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Salari E, Chen X, Wynne JF, Qiu RLJ, Roper J, Shu HK, Yang X. Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images. Med Phys 2024. [PMID: 39221589 DOI: 10.1002/mp.17382] [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: 03/22/2024] [Revised: 08/12/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Adult-type diffuse gliomas are among the central nervous system's most aggressive malignant primary neoplasms. Despite advancements in systemic therapies and technological improvements in radiation oncology treatment delivery, the survival outcome for these patients remains poor. Fast and accurate assessment of tumor response to oncologic treatments is crucial, as it can enable the early detection of recurrent or refractory gliomas, thereby allowing timely intervention with life-prolonging salvage therapies. PURPOSE Radiomics is a developing field with great potential to improve medical image interpretation. This study aims to apply a radiomics-based predictive model for classifying response to radiotherapy within the first 3 months post-treatment. METHODS Ninety-five patients were selected from the Burdenko Glioblastoma Progression Dataset. Tumor regions were delineated in the axial plane on contrast-enhanced T1(CE T1W) and T2 fluid-attenuated inversion recovery (T2_FLAIR) magnetic resonance imaging (MRI). Hand-crafted radiomic (HCR) features, including first- and second-order features, were extracted using PyRadiomics (3.7.6) in Python (3.10). Then, recursive feature elimination with a random forest (RF) classifier was applied for feature dimensionality reduction. RF and support vector machine (SVM) classifiers were built to predict treatment outcomes using the selected features. Leave-one-out cross-validation was employed to tune hyperparameters and evaluate the models. RESULTS For each segmented target, 186 HCR features were extracted from the MRI sequence. Using the top-ranked radiomic features from a combination of CE T1W and T2_FLAIR, an optimized classifier achieved the highest averaged area under the curve (AUC) of 0.829 ± 0.075 using the RF classifier. The HCR features of CE T1W produced the worst outcomes among all models (0.603 ± 0.024 and 0.615 ± 0.075 for RF and SVM classifiers, respectively). CONCLUSIONS We developed and evaluated a radiomics-based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi-modal MRI, showed superior predictive performance compared to single-modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process.
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Affiliation(s)
- Elahheh Salari
- Department of Radiation Oncology and Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Xuxin Chen
- Department of Radiation Oncology and Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Jacob Frank Wynne
- Department of Radiation Oncology and Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Richard L J Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Hui-Kuo Shu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University School of Medicine, Atlanta, Georgia, USA
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Bodalal Z, Hong EK, Trebeschi S, Kurilova I, Landolfi F, Bogveradze N, Castagnoli F, Randon G, Snaebjornsson P, Pietrantonio F, Lee JM, Beets G, Beets-Tan R. Non-invasive CT radiomic biomarkers predict microsatellite stability status in colorectal cancer: a multicenter validation study. Eur Radiol Exp 2024; 8:98. [PMID: 39186200 PMCID: PMC11347521 DOI: 10.1186/s41747-024-00484-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 05/30/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Microsatellite instability (MSI) status is a strong predictor of response to immunotherapy of colorectal cancer. Radiogenomic approaches promise the ability to gain insight into the underlying tumor biology using non-invasive routine clinical images. This study investigates the association between tumor morphology and the status of MSI versus microsatellite stability (MSS), validating a novel radiomic signature on an external multicenter cohort. METHODS Preoperative computed tomography scans with matched MSI status were retrospectively collected for 243 colorectal cancer patients from three hospitals: Seoul National University Hospital (SNUH); Netherlands Cancer Institute (NKI); and Fondazione IRCCS Istituto Nazionale dei Tumori, Milan Italy (INT). Radiologists delineated primary tumors in each scan, from which radiomic features were extracted. Machine learning models trained on SNUH data to identify MSI tumors underwent external validation using NKI and INT images. Performances were compared in terms of area under the receiving operating curve (AUROC). RESULTS We identified a radiomic signature comprising seven radiomic features that were predictive of tumors with MSS or MSI (AUROC 0.69, 95% confidence interval [CI] 0.54-0.84, p = 0.018). Integrating radiomic and clinical data into an algorithm improved predictive performance to an AUROC of 0.78 (95% CI 0.60-0.91, p = 0.002) and enhanced the reliability of the predictions. CONCLUSION Differences in the radiomic morphological phenotype between tumors MSS or MSI could be detected using radiogenomic approaches. Future research involving large-scale multicenter prospective studies that combine various diagnostic data is necessary to refine and validate more robust, potentially tumor-agnostic MSI radiogenomic models. RELEVANCE STATEMENT Noninvasive radiomic signatures derived from computed tomography scans can predict MSI in colorectal cancer, potentially augmenting traditional biopsy-based methods and enhancing personalized treatment strategies. KEY POINTS Noninvasive CT-based radiomics predicted MSI in colorectal cancer, enhancing stratification. A seven-feature radiomic signature differentiated tumors with MSI from those with MSS in multicenter cohorts. Integrating radiomic and clinical data improved the algorithm's predictive performance.
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Affiliation(s)
- Zuhir Bodalal
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Eun Kyoung Hong
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Seoul National University Hospital, Seoul, South Korea
| | - Stefano Trebeschi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Ieva Kurilova
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Federica Landolfi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Radiology Unit, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Radiology, American Hospital Tbilisi, Tbilisi, Georgia
| | - Francesca Castagnoli
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Radiology, Royal Marsden Hospital, London, UK
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, UK
| | - Giovanni Randon
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Petur Snaebjornsson
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Filippo Pietrantonio
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
- Oncology and Hemato-oncology Department, University of Milan, Milan, Italy
| | - Jeong Min Lee
- Seoul National University Hospital, Seoul, South Korea
| | - Geerard Beets
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark.
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Teng X, Wang Y, Nicol AJ, Ching JCF, Wong EKY, Lam KTC, Zhang J, Lee SWY, Cai J. Enhancing the Clinical Utility of Radiomics: Addressing the Challenges of Repeatability and Reproducibility in CT and MRI. Diagnostics (Basel) 2024; 14:1835. [PMID: 39202322 PMCID: PMC11353986 DOI: 10.3390/diagnostics14161835] [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: 06/26/2024] [Revised: 08/03/2024] [Accepted: 08/16/2024] [Indexed: 09/03/2024] Open
Abstract
Radiomics, which integrates the comprehensive characterization of imaging phenotypes with machine learning algorithms, is increasingly recognized for its potential in the diagnosis and prognosis of oncological conditions. However, the repeatability and reproducibility of radiomic features are critical challenges that hinder their widespread clinical adoption. This review aims to address the paucity of discussion regarding the factors that influence the reproducibility and repeatability of radiomic features and their subsequent impact on the application of radiomic models. We provide a synthesis of the literature on the repeatability and reproducibility of CT/MR-based radiomic features, examining sources of variation, the number of reproducible features, and the availability of individual feature repeatability indices. We differentiate sources of variation into random effects, which are challenging to control but can be quantified through simulation methods such as perturbation, and biases, which arise from scanner variability and inter-reader differences and can significantly affect the generalizability of radiomic model performance in diverse settings. Four suggestions for repeatability and reproducibility studies are suggested: (1) detailed reporting of variation sources, (2) transparent disclosure of calculation parameters, (3) careful selection of suitable reliability indices, and (4) comprehensive reporting of reliability metrics. This review underscores the importance of random effects in feature selection and harmonizing biases between development and clinical application settings to facilitate the successful translation of radiomic models from research to clinical practice.
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Affiliation(s)
- Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Yongqiang Wang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Alexander James Nicol
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Jerry Chi Fung Ching
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Edwin Ka Yiu Wong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Kenneth Tsz Chun Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Shara Wee-Yee Lee
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, 11 Yuk Choi Rd, Hung Hom, Hong Kong SAR, China; (X.T.); (Y.W.); (A.J.N.); (J.C.F.C.); (E.K.Y.W.); (K.T.C.L.); (J.Z.)
- Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen 518057, China
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Trojani V, Bassi MC, Verzellesi L, Bertolini M. Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review. Cancers (Basel) 2024; 16:2668. [PMID: 39123396 PMCID: PMC11311340 DOI: 10.3390/cancers16152668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 07/16/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. MATERIALS AND METHODS We performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters' influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups. RESULTS After applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models' best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works. CONCLUSIONS From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.
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Affiliation(s)
- Valeria Trojani
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
| | | | - Laura Verzellesi
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
| | - Marco Bertolini
- Medical Physics, Azienda USL-IRCCS, 42123 Reggio Emilia, Italy; (L.V.); (M.B.)
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Ramlee S, Manavaki R, Aloj L, Escudero Sanchez L. Mitigating the impact of image processing variations on tumour [ 18F]-FDG-PET radiomic feature robustness. Sci Rep 2024; 14:16294. [PMID: 39009706 PMCID: PMC11251269 DOI: 10.1038/s41598-024-67239-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 07/09/2024] [Indexed: 07/17/2024] Open
Abstract
Radiomics analysis of [18F]-fluorodeoxyglucose ([18F]-FDG) PET images could be leveraged for personalised cancer medicine. However, the inherent sensitivity of radiomic features to intensity discretisation and voxel interpolation complicates its clinical translation. In this work, we evaluated the robustness of tumour [18F]-FDG-PET radiomic features to 174 different variations in intensity resolution or voxel size, and determined whether implementing parameter range conditions or dependency corrections could improve their robustness. Using 485 patient images spanning three cancer types: non-small cell lung cancer (NSCLC), melanoma, and lymphoma, we observed features were more sensitive to intensity discretisation than voxel interpolation, especially texture features. In most of our investigations, the majority of non-robust features could be made robust by applying parameter range conditions. Correctable features, which were generally fewer than conditionally robust, showed systematic dependence on bin configuration or voxel size that could be minimised by applying corrections based on simple mathematical equations. Melanoma images exhibited limited robustness and correctability relative to NSCLC and lymphoma. Our study provides an in-depth characterisation of the sensitivity of [18F]-FDG-PET features to image processing variations and reinforces the need for careful selection of imaging biomarkers prior to any clinical application.
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Affiliation(s)
- Syafiq Ramlee
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.
| | - Roido Manavaki
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Luigi Aloj
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Lorena Escudero Sanchez
- Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
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Pacchiano F, Tortora M, Doneda C, Izzo G, Arrigoni F, Ugga L, Cuocolo R, Parazzini C, Righini A, Brunetti A. Radiomics and artificial intelligence applications in pediatric brain tumors. World J Pediatr 2024:10.1007/s12519-024-00823-0. [PMID: 38935233 DOI: 10.1007/s12519-024-00823-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024]
Abstract
BACKGROUND The study of central nervous system (CNS) tumors is particularly relevant in the pediatric population because of their relatively high frequency in this demographic and the significant impact on disease- and treatment-related morbidity and mortality. While both morphological and non-morphological magnetic resonance imaging techniques can give important information concerning tumor characterization, grading, and patient prognosis, increasing evidence in recent years has highlighted the need for personalized treatment and the development of quantitative imaging parameters that can predict the nature of the lesion and its possible evolution. For this purpose, radiomics and the use of artificial intelligence software, aimed at obtaining valuable data from images beyond mere visual observation, are gaining increasing importance. This brief review illustrates the current state of the art of this new imaging approach and its contributions to understanding CNS tumors in children. DATA SOURCES We searched the PubMed, Scopus, and Web of Science databases using the following key search terms: ("radiomics" AND/OR "artificial intelligence") AND ("pediatric AND brain tumors"). Basic and clinical research literature related to the above key research terms, i.e., studies assessing the key factors, challenges, or problems of using radiomics and artificial intelligence in pediatric brain tumors management, was collected. RESULTS A total of 63 articles were included. The included ones were published between 2008 and 2024. Central nervous tumors are crucial in pediatrics due to their high frequency and impact on disease and treatment. MRI serves as the cornerstone of neuroimaging, providing cellular, vascular, and functional information in addition to morphological features for brain malignancies. Radiomics can provide a quantitative approach to medical imaging analysis, aimed at increasing the information obtainable from the pixels/voxel grey-level values and their interrelationships. The "radiomic workflow" involves a series of iterative steps for reproducible and consistent extraction of imaging data. These steps include image acquisition for tumor segmentation, feature extraction, and feature selection. Finally, the selected features, via training predictive model (CNN), are used to test the final model. CONCLUSIONS In the field of personalized medicine, the application of radiomics and artificial intelligence (AI) algorithms brings up new and significant possibilities. Neuroimaging yields enormous amounts of data that are significantly more than what can be gained from visual studies that radiologists can undertake on their own. Thus, new partnerships with other specialized experts, such as big data analysts and AI specialists, are desperately needed. We believe that radiomics and AI algorithms have the potential to move beyond their restricted use in research to clinical applications in the diagnosis, treatment, and follow-up of pediatric patients with brain tumors, despite the limitations set out.
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Affiliation(s)
- Francesco Pacchiano
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Caserta, Italy
| | - Mario Tortora
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy.
- Department of Head and Neck, Neuroradiology Unit, AORN Moscati, Avellino, Italy.
| | - Chiara Doneda
- Department of Pediatric Radiology and Neuroradiology, V. Buzzi Children's Hospital, Milan, Italy
| | - Giana Izzo
- Department of Pediatric Radiology and Neuroradiology, V. Buzzi Children's Hospital, Milan, Italy
| | - Filippo Arrigoni
- Department of Pediatric Radiology and Neuroradiology, V. Buzzi Children's Hospital, Milan, Italy
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Cecilia Parazzini
- Department of Pediatric Radiology and Neuroradiology, V. Buzzi Children's Hospital, Milan, Italy
| | - Andrea Righini
- Department of Pediatric Radiology and Neuroradiology, V. Buzzi Children's Hospital, Milan, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy
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Bijari S, Sayfollahi S, Mardokh-Rouhani S, Bijari S, Moradian S, Zahiri Z, Rezaeijo SM. Radiomics and Deep Features: Robust Classification of Brain Hemorrhages and Reproducibility Analysis Using a 3D Autoencoder Neural Network. Bioengineering (Basel) 2024; 11:643. [PMID: 39061725 PMCID: PMC11273742 DOI: 10.3390/bioengineering11070643] [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: 05/08/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024] Open
Abstract
This study evaluates the reproducibility of machine learning models that integrate radiomics and deep features (features extracted from a 3D autoencoder neural network) to classify various brain hemorrhages effectively. Using a dataset of 720 patients, we extracted 215 radiomics features (RFs) and 15,680 deep features (DFs) from CT brain images. With rigorous screening based on Intraclass Correlation Coefficient thresholds (>0.75), we identified 135 RFs and 1054 DFs for analysis. Feature selection techniques such as Boruta, Recursive Feature Elimination (RFE), XGBoost, and ExtraTreesClassifier were utilized alongside 11 classifiers, including AdaBoost, CatBoost, Decision Trees, LightGBM, Logistic Regression, Naive Bayes, Neural Networks, Random Forest, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). Evaluation metrics included Area Under the Curve (AUC), Accuracy (ACC), Sensitivity (SEN), and F1-score. The model evaluation involved hyperparameter optimization, a 70:30 train-test split, and bootstrapping, further validated with the Wilcoxon signed-rank test and q-values. Notably, DFs showed higher accuracy. In the case of RFs, the Boruta + SVM combination emerged as the optimal model for AUC, ACC, and SEN, while XGBoost + Random Forest excelled in F1-score. Specifically, RFs achieved AUC, ACC, SEN, and F1-scores of 0.89, 0.85, 0.82, and 0.80, respectively. Among DFs, the ExtraTreesClassifier + Naive Bayes combination demonstrated remarkable performance, attaining an AUC of 0.96, ACC of 0.93, SEN of 0.92, and an F1-score of 0.92. Distinguished models in the RF category included SVM with Boruta, Logistic Regression with XGBoost, SVM with ExtraTreesClassifier, CatBoost with XGBoost, and Random Forest with XGBoost, each yielding significant q-values of 42. In the DFs realm, ExtraTreesClassifier + Naive Bayes, ExtraTreesClassifier + Random Forest, and Boruta + k-NN exhibited robustness, with 43, 43, and 41 significant q-values, respectively. This investigation underscores the potential of synergizing DFs with machine learning models to serve as valuable screening tools, thereby enhancing the interpretation of head CT scans for patients with brain hemorrhages.
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Affiliation(s)
- Salar Bijari
- Department of Radiology, Faculty of Paramedical, Kurdistan University of Medical Sciences, Sanandaj P.O. Box 66177-13446, Iran;
| | - Sahar Sayfollahi
- Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences, Tehran P.O. Box 14496-14535, Iran;
| | - Shiwa Mardokh-Rouhani
- Mechanical Engineering Group, Faculty of Engineering, University of Kurdistan, Sanandaj P.O. Box 66177-15175, Iran;
| | - Sahar Bijari
- Department of Aging and Health, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd P.O. Box 89151-73160, Iran;
| | - Sadegh Moradian
- Department of Radiology, Tehran University of Medical Sciences, Tehran P.O. Box 14197-33151, Iran;
| | - Ziba Zahiri
- Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz P.O. Box 61357-15794, Iran;
| | - Seyed Masoud Rezaeijo
- Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz P.O. Box 61357-15794, Iran
- Cancer Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz P.O. Box 61357-15794, Iran
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Zhang X, Iqbal Bin Saripan M, Wu Y, Wang Z, Wen D, Cao Z, Wang B, Xu S, Liu Y, Marhaban MH, Dong X. The impact of the combat method on radiomics feature compensation and analysis of scanners from different manufacturers. BMC Med Imaging 2024; 24:137. [PMID: 38844854 PMCID: PMC11157873 DOI: 10.1186/s12880-024-01306-4] [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: 01/02/2023] [Accepted: 05/23/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND This study investigated whether the Combat compensation method can remove the variability of radiomic features extracted from different scanners, while also examining its impact on the subsequent predictive performance of machine learning models. MATERIALS AND METHODS 135 CT images of Credence Cartridge Radiomic phantoms were collected and screened from three scanners manufactured by Siemens, Philips, and GE. 100 radiomic features were extracted and 20 radiomic features were screened according to the Lasso regression method. The radiomic features extracted from the rubber and resin-filled regions in the cartridges were labeled into different categories for evaluating the performance of the machine learning model. Radiomics features were divided into three groups based on the different scanner manufacturers. The radiomic features were randomly divided into training and test sets with a ratio of 8:2. Five machine learning models (lasso, logistic regression, random forest, support vector machine, neural network) were employed to evaluate the impact of Combat on radiomic features. The variability among radiomic features were assessed using analysis of variance (ANOVA) and principal component analysis (PCA). Accuracy, precision, recall, and area under the receiver curve (AUC) were used as evaluation metrics for model classification. RESULTS The principal component and ANOVA analysis results show that the variability of different scanner manufacturers in radiomic features was removed (P˃0.05). After harmonization with the Combat algorithm, the distributions of radiomic features were aligned in terms of location and scale. The performance of machine learning models for classification improved, with the Random Forest model showing the most significant enhancement. The AUC value increased from 0.88 to 0.92. CONCLUSIONS The Combat algorithm has reduced variability in radiomic features from different scanners. In the phantom CT dataset, it appears that the machine learning model's classification performance may have improved after Combat harmonization. However, further investigation and validation are required to fully comprehend Combat's impact on radiomic features in medical imaging.
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Affiliation(s)
- Xiaolei Zhang
- Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia.
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
- Department of Biomedical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
| | | | - Yanjun Wu
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | - Zhongxiao Wang
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | - Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
| | - Zhendong Cao
- Department of Radiology, the Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China
| | - Bingzhen Wang
- Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | - Shiqi Xu
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | - Yanli Liu
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China
| | | | - Xianling Dong
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Chengde City, Hebei Province, China.
- Hebei Provincial Key Laboratory of Nerve Injury and Repair, Chengde Medical University, Chengde City, Hebei Province, China.
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9
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Koçak B, Yüzkan S, Mutlu S, Karagülle M, Kala A, Kadıoğlu M, Solak S, Sunman Ş, Temiz ZH, Ganiyusufoğlu AK. Influence of image preprocessing on the segmentation-based reproducibility of radiomic features: in vivo experiments on discretization and resampling parameters. Diagn Interv Radiol 2024; 30:152-162. [PMID: 38073244 PMCID: PMC11095065 DOI: 10.4274/dir.2023.232543] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 11/14/2023] [Indexed: 05/15/2024]
Abstract
PURPOSE To systematically investigate the impact of image preprocessing parameters on the segmentation-based reproducibility of magnetic resonance imaging (MRI) radiomic features. METHODS The MRI scans of 50 patients were included from the multi-institutional Brain Tumor Segmentation 2021 public glioma dataset. Whole tumor volumes were manually segmented by two independent readers, with the participation of eight readers. Radiomic features were extracted from two sequences: T2-weighted (T2) and contrast-enhanced T1-weighted (T1ce). Two methods were considered for discretization: bin count (i.e., relative discretization) and bin width (i.e., absolute discretization). Ten discretization (five for each method) and five resampling parameters were varied while other parameters were fixed. The intraclass correlation coefficient (ICC) was used for reliability analysis based on two commonly used cut-off values (0.75 and 0.90). RESULTS Image preprocessing parameters had a significant impact on the segmentation-based reproducibility of radiomic features. The bin width method yielded more reproducible features than the bin count method. In discretization experiments using the bin width on both sequences, according to the ICC cut-off values of 0.75 and 0.90, the rate of reproducible features ranged from 70% to 84% and from 35% to 57%, respectively, with an increasing percentage trend as parameter values decreased (from 84 to 5 for T2; 100 to 6 for T1ce). In the resampling experiments, these ranged from 53% to 74% and from 10% to 20%, respectively, with an increasing percentage trend from lower to higher parameter values (physical voxel size; from 1 x 1 x 1 to 2 x 2 x 2 mm3). CONCLUSION The segmentation-based reproducibility of radiomic features appears to be substantially influenced by discretization and resampling parameters. Our findings indicate that the bin width method should be used for discretization and lower bin width and higher resampling values should be used to allow more reproducible features.
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Affiliation(s)
- Burak Koçak
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Sabahattin Yüzkan
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Samet Mutlu
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Mehmet Karagülle
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Ahmet Kala
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Mehmet Kadıoğlu
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Sıla Solak
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Şeyma Sunman
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Zişan Hayriye Temiz
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
| | - Ali Kürşad Ganiyusufoğlu
- University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Clinic of Radiology, İstanbul, Türkiye
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10
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Liu Y, Zhang Z, Zhang H, Wang X, Wang K, Yang R, Han P, Luan K, Zhou Y. Clinical prediction of microvascular invasion in hepatocellular carcinoma using an MRI-based graph convolutional network model integrated with nomogram. Br J Radiol 2024; 97:938-946. [PMID: 38552308 DOI: 10.1093/bjr/tqae056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 02/07/2024] [Accepted: 03/06/2024] [Indexed: 05/09/2024] Open
Abstract
OBJECTIVES Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram. METHODS We retrospectively collected 182 HCC patients confirmed histopathologically, all of them performed enhanced MRI before surgery. The patients were randomly divided into training and validation groups. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), respectively. After removing redundant features, the graph structure by constructing the distance matrix with the feature matrix was built. Screening the superior phases and acquired GCN Score (GS). Finally, combining clinical, radiological and GS established the predicting nomogram. RESULTS 27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoural artery (P = 0.007) was an independent predictor of MVI. GCN model with grey-level cooccurrence matrix-grey-level run length matrix features exhibited area under the curves of the training group was 0.532, 0.690, and 0.885 and the validation group was 0.583, 0.580, and 0.854 for AP, PVP, and DP, respectively. DP was selected to develop final model and got GS. Combining GS with diameter, corona enhancement, mosaic architecture, and intratumoural artery constructed a nomogram which showed a C-index of 0.884 (95% CI: 0.829-0.927). CONCLUSIONS The GCN model based on DP has a high predictive ability. A nomogram combining GS, clinical and radiological characteristics can be a simple and effective guiding tool for selecting HCC treatment options. ADVANCES IN KNOWLEDGE GCN based on MRI could predict MVI on HCC.
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Affiliation(s)
- Yang Liu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150010, Heilongjiang, China
| | - Ziqian Zhang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150010, Heilongjiang, China
| | - Hongxia Zhang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150010, Heilongjiang, China
| | - Xinxin Wang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150010, Heilongjiang, China
| | - Kun Wang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Rui Yang
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, No.150 Haping Road, Nangang District, Harbin 150081, Heilongjiang Province, China
| | - Peng Han
- Department of Surgical Oncology, Harbin Medical University Cancer Hospital, No.150 Haping Road, Nangang District, Harbin 150081, Heilongjiang Province, China
| | - Kuan Luan
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Yang Zhou
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150010, Heilongjiang, China
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11
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Scapicchio C, Imbriani M, Lizzi F, Quattrocchi M, Retico A, Saponaro S, Tenerani MI, Tofani A, Zafaranchi A, Fantacci ME. Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study. Biomed Phys Eng Express 2024; 10:045006. [PMID: 38653209 DOI: 10.1088/2057-1976/ad41e7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/23/2024] [Indexed: 04/25/2024]
Abstract
Objective. Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters. This limited robustness hinders the generalizable validity of radiomics-assisted models. Our aim is to investigate a possible harmonization strategy based on matching image quality to improve feature robustness.Approach.We acquired CT scans of a phantom with two scanners across different dose levels and percentages of Iterative Reconstruction algorithms. The detectability index was used as a comprehensive task-based image quality metric. A statistical analysis based on the Intraclass Correlation Coefficient was performed to determine if matching image quality/appearance could enhance the robustness of radiomics features extracted from the phantom images. Additionally, an Artificial Neural Network was trained on these features to automatically classify the scanner used for image acquisition.Main results.We found that the ICC of the features across protocols providing a similar detectability index improves with respect to the ICC of the features across protocols providing a different detectability index. This improvement was particularly noticeable in features relevant for distinguishing between scanners.Significance.This preliminary study demonstrates that a harmonization based on image quality/appearance matching could improve radiomics features robustness and heterogeneous protocols can be used to obtain a similar image appearance in terms of the detectability index. Thus protocols with a lower dose level could be selected to reduce the amount of radiation dose delivered to the patient and simultaneously obtain a more robust quantitative analysis.
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Affiliation(s)
- Camilla Scapicchio
- Department of Physics, University of Pisa, Pisa, Italy
- National Institute for Nuclear Physics, Pisa Division, Italy
| | | | - Francesca Lizzi
- National Institute for Nuclear Physics, Pisa Division, Italy
| | | | | | - Sara Saponaro
- National Institute for Nuclear Physics, Pisa Division, Italy
| | - Maria Irene Tenerani
- Department of Physics, University of Pisa, Pisa, Italy
- National Institute for Nuclear Physics, Pisa Division, Italy
| | - Alessandro Tofani
- Medical Physics Department, Azienda Toscana Nord Ovest Area Nord, Lucca, Italy
| | - Arman Zafaranchi
- Department of Physics, University of Pisa, Pisa, Italy
- National Institute for Nuclear Physics, Pisa Division, Italy
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Maria Evelina Fantacci
- Department of Physics, University of Pisa, Pisa, Italy
- National Institute for Nuclear Physics, Pisa Division, Italy
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12
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Watzenboeck ML, Beer L, Kifjak D, Röhrich S, Heidinger BH, Prayer F, Milos RI, Apfaltrer P, Langs G, Baltzer PAT, Prosch H. Contrast Agent Dynamics Determine Radiomics Profiles in Oncologic Imaging. Cancers (Basel) 2024; 16:1519. [PMID: 38672601 PMCID: PMC11049400 DOI: 10.3390/cancers16081519] [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: 03/12/2024] [Revised: 04/11/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The reproducibility of radiomics features extracted from CT and MRI examinations depends on several physiological and technical factors. The aim was to evaluate the impact of contrast agent timing on the stability of radiomics features using dynamic contrast-enhanced perfusion CT (dceCT) or MRI (dceMRI) in prostate and lung cancers. METHODS Radiomics features were extracted from dceCT or dceMRI images in patients with biopsy-proven peripheral prostate cancer (pzPC) or biopsy-proven non-small cell lung cancer (NSCLC), respectively. Features that showed significant differences between contrast phases were identified using linear mixed models. An L2-penalized logistic regression classifier was used to predict class labels for pzPC and unaffected prostate regions-of-interest (ROIs). RESULTS Nine pzPC and 28 NSCLC patients, who were imaged with dceCT and/or dceMRI, were included in this study. After normalizing for individual enhancement patterns by defining seven individual phases based on a reference vessel, 19, 467 and 128 out of 1204 CT features showed significant temporal dynamics in healthy prostate parenchyma, prostate tumors and lung tumors, respectively. CT radiomics-based classification accuracy of healthy and tumor ROIs was highly dependent on contrast agent phase. For dceMRI, 899 and 1027 out of 1118 features were significantly dependent on time after contrast agent injection for prostate and lung tumors. CONCLUSIONS CT and MRI radiomics features in both prostate and lung tumors are significantly affected by interindividual differences in contrast agent dynamics.
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Affiliation(s)
- Martin L. Watzenboeck
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Lucian Beer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Daria Kifjak
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Sebastian Röhrich
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Benedikt H. Heidinger
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Florian Prayer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Ruxandra-Iulia Milos
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Paul Apfaltrer
- Zentralröntgeninstitut für Diagnostik, Interventionelle Radiologie und Nuklearmedizin, Landesklinikum Wiener Neustadt, 2700 Wiener Neustadt, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Pascal A. T. Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
| | - Helmut Prosch
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria (G.L.); (P.A.T.B.); (H.P.)
- Computational Imaging Research Lab, Christian Doppler Laboratory for Machine Learning Driven Precision Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
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Daher H, Punchayil SA, Ismail AAE, Fernandes RR, Jacob J, Algazzar MH, Mansour M. Advancements in Pancreatic Cancer Detection: Integrating Biomarkers, Imaging Technologies, and Machine Learning for Early Diagnosis. Cureus 2024; 16:e56583. [PMID: 38646386 PMCID: PMC11031195 DOI: 10.7759/cureus.56583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2024] [Indexed: 04/23/2024] Open
Abstract
Artificial intelligence (AI) has come to play a pivotal role in revolutionizing medical practices, particularly in the field of pancreatic cancer detection and management. As a leading cause of cancer-related deaths, pancreatic cancer warrants innovative approaches due to its typically advanced stage at diagnosis and dismal survival rates. Present detection methods, constrained by limitations in accuracy and efficiency, underscore the necessity for novel solutions. AI-driven methodologies present promising avenues for enhancing early detection and prognosis forecasting. Through the analysis of imaging data, biomarker profiles, and clinical information, AI algorithms excel in discerning subtle abnormalities indicative of pancreatic cancer with remarkable precision. Moreover, machine learning (ML) algorithms facilitate the amalgamation of diverse data sources to optimize patient care. However, despite its huge potential, the implementation of AI in pancreatic cancer detection faces various challenges. Issues such as the scarcity of comprehensive datasets, biases in algorithm development, and concerns regarding data privacy and security necessitate thorough scrutiny. While AI offers immense promise in transforming pancreatic cancer detection and management, ongoing research and collaborative efforts are indispensable in overcoming technical hurdles and ethical dilemmas. This review delves into the evolution of AI, its application in pancreatic cancer detection, and the challenges and ethical considerations inherent in its integration.
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Affiliation(s)
- Hisham Daher
- Internal Medicine, University of Debrecen, Debrecen, HUN
| | - Sneha A Punchayil
- Internal Medicine, University Hospital of North Tees, Stockton-on-Tees, GBR
| | | | | | - Joel Jacob
- General Medicine, Diana Princess of Wales Hospital, Grimsby, GBR
| | | | - Mohammad Mansour
- General Medicine, University of Debrecen, Debrecen, HUN
- General Medicine, Jordan University Hospital, Amman, JOR
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14
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Mukherjee S, Korfiatis P, Patnam NG, Trivedi KH, Karbhari A, Suman G, Fletcher JG, Goenka AH. Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods. Abdom Radiol (NY) 2024; 49:964-974. [PMID: 38175255 DOI: 10.1007/s00261-023-04127-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 11/08/2023] [Accepted: 11/12/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE To evaluate robustness of a radiomics-based support vector machine (SVM) model for detection of visually occult PDA on pre-diagnostic CTs by simulating common variations in image acquisition and radiomics workflow using image perturbation methods. METHODS Eighteen algorithmically generated-perturbations, which simulated variations in image noise levels (σ, 2σ, 3σ, 5σ), image rotation [both CT image and the corresponding pancreas segmentation mask by 45° and 90° in axial plane], voxel resampling (isotropic and anisotropic), gray-level discretization [bin width (BW) 32 and 64)], and pancreas segmentation (sequential erosions by 3, 4, 6, and 8 pixels and dilations by 3, 4, and 6 pixels from the boundary), were introduced to the original (unperturbed) test subset (n = 128; 45 pre-diagnostic CTs, 83 control CTs with normal pancreas). Radiomic features were extracted from pancreas masks of these additional test subsets, and the model's performance was compared vis-a-vis the unperturbed test subset. RESULTS The model correctly classified 43 out of 45 pre-diagnostic CTs and 75 out of 83 control CTs in the unperturbed test subset, achieving 92.2% accuracy and 0.98 AUC. Model's performance was unaffected by a three-fold increase in noise level except for sensitivity declining to 80% at 3σ (p = 0.02). Performance remained comparable vis-a-vis the unperturbed test subset despite variations in image rotation (p = 0.99), voxel resampling (p = 0.25-0.31), change in gray-level BW to 32 (p = 0.31-0.99), and erosions/dilations up to 4 pixels from the pancreas boundary (p = 0.12-0.34). CONCLUSION The model's high performance for detection of visually occult PDA was robust within a broad range of clinically relevant variations in image acquisition and radiomics workflow.
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Affiliation(s)
- Sovanlal Mukherjee
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Panagiotis Korfiatis
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Nandakumar G Patnam
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Kamaxi H Trivedi
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Aashna Karbhari
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Garima Suman
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Joel G Fletcher
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA
| | - Ajit H Goenka
- Divisions of Abdominal and Nuclear Imaging, Nuclear Radiology Fellowship, Nuclear Radiology Research Operations, Enterprise PET/MR Research and Development, Department of Radiology, Mayo Clinic, 200 First St SW, Charlton 1, Rochester, MN, 55905, USA.
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15
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Thomas HMT, Wang HYC, Varghese AJ, Donovan EM, South CP, Saxby H, Nisbet A, Prakash V, Sasidharan BK, Pavamani SP, Devadhas D, Mathew M, Isiah RG, Evans PM. Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets. APPLIED SCIENCES (BASEL, SWITZERLAND) 2024; 166:s00701-024-05977-4. [PMID: 38725869 PMCID: PMC7615943 DOI: 10.3390/app13127291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/12/2024]
Abstract
Radiomics involves the extraction of information from medical images that are not visible to the human eye. There is evidence that these features can be used for treatment stratification and outcome prediction. However, there is much discussion about the reproducibility of results between different studies. This paper studies the reproducibility of CT texture features used in radiomics, comparing two feature extraction implementations, namely the MATLAB toolkit and Pyradiomics, when applied to independent datasets of CT scans of patients: (i) the open access RIDER dataset containing a set of repeat CT scans taken 15 min apart for 31 patients (RIDER Scan 1 and Scan 2, respectively) treated for lung cancer; and (ii) the open access HN1 dataset containing 137 patients treated for head and neck cancer. Gross tumor volume (GTV), manually outlined by an experienced observer available on both datasets, was used. The 43 common radiomics features available in MATLAB and Pyradiomics were calculated using two intensity-level quantization methods with and without an intensity threshold. Cases were ranked for each feature for all combinations of quantization parameters, and the Spearman's rank coefficient, rs, calculated. Reproducibility was defined when a highly correlated feature in the RIDER dataset also correlated highly in the HN1 dataset, and vice versa. A total of 29 out of the 43 reported stable features were found to be highly reproducible between MATLAB and Pyradiomics implementations, having a consistently high correlation in rank ordering for RIDER Scan 1 and RIDER Scan 2 (rs > 0.8). 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets.
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Affiliation(s)
- Hannah Mary T. Thomas
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Helen Y. C. Wang
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK
- Department of Medical Physics, Royal Surrey NHS Foundation Trust, Guildford GU2 7XX, UK
| | - Amal Joseph Varghese
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Ellen M. Donovan
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK
| | - Chris P. South
- Department of Medical Physics, Royal Surrey NHS Foundation Trust, Guildford GU2 7XX, UK
| | - Helen Saxby
- St Luke’s Cancer Centre, Royal Surrey NHS Foundation Trust, Guildford GU2 7XX, UK
| | - Andrew Nisbet
- Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Vineet Prakash
- St Luke’s Cancer Centre, Royal Surrey NHS Foundation Trust, Guildford GU2 7XX, UK
| | - Balu Krishna Sasidharan
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Simon Pradeep Pavamani
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Devakumar Devadhas
- Department of Nuclear Medicine, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Manu Mathew
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Rajesh Gunasingam Isiah
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore 632004, Tamil Nadu, India
| | - Philip M. Evans
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK
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16
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Pal S, Singh RP, Kumar A. Analysis of Hybrid Feature Optimization Techniques Based on the Classification Accuracy of Brain Tumor Regions Using Machine Learning and Further Evaluation Based on the Institute Test Data. J Med Phys 2024; 49:22-32. [PMID: 38828069 PMCID: PMC11141750 DOI: 10.4103/jmp.jmp_77_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 02/23/2024] [Accepted: 02/23/2024] [Indexed: 06/05/2024] Open
Abstract
Aim The goal of this study was to get optimal brain tumor features from magnetic resonance imaging (MRI) images and classify them based on the three groups of the tumor region: Peritumoral edema, enhancing-core, and necrotic tumor core, using machine learning classification models. Materials and Methods This study's dataset was obtained from the multimodal brain tumor segmentation challenge. A total of 599 brain MRI studies were employed, all in neuroimaging informatics technology initiative format. The dataset was divided into training, validation, and testing subsets online test dataset (OTD). The dataset includes four types of MRI series, which were combined together and processed for intensity normalization using contrast limited adaptive histogram equalization methodology. To extract radiomics features, a python-based library called pyRadiomics was employed. Particle-swarm optimization (PSO) with varying inertia weights was used for feature optimization. Inertia weight with a linearly decreasing strategy (W1), inertia weight with a nonlinear coefficient decreasing strategy (W2), and inertia weight with a logarithmic strategy (W3) were different strategies used to vary the inertia weight for feature optimization in PSO. These selected features were further optimized using the principal component analysis (PCA) method to further reducing the dimensionality and removing the noise and improve the performance and efficiency of subsequent algorithms. Support vector machine (SVM), light gradient boosting (LGB), and extreme gradient boosting (XGB) machine learning classification algorithms were utilized for the classification of images into different tumor regions using optimized features. The proposed method was also tested on institute test data (ITD) for a total of 30 patient images. Results For OTD test dataset, the classification accuracy of SVM was 0.989, for the LGB model (LGBM) was 0.992, and for the XGB model (XGBM) was 0.994, using the varying inertia weight-PSO optimization method and the classification accuracy of SVM was 0.996 for the LGBM was 0.998, and for the XGBM was 0.994, using PSO and PCA-a hybrid optimization technique. For ITD test dataset, the classification accuracy of SVM was 0.994 for the LGBM was 0.993, and for the XGBM was 0.997, using the hybrid optimization technique. Conclusion The results suggest that the proposed method can be used to classify a brain tumor as used in this study to classify the tumor region into three groups: Peritumoral edema, enhancing-core, and necrotic tumor core. This was done by extracting the different features of the tumor, such as its shape, grey level, gray-level co-occurrence matrix, etc., and then choosing the best features using hybrid optimal feature selection techniques. This was done without much human expertise and in much less time than it would take a person.
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Affiliation(s)
- Soniya Pal
- Department of Physics, GLA University, Mathura, Uttar Pradesh, India
- Batra Hospital and Medical Research Center, New Delhi, India
| | - Raj Pal Singh
- Department of Physics, GLA University, Mathura, Uttar Pradesh, India
| | - Anuj Kumar
- Department of Radiotherapy, S. N. Medical College, Agra, Uttar Pradesh, India
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Xing X, Li L, Sun M, Zhu X, Feng Y. A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma. Ther Adv Respir Dis 2024; 18:17534666241249168. [PMID: 38757628 PMCID: PMC11102675 DOI: 10.1177/17534666241249168] [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: 10/19/2023] [Accepted: 04/05/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment. OBJECTIVE This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers. DESIGN A retrospective case control, diagnostic accuracy study. METHODS This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines. RESULTS The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively. CONCLUSION This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.
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Affiliation(s)
- Xiaowei Xing
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital, (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Liangping Li
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Mingxia Sun
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Xinhai Zhu
- Department of Thoracic Surgery, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Yue Feng
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital, (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
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18
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Shang Y, Chen W, Li G, Huang Y, Wang Y, Kui X, Li M, Zheng H, Zhao W, Liu J. Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma. LA RADIOLOGIA MEDICA 2023; 128:1483-1496. [PMID: 37749461 PMCID: PMC10700425 DOI: 10.1007/s11547-023-01722-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 09/04/2023] [Indexed: 09/27/2023]
Abstract
OBJECTIVE To investigate the value of Computed Tomography (CT) radiomics derived from different peritumoral volumes of interest (VOIs) in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients. MATERIALS AND METHODS A retrospective cohort of 779 patients who had pathologically confirmed lung adenocarcinoma were enrolled. 640 patients were randomly divided into a training set, a validation set, and an internal testing set (3:1:1), and the remaining 139 patients were defined as an external testing set. The intratumoral VOI (VOI_I) was manually delineated on the thin-slice CT images, and seven peritumoral VOIs (VOI_P) were automatically generated with 1, 2, 3, 4, 5, 10, and 15 mm expansion along the VOI_I. 1454 radiomic features were extracted from each VOI. The t-test, the least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) algorithm were used for feature selection, followed by the construction of radiomics models (VOI_I model, VOI_P model and combined model). The performance of the models were evaluated by the area under the curve (AUC). RESULTS 399 patients were classified as EGFR mutant (EGFR+), while 380 were wild-type (EGFR-). In the training and validation sets, internal and external testing sets, VOI4 (intratumoral and peritumoral 4 mm) model achieved the best predictive performance, with AUCs of 0.877, 0.727, and 0.701, respectively, outperforming the VOI_I model (AUCs of 0.728, 0.698, and 0.653, respectively). CONCLUSIONS Radiomics extracted from peritumoral region can add extra value in predicting EGFR mutation status of lung adenocarcinoma patients, with the optimal peritumoral range of 4 mm.
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Affiliation(s)
- Youlan Shang
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
| | - Weidao Chen
- Infervision, Chaoyang District, Beijing, 100025, China
| | - Ge Li
- Department of Radiology, Xiangya Hospital, Central South University, No. 87 Xiangya Rd, Changsha, 410008, Hunan, People's Republic of China
| | - Yijie Huang
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
| | - Yisong Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China
| | - Xiaoyan Kui
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China
| | - Ming Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, People's Republic of China
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Wei Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China.
- Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China.
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan Province, People's Republic of China.
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Changsha, 410011, Hunan, People's Republic of China.
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan Province, People's Republic of China.
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Mese I, Taslicay CA, Sivrioglu AK. Improving radiology workflow using ChatGPT and artificial intelligence. Clin Imaging 2023; 103:109993. [PMID: 37812965 DOI: 10.1016/j.clinimag.2023.109993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/19/2023] [Accepted: 09/28/2023] [Indexed: 10/11/2023]
Abstract
Artificial Intelligence is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence. One of the branches of artificial intelligence is natural language processing, which is dedicated to studying the interaction between computers and human language. ChatGPT is a sophisticated natural language processing tool that can understand and respond to complex questions and commands in natural language. Radiology is a vital aspect of modern medicine that involves the use of imaging technologies to diagnose and treat medical conditions artificial intelligence, including ChatGPT, can be integrated into radiology workflows to improve efficiency, accuracy, and patient care. ChatGPT can streamline various radiology workflow steps, including patient registration, scheduling, patient check-in, image acquisition, interpretation, and reporting. While ChatGPT has the potential to transform radiology workflows, there are limitations to the technology that must be addressed, such as the potential for bias in artificial intelligence algorithms and ethical concerns. As technology continues to advance, ChatGPT is likely to become an increasingly important tool in the field of radiology, and in healthcare more broadly.
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Affiliation(s)
- Ismail Mese
- Department of Radiology, Health Sciences University, Erenkoy Mental Health and Neurology Training and Research Hospital, 19 Mayıs, Sinan Ercan Cd. No: 23, Kadıköy/Istanbul 34736, Turkey.
| | | | - Ali Kemal Sivrioglu
- Department of Radiology, Liv Hospital Vadistanbul, Ayazağa Mahallesi, Kemerburgaz Caddesi, Vadistanbul Park Etabı, 7F Blok, 34396 Sarıyer/İstanbul, Turkey
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20
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Xiao DX, Zhong JP, Peng JD, Fan CG, Wang XC, Wen XL, Liao WW, Wang J, Yin XF. Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics. BMC Med Imaging 2023; 23:159. [PMID: 37845636 PMCID: PMC10580667 DOI: 10.1186/s12880-023-01106-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 09/20/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND There is a paucity of research investigating the application of machine learning techniques for distinguishing between lipid-poor adrenal adenoma (LPA) and subclinical pheochromocytoma (sPHEO) based on radiomic features extracted from non-contrast and dynamic contrast-enhanced computed tomography (CT) scans of the abdomen. METHODS We conducted a retrospective analysis of multiphase spiral CT scans, including non-contrast, arterial, venous, and delayed phases, as well as thin- and thick-thickness images from 134 patients with surgically and pathologically confirmed. A total of 52 patients with LPA and 44 patients with sPHEO were randomly assigned to training/testing sets in a 7:3 ratio. Additionally, a validation set was comprised of 22 LPA cases and 16 sPHEO cases from two other hospitals. We used 3D Slicer and PyRadiomics to segment tumors and extract radiomic features, respectively. We then applied T-test and least absolute shrinkage and selection operator (LASSO) to select features. Six binary classifiers, including K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP), were employed to differentiate LPA from sPHEO. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were compared using DeLong's method. RESULTS All six classifiers showed good diagnostic performance for each phase and slice thickness, as well as for the entire CT data, with AUC values ranging from 0.706 to 1. Non-contrast CT densities of LPA were significantly lower than those of sPHEO (P < 0.001). However, using the optimal threshold for non-contrast CT density, sensitivity was only 0.743, specificity 0.744, and AUC 0.828. Delayed phase CT density yielded a sensitivity of 0.971, specificity of 0.641, and AUC of 0.814. In radiomics, AUC values for the testing set using non-contrast CT images were: KNN 0.919, LR 0.979, DT 0.835, RF 0.967, SVM 0.979, and MLP 0.981. In the validation set, AUC values were: KNN 0.891, LR 0.974, DT 0.891, RF 0.964, SVM 0.949, and MLP 0.979. CONCLUSIONS The machine learning model based on CT radiomics can accurately differentiate LPA from sPHEO, even using non-contrast CT data alone, making contrast-enhanced CT unnecessary for diagnosing LPA and sPHEO.
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Affiliation(s)
- Dao-Xiong Xiao
- Department of Medical Imaging, Ganzhou Hospital affiliated to Nanchang University, Ganzhou People's Hospital, Ganzhou, Jiangxi province, China.
| | - Jian-Ping Zhong
- Department of Medical Imaging, Ganzhou Hospital affiliated to Nanchang University, Ganzhou People's Hospital, Ganzhou, Jiangxi province, China
| | - Ji-Dong Peng
- Department of Medical Imaging, Ganzhou Hospital affiliated to Nanchang University, Ganzhou People's Hospital, Ganzhou, Jiangxi province, China
| | - Cun-Geng Fan
- Department of Medical Imaging, Ganzhou Hospital affiliated to Nanchang University, Ganzhou People's Hospital, Ganzhou, Jiangxi province, China
| | - Xiao-Chun Wang
- Department of Medical Imaging, Ganzhou Hospital affiliated to Nanchang University, Ganzhou People's Hospital, Ganzhou, Jiangxi province, China
| | - Xing-Lin Wen
- Department of Medical Imaging, Ganzhou Hospital affiliated to Nanchang University, Ganzhou People's Hospital, Ganzhou, Jiangxi province, China
| | - Wei-Wei Liao
- Department of Medical Imaging, Ganzhou Hospital affiliated to Nanchang University, Ganzhou People's Hospital, Ganzhou, Jiangxi province, China
| | - Jun Wang
- Department of Medical Imaging, the First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi province, China
| | - Xiao-Feng Yin
- Department of Medical Imaging, Nankang District People's Hospital, Nankang District, Ganzhou, Jiangxi province, China
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21
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Libling WA, Korn R, Weiss GJ. Review of the use of radiomics to assess the risk of recurrence in early-stage non-small cell lung cancer. Transl Lung Cancer Res 2023; 12:1575-1589. [PMID: 37577298 PMCID: PMC10413018 DOI: 10.21037/tlcr-23-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 06/13/2023] [Indexed: 08/15/2023]
Abstract
Background and Objective Radiomics is an emerging field of advanced image analysis that has shown promise as a non-invasive, companion diagnostic in predicting clinical outcomes and response assessments in solid tumors. Radiomics aims to extract high-content information from medical images not visible to the naked eye, especially in early-stage non-small cell lung cancer (NSCLC) patients. Although these patients are being identified by early detection programs, it remains unclear which patients would benefit from adjuvant treatment versus active surveillance. Having a radiomic signature(s) that could predict early recurrence would be beneficial. In this review, an overview of the basic radiomic approaches used to evaluate solid tumors on radiologic scans, including NSCLC is provided followed by a review of relevant literature that supports the use of radiomics to help predict tumor recurrence in early-stage NSCLC patients. Methods A review of the radiomic literature from 1985 to present focusing on the prediction of disease recurrence in early-stage NSCLC was conducted. PubMed database was searched using key terms for radiomics and NSCLC. A total of 41 articles were identified and 13 studies were considered suitable for inclusion based upon study population, patient number (n>50), use of well described radiomic methodologies, suitable model building features, and well-defined testing/training and validation where feasible. Key Content and Findings Examples of using radiomics in early-stage NSCLC patients will be presented, where disease free survival is a primary consideration. A summary of the findings demonstrates the importance of both the intratumor and peritumoral radiomic signals as a marker of outcomes. Conclusions The value of radiomic information for predicting disease recurrence in early-stage NSCLC patients is accumulating. However, overcoming several challenges along with the lack of prospective trials, has inhibited it use as a clinical decision-making support tool in early-stage NSCLC.
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Affiliation(s)
- William Adam Libling
- Arizona College of Osteopathic Medicine, Midwestern University, Glendale, AZ, USA
| | - Ronald Korn
- Virginia G Piper Cancer Center at HonorHealth, Scottsdale, AZ, USA
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Waldenberg C, Brisby H, Hebelka H, Lagerstrand KM. Associations between Vertebral Localized Contrast Changes and Adjacent Annular Fissures in Patients with Low Back Pain: A Radiomics Approach. J Clin Med 2023; 12:4891. [PMID: 37568293 PMCID: PMC10420134 DOI: 10.3390/jcm12154891] [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: 07/06/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 08/13/2023] Open
Abstract
Low back pain (LBP) is multifactorial and associated with various spinal tissue changes, including intervertebral disc fissures, vertebral pathology, and damaged endplates. However, current radiological markers lack specificity and individualized diagnostic capability, and the interactions between the various markers are not fully clear. Radiomics, a data-driven analysis of radiological images, offers a promising approach to improve evaluation and deepen the understanding of spinal changes related to LBP. This study investigated possible associations between vertebral changes and annular fissures using radiomics. A dataset of 61 LBP patients who underwent conventional magnetic resonance imaging followed by discography was analyzed. Radiomics features were extracted from segmented vertebrae and carefully reduced to identify the most relevant features associated with annular fissures. The results revealed three important texture features that display concentrated high-intensity gray levels, extensive regions with elevated gray levels, and localized areas with reduced gray levels within the vertebrae. These features highlight patterns within vertebrae that conventional classification systems cannot reflect on distinguishing between vertebrae adjacent to an intervertebral disc with or without an annular fissure. As such, the present study reveals associations that contribute to the understanding of pathophysiology and may provide improved diagnostics of LBP.
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Affiliation(s)
- Christian Waldenberg
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden;
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Helena Brisby
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Orthopaedics, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Hanna Hebelka
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Radiology, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Kerstin Magdalena Lagerstrand
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 413 45 Gothenburg, Sweden;
- Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden; (H.B.); (H.H.)
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
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Loi S, Mori M, Palumbo D, Crippa S, Palazzo G, Spezi E, Del Vecchio A, Falconi M, De Cobelli F, Fiorino C. Limited impact of discretization/interpolation parameters on the predictive power of CT radiomic features in a surgical cohort of pancreatic cancer patients. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01649-y. [PMID: 37289267 DOI: 10.1007/s11547-023-01649-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/15/2023] [Indexed: 06/09/2023]
Abstract
PURPOSE To explore the variation of the discriminative power of CT (Computed Tomography) radiomic features (RF) against image discretization/interpolation in predicting early distant relapses (EDR) after upfront surgery. MATERIALS AND METHODS Data of 144 patients with pre-surgical high contrast CT were processed consistently with IBSI (Image Biomarker Standardization Initiative) guidelines. Image interpolation/discretization parameters were intentionally changed, including cubic voxel size (0.21-27 mm3) and binning (32-128 grey levels) in a 15 parameter's sets. After excluding RF with poor inter-observer delineation agreement (ICC < 0.80) and not negligible inter-scanner variability, the variation of 80 RF against discretization/interpolation was first quantified. Then, their ability in classifying patients with early distant relapses (EDR, < 10 months, previously assessed at the first quartile value of time-to-relapse) was investigated in terms of AUC (Area Under Curve) variation for those RF significantly associated to EDR. RESULTS Despite RF variability against discretization/interpolation parameters was large and only 30/80 RF showed %COV < 20 (%COV = 100*STDEV/MEAN), AUC changes were relatively limited: for 30 RF significantly associated with EDR (AUC values around 0.60-0.70), the mean values of SD of AUC variability and AUC range were 0.02 and 0.05 respectively. AUC ranges were between 0.00 and 0.11, with values ≤ 0.05 in 16/30 RF. These variations were further reduced when excluding the extreme values of 32 and 128 for grey levels (Average AUC range 0.04, with values between 0.00 and 0.08). CONCLUSIONS The discriminative power of CT RF in the prediction of EDR after upfront surgery for pancreatic cancer is relatively invariant against image interpolation/discretization within a large range of voxel sizes and binning.
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Affiliation(s)
- Sara Loi
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 690, 20132, Milan, Italy
| | - Martina Mori
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 690, 20132, Milan, Italy
| | | | - Stefano Crippa
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Milan, Italy
- Università Vita-Salute, Milan, Italy
| | - Gabriele Palazzo
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 690, 20132, Milan, Italy
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, Wales, UK
| | - Antonella Del Vecchio
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 690, 20132, Milan, Italy
| | - Massimo Falconi
- Pancreatic Surgery Unit, Pancreas Translational and Clinical Research Center, San Raffaele Scientific Institute, Milan, Italy
- Università Vita-Salute, Milan, Italy
| | - Francesco De Cobelli
- San Raffaele Scientific Institute, Milan, Italy
- Università Vita-Salute, Milan, Italy
| | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 690, 20132, Milan, Italy.
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O'Sullivan NJ, Kelly ME. Radiomics and Radiogenomics in Pelvic Oncology: Current Applications and Future Directions. Curr Oncol 2023; 30:4936-4945. [PMID: 37232830 DOI: 10.3390/curroncol30050372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/19/2023] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Radiomics refers to the conversion of medical imaging into high-throughput, quantifiable data in order to analyse disease patterns, guide prognosis and aid decision making. Radiogenomics is an extension of radiomics that combines conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data, serving as an alternative to costly, labour-intensive genetic testing. Data on radiomics and radiogenomics in the field of pelvic oncology remain novel concepts in the literature. We aim to perform an up-to-date analysis of current applications of radiomics and radiogenomics in the field of pelvic oncology, particularly focusing on the prediction of survival, recurrence and treatment response. Several studies have applied these concepts to colorectal, urological, gynaecological and sarcomatous diseases, with individual efficacy yet poor reproducibility. This article highlights the current applications of radiomics and radiogenomics in pelvic oncology, as well as the current limitations and future directions. Despite a rapid increase in publications investigating the use of radiomics and radiogenomics in pelvic oncology, the current evidence is limited by poor reproducibility and small datasets. In the era of personalised medicine, this novel field of research has significant potential, particularly for predicting prognosis and guiding therapeutic decisions. Future research may provide fundamental data on how we treat this cohort of patients, with the aim of reducing the exposure of high-risk patients to highly morbid procedures.
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Affiliation(s)
- Niall J O'Sullivan
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
| | - Michael E Kelly
- The Trinity St. James's Cancer Institute, D08 NHY1 Dublin, Ireland
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland
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Zhou Y, Yuan J, Xue C, Poon DMC, Yang B, Yu SK, Cheung KY. A pilot study of MRI radiomics for high-risk prostate cancer stratification in 1.5 T MR-guided radiotherapy. Magn Reson Med 2023; 89:2088-2099. [PMID: 36572990 DOI: 10.1002/mrm.29564] [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: 08/30/2022] [Revised: 11/09/2022] [Accepted: 12/09/2022] [Indexed: 12/28/2022]
Abstract
PURPOSE To investigate the potential value of MRI radiomics obtained from a 1.5 T MRI-guided linear accelerator (MR-LINAC) for D'Amico high-risk prostate cancer (PC) classification in MR-guided radiotherapy (MRgRT). METHODS One hundred seventy-six consecutive PC patients underwent 1.5 T MRgRT treatment were retrospectively enrolled. Each patient received one or two pretreatment T2 -weighted MRI scans on a 1.5 T MR-LINAC. The endpoint was to differentiate high-risk from low/intermediate-risk PC based on D'Amico criteria using MRI-radiomics. Totally 1023 features were extracted from clinical target volume (CTV) and planning target volume (PTV). Intraclass correlation coefficient of scan-rescan repeatability, feature correlation, and recursive feature elimination were used for feature dimension reduction. Least absolute shrinkage and selection operator regression was employed for model construction. Receiver operating characteristic area under the curve (AUC) analysis was used for model performance assessment in both training and testing data. RESULTS One hundred and eleven patients fulfilled all criteria were finally included: 76 for training and 35 for testing. The constructed MRI-radiomics models extracted from CTV and PTV achieved the AUC of 0.812 and 0.867 in the training data, without significant difference (P = 0.083). The model performances remained in the testing. The sensitivity, specificity, and accuracy were 85.71%, 64.29%, and 77.14% for the PTV-based model; and 71.43%, 71.43%, and 71.43% for the CTV-based model. The corresponding AUCs were 0.718 and 0.750 (P = 0.091) for CTV- and PTV-based models. CONCLUSION MRI-radiomics obtained from a 1.5 T MR-LINAC showed promising results in D'Amico high-risk PC stratification, potentially helpful for the future PC MRgRT. Prospective studies with larger sample sizes and external validation are warranted for further verification.
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Affiliation(s)
- Yihang Zhou
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Jing Yuan
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Cindy Xue
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Darren M C Poon
- Comprehensive Oncology Center, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Bin Yang
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Siu Ki Yu
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Kin Yin Cheung
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
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Gidwani M, Chang K, Patel JB, Hoebel KV, Ahmed SR, Singh P, Fuller CD, Kalpathy-Cramer J. Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models. Radiology 2023; 307:e220715. [PMID: 36537895 PMCID: PMC10068883 DOI: 10.1148/radiol.220715] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/19/2022] [Accepted: 11/01/2022] [Indexed: 12/24/2022]
Abstract
Background Radiomics is the extraction of predefined mathematic features from medical images for the prediction of variables of clinical interest. While some studies report superlative accuracy of radiomic machine learning (ML) models, the published methodology is often incomplete, and the results are rarely validated in external testing data sets. Purpose To characterize the type, prevalence, and statistical impact of methodologic errors present in radiomic ML studies. Materials and Methods Radiomic ML publications were reviewed for the presence of performance-inflating methodologic flaws. Common flaws were subsequently reproduced with randomly generated features interpolated from publicly available radiomic data sets to demonstrate the precarious nature of reported findings. Results In an assessment of radiomic ML publications, the authors uncovered two general categories of data analysis errors: inconsistent partitioning and unproductive feature associations. In simulations, the authors demonstrated that inconsistent partitioning augments radiomic ML accuracy by 1.4 times from unbiased performance and that correcting for flawed methodologic results in areas under the receiver operating characteristic curve approaching a value of 0.5 (random chance). With use of randomly generated features, the authors illustrated that unproductive associations between radiomic features and gene sets can imply false causality for biologic phenomenon. Conclusion Radiomic machine learning studies may contain methodologic flaws that undermine their validity. This study provides a review template to avoid such flaws. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Jacobs in this issue.
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Affiliation(s)
- Mishka Gidwani
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Ken Chang
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Jay Biren Patel
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Katharina Viktoria Hoebel
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Syed Rakin Ahmed
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Praveer Singh
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Clifton David Fuller
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Jayashree Kalpathy-Cramer
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
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Varghese AJ, Gouthamchand V, Sasidharan BK, Wee L, Sidhique SK, Rao JP, Dekker A, Hoebers F, Devakumar D, Irodi A, Balasingh TP, Godson HF, Joel T, Mathew M, Gunasingam Isiah R, Pavamani SP, Thomas HMT. Multi-centre radiomics for prediction of recurrence following radical radiotherapy for head and neck cancers: Consequences of feature selection, machine learning classifiers and batch-effect harmonization. Phys Imaging Radiat Oncol 2023; 26:100450. [PMID: 37260438 PMCID: PMC10227455 DOI: 10.1016/j.phro.2023.100450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 06/02/2023] Open
Abstract
Background and purpose Radiomics models trained with limited single institution data are often not reproducible and generalisable. We developed radiomics models that predict loco-regional recurrence within two years of radiotherapy with private and public datasets and their combinations, to simulate small and multi-institutional studies and study the responsiveness of the models to feature selection, machine learning algorithms, centre-effect harmonization and increased dataset sizes. Materials and methods 562 patients histologically confirmed and treated for locally advanced head-and-neck cancer (LA-HNC) from two public and two private datasets; one private dataset exclusively reserved for validation. Clinical contours of primary tumours were not recontoured and were used for Pyradiomics based feature extraction. ComBat harmonization was applied, and LASSO-Logistic Regression (LR) and Support Vector Machine (SVM) models were built. 95% confidence interval (CI) of 1000 bootstrapped area-under-the-Receiver-operating-curves (AUC) provided predictive performance. Responsiveness of the models' performance to the choice of feature selection methods, ComBat harmonization, machine learning classifier, single and pooled data was evaluated. Results LASSO and SelectKBest selected 14 and 16 features, respectively; three were overlapping. Without ComBat, the LR and SVM models for three institutional data showed AUCs (CI) of 0.513 (0.481-0.559) and 0.632 (0.586-0.665), respectively. Performances following ComBat revealed AUCs of 0.559 (0.536-0.590) and 0.662 (0.606-0.690), respectively. Compared to single cohort AUCs (0.562-0.629), SVM models from pooled data performed significantly better at AUC = 0.680. Conclusions Multi-institutional retrospective data accentuates the existing variabilities that affect radiomics. Carefully designed prospective, multi-institutional studies and data sharing are necessary for clinically relevant head-and-neck cancer prognostication models.
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Affiliation(s)
- Amal Joseph Varghese
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Varsha Gouthamchand
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Leonard Wee
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sharief K Sidhique
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | | | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Devadhas Devakumar
- Department of Nuclear Medicine, Christian Medical College, Vellore, Tamil Nadu, India
| | - Aparna Irodi
- Department of Radiology, Christian Medical College, Vellore, Tamil Nadu, India
| | | | - Henry Finlay Godson
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | - T Joel
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | - Manu Mathew
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
| | | | | | - Hannah Mary T Thomas
- Department of Radiation Oncology, Christian Medical College, Vellore, Tamil Nadu, India
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28
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Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review. Cancers (Basel) 2023; 15:cancers15041174. [PMID: 36831517 PMCID: PMC9954362 DOI: 10.3390/cancers15041174] [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: 12/21/2022] [Revised: 01/31/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.
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29
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Xue C, Chu WCW, Yuan J, Poon DMC, Yang B, Zhou Y, Yu SK, Cheung KY. Determining the reliable feature change in longitudinal radiomics studies: A methodological approach using the reliable change index. Med Phys 2023; 50:958-969. [PMID: 36251320 DOI: 10.1002/mp.16046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/28/2022] [Accepted: 09/30/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Determination of reliable change of radiomics feature over time is essential and vital in delta-radiomics, but has not yet been rigorously examined. This study attempts to propose a methodological approach using reliable change index (RCI), a statistical metric to determine the reliability of quantitative biomarker changes by accounting for the baseline measurement standard error, in delta-radiomics. The use of RCI was demonstrated with the MRI data acquired from a group of prostate cancer (PCa) patients treated by 1.5 T MRI-guided radiotherapy (MRgRT). METHODS Fifty consecutive PCa patients who underwent five-fractionated MRgRT were retrospectively included, and 1023 radiomics features were extracted from the clinical target volume (CTV) and planning target volume (PTV). The two MRI datasets acquired at the first fraction (MRI11 and MRI21) were used to calculate the baseline feature reliability against image acquisition using intraclass correlation coefficient (ICC). The RCI was constructed based on the baseline feature measurement standard deviation, ICC, and feature value differences at two time points between the fifth (MRI51) and the first fraction MRI (MRI11). The reliable change of features was determined in each patient only if the calculated RCI was over 1.96 or smaller than -1.96. The feature changes between MRI51 and MRI11 were correlated to two patient-reported quality-of-life clinical endpoints of urinary domain summary score (UDSS) and bowel domain summary score (BDSS) in 35 patients using the Spearman correlation test. Only the significant correlations between a feature that was reliably changed in ≥7 patients (20%) by RCI and an endpoint were considered as true significant correlations. RESULTS The 352 (34.4%) and 386 (37.7%) features among all 1023 features were determined by RCI to be reliably changed in more than five (10%) patients in the CTV and PTV, respectively. Nineteen features were found reliably changed in the CTV and 31 features in the PTV, respectively, in 10 (20%) or more patients. These features were not necessarily associated with significantly different longitudinal feature values (group p-value < 0.05). Most reliably changed features in more than 10 patients had excellent or good baseline test-retest reliability ICC, while none showed poor reliability. The RCI method ruled out the features to be reliably changed when substantial feature measurement bias was presented. After applying the RCI criterion, only four and five true significant correlations were confirmed with UDSS and BDSS in the CTV, respectively, with low true significance correlation rates of 10.8% (4/37) and 17.9% (5/28). No true significant correlations were found in the PTV. CONCLUSIONS The RCI method was proposed for delta-radiomics and demonstrated using PCa MRgRT data. The RCI has advantages over some other statistical metrics commonly used in the previous delta-radiomics studies, and is useful to reliably identify the longitudinal radiomics feature change on an individual basis. This proposed RCI method should be helpful for the development of essential feature selection methodology in delta-radiomics.
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Affiliation(s)
- Cindy Xue
- Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China.,Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jing Yuan
- Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Darren M C Poon
- Comprehensive Oncology Center, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Bin Yang
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Yihang Zhou
- Research Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Siu Ki Yu
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
| | - Kin Yin Cheung
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong SAR, China
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30
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Adams SJ, Madtes DK, Burbridge B, Johnston J, Goldberg IG, Siegel EL, Babyn P, Nair VS, Calhoun ME. Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT. J Am Coll Radiol 2023; 20:232-242. [PMID: 36064040 DOI: 10.1016/j.jacr.2022.08.006] [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: 05/09/2022] [Revised: 08/19/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022]
Abstract
OBJECTIVE To evaluate whether an imaging classifier for radiology practice can improve lung nodule classification and follow-up. METHODS A machine learning classifier was developed and trained using imaging data from the National Lung Screening Trial (NSLT) to produce a malignancy risk score (malignancy Similarity Index [mSI]) for individual lung nodules. In addition to NLST cohorts, external cohorts were developed from a tertiary referral lung cancer screening program data set and an external nonscreening data set of all nodules detected on CT. Performance of the mSI combined with Lung-RADS was compared with Lung-RADS alone and the Mayo and Brock risk calculators. RESULTS We analyzed 963 subjects and 1,331 nodules across these cohorts. The mSI was comparable in accuracy (area under the curve = 0.89) to existing clinical risk models (area under the curve = 0.86-0.88) and independently predictive in the NLST cohort of 704 nodules. When compared with Lung-RADS, the mSI significantly increased sensitivity across all cohorts (25%-117%), with significant increases in specificity in the screening cohorts (17%-33%). When used in conjunction with Lung-RADS, use of mSI would result in earlier diagnoses and reduced follow-up across cohorts, including the potential for early diagnosis in 42% of malignant NLST nodules from prior-year CT scans. CONCLUSION A computer-assisted diagnosis software improved risk classification from chest CTs of screening and incidentally detected lung nodules compared with Lung-RADS. mSI added predictive value independent of existing radiological and clinical variables. These results suggest the generalizability and potential clinical impact of a tool that is straightforward to implement in practice.
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Affiliation(s)
- Scott J Adams
- Department of Medical Imaging, University of Saskatchewan, Saskatoon, Canada; Scientific Director of the National Medical Imaging Clinic in Saskatoon
| | - David K Madtes
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Brent Burbridge
- Department of Medical Imaging, University of Saskatchewan, Saskatoon, Canada
| | | | | | - Eliot L Siegel
- Professor and Vice Chair, Department of Diagnostic Radiology, University of Maryland School of Medicine; Chief of Radiology and Nuclear Medicine for the Veterans Affairs Maryland Healthcare System; and Fellow of the American College of Radiology
| | - Paul Babyn
- Department of Medical Imaging, University of Saskatchewan, Saskatoon, Canada; recently retired as Physician Executive, Provincial Programs for the Saskatchewan Health Authority
| | - Viswam S Nair
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington; Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington School of Medicine, Seattle, Washington
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Capobianco E, Dominietto M. Translating Data Science Results into Precision Oncology Decisions: A Mini Review. J Clin Med 2023; 12:438. [PMID: 36675367 PMCID: PMC9862106 DOI: 10.3390/jcm12020438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations.
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Affiliation(s)
- Enrico Capobianco
- The Jackson Laboratory, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Marco Dominietto
- Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen, Switzerland
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Ge G, Zhang J. Feature selection methods and predictive models in CT lung cancer radiomics. J Appl Clin Med Phys 2023; 24:e13869. [PMID: 36527376 PMCID: PMC9860004 DOI: 10.1002/acm2.13869] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/31/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Radiomics is a technique that extracts quantitative features from medical images using data-characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes cohort selection, radiomic feature extraction, feature and predictive model selection, and model training and validation. While there has been increasing attention given to radiomic feature extraction, standardization, and reproducibility, currently, there is a lack of rigorous evaluation of feature selection methods and predictive models. Herein, we review the published radiomics investigations in CT lung cancer and provide an overview of the commonly used radiomic feature selection methods and predictive models. We also compare limitations of various methods in clinical applications and present sources of uncertainty associated with those methods. This review is expected to help raise awareness of the impact of radiomic feature and model selection methods on the integrity of radiomics studies.
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Affiliation(s)
- Gary Ge
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
| | - Jie Zhang
- Department of Radiology, University of Kentucky, Lexington, Kentucky, USA
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Huang J, Chen X, Xia B, Ma S. Advances in CT features and radiomics of checkpoint inhibitor-related pneumonitis: A short review. Front Immunol 2023; 14:1082980. [PMID: 36756121 PMCID: PMC9899831 DOI: 10.3389/fimmu.2023.1082980] [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: 10/28/2022] [Accepted: 01/09/2023] [Indexed: 01/24/2023] Open
Abstract
Checkpoint inhibitor-related pneumonitis (CIP) is a complication of immunotherapy for malignant tumors that severely limits the treatment cycles as well as endangers patients' health. The chest CT imaging features or typing of CIP and the application of radiomics will contribute to the precise prevention, early diagnosis and instant treatment of CIP. This article reviews the advances in the CT features and the application of radiomics in CIP.
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Affiliation(s)
- Jie Huang
- Department of Thoracic Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Chen
- Department of Thoracic Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bing Xia
- Department of Thoracic Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shenglin Ma
- Department of Thoracic Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China
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34
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Li S, Zhou B. A review of radiomics and genomics applications in cancers: the way towards precision medicine. Radiat Oncol 2022; 17:217. [PMID: 36585716 PMCID: PMC9801589 DOI: 10.1186/s13014-022-02192-2] [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: 09/15/2022] [Accepted: 12/27/2022] [Indexed: 01/01/2023] Open
Abstract
The application of radiogenomics in oncology has great prospects in precision medicine. Radiogenomics combines large volumes of radiomic features from medical digital images, genetic data from high-throughput sequencing, and clinical-epidemiological data into mathematical modelling. The amalgamation of radiomics and genomics provides an approach to better study the molecular mechanism of tumour pathogenesis, as well as new evidence-supporting strategies to identify the characteristics of cancer patients, make clinical decisions by predicting prognosis, and improve the development of individualized treatment guidance. In this review, we summarized recent research on radiogenomics applications in solid cancers and presented the challenges impeding the adoption of radiomics in clinical practice. More standard guidelines are required to normalize radiomics into reproducible and convincible analyses and develop it as a mature field.
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Affiliation(s)
- Simin Li
- grid.412636.40000 0004 1757 9485Department of Clinical Epidemiology and Center of Evidence-Based Medicine, The First Hospital of China Medical University, Shenyang, 110001 Liaoning People’s Republic of China
| | - Baosen Zhou
- grid.412636.40000 0004 1757 9485Department of Clinical Epidemiology and Center of Evidence-Based Medicine, The First Hospital of China Medical University, Shenyang, 110001 Liaoning People’s Republic of China
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35
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Singh A, Horng H, Chitalia R, Roshkovan L, Katz SI, Noël P, Shinohara RT, Kontos D. Resampling and harmonization for mitigation of heterogeneity in image parameters of baseline scans. Sci Rep 2022; 12:21505. [PMID: 36513760 PMCID: PMC9747915 DOI: 10.1038/s41598-022-26083-4] [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: 08/12/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Our study investigates the effects of heterogeneity in image parameters on the reproducibility of prognostic performance of models built using radiomic biomarkers. We compare the prognostic performance of models derived from the heterogeneity-mitigated features with that of models obtained from raw features, to assess whether reproducibility of prognostic scores improves upon application of our methods. We used two datasets: The Breast I-SPY1 dataset-Baseline DCE-MRI scans of 156 women with locally advanced breast cancer, treated with neoadjuvant chemotherapy, publicly available via The Cancer Imaging Archive (TCIA); The NSCLC IO dataset-Baseline CT scans of 107 patients with stage 4 non-small cell lung cancer (NSCLC), treated with pembrolizumab immunotherapy at our institution. Radiomic features (n = 102) are extracted from the tumor ROIs. We use a variety of resampling and harmonization scenarios to mitigate the heterogeneity in image parameters. The patients were divided into groups based on batch variables. For each group, the radiomic phenotypes are combined with the clinical covariates into a prognostic model. The performance of the groups is assessed using the c-statistic, derived from a Cox proportional hazards model fitted on all patients within a group. The heterogeneity-mitigation scenario (radiomic features, derived from images that have been resampled to minimum voxel spacing, are harmonized using the image acquisition parameters as batch variables) gave models with highest prognostic scores (for e.g., IO dataset; batch variable: high kernel resolution-c-score: 0.66). The prognostic performance of patient groups is not comparable in case of models built using non-heterogeneity mitigated features (for e.g., I-SPY1 dataset; batch variable: small pixel spacing-c-score: 0.54, large pixel spacing-c-score: 0.65). The prognostic performance of patient groups is closer in case of heterogeneity-mitigated scenarios (for e.g., scenario-harmonize by voxel spacing parameters: IO dataset; thin slice-c-score: 0.62, thick slice-c-score: 0.60). Our results indicate that accounting for heterogeneity in image parameters is important to obtain more reproducible prognostic scores, irrespective of image site or modality. For non-heterogeneity mitigated models, the prognostic scores are not comparable across patient groups divided based on batch variables. This study can be a step in the direction of constructing reproducible radiomic biomarkers, thus increasing their application in clinical decision making.
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Affiliation(s)
- Apurva Singh
- grid.25879.310000 0004 1936 8972Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA ,grid.25879.310000 0004 1936 8972Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Hannah Horng
- grid.25879.310000 0004 1936 8972Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Rhea Chitalia
- grid.25879.310000 0004 1936 8972Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA ,grid.25879.310000 0004 1936 8972Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Leonid Roshkovan
- grid.25879.310000 0004 1936 8972Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Sharyn I. Katz
- grid.25879.310000 0004 1936 8972Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Peter Noël
- grid.25879.310000 0004 1936 8972Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Russell T. Shinohara
- grid.25879.310000 0004 1936 8972Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Despina Kontos
- grid.25879.310000 0004 1936 8972Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 USA ,grid.25879.310000 0004 1936 8972Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Rm D702 Richards Bldg., 3700 Hamilton Walk, Philadelphia, PA 19104 USA
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36
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Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions. Sci Rep 2022; 12:19594. [PMID: 36379992 PMCID: PMC9665022 DOI: 10.1038/s41598-022-22877-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 10/20/2022] [Indexed: 11/16/2022] Open
Abstract
Feature stability and standardization remain challenges that impede the clinical implementation of radiomics. This study investigates the potential of spectral reconstructions from photon-counting computed tomography (PCCT) regarding organ-specific radiomics feature stability. Abdominal portal-venous phase PCCT scans of 10 patients in virtual monoenergetic (VM) (keV 40-120 in steps of 10), polyenergetic, virtual non-contrast (VNC), and iodine maps were acquired. Two 2D and 3D segmentations measuring 1 and 2 cm in diameter of the liver, lung, spleen, psoas muscle, subcutaneous fat, and air were obtained for spectral reconstructions. Radiomics features were extracted with pyradiomics. The calculation of feature-specific intraclass correlation coefficients (ICC) was performed by comparing all segmentation approaches and organs. Feature-wise and organ-wise correlations were evaluated. Segmentation-resegmentation stability was evaluated by concordance correlation coefficient (CCC). Compared to non-VM, VM-reconstruction features tended to be more stable. For VM reconstructions, 3D 2 cm segmentation showed the highest average ICC with 0.63. Based on a criterion of ≥ 3 stable organs and an ICC of ≥ 0.75, 12-mainly non-first-order features-are shown to be stable between the VM reconstructions. In a segmentation-resegmentation analysis in 3D 2 cm, three features were identified as stable based on a CCC of > 0.6 in ≥ 3 organs in ≥ 6 VM reconstructions. Certain radiomics features vary between monoenergetic reconstructions and depend on the ROI size. Feature stability was also shown to differ between different organs. Yet, glcm_JointEntropy, gldm_GrayLevelNonUniformity, and firstorder_Entropy could be identified as features that could be interpreted as energy-independent and segmentation-resegmentation stable in this PCCT collective. PCCT may support radiomics feature standardization and comparability between sites.
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Wright DE, Mukherjee S, Patra A, Khasawneh H, Korfiatis P, Suman G, Chari ST, Kudva YC, Kline TL, Goenka AH. Radiomics-based machine learning (ML) classifier for detection of type 2 diabetes on standard-of-care abdomen CTs: a proof-of-concept study. Abdom Radiol (NY) 2022; 47:3806-3816. [PMID: 36085379 DOI: 10.1007/s00261-022-03668-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/26/2022] [Accepted: 08/27/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D). METHODS Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset. RESULTS Model correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance was equivalent across gender, CT slice thicknesses, and CT vendors (p values > 0.05). There was no difference between correctly classified versus misclassified patients in the mean (range) T2D duration [4.5 (0-15.4) versus 4.8 (0-15.7) years, p = 0.8], antidiabetic treatment [insulin (22% versus 18%), oral antidiabetics (10% versus 18%), both (41% versus 39%) (p > 0.05)], and treatment duration [5.4 (0-15) versus 5 (0-13) years, p = 0.4]. CONCLUSION Pancreas radiomics-based AI model can detect the imaging signature of T2D. Further refinement and validation are needed to evaluate its potential for opportunistic T2D detection on millions of CTs that are performed annually.
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Affiliation(s)
- Darryl E Wright
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Sovanlal Mukherjee
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Anurima Patra
- Department of Radiology, Tata Medical Center, Kolkata, 700160, India
| | - Hala Khasawneh
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Panagiotis Korfiatis
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Garima Suman
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Suresh T Chari
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
- Department of Gastroenterology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Yogish C Kudva
- Department of Endocrinology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Timothy L Kline
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA
| | - Ajit H Goenka
- Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA.
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Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J. Artificial intelligence in pancreatic cancer. Theranostics 2022; 12:6931-6954. [PMID: 36276650 PMCID: PMC9576619 DOI: 10.7150/thno.77949] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.
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Affiliation(s)
- Bowen Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Haoran Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Shuting Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Dingyue Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Qingya Shi
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jianzhou Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Junchao Guo
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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Ibrahim A, Lu L, Yang H, Akin O, Schwartz LH, Zhao B. The Impact of Image Acquisition Parameters and ComBat Harmonization on the Predictive Performance of Radiomics: A Renal Cell Carcinoma Model. APPLIED SCIENCES (BASEL, SWITZERLAND) 2022; 12:9824. [PMID: 37091743 PMCID: PMC10121203 DOI: 10.3390/app12199824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field.
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Affiliation(s)
- Abdalla Ibrahim
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Correspondence:
| | - Lin Lu
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Hao Yang
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Lawrence H. Schwartz
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
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40
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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.
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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.)
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Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study. Cancers (Basel) 2022; 14:cancers14153656. [PMID: 35954318 PMCID: PMC9367613 DOI: 10.3390/cancers14153656] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/23/2022] [Accepted: 07/25/2022] [Indexed: 02/04/2023] Open
Abstract
The tumour immune microenvironment influences the efficacy of immune checkpoint inhibitors. Within this microenvironment are CD8-expressing tumour-infiltrating lymphocytes (CD8+ TILs), which are an important mediator and marker of anti-tumour response. In practice, the assessment of CD8+ TILs via tissue sampling involves logistical challenges. Radiomics, the high-throughput extraction of features from medical images, may offer a novel and non-invasive alternative. We performed a systematic review of the available literature reporting radiomic signatures associated with CD8+ TILs. We also aimed to evaluate the methodological quality of the identified studies using the Radiomics Quality Score (RQS) tool, and the risk of bias and applicability with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Articles were searched from inception until 31 December 2021, in three electronic databases, and screened against eligibility criteria. Twenty-seven articles were included. A wide variety of cancers have been studied. The reported radiomic signatures were heterogeneous, with very limited reproducibility between studies of the same cancer group. The overall quality of studies was found to be less than desirable (mean RQS = 33.3%), indicating a need for technical maturation. Some potential avenues for further investigation are also discussed.
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Kothari G, Woon B, Patrick CJ, Korte J, Wee L, Hanna GG, Kron T, Hardcastle N, Siva S. The impact of inter-observer variation in delineation on robustness of radiomics features in non-small cell lung cancer. Sci Rep 2022; 12:12822. [PMID: 35896707 PMCID: PMC9329346 DOI: 10.1038/s41598-022-16520-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/11/2022] [Indexed: 11/25/2022] Open
Abstract
Artificial intelligence and radiomics have the potential to revolutionise cancer prognostication and personalised treatment. Manual outlining of the tumour volume for extraction of radiomics features (RF) is a subjective process. This study investigates robustness of RF to inter-observer variation (IOV) in contouring in lung cancer. We utilised two public imaging datasets: ‘NSCLC-Radiomics’ and ‘NSCLC-Radiomics-Interobserver1’ (‘Interobserver’). For ‘NSCLC-Radiomics’, we created an additional set of manual contours for 92 patients, and for ‘Interobserver’, there were five manual and five semi-automated contours available for 20 patients. Dice coefficients (DC) were calculated for contours. 1113 RF were extracted including shape, first order and texture features. Intraclass correlation coefficient (ICC) was computed to assess robustness of RF to IOV. Cox regression analysis for overall survival (OS) was performed with a previously published radiomics signature. The median DC ranged from 0.81 (‘NSCLC-Radiomics’) to 0.85 (‘Interobserver’—semi-automated). The median ICC for the ‘NSCLC-Radiomics’, ‘Interobserver’ (manual) and ‘Interobserver’ (semi-automated) were 0.90, 0.88 and 0.93 respectively. The ICC varied by feature type and was lower for first order and gray level co-occurrence matrix (GLCM) features. Shape features had a lower median ICC in the ‘NSCLC-Radiomics’ dataset compared to the ‘Interobserver’ dataset. Survival analysis showed similar separation of curves for three of four RF apart from ‘original_shape_Compactness2’, a feature with low ICC (0.61). The majority of RF are robust to IOV, with first order, GLCM and shape features being the least robust. Semi-automated contouring improves feature stability. Decreased robustness of a feature is significant as it may impact upon the features’ prognostic capability.
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Affiliation(s)
- Gargi Kothari
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre Building, 305 Grattan Street, Melbourne, VIC, 3000, Australia. .,Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.
| | - Beverley Woon
- Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.,Department of Radiology, Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Cameron J Patrick
- Statistical Consulting Centre, University of Melbourne, Parkville, Australia
| | - James Korte
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Department of Biomedical Engineering, School of Chemical and Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia
| | - Leonard Wee
- Department of Radiotherapy (MAASTRO), GROW School of Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Clinical Data Science, Maastricht University, Maastricht, The Netherlands
| | - Gerard G Hanna
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre Building, 305 Grattan Street, Melbourne, VIC, 3000, Australia.,Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia
| | - Tomas Kron
- Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Nicholas Hardcastle
- Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Shankar Siva
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre Building, 305 Grattan Street, Melbourne, VIC, 3000, Australia.,Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia
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43
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Rai R, Barton MB, Chlap P, Liney G, Brink C, Vinod S, Heinke M, Trada Y, Holloway LC. Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer. J Med Imaging (Bellingham) 2022; 9:044005. [PMID: 35992729 PMCID: PMC9386367 DOI: 10.1117/1.jmi.9.4.044005] [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: 02/04/2022] [Accepted: 08/09/2022] [Indexed: 08/20/2023] Open
Abstract
Purpose: Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer. Approach: An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of 10 patients with test-retest imaging among two observers. Results: We found that many radiomic features, particularly from texture classes, were highly sensitive to changes in image resolution and noise. About 49% of features had coefficient of variations ≤ 10 % in longitudinal phantom measurements. About 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8 . We saw excellent interobserver agreement with mean Dice similarity coefficient of 0.95 ± 0.04 for test and retest scans. Conclusions: The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.
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Affiliation(s)
- Robba Rai
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Michael B. Barton
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Phillip Chlap
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Gary Liney
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Carsten Brink
- Odense University Hospital, Laboratory of Radiation Physics, Department of Oncology, Odense, Denmark
- University of Southern Denmark, Department of Clinical Research, Odense, Denmark
| | - Shalini Vinod
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | | | - Yuvnik Trada
- Calvary Mater Newcastle, Department of Radiation Oncology, Newcastle, New South Wales, Australia
| | - Lois C. Holloway
- University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia
- Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- University of Wollongong, Centre of Radiation Physics, Wollongong, New South Wales, Australia
- University of Sydney, Institute of Medical Physics, School of Physics, Sydney, New South Wales, Australia
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Mirón Mombiela R, Arildskov AR, Bruun FJ, Hasselbalch LH, Holst KB, Rasmussen SH, Borrás C. What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies. Int J Mol Sci 2022; 23:6504. [PMID: 35742947 PMCID: PMC9224495 DOI: 10.3390/ijms23126504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 02/04/2023] Open
Abstract
(1) Background: Radiogenomics is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology. This review focused on papers that used genetics to validate their radiomics models and outcomes and assess their contribution to this emerging field. (2) Methods: All original research with the words radiomics and genomics in English and performed in humans up to 31 January 2022, were identified on Medline and Embase. The quality of the studies was assessed with Radiomic Quality Score (RQS) and the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. (3) Results: 45 studies were included in our systematic review, and more than 50% were published in the last two years. The studies had a mean RQS of 12, and the studied tumors were very diverse. Up to 83% investigated the prognosis as the main outcome, with the rest focusing on response to treatment and risk assessment. Most applied either transcriptomics (54%) and/or genetics (35%) for genetic validation. (4) Conclusions: There is enough evidence to state that new science has emerged, focusing on establishing an association between radiological features and genomic/molecular expression to explain underlying disease mechanisms and enhance prognostic, risk assessment, and treatment response radiomics models in cancer patients.
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Affiliation(s)
- Rebeca Mirón Mombiela
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Anne Rix Arildskov
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Frederik Jager Bruun
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Lotte Harries Hasselbalch
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Kristine Bærentz Holst
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Sine Hvid Rasmussen
- Radiology Derpartment, Herlev og Gentofte Hospital, Borgmester Ib Juuls Vej 17, Opgang 4, 4.Etage, E2, 2730 Herlev, Denmark; (A.R.A.); (F.J.B.); (L.H.H.); (K.B.H.); (S.H.R.)
| | - Consuelo Borrás
- Freshage Research Group, Department of Physiology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain
- CIBERFES, Institute of Health Research-INCLIVA, 46010 Valencia, Spain
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45
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Scalco E, Rizzo G, Mastropietro A. The stability of oncologic MRI radiomic features and the potential role of deep learning: a review. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac60b9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/24/2022] [Indexed: 11/11/2022]
Abstract
Abstract
The use of MRI radiomic models for the diagnosis, prognosis and treatment response prediction of tumors has been increasingly reported in literature. However, its widespread adoption in clinics is hampered by issues related to features stability. In the MRI radiomic workflow, the main factors that affect radiomic features computation can be found in the image acquisition and reconstruction phase, in the image pre-processing steps, and in the segmentation of the region of interest on which radiomic indices are extracted. Deep Neural Networks (DNNs), having shown their potentiality in the medical image processing and analysis field, can be seen as an attractive strategy to partially overcome the issues related to radiomic stability and mitigate their impact. In fact, DNN approaches can be prospectively integrated in the MRI radiomic workflow to improve image quality, obtain accurate and reproducible segmentations and generate standardized images. In this review, DNN methods that can be included in the image processing steps of the radiomic workflow are described and discussed, in the light of a detailed analysis of the literature in the context of MRI radiomic reliability.
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Wang Z, Yang C, Han W, Sui X, Zheng F, Xue F, Xu X, Wu P, Chen Y, Gu W, Song W, Jiang J. Quantifying lung cancer heterogeneity using novel CT features: a cross-institute study. Insights Imaging 2022; 13:82. [PMID: 35482262 PMCID: PMC9050978 DOI: 10.1186/s13244-022-01204-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/04/2022] [Indexed: 11/12/2022] Open
Abstract
Background Radiomics-based image metrics are not used in the clinic despite the rapidly growing literature. We selected eight promising radiomic features and validated their value in decoding lung cancer heterogeneity. Methods CT images of 236 lung cancer patients were obtained from three different institutes, whereupon radiomic features were extracted according to a standardized procedure. The predictive value for patient long-term prognosis and association with routinely used semantic, genetic (e.g., epidermal growth factor receptor (EGFR)), and histopathological cancer profiles were validated. Feature measurement reproducibility was assessed. Results All eight selected features were robust across repeat scans (intraclass coefficient range: 0.81–0.99), and were associated with at least one of the cancer profiles: prognostic, semantic, genetic, and histopathological. For instance, “kurtosis” had a high predictive value of early death (AUC at first year: 0.70–0.75 in two independent cohorts), negative association with histopathological grade (Spearman’s r: − 0.30), and altered expression levels regarding EGFR mutation and semantic characteristics (solid intensity, spiculated shape, juxtapleural location, and pleura tag; all p < 0.05). Combined as a radiomic score, the features had a higher area under curve for predicting 5-year survival (train: 0.855, test: 0.780, external validation: 0.760) than routine characteristics (0.733, 0.622, 0.613, respectively), and a better capability in patient death risk stratification (hazard ratio: 5.828, 95% confidence interval: 2.915–11.561) than histopathological staging and grading. Conclusions We highlighted the clinical value of radiomic features. Following confirmation, these features may change the way in which we approach CT imaging and improve the individualized care of lung cancer patients.
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Affiliation(s)
- Zixing Wang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Cuihong Yang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Wei Han
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Xin Sui
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Fuling Zheng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Fang Xue
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Xiaoli Xu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.,Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Peng Wu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Yali Chen
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Wentao Gu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Wei Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
| | - Jingmei Jiang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, China.
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Duan J, Qiu Q, Zhu J, Shang D, Dou X, Sun T, Yin Y, Meng X. Reproducibility for Hepatocellular Carcinoma CT Radiomic Features: Influence of Delineation Variability Based on 3D-CT, 4D-CT and Multiple-Parameter MR Images. Front Oncol 2022; 12:881931. [PMID: 35494061 PMCID: PMC9047864 DOI: 10.3389/fonc.2022.881931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 03/21/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose Accurate lesion segmentation is a prerequisite for radiomic feature extraction. It helps to reduce the features variability so as to improve the reporting quality of radiomics study. In this research, we aimed to conduct a radiomic feature reproducibility test of inter-/intra-observer delineation variability in hepatocellular carcinoma using 3D-CT images, 4D-CT images and multiple-parameter MR images. Materials and Methods For this retrospective study, 19 HCC patients undergoing 3D-CT, 4D-CT and multiple-parameter MR scans were included in this study. The gross tumor volume (GTV) was independently delineated twice by two observers based on contrast-enhanced computed tomography (CECT), maximum intensity projection (MIP), LAVA-Flex, T2W FRFSE and DWI-EPI images. We also delineated the peritumoral region, which was defined as 0 to 5 mm radius surrounding the GTV. 107 radiomic features were automatically extracted from CECT images using 3D-Slicer software. Quartile coefficient of dispersion (QCD) and intraclass correlation coefficient (ICC) were applied to assess the variability of each radiomic feature. QCD<10% and ICC≥0.75 were considered small variations and excellent reliability. Finally, the principal component analysis (PCA) was used to test the feasibility of dimensionality reduction. Results For tumor tissues, the numbers of radiomic features with QCD<10% indicated no obvious inter-/intra-observer differences or discrepancies in 3D-CT, 4D-CT and multiple-parameter MR delineation. However, the number of radiomic features (mean 89) with ICC≥0.75 was the highest in the multiple-parameter MR group, followed by the 3DCT group (mean 77) and the MIP group (mean 73). The peritumor tissues also showed similar results. A total of 15 and 7 radiomic features presented excellent reproducibility and small variation in tumor and peritumoral tissues, respectively. Two robust features showed excellent reproducibility and small variation in tumor and peritumoral tissues. In addition, the values of the two features both represented statistically significant differences among tumor and peritumoral tissues (P<0.05). The PCA results indicated that the first seven principal components could preserve at least 90% of the variance of the original set of features. Conclusion Delineation on multiple-parameter MR images could help to improve the reproducibility of the HCC CT radiomic features and weaken the inter-/intra-observer influence.
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Affiliation(s)
- Jinghao Duan
- School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin, China
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Qingtao Qiu
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jian Zhu
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Dongping Shang
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xue Dou
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Tao Sun
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yong Yin
- Department of Radiotherapy, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiangjuan Meng
- Department of Clinical Laboratory, Shandong First Medical University and Shandong Academy of Medical Sciences, Shandong Cancer Hospital and Institute, Jinan, China
- *Correspondence: Xiangjuan Meng,
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Xue C, Yuan J, Zhou Y, Wong OL, Cheung KY, Yu SK. Acquisition repeatability of MRI radiomics features in the head and neck: a dual-3D-sequence multi-scan study. Vis Comput Ind Biomed Art 2022; 5:10. [PMID: 35359245 PMCID: PMC8971276 DOI: 10.1186/s42492-022-00106-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/23/2022] [Indexed: 02/08/2023] Open
Abstract
Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer (HNC), a group of malignancies associated with high heterogeneity. However, the feature reliability of radiomics is a major obstacle to its broad validity and generality in application to the highly heterogeneous head and neck (HN) tissues. In particular, feature repeatability of radiomics in magnetic resonance imaging (MRI) acquisition, which is considered a crucial confounding factor of radiomics feature reliability, is still sparsely investigated. This study prospectively investigated the acquisition repeatability of 93 MRI radiomics features in ten HN tissues of 15 healthy volunteers, aiming for potential magnetic resonance-guided radiotherapy (MRgRT) treatment of HNC. Each subject underwent four MRI acquisitions with MRgRT treatment position and immobilization using two pulse sequences of 3D T1-weighed turbo spin-echo and 3D T2-weighed turbo spin-echo on a 1.5 T MRI simulator. The repeatability of radiomics feature acquisition was evaluated in terms of the intraclass correlation coefficient (ICC), whereas within-subject acquisition variability was evaluated in terms of the coefficient of variation (CV). The results showed that MRI radiomics features exhibited heterogeneous acquisition variability and uncertainty dependent on feature types, tissues, and pulse sequences. Only a small fraction of features showed excellent acquisition repeatability (ICC > 0.9) and low within-subject variability. Multiple MRI scans improved the accuracy and confidence of the identification of reliable features concerning MRI acquisition compared to simple test-retest repeated scans. This study contributes to the literature on the reliability of radiomics features with respect to MRI acquisition and the selection of reliable radiomics features for use in modeling in future HNC MRgRT applications.
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Affiliation(s)
- Cindy Xue
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Jing Yuan
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China.
| | - Yihang Zhou
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Oi Lei Wong
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Kin Yin Cheung
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong, China
| | - Siu Ki Yu
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong, China
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CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features. J Pers Med 2022; 12:jpm12040553. [PMID: 35455668 PMCID: PMC9030848 DOI: 10.3390/jpm12040553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/27/2022] [Accepted: 03/28/2022] [Indexed: 02/06/2023] Open
Abstract
Handcrafted radiomics features (HRFs) are quantitative features extracted from medical images to decode biological information to improve clinical decision making. Despite the potential of the field, limitations have been identified. The most important identified limitation, currently, is the sensitivity of HRF to variations in image acquisition and reconstruction parameters. In this study, we investigated the use of Reconstruction Kernel Normalization (RKN) and ComBat harmonization to improve the reproducibility of HRFs across scans acquired with different reconstruction kernels. A set of phantom scans (n = 28) acquired on five different scanner models was analyzed. HRFs were extracted from the original scans, and scans were harmonized using the RKN method. ComBat harmonization was applied on both sets of HRFs. The reproducibility of HRFs was assessed using the concordance correlation coefficient. The difference in the number of reproducible HRFs in each scenario was assessed using McNemar’s test. The majority of HRFs were found to be sensitive to variations in the reconstruction kernels, and only six HRFs were found to be robust with respect to variations in reconstruction kernels. The use of RKN resulted in a significant increment in the number of reproducible HRFs in 19 out of the 67 investigated scenarios (28.4%), while the ComBat technique resulted in a significant increment in 36 (53.7%) scenarios. The combination of methods resulted in a significant increment in 53 (79.1%) scenarios compared to the HRFs extracted from original images. Since the benefit of applying the harmonization methods depended on the data being harmonized, reproducibility analysis is recommended before performing radiomics analysis. For future radiomics studies incorporating images acquired with similar image acquisition and reconstruction parameters, except for the reconstruction kernels, we recommend the systematic use of the pre- and post-processing approaches (respectively, RKN and ComBat).
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50
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Tharmaseelan H, Hertel A, Tollens F, Rink J, Woźnicki P, Haselmann V, Ayx I, Nörenberg D, Schoenberg SO, Froelich MF. Identification of CT Imaging Phenotypes of Colorectal Liver Metastases from Radiomics Signatures-Towards Assessment of Interlesional Tumor Heterogeneity. Cancers (Basel) 2022; 14:cancers14071646. [PMID: 35406418 PMCID: PMC8997087 DOI: 10.3390/cancers14071646] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/17/2022] [Accepted: 03/21/2022] [Indexed: 02/06/2023] Open
Abstract
(1) Background: Tumoral heterogeneity (TH) is a major challenge in the treatment of metastatic colorectal cancer (mCRC) and is associated with inferior response. Therefore, the identification of TH would be beneficial for treatment planning. TH can be assessed by identifying genetic alterations. In this work, a radiomics-based approach for assessment of TH in colorectal liver metastases (CRLM) in CT scans is demonstrated. (2) Methods: In this retrospective study, CRLM of mCRC were segmented and radiomics features extracted using pyradiomics. Unsupervised k-means clustering was applied to features and lesions. Feature redundancy was evaluated by principal component analysis and reduced by Pearson correlation coefficient cutoff. Feature selection was conducted by LASSO regression and visual analysis of the clusters by radiologists. (3) Results: A total of 47 patients’ (36% female, median age 64) CTs with 261 lesions were included. Five clusters were identified, and the categories small disseminated (n = 31), heterogeneous (n = 105), homogeneous (n = 64), mixed (n = 59), and very large type (n = 2) were assigned based on visual characteristics. Further statistical analysis showed correlation (p < 0.01) of clusters with sex, primary location, T- and N-status, and mutational status. Feature reduction and selection resulted in the identification of four features as a final set for cluster definition. (4) Conclusions: Radiomics features can characterize TH in liver metastases of mCRC in CT scans, and may be suitable for a better pretherapeutic classification of liver lesion phenotypes.
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Affiliation(s)
- Hishan Tharmaseelan
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
| | - Alexander Hertel
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
| | - Fabian Tollens
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
| | - Johann Rink
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
| | - Piotr Woźnicki
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
| | - Verena Haselmann
- Institute of Clinical Chemistry, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany;
| | - Isabelle Ayx
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
| | - Dominik Nörenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
| | - Stefan O. Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
| | - Matthias F. Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, 68167 Mannheim, Germany; (H.T.); (A.H.); (F.T.); (J.R.); (P.W.); (I.A.); (D.N.); (S.O.S.)
- Correspondence:
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