1
|
Fan L, Yang Z, Chang M, Chen Z, Wen Q. CT-based delta-radiomics nomogram to predict pathological complete response after neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma patients. J Transl Med 2024; 22:579. [PMID: 38890720 PMCID: PMC11186275 DOI: 10.1186/s12967-024-05392-4] [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: 07/21/2023] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND This study developed a nomogram model using CT-based delta-radiomics features and clinical factors to predict pathological complete response (pCR) in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiotherapy (nCRT). METHODS The study retrospectively analyzed 232 ESCC patients who underwent pretreatment and post-treatment CT scans. Patients were divided into training (n = 186) and validation (n = 46) sets through fivefold cross-validation. 837 radiomics features were extracted from regions of interest (ROIs) delineations on CT images before and after nCRT to calculate delta values. The LASSO algorithm selected delta-radiomics features (DRF) based on classification performance. Logistic regression constructed a nomogram incorporating DRFs and clinical factors. Receiver operating characteristic (ROC) and area under the curve (AUC) analyses evaluated nomogram performance for predicting pCR. RESULTS No significant differences existed between the training and validation datasets. The 4-feature delta-radiomics signature (DRS) demonstrated good predictive accuracy for pCR, with α-binormal-based and empirical AUCs of 0.871 and 0.869. T-stage (p = 0.001) and differentiation degree (p = 0.018) were independent predictors of pCR. The nomogram combined the DRS and clinical factors improved the classification performance in the training dataset (AUCαbin = 0.933 and AUCemp = 0.941). The validation set showed similar performance with AUCs of 0.958 and 0.962. CONCLUSIONS The CT-based delta-radiomics nomogram model with clinical factors provided high predictive accuracy for pCR in ESCC patients after nCRT.
Collapse
Affiliation(s)
- Liyuan Fan
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China
| | - Zhe Yang
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, 250021, Shandong, China
| | - Minghui Chang
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, 250021, Shandong, China
| | - Zheng Chen
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, 250021, Shandong, China
| | - Qiang Wen
- Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, 250021, Shandong, China.
| |
Collapse
|
2
|
Rong Y, Chen Q, Fu Y, Yang X, Al-Hallaq HA, Wu QJ, Yuan L, Xiao Y, Cai B, Latifi K, Benedict SH, Buchsbaum JC, Qi XS. NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions. Int J Radiat Oncol Biol Phys 2024; 119:261-280. [PMID: 37972715 PMCID: PMC11023777 DOI: 10.1016/j.ijrobp.2023.10.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 09/16/2023] [Accepted: 10/14/2023] [Indexed: 11/19/2023]
Abstract
Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.
Collapse
Affiliation(s)
- Yi Rong
- Mayo Clinic Arizona, Phoenix, AZ
| | - Quan Chen
- City of Hope Comprehensive Cancer Center Duarte, CA
| | - Yabo Fu
- Memorial Sloan Kettering Cancer Center, Commack, NY
| | | | | | | | - Lulin Yuan
- Virginia Commonwealth University, Richmond, VA
| | - Ying Xiao
- University of Pennsylvania/Abramson Cancer Center, Philadelphia, PA
| | - Bin Cai
- The University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Stanley H Benedict
- University of California Davis Comprehensive Cancer Center, Sacramento, CA
| | | | - X Sharon Qi
- University of California Los Angeles, Los Angeles, CA
| |
Collapse
|
3
|
Yuan S, Liu Y, Wei R, Zhu J, Men K, Dai J. A novel loss function to reproduce texture features for deep learning-based MRI-to-CT synthesis. Med Phys 2024; 51:2695-2706. [PMID: 38043105 DOI: 10.1002/mp.16850] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 10/03/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Studies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel-wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention. PURPOSE This study aimed to propose a novel loss function to reproduce texture features of ROIs and pixel-wise consistency for deep learning-based MRI-to-CT synthesis. The method was expected to assist the multi-modality studies for radiomics. METHODS The study retrospectively enrolled 127 patients with nasopharyngeal carcinoma. CT and MRI images were collected for each patient, and then rigidly registered as pre-procession. We proposed a gray-level co-occurrence matrix (GLCM)-based loss function to improve the reproducibility of texture features. This novel loss function could be embedded into the present deep learning-based framework for image synthesis. In this study, a typical image synthesis model was selected as the baseline, which contained a Unet trained mean square error (MSE) loss function. We embedded the proposed loss function and designed experiments to supervise different ROIs to prove its effectiveness. The concordance correlation coefficient (CCC) of the GLCM feature was employed to evaluate the reproducibility of GLCM features, which are typical texture features. Besides, we used a publicly available dataset of brain tumors to verify our loss function. RESULTS Compared with the baseline, the proposed method improved the pixel-wise image quality metrics (MAE: 107.5 to 106.8 HU; SSIM: 0.9728 to 0.9730). CCC values of the GLCM features in GTVnx were significantly improved from 0.78 ± 0.12 to 0.82 ± 0.11 (p < 0.05 for paired t-test). Generally, > 90% (22/24) of the GLCM-based features were improved compared with the baseline, where the Informational Measure of Correlation feature was improved the most (CCC: 0.74 to 0.83). For the public dataset, the loss function also shows its effectiveness. With our proposed loss function added, the ability to reproduce texture features was improved in the ROIs. CONCLUSIONS The proposed method reproduced texture features for MRI-to-CT synthesis, which would benefit radiomics studies based on image multi-modality synthesis.
Collapse
Affiliation(s)
- Siqi Yuan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ran Wei
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
4
|
Demetriou D, Lockhat Z, Brzozowski L, Saini KS, Dlamini Z, Hull R. The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics. Cancers (Basel) 2024; 16:1076. [PMID: 38473432 DOI: 10.3390/cancers16051076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/09/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.
Collapse
Affiliation(s)
- Demetra Demetriou
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Zarina Lockhat
- Department of Radiology, Faculty of Health Sciences, Steve Biko Academic Hospital, University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Luke Brzozowski
- Translational Research and Core Facilities, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Kamal S Saini
- Fortrea Inc., 8 Moore Drive, Durham, NC 27709, USA
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Zodwa Dlamini
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Rodney Hull
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
| |
Collapse
|
5
|
Triggiani S, Contaldo MT, Mastellone G, Cè M, Ierardi AM, Carrafiello G, Cellina M. The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review. Crit Rev Oncog 2024; 29:37-52. [PMID: 38505880 DOI: 10.1615/critrevoncog.2023049855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions. Furthermore, the review addresses the challenges associated with the implementation of AI and texture analysis in clinical practice, including data acquisition, standardization of imaging protocols, and model validation. Future directions and potential advancements in this field are discussed. Integration of multi-modal imaging data, incorporation of genomics and clinical data, and the development of predictive models with enhanced interpretability are proposed as potential avenues for further research. In conclusion, the application of AI and texture analysis in predicting outcomes of interventional radiological treatment for liver lesions shows great promise in augmenting clinical decision-making and improving patient care. By leveraging these technologies, clinicians can potentially enhance treatment planning, optimize intervention strategies, and ultimately improve patient outcomes in the management of liver lesions.
Collapse
Affiliation(s)
- Sonia Triggiani
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maria T Contaldo
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Giulia Mastellone
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Anna M Ierardi
- Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, 20122 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
| |
Collapse
|
6
|
Bagher-Ebadian H, Brown SL, Ghassemi MM, Nagaraja TN, Movsas B, Ewing JR, Chetty IJ. Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI. Sci Rep 2023; 13:10693. [PMID: 37394559 DOI: 10.1038/s41598-023-37723-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/27/2023] [Indexed: 07/04/2023] Open
Abstract
Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs' feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.
Collapse
Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Physics, Oakland University, Rochester, MI, 48309, USA.
| | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Mohammad M Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tavarekere N Nagaraja
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - James R Ewing
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Wayne State University, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| |
Collapse
|
7
|
Nie K, Xiao Y. Radiomics in clinical trials: perspectives on standardization. Phys Med Biol 2022; 68. [PMID: 36384049 DOI: 10.1088/1361-6560/aca388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 11/16/2022] [Indexed: 11/17/2022]
Abstract
The term biomarker is used to describe a biological measure of the disease behavior. The existing imaging biomarkers are associated with the known tissue biological characteristics and follow a well-established roadmap to be implemented in routine clinical practice. Recently, a new quantitative imaging analysis approach named radiomics has emerged. It refers to the extraction of a large number of advanced imaging features with high-throughput computing. Extensive research has demonstrated its value in predicting disease behavior, progression, and response to therapeutic options. However, there are numerous challenges to establishing it as a clinically viable solution, including lack of reproducibility and transparency. The data-driven nature also does not offer insights into the underpinning biology of the observed relationships. As such, additional effort is needed to establish it as a qualified biomarker to inform clinical decisions. Here we review the technical difficulties encountered in the clinical applications of radiomics and current effort in addressing some of these challenges in clinical trial designs. By addressing these challenges, the true potential of radiomics can be unleashed.
Collapse
Affiliation(s)
- Ke Nie
- Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, Department of Radiation Oncology, New Brunswick, NJ, 08901, United States of America
| | - Ying Xiao
- University of Pennsylvania, Department of Radiation Oncology, 3400 Civic Center Blvd, TRC-2 West Philadelphia, PA 19104, United States of America
| |
Collapse
|
8
|
Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology. Biomedicines 2022; 10:biomedicines10123205. [PMID: 36551961 PMCID: PMC9775324 DOI: 10.3390/biomedicines10123205] [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: 11/01/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.
Collapse
|
9
|
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
|
10
|
Moskowitz CS, Welch ML, Jacobs MA, Kurland BF, Simpson AL. Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies. Radiology 2022; 304:265-273. [PMID: 35579522 PMCID: PMC9340236 DOI: 10.1148/radiol.211597] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 01/30/2022] [Accepted: 02/02/2022] [Indexed: 12/13/2022]
Abstract
Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described.
Collapse
Affiliation(s)
| | | | - Michael A. Jacobs
- From the Department of Epidemiology and Biostatistics, Memorial Sloan
Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017
(C.S.M.); Cancer Digital Intelligence Program, University Health Network,
Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and
Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns
Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa
(B.F.K.); and School of Computing, Department of Biomedical and Molecular
Sciences, Queen’s University, Kingston, ON, Canada (A.L.S.)
| | - Brenda F. Kurland
- From the Department of Epidemiology and Biostatistics, Memorial Sloan
Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017
(C.S.M.); Cancer Digital Intelligence Program, University Health Network,
Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and
Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns
Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa
(B.F.K.); and School of Computing, Department of Biomedical and Molecular
Sciences, Queen’s University, Kingston, ON, Canada (A.L.S.)
| | - Amber L. Simpson
- From the Department of Epidemiology and Biostatistics, Memorial Sloan
Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017
(C.S.M.); Cancer Digital Intelligence Program, University Health Network,
Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and
Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns
Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa
(B.F.K.); and School of Computing, Department of Biomedical and Molecular
Sciences, Queen’s University, Kingston, ON, Canada (A.L.S.)
| |
Collapse
|
11
|
Nie K, Hu P, Zheng J, Zhang Y, Yang P, Jabbour SK, Yue N, Dong X, Xu S, Shen B, Niu T, Hu X, Cai X, Sun J. Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II-III Rectal Cancer. Front Oncol 2022; 12:779030. [PMID: 35847948 PMCID: PMC9279662 DOI: 10.3389/fonc.2022.779030] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
Although rectal cancer comprises up to one-third of colorectal cancer cases and several prognosis nomograms have been established for colon cancer, statistical tools for predicting long-term survival in rectal cancer are lacking. In addition, previous prognostic studies did not include much imaging findings, qualitatively or quantitatively. Therefore, we include multiparametric MRI information from both radiologists' readings and quantitative radiomics signatures to construct a prognostic model that allows 5-year overall survival (OS) prediction for advance-staged rectal cancer patients. The result suggested that the model combined with quantitative imaging findings might outperform that of conventional TNM staging or other clinical prognostic factors. It was noteworthy that the identified radiomics signature consisted of three from dynamic contrast-enhanced (DCE)-MRI, four from anatomical MRI, and one from functional diffusion-weighted imaging (DWI). This highlighted the importance of multiparametric MRI to address the issue of long-term survival estimation in rectal cancer. Additionally, the constructed radiomics signature demonstrated value to the conventional prognostic factors in predicting 5-year OS for stage II-III rectal cancer. The presented nomogram also provides a practical example of individualized prognosis estimation and may potentially impact treatment strategies.
Collapse
Affiliation(s)
- Ke Nie
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Peng Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianjun Zheng
- Department of Radiology, Hwa Mei Hospital, Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province, University of Chinese Academy of Sciences, Ningbo, China
| | - Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Pengfei Yang
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Salma K. Jabbour
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Ning Yue
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Xue Dong
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shufeng Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bo Shen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tianye Niu
- Institute of Translational Medicine, Zhejiang University, Hangzhou, China
| | - Xiaotong Hu
- Biomedical Research Center and Key Laboratory of Biotherapy of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiujun Cai
- Department of General Surgery, Innovation Center for Minimally Invasive Techniques and Devices, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jihong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Innovation Center for Minimally Invasive Techniques and Devices, Zhejiang University, Hangzhou, China
| |
Collapse
|
12
|
Extermann M, Chetty IJ, Brown SL, Al-Jumayli M, Movsas B. Predictors of Toxicity Among Older Adults with Cancer. Semin Radiat Oncol 2022; 32:179-185. [DOI: 10.1016/j.semradonc.2021.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
13
|
High-dimensional role of AI and machine learning in cancer research. Br J Cancer 2022; 126:523-532. [PMID: 35013580 PMCID: PMC8854697 DOI: 10.1038/s41416-021-01689-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 11/23/2021] [Accepted: 12/23/2021] [Indexed: 01/12/2023] Open
Abstract
The role of Artificial Intelligence and Machine Learning in cancer research offers several advantages, primarily scaling up the information processing and increasing the accuracy of the clinical decision-making. The key enabling tools currently in use in Precision, Digital and Translational Medicine, here named as 'Intelligent Systems' (IS), leverage unprecedented data volumes and aim to model their underlying heterogeneous influences and variables correlated with patients' outcomes. As functionality and performance of IS are associated with complex diagnosis and therapy decisions, a rich spectrum of patterns and features detected in high-dimensional data may be critical for inference purposes. Many challenges are also present in such discovery task. First, the generation of interpretable model results from a mix of structured and unstructured input information. Second, the design, and implementation of automated clinical decision processes for drawing disease trajectories and patient profiles. Ultimately, the clinical impacts depend on the data effectively subjected to steps such as harmonisation, integration, validation, etc. The aim of this work is to discuss the transformative value of IS applied to multimodal data acquired through various interrelated cancer domains (high-throughput genomics, experimental biology, medical image processing, radiomics, patient electronic records, etc.).
Collapse
|
14
|
Lee SH, Geng H, Xiao Y. Radiotherapy Standardisation and Artificial Intelligence within the National Cancer Institute's Clinical Trials Network. Clin Oncol (R Coll Radiol) 2022; 34:128-134. [PMID: 34906407 PMCID: PMC8792288 DOI: 10.1016/j.clon.2021.11.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/09/2021] [Accepted: 11/25/2021] [Indexed: 02/03/2023]
Abstract
Artificial intelligence in healthcare refers to the use of complex algorithms designed to conduct certain tasks in an automated manner. Artificial intelligence has a transformative power in radiation oncology to improve the quality and efficiency of patient care, given the increase in volume and complexity of digital data, as well as the multi-faceted and highly technical nature of this field of medicine. However, artificial intelligence alone will not be able to fix healthcare's problem, because new technologies bring unexpected and potentially underappreciated obstacles. The inclusion of multicentre datasets, the incorporation of time-varying data, the assessment of missing data as well as informative censoring and the addition of clinical utility could significantly improve artificial intelligence models. Standardisation plays a crucial, supportive and leading role in artificial intelligence. Clinical trials are the most reliable method of demonstrating the efficacy and safety of a treatment or clinical approach, as well as providing high-level evidence to justify artificial intelligence. The National Surgical Adjuvant Breast and Bowel Project, the Radiation Therapy Oncology Group and the Gynecologic Oncology Group collaborated to form NRG Oncology (acronym NRG derived from the names of the parental groups). NRG Oncology is one of the adult cancer clinical trial groups containing radiotherapy specialty of the National Cancer Institute's Clinical Trials Network (NCTN). Standardisation from NRG/NCTN has the potential to reduce variation in clinical treatment and patient outcome by eliminating potential errors, enabling broader application of artificial intelligence tools. NCTN, NRG and Imaging and Radiation Oncology Core are in a unique position to help with standards development, advocacy and enforcement, all of which can benefit from artificial intelligence, as artificial intelligence has the ability to improve trial success rates by transforming crucial phases in clinical trial design, from study planning through to execution. Here we will examine: (i) how to conduct technical and clinical evaluations before adopting artificial intelligence technologies, (ii) how to obtain high-quality data for artificial intelligence, (iii) the NCTN infrastructure and standards, (iv) radiotherapy standardisation for clinical trials and (v) artificial intelligence applications in standardisation.
Collapse
Affiliation(s)
- S H Lee
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
| | - H Geng
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
| | - Y Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
| |
Collapse
|
15
|
Bagher Ebadian H, Siddiqui F, Ghanem A, Zhu S, Lu M, Movsas B, Chetty IJ. Radiomics outperforms clinical factors in characterizing human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinomas. Biomed Phys Eng Express 2021; 8. [PMID: 34781281 DOI: 10.1088/2057-1976/ac39ab] [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: 08/05/2021] [Accepted: 11/15/2021] [Indexed: 11/11/2022]
Abstract
Purpose:To utilize radiomic features extracted from CT images to characterize Human Papilloma Virus (HPV) for patients with oropharyngeal cancer squamous cell carcinoma (OPSCC).Methods:One hundred twenty-eight OPSCC patients with known HPV-status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16-protein testing) were retrospectively studied. Radiomic features (11 feature-categories) were extracted in 3D from contrast-enhanced (CE)-CT images of gross-tumor-volumes using 'in-house' software ('ROdiomiX') developed and validated following the image-biomarker-standardization-initiative (IBSI) guidelines. Six clinical factors were investigated: Age-at-Diagnosis, Gender, Total-Charlson, Alcohol-Use, Smoking-History, and T-Stage. A Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) technique combined with a Generalized-Linear-Model (Lasso-GLM) were applied to perform regularization in the radiomic and clinical feature spaces to identify the ranking of optimal feature subsets with most representative information for prediction of HPV. Lasso-GLM models/classifiers based on clinical factors only, radiomics only, and combined clinical and radiomics (ensemble/integrated) were constructed using random-permutation-sampling. Tests of significance (One-way ANOVA), average Area-Under-Receiver-Operating-Characteristic (AUC), and Positive and Negative Predictive values (PPV and NPV) were computed to estimate the generalization-error and prediction performance of the classifiers.Results:Five clinical factors, including T-stage, smoking status, and age, and 14 radiomic features, including tumor morphology, and intensity contrast were found to be statistically significant discriminators between HPV positive and negative cohorts. Performances for prediction of HPV for the 3 classifiers were: Radiomics-Lasso-GLM: AUC/PPV/NPV=0.789/0.755/0.805; Clinical-Lasso-GLM: 0.676/0.747/0.672, and Integrated/Ensemble-Lasso-GLM: 0.895/0.874/0.844. Results imply that the radiomics-based classifier enabled better characterization and performance prediction of HPV relative to clinical factors, and that the combination of both radiomics and clinical factors yields even higher accuracy characterization and predictive performance.Conclusion:Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results in support of the role of radiomic features towards characterization of HPV in patients with OPSCC.
Collapse
Affiliation(s)
- Hassan Bagher Ebadian
- Department of Radiation Oncology , Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Detroit, Michigan, 48202, UNITED STATES
| | - Farzan Siddiqui
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Ahmed Ghanem
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Simeng Zhu
- Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Mei Lu
- Henry Ford Hospital, 2799 West Grand Blvd., Detroit, Michigan, 48202, UNITED STATES
| | - Benjamin Movsas
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd., Detroit, 48202, UNITED STATES
| | - Indrin J Chetty
- Dept of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI 48202-2689, USA, Detroit, Michigan, 48202, UNITED STATES
| |
Collapse
|
16
|
Bagher-Ebadian H, Zhu S, Siddiqui F, Lu M, Movsas B, Chetty IJ. Technical Note: On the development of an outcome-driven frequency filter for improving Radiomics-based modeling of Human Papilloma Virus (HPV) in patients with oropharyngeal squamous cell carcinomas. Med Phys 2021; 48:7552-7562. [PMID: 34390003 DOI: 10.1002/mp.15159] [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: 05/12/2021] [Revised: 07/08/2021] [Accepted: 08/03/2021] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To implement an outcome-driven frequency filter for improving radiomics-based modeling of human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinoma (OPSCC). METHODS AND MATERIALS One hundred twenty-eight OPSCC patients with known HPV status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16 protein testing) were retrospectively studied. A 3D Discrete Fourier Transform was applied on contrast-enhanced CT images of patient gross tumor volumes (GTV's) to transform intensity distributions to the frequency domain and estimate frequency power spectrums of HPV- and HPV+ patient cohorts. Statistical analyses were performed to rank frequency bands contributing towards prediction of HPV status. An outcome-driven frequency filter was designed accordingly and applied to GTV frequency information. 3D Inverse-Discrete-Fourier-Transform was applied to reconstruct HPV-related frequency-filtered images. Radiomics features (11 feature-categories) were extracted from pre- and post- frequency filtered images using our previously published 'ROdiomiX' software. Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) combined with a Generalized-Linear-Model (Lasso-GLM) was developed to identify and rank feature subsets with optimal information for prediction of HPV+/-. Radiomics-based Lasso-GLM classifiers (pre- and post-frequency filtered) were constructed and validated using a random permutation sampling and nested cross-validation techniques. Average Area Under Receiver Operating Characteristic (AUC), and Positive and Negative Predictive values (PPV, NPV) were computed to estimate generalization error and prediction performance. RESULTS Among 192 radiomic features, 15 features were found to be statistically significant discriminators between HPV+/- cohorts on post-frequency filtered CE-CT images; 14 such radiomic features were observed on pre-frequency filtered datasets. Discriminant features included tumor morphology and intensity contrast. Performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.789/0.755/0.805 and 0.850/0.808/0.877 respectively. Nested-CV performances for prediction of HPV for the pre- and post-frequency filtered Lasso-GLM classifiers were: AUC/PPV/NPV = 0.814/0.725/0.877 and 0.890/0.820/0.911 respectively. CONCLUSION Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results on the importance of frequency analysis prior to radiomic feature extraction toward enhancement of model performance for characterizing HPV in patients with OPSCC. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Simeng Zhu
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Farzan Siddiqui
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Mei Lu
- Department of Public Health, Henry Ford Health System, Michigan, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Hospital, 2799 West Grand Blvd, Detroit, MI, 48202, USA
| |
Collapse
|
17
|
Fournier L, Costaridou L, Bidaut L, Michoux N, Lecouvet FE, de Geus-Oei LF, Boellaard R, Oprea-Lager DE, Obuchowski NA, Caroli A, Kunz WG, Oei EH, O'Connor JPB, Mayerhoefer ME, Franca M, Alberich-Bayarri A, Deroose CM, Loewe C, Manniesing R, Caramella C, Lopci E, Lassau N, Persson A, Achten R, Rosendahl K, Clement O, Kotter E, Golay X, Smits M, Dewey M, Sullivan DC, van der Lugt A, deSouza NM, European Society Of Radiology. Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers. Eur Radiol 2021; 31:6001-6012. [PMID: 33492473 PMCID: PMC8270834 DOI: 10.1007/s00330-020-07598-8] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/16/2020] [Accepted: 12/03/2020] [Indexed: 02/07/2023]
Abstract
Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.
Collapse
Affiliation(s)
- Laure Fournier
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
| | - Lena Costaridou
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- School of Medicine, University of Patras, University Campus, Rio, 26 500, Patras, Greece
| | - Luc Bidaut
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- College of Science, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Nicolas Michoux
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Frederic E Lecouvet
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), B-1200, Brussels, Belgium
| | - Lioe-Fee de Geus-Oei
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands
| | - Ronald Boellaard
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
| | - Daniela E Oprea-Lager
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers (VU University), Amsterdam, The Netherlands
| | - Nancy A Obuchowski
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - Anna Caroli
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Engineering, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy
| | - Wolfgang G Kunz
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Edwin H Oei
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - James P B O'Connor
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Marius E Mayerhoefer
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Manuela Franca
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Centro Hospitalar Universitário do Porto, Instituto de Ciências Biomédicas de Abel Salazar, University of Porto, Porto, Portugal
| | - Angel Alberich-Bayarri
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers in Medicine (QUIBIM), Valencia, Spain
| | - Christophe M Deroose
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Christian Loewe
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Division of Cardiovascular and Interventional Radiology, Dept. for Bioimaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Rashindra Manniesing
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525 GA, Nijmegen, The Netherlands
| | - Caroline Caramella
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Radiology Department, Hôpital Marie Lannelongue, Institut d'Oncologie Thoracique, Université Paris-Saclay, Le Plessis-Robinson, France
| | - Egesta Lopci
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, Humanitas Clinical and Research Hospital - IRCCS, Rozzano, MI, Italy
| | - Nathalie Lassau
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Imaging Department, Gustave Roussy Cancer Campus Grand, Paris, UMR 1281, INSERM, CNRS, CEA, Universite Paris-Saclay, Saint-Aubin, France
| | - Anders Persson
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, and Department of Health, Medicine and Caring Sciences, Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Rik Achten
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology and Medical Imaging, Ghent University Hospital, Gent, Belgium
| | - Karen Rosendahl
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Hospital of North Norway, Tromsø, Norway
| | - Olivier Clement
- PARCC, INSERM, Radiology Department, AP-HP, Hopital europeen Georges Pompidou, Université de Paris, F-75015, Paris, France
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
| | - Elmar Kotter
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Xavier Golay
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Queen Square Institute of Neurology, University College London, London, UK
| | - Marion Smits
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Marc Dewey
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel C Sullivan
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA
- Dept. of Radiology, Duke University, 311 Research Dr, Durham, NC, 27710, USA
| | - Aad van der Lugt
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Nandita M deSouza
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria.
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium.
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, USA.
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK.
| | | |
Collapse
|
18
|
Wang H, Xue J, Qu T, Bernstein K, Chen T, Barbee D, Silverman JS, Kondziolka D. Predicting local failure of brain metastases after stereotactic radiosurgery with radiomics on planning MR images and dose maps. Med Phys 2021; 48:5522-5530. [PMID: 34287940 DOI: 10.1002/mp.15110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 05/10/2021] [Accepted: 07/12/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Stereotactic radiosurgery (SRS) has become an important modality in the treatment of brain metastases. The purpose of this study is to investigate the potential of radiomic features from planning magnetic resonance (MR) images and dose maps to predict local failure after SRS for brain metastases. MATERIALS/METHODS Twenty-eight patients who received Gamma Knife (GK) radiosurgery for brain metastases were retrospectively reviewed in this IRB-approved study. 179 irradiated tumors included 42 that locally failed within one-year follow-up. Using SRS tumor volumes, radiomic features were calculated on T1-weighted contrast-enhanced MR images acquired for treatment planning and planned dose maps. 125 radiomic features regarding tumor shape, dose distribution, MR intensities and textures were extracted for each tumor. Logistic regression with automatic feature selection was built to predict tumor progression from local control after SRS. Feature selection and model evaluation using receiver operating characteristic (ROC) curves were performed in a nested cross validation (CV) scheme. The associations between selected radiomic features and treatment outcomes were statistically assessed by univariate analysis. RESULTS The logistic model with feature selection achieved ROC AUC of 0.82 ± 0.09 on 5-fold CV, providing 83% sensitivity and 70% specificity for predicting local failure. A total of 10 radiomic features including 1 shape feature, 6 MR images and 3 dose distribution features were selected. These features were significantly associated with treatment outcomes (p < 0.05). The model was validated on independent holdout data with an AUC of 0.78. CONCLUSIONS Radiomic features from planning MR images and dose maps provided prognostic information in SRS for brain metastases. A model built on the radiomic features shows promise for early prediction of tumor local failure after treatment, potentially aiding in personalized care for brain metastases.
Collapse
Affiliation(s)
- Hesheng Wang
- Department of Radiation Oncology, NYU Langone Medical Center, New York University, New York, New York, USA
| | - Jinyu Xue
- Department of Radiation Oncology, NYU Langone Medical Center, New York University, New York, New York, USA
| | - Tanxia Qu
- Department of Radiation Oncology, NYU Langone Medical Center, New York University, New York, New York, USA
| | - Kenneth Bernstein
- Department of Radiation Oncology, NYU Langone Medical Center, New York University, New York, New York, USA
| | - Ting Chen
- Department of Radiation Oncology, NYU Langone Medical Center, New York University, New York, New York, USA
| | - David Barbee
- Department of Radiation Oncology, NYU Langone Medical Center, New York University, New York, New York, USA
| | - Joshua S Silverman
- Department of Radiation Oncology, NYU Langone Medical Center, New York University, New York, New York, USA
| | - Douglas Kondziolka
- Department of Radiation Oncology, NYU Langone Medical Center, New York University, New York, New York, USA.,Department of Neurosurgery, NYU Langone Medical Center, New York University, New York, New York, USA
| |
Collapse
|
19
|
Coates JTT, Pirovano G, El Naqa I. Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges. J Med Imaging (Bellingham) 2021; 8:031902. [PMID: 33768134 PMCID: PMC7985651 DOI: 10.1117/1.jmi.8.3.031902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/12/2021] [Indexed: 12/14/2022] Open
Abstract
The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed.
Collapse
Affiliation(s)
- James T. T. Coates
- Massachusetts General Hospital & Harvard Medical School, Center for Cancer Research, Boston, Massachusetts, United States
| | - Giacomo Pirovano
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, New York, United States
| | - Issam El Naqa
- Moffitt Cancer Center and Research Institute, Department of Machine Learning, Tampa, Florida, United States
| |
Collapse
|
20
|
Integration of Risk Survival Measures Estimated From Pre- and Posttreatment Computed Tomography Scans Improves Stratification of Patients With Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys 2021; 109:1647-1656. [PMID: 33333202 PMCID: PMC7965338 DOI: 10.1016/j.ijrobp.2020.12.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/20/2020] [Accepted: 12/11/2020] [Indexed: 01/07/2023]
Abstract
PURPOSE To predict overall survival of patients receiving stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (ES-NSCLC), we developed a radiomic model that integrates risk of death estimates and changes based on pre- and posttreatment computed tomography (CT) scans. We hypothesize this innovation will improve our ability to stratify patients into various oncologic outcomes with greater accuracy. METHODS AND MATERIALS Two cohorts of patients with ES-NSCLC uniformly treated with SBRT (a median dose of 50 Gy in 4-5 fractions) were studied. Prediction models were built on a discovery cohort of 100 patients with treatment planning CT scans, and then were applied to a separate validation cohort of 60 patients with pre- and posttreatment CT scans for evaluating their performance. RESULTS Prediction models achieved a c-index up to 0.734 in predicting survival outcomes of the validation cohort. The integration of the pretreatment risk of survival measures (risk-high vs risk-low) and changes (risk-increase vs risk-decrease) in risk of survival measures between the pretreatment and posttreatment scans further stratified the patients into 4 subgroups (risk: high, increase; risk: high, decrease; risk: low, increase; risk: low, decrease) with significant difference (χ2 = 18.549, P = .0003, log-rank test). There was also a significant difference between the risk-increase and risk-decrease groups (χ2 = 6.80, P = .0091, log-rank test). In addition, a significant difference (χ2 = 7.493, P = .0062, log-rank test) was observed between the risk-high and risk-low groups obtained based on the pretreatment risk of survival measures. CONCLUSION The integration of risk of survival measures estimated from pre- and posttreatment CT scans can help differentiate patients with good expected survival from those who will do more poorly following SBRT. The analysis of these radiomics-based longitudinal risk measures may help identify patients with early-stage NSCLC who will benefit from adjuvant treatment after lung SBRT, such as immunotherapy.
Collapse
|
21
|
Zhang Z, Huang M, Jiang Z, Chang Y, Torok J, Yin FF, Ren L. 4D radiomics: impact of 4D-CBCT image quality on radiomic analysis. Phys Med Biol 2021; 66:045023. [PMID: 33361574 DOI: 10.1088/1361-6560/abd668] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE To investigate the impact of 4D-CBCT image quality on radiomic analysis and the efficacy of using deep learning based image enhancement to improve the accuracy of radiomic features of 4D-CBCT. MATERIAL AND METHODS In this study, 4D-CT data from 16 lung cancer patients were obtained. Digitally reconstructed radiographs (DRRs) were simulated from the 4D-CT, and then used to reconstruct 4D CBCT using the conventional FDK (Feldkamp et al 1984 J. Opt. Soc. Am. A 1 612-9) algorithm. Different projection numbers (i.e. 72, 120, 144, 180) and projection angle distributions (i.e. evenly distributed and unevenly distributed using angles from real 4D-CBCT scans) were simulated to generate the corresponding 4D-CBCT. A deep learning model (TecoGAN) was trained on 10 patients and validated on 3 patients to enhance the 4D-CBCT image quality to match with the corresponding ground-truth 4D-CT. The remaining 3 patients with different tumor sizes were used for testing. The radiomic features in 6 different categories, including histogram, GLCM, GLRLM, GLSZM, NGTDM, and wavelet, were extracted from the gross tumor volumes of each phase of original 4D-CBCT, enhanced 4D-CBCT, and 4D-CT. The radiomic features in 4D-CT were used as the ground-truth to evaluate the errors of the radiomic features in the original 4D-CBCT and enhanced 4D-CBCT. Errors in the original 4D-CBCT demonstrated the impact of image quality on radiomic features. Comparison between errors in the original 4D-CBCT and enhanced 4D-CBCT demonstrated the efficacy of using deep learning to improve the radiomic feature accuracy. RESULTS 4D-CBCT image quality can substantially affect the accuracy of the radiomic features, and the degree of impact is feature-dependent. The deep learning model was able to enhance the anatomical details and edge information in the 4D-CBCT as well as removing other image artifacts. This enhancement of image quality resulted in reduced errors for most radiomic features. The average reduction of radiomics errors for 3 patients are 20.0%, 31.4%, 36.7%, 50.0%, 33.6% and 11.3% for histogram, GLCM, GLRLM, GLSZM, NGTDM and Wavelet features. And the error reduction was more significant for patients with larger tumors. The findings were consistent across different respiratory phases, projection numbers, and angle distributions. CONCLUSIONS The study demonstrated that 4D-CBCT image quality has a significant impact on the radiomic analysis. The deep learning-based augmentation technique proved to be an effective approach to enhance 4D-CBCT image quality to improve the accuracy of radiomic analysis.
Collapse
Affiliation(s)
- Zeyu Zhang
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America.,Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America
| | - Mi Huang
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America
| | - Zhuoran Jiang
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America.,School of Electronic Science and Engineering, Nanjing University, 163 Xianlin Road, Nanjing, Jiangsu, 210046, People's Republic of China
| | - Yushi Chang
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America.,Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America
| | - Jordan Torok
- Department of Radiation Oncology, University of Pittsburgh Medical Center, 5150 Centre Ave, Pittsburgh, PA 15232, United States of America
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America.,Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America.,Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, 215316, People's Republic of China
| | - Lei Ren
- Department of Radiation Oncology, Duke University Medical Center, DUMC Box 3295, Durham, NC, 27710, United States of America.,Medical Physics Graduate Program, Duke University, 2424 Erwin Road Suite 101, Durham, NC 27705, United States of America
| |
Collapse
|
22
|
The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients. J Imaging 2021; 7:jimaging7020017. [PMID: 34460616 PMCID: PMC8321255 DOI: 10.3390/jimaging7020017] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/17/2021] [Accepted: 01/25/2021] [Indexed: 11/19/2022] Open
Abstract
Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clinical features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre-RT MRIs.
Collapse
|
23
|
Shui L, Ren H, Yang X, Li J, Chen Z, Yi C, Zhu H, Shui P. The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology. Front Oncol 2021; 10:570465. [PMID: 33575207 PMCID: PMC7870863 DOI: 10.3389/fonc.2020.570465] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 12/08/2020] [Indexed: 02/05/2023] Open
Abstract
With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.
Collapse
Affiliation(s)
- Lin Shui
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Haoyu Ren
- Department of General, Visceral and Transplantation Surgery, University Hospital, LMU Munich, Munich, Germany
| | - Xi Yang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jian Li
- Department of Pharmacy, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Ziwei Chen
- Department of Nephrology, Chengdu Integrated TCM and Western Medicine Hospital, Chengdu, China
| | - Cheng Yi
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Zhu
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Pixian Shui
- School of Pharmacy, Southwest Medical University, Luzhou, China
| |
Collapse
|
24
|
Yoon J, Lee E, Kang SW, Han K, Park VY, Kwak JY. Implications of US radiomics signature for predicting malignancy in thyroid nodules with indeterminate cytology. Eur Radiol 2021; 31:5059-5067. [PMID: 33459858 DOI: 10.1007/s00330-020-07670-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 11/16/2020] [Accepted: 12/23/2020] [Indexed: 12/13/2022]
Abstract
OBJECTIVES The purpose of this study was to evaluate the role of the radiomics score using US images to predict malignancy in AUS/FLUS and FN/SFN nodules. METHODS One hundred fifty-five indeterminate thyroid nodules in 154 patients who received initial US-guided FNA for diagnostic purposes were included in this retrospective study. A representative US image of each tumor was acquired, and square ROIs covering the whole nodule were drawn using the Paint program of Windows 7. Texture features were extracted by in-house texture analysis algorithms implemented in MATLAB 2019b. The LASSO logistic regression model was used to choose the most useful predictive features, and ten-fold cross-validation was performed. Two prediction models were constructed using multivariable logistic regression analysis: one based on clinical variables, and the other based on clinical variables with the radiomics score. Predictability of the two models was assessed with the AUC of the ROC curves. RESULTS Clinical characteristics did not significantly differ between malignant and benign nodules, except for mean nodule size. Among 730 candidate texture features generated from a single US image, 15 features were selected. Radiomics signatures were constructed with a radiomics score, using selected features. In multivariable logistic regression analysis, higher radiomics score was associated with malignancy (OR = 10.923; p < 0.001). The AUC of the malignancy prediction model composed of clinical variables with the radiomics score was significantly higher than the model composed of clinical variables alone (0.839 vs 0.583). CONCLUSIONS Quantitative US radiomics features can help predict malignancy in thyroid nodules with indeterminate cytology.
Collapse
Affiliation(s)
- Jiyoung Yoon
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Eunjung Lee
- Department of Computational Science and Engineering, Yonsei University, Seoul, South Korea
| | - Sang-Wook Kang
- Department of Surgery, Yonsei University, College of Medicine, Seoul, South Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Vivian Youngjean Park
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea
| | - Jin Young Kwak
- Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
| |
Collapse
|
25
|
Masson I, Dutreix M, Supiot S. [Innovation in radiotherapy in 2021]. Bull Cancer 2020; 108:42-49. [PMID: 33303195 DOI: 10.1016/j.bulcan.2020.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 10/26/2020] [Indexed: 11/25/2022]
Affiliation(s)
- Ingrid Masson
- Département de radiothérapie, Institut de cancérologie de l'Ouest René-Gauducheau, boulevard Jacques-Monod, 44805 Saint-Herblain, France
| | - Marie Dutreix
- Institut Curie, Université PSL, CNRS, Inserm, UMR 3347; Université Paris Sud, Université Paris-Saclay, 91405 Orsay, France
| | - Stéphane Supiot
- Département de radiothérapie, Institut de cancérologie de l'Ouest René-Gauducheau, boulevard Jacques-Monod, 44805 Saint-Herblain, France; Centre de Recherche en Cancéro-Immunologie Nantes/Angers (CRCINA, UMR 892 Inserm), Institut de Recherche en Santé de l'Université de Nantes, Nantes cedex 1, France.
| |
Collapse
|
26
|
Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence. Invest Radiol 2020; 55:601-616. [PMID: 32209816 PMCID: PMC7413678 DOI: 10.1097/rli.0000000000000666] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.
Collapse
Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| | | | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| |
Collapse
|
27
|
Magnetic resonance imaging-derived radiomic signature predicts locoregional failure after organ preservation therapy in patients with hypopharyngeal squamous cell carcinoma. Clin Transl Radiat Oncol 2020; 25:1-9. [PMID: 33426314 PMCID: PMC7780126 DOI: 10.1016/j.ctro.2020.08.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/16/2020] [Accepted: 08/24/2020] [Indexed: 01/08/2023] Open
Abstract
The first study to develop and validate an MRI-derived radiomic signature (RS) for the prediction of 1-year locoregional failure in HPSCC patients receiving OPT. The RS-based model (RS of 0.0326 as the cut-off value) provides a novel and convenient approach for the prediction of the 1-year clinical outcomes. The proposed RS-based model can help physicians characterize and facilitate decision-making for the clinical management of patients with locally advanced HPSCC.
Background and purpose To develop and validate a magnetic resonance imaging (MRI)-derived radiomic signature (RS) for the prediction of 1-year locoregional failure (LRF) in patients with hypopharyngeal squamous cell carcinoma (HPSCC) who received organ preservation therapy (OPT) Material and methods A total of 800 MRI-based features of pretreatment tumors were obtained from 116 patients with HPSCC who received OPT from two independent cohorts. The least absolute shrinkage and selection operator regression model were used to select the features used to develop the RS. Harrell’s C-index and corrected C-index were used to evaluate the discriminative ability of RS. The Youden index was used to select the optimal cut-point for risk category. Results The RS yielded 1000 times bootstrapping corrected C-index of 0.8036 and 0.78235 in the experimental (n = 82) and validation cohorts (n = 34), respectively. With respect to the subgroup of patients with stage III/IV and cT4 disease, the RS also showed good predictive performance with corrected C-indices of 0.760 and 0.754, respectively. The dichotomized risk category using an RS of 0.0326 as the cut-off value yielded a 1-year LRF predictive accuracy of 79.27%, 79.41%, 76.74%, and 71.15% in the experimental, validation, stage III/IV, and cT4a cohorts, respectively. The low-risk group was associated with a significantly better progression-free laryngectomy-free and overall survival outcome in two independent institutions, stage III/IV, and cT4a cohorts. Conclusion The RS-based model provides a novel and convenient approach for the prediction of the 1-year LRF and survival outcome in patients with HPSCC who received OPT.
Collapse
|
28
|
Li H, El Naqa I, Rong Y. Current status of Radiomics for cancer management: Challenges versus opportunities for clinical practice. J Appl Clin Med Phys 2020; 21:7-10. [PMID: 32697032 PMCID: PMC7386181 DOI: 10.1002/acm2.12982] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 01/31/2023] Open
Affiliation(s)
- Hua Li
- Department of BioengineeringUniversity of Illinois at Urbana‐ChampaignUrbanaILUSA,Department of Radiation OncologyCarle Cancer Center, Carle Foundation HospitalUrbanaILUSA
| | - Issam El Naqa
- Department of Radiation OncologyUniversity of MichiganAnn ArborILUSA
| | - Yi Rong
- Department of Radiation OncologyUniversity of California Davis Cancer CenterSacramentoCAUSA
| |
Collapse
|
29
|
O'Sullivan S, McDermott R, Keys M, O'Sullivan M, Armstrong J, Faul C. Imaging response assessment following stereotactic body radiotherapy for solid tumour metastases of the spine: Current challenges and future directions. J Med Imaging Radiat Oncol 2020; 64:385-397. [PMID: 32293114 DOI: 10.1111/1754-9485.13032] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 03/09/2020] [Indexed: 01/01/2023]
Abstract
Patients with metastatic disease are routinely serially imaged to assess disease burden and response to systemic and local therapies, which places ever-expanding demands on our healthcare resources. Image interpretation following stereotactic body radiotherapy (SBRT) for spine metastases can be challenging; however, appropriate and accurate assessment is critical to ensure patients are managed correctly and resources are optimised. Here, we take a critical review of the merits and pitfalls of various imaging modalities, current response assessment guidelines, and explore novel imaging approaches and the potential for radiomics to add value in imaging assessment.
Collapse
Affiliation(s)
- Siobhra O'Sullivan
- St Luke's Institute of Cancer Research, St Luke's Radiation Oncology Network, Dublin 6, Ireland.,Department of Radiation Oncology, St Luke's Radiation Oncology Network, Dublin 6, Ireland
| | - Ronan McDermott
- St Luke's Institute of Cancer Research, St Luke's Radiation Oncology Network, Dublin 6, Ireland.,Department of Radiation Oncology, St Luke's Radiation Oncology Network, Dublin 6, Ireland
| | - Maeve Keys
- Department of Radiation Oncology, St Luke's Radiation Oncology Network, Dublin 6, Ireland
| | - Maeve O'Sullivan
- Department of Radiology, Beaumont Hospital, Royal College of Surgeons of Ireland, Dublin 9, Ireland
| | - John Armstrong
- Department of Radiation Oncology, St Luke's Radiation Oncology Network, Dublin 6, Ireland
| | - Clare Faul
- Department of Radiation Oncology, St Luke's Radiation Oncology Network, Dublin 6, Ireland
| |
Collapse
|
30
|
Capobianco E, Dominietto M. From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health. J Pers Med 2020; 10:jpm10010015. [PMID: 32121633 PMCID: PMC7151556 DOI: 10.3390/jpm10010015] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 02/17/2020] [Indexed: 12/17/2022] Open
Abstract
Treating disease according to precision health requires the individualization of therapeutic solutions as a cardinal step that is part of a process that typically depends on multiple factors. The starting point is the collection and assembly of data over time to assess the patient’s health status and monitor response to therapy. Radiomics is a very important component of this process. Its main goal is implementing a protocol to quantify the image informative contents by first mining and then extracting the most representative features. Further analysis aims to detect potential disease phenotypes through signs and marks of heterogeneity. As multimodal images hinge on various data sources, and these can be integrated with treatment plans and follow-up information, radiomics is naturally centered on dynamically monitoring disease progression and/or the health trajectory of patients. However, radiomics creates critical needs too. A concise list includes: (a) successful harmonization of intra/inter-modality radiomic measurements to facilitate the association with other data domains (genetic, clinical, lifestyle aspects, etc.); (b) ability of data science to revise model strategies and analytics tools to tackle multiple data types and structures (electronic medical records, personal histories, hospitalization data, genomic from various specimens, imaging, etc.) and to offer data-agnostic solutions for patient outcomes prediction; (c) and model validation with independent datasets to ensure generalization of results, clinical value of new risk stratifications, and support to clinical decisions for highly individualized patient management.
Collapse
Affiliation(s)
- Enrico Capobianco
- Center for Computational Science, University of Miami, FL 33146, USA
- Correspondence:
| | | |
Collapse
|
31
|
Bagher-Ebadian H, Lu M, Siddiqui F, Ghanem AI, Wen N, Wu Q, Liu C, Movsas B, Chetty IJ. Application of radiomics for the prediction of HPV status for patients with head and neck cancers. Med Phys 2020; 47:563-575. [PMID: 31853980 DOI: 10.1002/mp.13977] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 10/28/2019] [Accepted: 11/22/2019] [Indexed: 12/19/2022] Open
Abstract
PURPOSE To perform radiomic analysis of primary tumors extracted from pretreatment contrast-enhanced computed tomography (CE-CT) images for patients with oropharyngeal cancers to identify discriminant features and construct an optimal classifier for the characterization and prediction of human papilloma virus (HPV) status. MATERIALS AND METHODS One hundred and eighty seven patients with oropharyngeal cancers with known HPV status (confirmed by immunohistochemistry-p16 protein testing) were retrospectively studied as follows: Group A: 95 patients (19HPV- and 76HPV+) from the MICAII grand challenge. Group B: 92 patients (52HPV- and 40HPV+) from our institution. Radiomic features (172) were extracted from pretreatment diagnostic CE-CT images of the gross tumor volume (GTV). Levene and Kolmogorov-Smirnov's tests with absolute biserial correlation (>0.48) were used to identify the discriminant features between the HPV+ and HPV- groups. The discriminant features were used to train and test eight different classifiers. Area under receiver operating characteristic (AUC), positive predictive and negative predictive values (PPV and NPV, respectively) were used to evaluate the performance of the classifiers. Principal component analysis (PCA) was applied on the discriminant feature set and seven PCs were used to train and test a generalized linear model (GLM) classifier. RESULTS Among 172 radiomic features only 12 radiomic features (from 3 categories) were significantly different (P < 0.05, |BSC| > 0.48) between the HPV+ and HPV- groups. Among the eight classifiers trained and applied for prediction of HPV status, the GLM showed the highest performance for each discriminant feature and the combined 12 features: AUC/PPV/NPV = 0.878/0.834/0.811. The GLM high prediction power was AUC/PPV/NPV = 0.849/0.731/0.788 and AUC/PPV/NPV = 0.869/0.807/0.870 for unseen test datasets for groups A and B, respectively. After eliminating the correlation among discriminant features by applying PCA analysis, the performance of the GLM was improved by 3.3%, 2.2%, and 1.8% for AUC, PPV, and NPV, respectively. CONCLUSIONS Results imply that GTV's for HPV+ patients exhibit higher intensities, smaller lesion size, greater sphericity/roundness, and higher spatial intensity-variation/heterogeneity. Results are suggestive that radiomic features primarily associated with the spatial arrangement and morphological appearance of the tumor on contrast-enhanced diagnostic CT datasets may be potentially used for classification of HPV status.
Collapse
Affiliation(s)
| | - Mei Lu
- Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Farzan Siddiqui
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Ahmed I Ghanem
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA.,Department of Clinical Oncology, Alexandria University, Alexandria, Egypt
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Qixue Wu
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Chang Liu
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| |
Collapse
|
32
|
Waninger JJ, Green MD, Cheze Le Rest C, Rosen B, El Naqa I. Integrating radiomics into clinical trial design. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2019; 63:339-346. [PMID: 31527581 DOI: 10.23736/s1824-4785.19.03217-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In radiomics, quantitative features that describe phenotypic tumor characteristics are derived from radiographic images. Because radiomics generates information from routine medical images, it is a powerful way to non-invasively examine the spatial and temporal heterogeneity of disease, and thus has potential to significantly impact clinical trial design, execution, and ultimately patient care. The aim of this review article is to discuss how radiomics may address some of the current challenges in clinical randomized control trials, and the difficulties of integrating robust and repeatable radiomics analysis into trial design. Each step of the radiomics process, including image acquisition and reconstruction, image segmentation, feature extraction, and computational analysis, requires extensive standardization in order to be successfully incorporated into clinical trials and inform clinical decision making. By addressing these challenges, the potential of radiomics may be realized.
Collapse
Affiliation(s)
- Jessica J Waninger
- Department of Medical Education, University of Michigan School of Medicine, Ann Arbor, MI, USA.,Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Michael D Green
- Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, MI, USA.,University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | | | - Benjamin Rosen
- Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan School of Medicine, Ann Arbor, MI, USA -
| |
Collapse
|
33
|
Liang ZG, Tan HQ, Zhang F, Rui Tan LK, Lin L, Lenkowicz J, Wang H, Wen Ong EH, Kusumawidjaja G, Phua JH, Gan SA, Sin SY, Ng YY, Tan TW, Soong YL, Fong KW, Park SY, Soo KC, Wee JT, Zhu XD, Valentini V, Boldrini L, Sun Y, Chua ML. Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma. Br J Radiol 2019; 92:20190271. [PMID: 31453720 PMCID: PMC6774600 DOI: 10.1259/bjr.20190271] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Objective: Radiomics pipelines have been developed to extract novel information from radiological images, which may help in phenotypic profiling of tumours that would correlate to prognosis. Here, we compared two publicly available pipelines for radiomics analyses on head and neck CT and MRI in nasopharynx cancer (NPC). Methods and materials: 100 biopsy-proven NPC cases stratified by T- and N-categories were enrolled in this study. Two radiomics pipeline, Moddicom (v. 0.51) and Pyradiomics (v. 2.1.2) were used to extract radiomics features of CT and MRI. Segmentation of primary gross tumour volume was performed using Velocity v. 4.0 by consensus agreement between three radiation oncologists. Intraclass correlation between common features of the two pipelines was analysed by Spearman’s rank correlation. Unsupervised hierarchical clustering was used to determine association between radiomics features and clinical parameters. Results: We observed a high proportion of correlated features in the CT data set, but not for MRI; 76.1% (51 of 67 common between Moddicom and Pyradiomics) of CT features and 28.6% (20 of 70 common) of MRI features were significantly correlated. Of these, 100% were shape-related for both CT and MRI, 100 and 23.5% were first-order-related, 61.9 and 19.0% were texture-related, respectively. This interpipeline heterogeneity affected the downstream clustering with known prognostic clinical parameters of cTN-status and GTVp. Nonetheless, shape features were the most reproducible predictors of clinical parameters among the different radiomics modules. Conclusion: Here, we highlighted significant heterogeneity between two publicly available radiomics pipelines that could affect the downstream association with prognostic clinical factors in NPC Advances in knowledge: The present study emphasized the broader importance of selecting stable radiomics features for disease phenotyping, and it is necessary prior to any investigation of multicentre imaging datasets to validate the stability of CT-related radiomics features for clinical prognostication.
Collapse
Affiliation(s)
- Zhong-Guo Liang
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.,Division of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, P.R. China.,Division of Medical Sciences, National Cancer Centre Singapore, Singapore
| | - Hong Qi Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Fan Zhang
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore
| | - Lloyd Kuan Rui Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Li Lin
- Division of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, P.R. China
| | | | - Haitao Wang
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore
| | - Enya Hui Wen Ong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.,Division of Medical Sciences, National Cancer Centre Singapore, Singapore
| | | | - Jun Hao Phua
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Soon Ann Gan
- Division of Cancer Informatics, National Cancer Centre Singapore, Singapore
| | - Sze Yarn Sin
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Yan Yee Ng
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Terence Wee Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.,Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore
| | - Yoke Lim Soong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.,Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore
| | - Kam Weng Fong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.,Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore
| | - Sung Yong Park
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Khee-Chee Soo
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore.,Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore
| | - Joseph Tien Wee
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.,Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore
| | - Xiao-Dong Zhu
- Division of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, P.R. China
| | | | | | - Ying Sun
- Division of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, Guangdong, P.R. China
| | - Melvin Lee Chua
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore.,Division of Medical Sciences, National Cancer Centre Singapore, Singapore.,Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore
| |
Collapse
|