.01). CONCLUSIONS CT image normalization may improve prediction performance among patients with NSCLC, especially for patients with adenocarcinoma. KEY POINTS • After CT image normalization, more radiomic features were able to be identified. • Prognostic performance in patients was improved significantly after CT image normalization compared with before the CT image normalization. • The improvement in prognostic performance following CT image normalization was superior in patients with adenocarcinoma.
Collapse
Affiliation(s)
- Doohyun Park
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Daejoong Oh
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
- D&P BIOTECH Inc., Seoul, Republic of Korea
| | | | - Shin Yup Lee
- Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
- Lung Cancer Center, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
| | - Kyung Min Shin
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
| | | | - Dosik Hwang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
- Center for Healthcare Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea.
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea.
- Department of Radiology and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Republic of Korea.
| |
Collapse
|
49
|
Ren Q, Zhu P, Li C, Yan M, Liu S, Zheng C, Xia X. Pretreatment Computed Tomography-Based Machine Learning Models to Predict Outcomes in Hepatocellular Carcinoma Patients who Received Combined Treatment of Trans-Arterial Chemoembolization and Tyrosine Kinase Inhibitor. Front Bioeng Biotechnol 2022; 10:872044. [PMID: 35677305 PMCID: PMC9168370 DOI: 10.3389/fbioe.2022.872044] [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: 02/09/2022] [Accepted: 04/22/2022] [Indexed: 11/15/2022] Open
Abstract
Aim: Trans-arterial chemoembolization (TACE) in combination with tyrosine kinase inhibitor (TKI) has been evidenced to improve outcomes in a portion of patients with hepatocellular carcinoma (HCC). Developing biomarkers to identify patients who might benefit from the combined treatment is needed. This study aims to investigate the efficacy of radiomics/deep learning features-based models in predicting short-term disease control and overall survival (OS) in HCC patients who received the combined treatment. Materials and Methods: A total of 103 HCC patients who received the combined treatment from Sep. 2015 to Dec. 2019 were enrolled in the study. We exacted radiomics features and deep learning features of six pre-trained convolutional neural networks (CNNs) from pretreatment computed tomography (CT) images. The robustness of features was evaluated, and those with excellent stability were used to construct predictive models by combining each of the seven feature exactors, 13 feature selection methods and 12 classifiers. The models were evaluated for predicting short-term disease by using the area under the receiver operating characteristics curve (AUC) and relative standard deviation (RSD). The optimal models were further analyzed for predictive performance on overall survival. Results: A total of the 1,092 models (156 with radiomics features and 936 with deep learning features) were constructed. Radiomics_GINI_Nearest Neighbors (RGNN) and Resnet50_MIM_Nearest Neighbors (RMNN) were identified as optimal models, with the AUC of 0.87 and 0.94, accuracy of 0.89 and 0.92, sensitivity of 0.88 and 0.97, specificity of 0.90 and 0.90, precision of 0.87 and 0.83, F1 score of 0.89 and 0.92, and RSD of 1.30 and 0.26, respectively. Kaplan-Meier survival analysis showed that RGNN and RMNN were associated with better OS (p = 0.006 for RGNN and p = 0.033 for RMNN). Conclusion: Pretreatment CT-based radiomics/deep learning models could non-invasively and efficiently predict outcomes in HCC patients who received combined therapy of TACE and TKI.
Collapse
Affiliation(s)
- Qianqian Ren
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Peng Zhu
- Department of Hepatobiliary Surgery, Wuhan No.1 Hospital, Wuhan, China
| | - Changde Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Meijun Yan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Song Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xiangwen Xia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- *Correspondence: Xiangwen Xia,
| |
Collapse
|
50
|
Deep Learning-Based Image Conversion Improves the Reproducibility of Computed Tomography Radiomics Features: A Phantom Study. Invest Radiol 2022; 57:308-317. [PMID: 34839305 DOI: 10.1097/rli.0000000000000839] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVES This study aimed to evaluate the usefulness of deep learning-based image conversion to improve the reproducibility of computed tomography (CT) radiomics features. MATERIALS AND METHODS This study was conducted using an abdominal phantom with liver nodules. We developed an image conversion algorithm using a residual feature aggregation network to reproduce radiomics features with CT images under various CT protocols and reconstruction kernels. External validation was performed using images from different scanners, consisting of 8 different protocols. To evaluate the variability of radiomics features, regions of interest (ROIs) were drawn by targeting the liver parenchyma, vessels, paraspinal area, and liver nodules. We extracted 18 first-order, 68 second-order, and 688 wavelet radiomics features. Measurement variability was assessed using the concordance correlation coefficient (CCC), compared with the ground-truth image. RESULTS In the ROI-based analysis, there was an 83.3% improvement of CCC (80/96; 4 ROIs with 3 categories of radiomics features and 8 protocols) in synthetic images compared with the original images. Among them, the 56 CCC pairs showed a significant increase after image synthesis. In the radiomics feature-based analysis, 62.0% (3838 of 6192; 774 radiomics features with 8 protocols) features showed increased CCC after image synthesis, and a significant increase was noted in 26.9% (1663 of 6192) features. In particular, the first-order feature (79.9%, 115/144) showed better improvement in terms of the reproducibility of radiomics feature than the second-order (59.9%, 326/544) or wavelet feature (61.7%, 3397/5504). CONCLUSIONS Our study demonstrated that a deep learning model for image conversion can improve the reproducibility of radiomics features across various CT protocols, reconstruction kernels, and CT scanners.
Collapse
|