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Bortolotto C, Pinto A, Brero F, Messana G, Cabini RF, Postuma I, Robustelli Test A, Stella GM, Galli G, Mariani M, Figini S, Lascialfari A, Filippi AR, Bottinelli OM, Preda L. CT and MRI radiomic features of lung cancer (NSCLC): comparison and software consistency. Eur Radiol Exp 2024; 8:71. [PMID: 38880866 PMCID: PMC11180643 DOI: 10.1186/s41747-024-00468-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 04/10/2024] [Indexed: 06/18/2024] Open
Abstract
BACKGROUND Radiomics is a quantitative approach that allows the extraction of mineable data from medical images. Despite the growing clinical interest, radiomics studies are affected by variability stemming from analysis choices. We aimed to investigate the agreement between two open-source radiomics software for both contrast-enhanced computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) of lung cancers and to preliminarily evaluate the existence of radiomic features stable for both techniques. METHODS Contrast-enhanced CT and MRI images of 35 patients affected with non-small cell lung cancer (NSCLC) were manually segmented and preprocessed using three different methods. Sixty-six Image Biomarker Standardisation Initiative-compliant features common to the considered platforms, PyRadiomics and LIFEx, were extracted. The correlation among features with the same mathematical definition was analyzed by comparing PyRadiomics and LIFEx (at fixed imaging technique), and MRI with CT results (for the same software). RESULTS When assessing the agreement between LIFEx and PyRadiomics across the considered resampling, the maximum statistically significant correlations were observed to be 94% for CT features and 95% for MRI ones. When examining the correlation between features extracted from contrast-enhanced CT and MRI using the same software, higher significant correspondences were identified in 11% of features for both software. CONCLUSIONS Considering NSCLC, (i) for both imaging techniques, LIFEx and PyRadiomics agreed on average for 90% of features, with MRI being more affected by resampling and (ii) CT and MRI contained mostly non-redundant information, but there are shape features and, more importantly, texture features that can be singled out by both techniques. RELEVANCE STATEMENT Identifying and selecting features that are stable cross-modalities may be one of the strategies to pave the way for radiomics clinical translation. KEY POINTS • More than 90% of LIFEx and PyRadiomics features contain the same information. • Ten percent of features (shape, texture) are stable among contrast-enhanced CT and MRI. • Software compliance and cross-modalities stability features are impacted by the resampling method.
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Affiliation(s)
- Chandra Bortolotto
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
| | - Alessandra Pinto
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy.
| | - Francesca Brero
- Department of Physics, University of Pavia, Via Bassi 6, Pavia, 27100, Italy
- Istituto Nazionale Di Fisica Nucleare, Sezione Di Pavia, Pavia, 27100, Italy
| | - Gaia Messana
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
| | - Raffaella Fiamma Cabini
- Istituto Nazionale Di Fisica Nucleare, Sezione Di Pavia, Pavia, 27100, Italy.
- Department of Mathematics, University of Pavia, Via Ferrata 5, Pavia, 27100, Italy.
| | - Ian Postuma
- Istituto Nazionale Di Fisica Nucleare, Sezione Di Pavia, Pavia, 27100, Italy
| | - Agnese Robustelli Test
- Department of Physics, University of Pavia, Via Bassi 6, Pavia, 27100, Italy.
- Istituto Nazionale Di Fisica Nucleare, Sezione Di Pavia, Pavia, 27100, Italy.
| | - Giulia Maria Stella
- Department of Medical Sciences and Infective Diseases, Unit of Respiratory Diseases, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
- Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, 27100, Italy
| | - Giulia Galli
- Department of Medical Sciences and Infective Diseases, Unit of Respiratory Diseases, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
- Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, 27100, Italy
| | - Manuel Mariani
- Department of Physics, University of Pavia, Via Bassi 6, Pavia, 27100, Italy
| | - Silvia Figini
- Department of Political and Social Sciences, University of Pavia, Pavia, 27100, Italy
| | - Alessandro Lascialfari
- Department of Physics, University of Pavia, Via Bassi 6, Pavia, 27100, Italy
- Istituto Nazionale Di Fisica Nucleare, Sezione Di Pavia, Pavia, 27100, Italy
| | - Andrea Riccardo Filippi
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
- Department of Radiation Oncology, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
| | - Olivia Maria Bottinelli
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
| | - Lorenzo Preda
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, Pavia, 27100, Italy
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
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Floca R, Bohn J, Haux C, Wiestler B, Zöllner FG, Reinke A, Weiß J, Nolden M, Albert S, Persigehl T, Norajitra T, Baeßler B, Dewey M, Braren R, Büchert M, Fallenberg EM, Galldiks N, Gerken A, Götz M, Hahn HK, Haubold J, Haueise T, Große Hokamp N, Ingrisch M, Iuga AI, Janoschke M, Jung M, Kiefer LS, Lohmann P, Machann J, Moltz JH, Nattenmüller J, Nonnenmacher T, Oerther B, Othman AE, Peisen F, Schick F, Umutlu L, Wichtmann BD, Zhao W, Caspers S, Schlemmer HP, Schlett CL, Maier-Hein K, Bamberg F. Radiomics workflow definition & challenges - German priority program 2177 consensus statement on clinically applied radiomics. Insights Imaging 2024; 15:124. [PMID: 38825600 PMCID: PMC11144687 DOI: 10.1186/s13244-024-01704-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/20/2024] [Indexed: 06/04/2024] Open
Abstract
OBJECTIVES Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation. MATERIALS AND METHODS The consensus was achieved by a multi-stage process. Stage 1 comprised a definition screening, a retrospective analysis with semantic mapping of terms found in 22 workflow definitions, and the compilation of an initial baseline definition. Stages 2 and 3 consisted of a Delphi process with over 45 experts hailing from sites participating in the German Research Foundation (DFG) Priority Program 2177. Stage 2 aimed to achieve a broad consensus for a definition proposal, while stage 3 identified the importance of translational challenges. RESULTS Workflow definitions from 22 publications (published 2012-2020) were analyzed. Sixty-nine definition terms were extracted, mapped, and semantic ambiguities (e.g., homonymous and synonymous terms) were identified and resolved. The consensus definition was developed via a Delphi process. The final definition comprising seven phases and 37 aspects reached a high overall consensus (> 89% of experts "agree" or "strongly agree"). Two aspects reached no strong consensus. In addition, the Delphi process identified and characterized from the participating experts' perspective the ten most important challenges in radiomics workflows. CONCLUSION To overcome semantic inconsistencies between existing definitions and offer a well-defined, broad, referenceable terminology, a consensus workflow definition for radiomics-based setups and a terms mapping to existing literature was compiled. Moreover, the most relevant challenges towards clinical application were characterized. CRITICAL RELEVANCE STATEMENT Lack of standardization represents one major obstacle to successful clinical translation of radiomics. Here, we report a consensus workflow definition on different aspects of radiomics studies and highlight important challenges to advance the clinical adoption of radiomics. KEY POINTS Published radiomics workflow terminologies are inconsistent, hindering standardization and translation. A consensus radiomics workflow definition proposal with high agreement was developed. Publicly available result resources for further exploitation by the scientific community.
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Affiliation(s)
- Ralf Floca
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany.
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
- National Center for Radiation Research in Oncology NCRO, Heidelberg Institute for Radiation Oncology HIRO, Heidelberg, Germany.
| | - Jonas Bohn
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Faculty of Bioscience, University of Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
- Translational Lung Research Center (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Christian Haux
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, TU Munich University Hospital, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, TU Munich, Munich, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Annika Reinke
- Intelligent Medical Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Weiß
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Marco Nolden
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Steffen Albert
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Tobias Norajitra
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Marc Dewey
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin Institute of Health, DZHK (German Centre for Cardiovascular Research), and DKTK (German Cancer Consortium), both partner sites Berlin, Berlin, Germany
| | - Rickmer Braren
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine & Health, Ismaninger Str. 22, 81675, München, Germany
- Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
- German Cancer Consortium (DKTK), Munich partner site, Heidelberg, Germany
| | - Martin Büchert
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Eva Maria Fallenberg
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine & Health, Ismaninger Str. 22, 81675, München, Germany
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3), Research Center Juelich (FZJ), Juelich, Germany
- Center of Integrated Oncology Aachen Bonn Cologne Duesseldorf (CIO ABCD), Aachen, Bonn, Cologne & Duesseldorf, Germany
| | - Annika Gerken
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Michael Götz
- Division of Experimental Radiology, Department for Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Horst K Hahn
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- Faculty 3, Mathematics and Computer Science, University of Bremen, Bremen, Germany
| | - Johannes Haubold
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Tobias Haueise
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Nils Große Hokamp
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Michael Ingrisch
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Andra-Iza Iuga
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Marco Janoschke
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Matthias Jung
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Lena Sophie Kiefer
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich (FZJ), Juelich, Germany
- Department of Nuclear Medicine, University Hospital RWTH Aachen, Aachen, Germany
| | - Jürgen Machann
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | | | - Johanna Nattenmüller
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Tobias Nonnenmacher
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Benedict Oerther
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Ahmed E Othman
- Department of Neuroradiology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Felix Peisen
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Fritz Schick
- Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Barbara D Wichtmann
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | - Wenzhao Zhao
- Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Institute for Anatomy I, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Heinz-Peter Schlemmer
- German Cancer Research Center (DKFZ) Heidelberg, Division of Radiology, Heidelberg, Germany
| | - Christopher L Schlett
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Freiburg, Germany
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Wang TW, Hong JS, Huang JW, Liao CY, Lu CF, Wu YT. Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation. Radiother Oncol 2024; 197:110344. [PMID: 38806113 DOI: 10.1016/j.radonc.2024.110344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
BACKGROUND Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable. METHODS We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined. RESULTS Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42. CONCLUSION This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.
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Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan; School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Jia-Sheng Hong
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Jing-Wen Huang
- Department of Radiation Oncology, Taichung Veterans General Hospital, Taichung 407, Taiwan
| | - Chien-Yi Liao
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei, Taiwan; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Chia-Feng Lu
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang-Ming Chiao Tung University, Taipei, Taiwan; National Yang Ming Chiao Tung University, Brain Research Center, Taiwan.
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Li T, Xu M, Yang S, Wang G, Liu Y, Liu K, Zhao K, Su X. Development and validation of [18 F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06734-6. [PMID: 38691111 DOI: 10.1007/s00259-024-06734-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/23/2024] [Indexed: 05/03/2024]
Abstract
PURPOSE Biochemical recurrence (BCR) following radical prostatectomy (RP) is a significant concern for patients with prostate cancer. Reliable prediction models are needed to identify patients at risk for BCR and facilitate appropriate management. This study aimed to develop and validate a clinical-radiomics model based on preoperative [18 F]PSMA-1007 PET for predicting BCR-free survival (BRFS) in patients who underwent RP for prostate cancer. MATERIALS AND METHODS A total of 236 patients with histologically confirmed prostate cancer who underwent RP were retrospectively analyzed. All patients had a preoperative [18 F]PSMA-1007 PET/CT scan. Radiomics features were extracted from the primary tumor region on PET images. A radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The performance of the radiomics signature in predicting BRFS was assessed using Harrell's concordance index (C-index). The clinical-radiomics nomogram was constructed using the radiomics signature and clinical features. The model was externally validated in an independent cohort of 98 patients. RESULTS The radiomics signature comprised three features and demonstrated a C-index of 0.76 (95% CI: 0.60-0.91) in the training cohort and 0.71 (95% CI: 0.63-0.79) in the validation cohort. The radiomics signature remained an independent predictor of BRFS in multivariable analysis (HR: 2.48, 95% CI: 1.47-4.17, p < 0.001). The clinical-radiomics nomogram significantly improved the prediction performance (C-index: 0.81, 95% CI: 0.66-0.95, p = 0.007) in the training cohort and (C-index: 0.78 95% CI: 0.63-0.89, p < 0.001) in the validation cohort. CONCLUSION We developed and validated a novel [18 F]PSMA-1007 PET-based clinical-radiomics model that can predict BRFS following RP in prostate cancer patients. This model may be useful in identifying patients with a higher risk of BCR, thus enabling personalized risk stratification and tailored management strategies.
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Affiliation(s)
- Tiancheng Li
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Mimi Xu
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Shuye Yang
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Guolin Wang
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Yinuo Liu
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Kaifeng Liu
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Kui Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China
| | - Xinhui Su
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, People's Republic of China.
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Zheng J, Hao Y, Guo Y, Du M, Wang P, Xin J. An 18F-FDG-PET/CT-based radiomics signature for estimating malignance probability of solitary pulmonary nodule. THE CLINICAL RESPIRATORY JOURNAL 2024; 18:e13751. [PMID: 38725315 PMCID: PMC11082539 DOI: 10.1111/crj.13751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 01/29/2024] [Accepted: 03/28/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND Some solitary pulmonary nodules (SPNs) as early manifestations of lung cancer, it is difficult to determine its nature, which brings great trouble to clinical diagnosis and treatment. Radiomics can deeply explore the essence of images and provide clinical decision support for clinicians. The purpose of our study was to explore the effect of positron emission tomography (PET) with 2-deoxy-2-[fluorine-18] fluoro-d-glucose integrated with computed tomography (CT; 18F-FDG-PET/CT) combined with radiomics for predicting probability of malignancy of SPNs. METHODS We retrospectively enrolled 190 patients with SPNs confirmed by pathology from January 2013 to December 2019 in our hospital. SPNs were benign in 69 patients and malignant in 121 patients. Patients were randomly divided into a training or testing group at a ratio of 7:3. Three-dimensional regions of interest (ROIs) were manually outlined on PET and CT images, and radiomics features were extracted. Synthetic minority oversampling technique (SMOTE) method was used to balance benign and malignant samples to a ratio of 1:1. In the training group, least absolute shrinkage and selection operator (LASSO) regression analyses and Spearman correlation analyses were used to select the strongest radiomics features. Three models including PET model, CT model, and joint model were constructed using multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were plotted to evaluate diagnostic efficiency, calibration degree, and clinical usefulness of all models in training and testing groups. RESULTS The estimative effectiveness of the joint model was superior to the CT or PET model alone in the training and testing groups. For the joint model, CT model, and PET model, area under the ROC curve was 0.929, 0.819, 0.833 in the training group, and 0.844, 0.759, 0.748 in the testing group, respectively. Calibration and decision curves showed good fit and clinical usefulness for the joint model in both training and testing groups. CONCLUSION Radiomics models constructed by combining PET and CT radiomics features are valuable for distinguishing benign and malignant SPNs. The combined effect is superior to qualitative diagnoses with CT or PET radiomics models alone.
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Affiliation(s)
- Jingchi Zheng
- Radiology DepartmentShengjing Hospital of China Medical UniversityShenyangChina
| | - Yue Hao
- Radiology DepartmentShengjing Hospital of China Medical UniversityShenyangChina
| | | | - Ming Du
- Nuclear Medicine DepartmentShengjing Hospital of China Medical UniversityShenyangChina
| | - Pengyuan Wang
- Nuclear Medicine DepartmentShengjing Hospital of China Medical UniversityShenyangChina
| | - Jun Xin
- Nuclear Medicine DepartmentShengjing Hospital of China Medical UniversityShenyangChina
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Hosseini SA, Shiri I, Ghaffarian P, Hajianfar G, Avval AH, Seyfi M, Servaes S, Rosa-Neto P, Zaidi H, Ay MR. The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods. Ann Nucl Med 2024:10.1007/s12149-024-01923-7. [PMID: 38575814 DOI: 10.1007/s12149-024-01923-7] [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: 12/21/2023] [Accepted: 03/07/2024] [Indexed: 04/06/2024]
Abstract
PURPOSE This study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC). METHODS We included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with 'n_splits' set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome. RESULTS From 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity. CONCLUSION Radiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.
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Affiliation(s)
- Seyyed Ali Hosseini
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | - Pardis Ghaffarian
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
- PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland
| | | | - Milad Seyfi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC, Canada
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, QC, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, QC, Canada
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211, Geneva 4, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, 500, Odense, Denmark.
- University Research and Innovation Center, Óbudabuda University, Budapest, Hungary.
| | - Mohammad Reza Ay
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
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Zhang Y, Liu H, Chang C, Yin Y, Wang R. Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics. PLoS One 2024; 19:e0300170. [PMID: 38568892 PMCID: PMC10990193 DOI: 10.1371/journal.pone.0300170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/22/2024] [Indexed: 04/05/2024] Open
Abstract
Noninvasive differentiation between the squamous cell carcinoma (SCC) and adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit patients who are unsuitable for invasive diagnostic procedures. Therefore, this study evaluates the predictive performance of a PET/CT-based radiomics model. It aims to distinguish between the histological subtypes of lung adenocarcinoma and squamous cell carcinoma, employing four different machine learning techniques. A total of 255 Non-Small Cell Lung Cancer (NSCLC) patients were retrospectively analyzed and randomly divided into the training (n = 177) and validation (n = 78) sets, respectively. Radiomics features were extracted, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was employed for feature selection. Subsequently, models were constructed using four distinct machine learning techniques, with the top-performing algorithm determined by evaluating metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC). The efficacy of the various models was appraised and compared using the DeLong test. A nomogram was developed based on the model with the best predictive efficiency and clinical utility, and it was validated using calibration curves. Results indicated that the logistic regression classifier had better predictive power in the validation cohort of the radiomic model. The combined model (AUC 0.870) exhibited superior predictive power compared to the clinical model (AUC 0.848) and the radiomics model (AUC 0.774). In this study, we discovered that the combined model, refined by the logistic regression classifier, exhibited the most effective performance in classifying the histological subtypes of NSCLC.
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Affiliation(s)
- Yalin Zhang
- Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China
- Xinjiang Key Laboratory of Oncology, Urumqi, China
| | - Huiling Liu
- Department of Radiation Oncology, Binzhou People’s Hospital, Binzhou, China
| | - Cheng Chang
- Department of Nuclear Medicine, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ruozheng Wang
- Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China
- Xinjiang Key Laboratory of Oncology, Urumqi, China
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8
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Wang Y, Lyu D, Hu L, Wu J, Duan S, Zhou T, Tu W, Xiao Y, Fan L, Liu S. CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:520-535. [PMID: 38343212 PMCID: PMC11031508 DOI: 10.1007/s10278-023-00939-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/14/2023] [Accepted: 09/22/2023] [Indexed: 04/20/2024]
Abstract
The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model. The GTV, PTV5, PTV10, PTV15, PTV20, GPTV5, GPTV10, GPTV15, and GPTV20 models were constructed based on intratumoral and peritumoral (5 mm, 10 mm, 15 mm, 20 mm) radiomics features. Additionally, the radscore of the optimal radiomics model and clinical-radiological predictors were used to construct a combined model and plot a nomogram. Lastly, the ROC curve and AUC value were used to evaluate the diagnostic performance of the model. Tumor density type (OR = 6.738) and distal ribbon sign (OR = 5.141) were independent risk factors for the occurrence of STAS. The GPTV10 model outperformed the other radiomics models, and its AUC values were 0.887, 0.876, and 0.868 in the three cohorts. The AUC values of the combined model constructed based on GPTV10 radscore and clinical-radiological predictors were 0.901, 0.875, and 0.878. DeLong test results revealed that the combined model was superior to the clinical model in the three cohorts. The nomogram based on GPTV10 radscore and clinical-radiological features exhibited high predictive efficiency for STAS status in NSCLC.
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Affiliation(s)
- Yun Wang
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China
| | - Deng Lyu
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China
| | - Lei Hu
- Department of Radiology Medicine, The People's Hospital of Chizhou, Chizhou, Anhui, 247100, China
| | - Junhong Wu
- Department of Radiology Medicine, The People's Hospital of Guigang, Guigang, Guangxi Zhuang Autonomous Region, 537100, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Taohu Zhou
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China
| | - Wenting Tu
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China
| | - Yi Xiao
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China
| | - Li Fan
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China.
| | - Shiyuan Liu
- Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, 200003, China.
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Zhao W, Ozawa Y, Hara M, Okuda K, Hiwatashi A. Computed tomography radiomic feature analysis of thymic epithelial tumors: Differentiation of thymic epithelial tumors from thymic cysts and prediction of histological subtypes. Jpn J Radiol 2024; 42:367-373. [PMID: 38010596 DOI: 10.1007/s11604-023-01512-0] [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: 07/24/2023] [Accepted: 10/28/2023] [Indexed: 11/29/2023]
Abstract
PURPOSE To investigate the value of computed tomography (CT) radiomic feature analysis for the differential diagnosis between thymic epithelial tumors (TETs) and thymic cysts, and prediction of histological subtypes of TETs. MATERIALS AND METHODS Twenty-four patients with TETs (13 low-risk and 9 high-risk thymomas, and 2 thymic carcinomas) and 12 with thymic cysts were included in this study. For each lesion, the radiomic features of a volume of interest covering the lesion were extracted from non-contrast enhanced CT images. The Least Absolute Shrinkage and Selection Operator (Lasso) method was used for the feature selection. Predictive models for differentiating TETs from thymic cysts (model A), and high risk thymomas + thymic carcinomas from low risk thymomas (model B) were created from the selected features. The receiver operating characteristic curve was used to evaluate the effectiveness of radiomic feature analysis for differentiating among these tumors. RESULTS In model A, the selected 5 radiomic features for the model A were NGLDM_Contrast, GLCM_Correlation, GLZLM_SZLGE, DISCRETIZED_HISTO_Entropy_log2, and DISCRETIZED_HUmin. In model B, sphericity was the only selected feature. The area under the curve, sensitivity, and specificity of radiomic feature analysis were 1 (95% confidence interval [CI]: 1-1), 100%, and 100%, respectively, for differentiating TETs from thymic cysts (model A), and 0.76 (95%CI: 0.53-0.99), 64%, and 100% respectively, for differentiating high-risk thymomas + thymic carcinomas from low-risk thymomas (model B). CONCLUSION CT radiomic analysis could be utilized as a non-invasive imaging technique for differentiating TETs from thymic cysts, and high-risk thymomas + thymic carcinomas from low-risk thymomas.
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Affiliation(s)
- Wenya Zhao
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Yoshiyuki Ozawa
- Department of Radiology, Fujita Health University School of Medicine, 1-98, Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan.
- Department of Radiology, Fujita Health University Okazaki Medical Center, Okazaki, Japan.
| | - Masaki Hara
- Nagoya Johoku Teleradiology Clinic, Nagoya, Japan
| | - Katsuhiro Okuda
- Department of Oncology, Immunology and Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Akio Hiwatashi
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
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10
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Sheikh TS, Cho M. Segmentation of Variants of Nuclei on Whole Slide Images by Using Radiomic Features. Bioengineering (Basel) 2024; 11:252. [PMID: 38534526 DOI: 10.3390/bioengineering11030252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/10/2024] [Accepted: 02/26/2024] [Indexed: 03/28/2024] Open
Abstract
The histopathological segmentation of nuclear types is a challenging task because nuclei exhibit distinct morphologies, textures, and staining characteristics. Accurate segmentation is critical because it affects the diagnostic workflow for patient assessment. In this study, a framework was proposed for segmenting various types of nuclei from different organs of the body. The proposed framework improved the segmentation performance for each nuclear type using radiomics. First, we used distinct radiomic features to extract and analyze quantitative information about each type of nucleus and subsequently trained various classifiers based on the best input sub-features of each radiomic feature selected by a LASSO operator. Second, we inputted the outputs of the best classifier to various segmentation models to learn the variants of nuclei. Using the MoNuSAC2020 dataset, we achieved state-of-the-art segmentation performance for each category of nuclei type despite the complexity, overlapping, and obscure regions. The generalized adaptability of the proposed framework was verified by the consistent performance obtained in whole slide images of different organs of the body and radiomic features.
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Affiliation(s)
- Taimoor Shakeel Sheikh
- AIMI-Artificial Intelligence and Medical Imaging Laboratory, Department of Computer & Media Engineering, Tongmyong University, Busan 48520, Republic of Korea
| | - Migyung Cho
- AIMI-Artificial Intelligence and Medical Imaging Laboratory, Department of Computer & Media Engineering, Tongmyong University, Busan 48520, Republic of Korea
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Abstract
At present, no biomarker exists which is truly specific for sarcoidosis and the ones available have modest sensitivity and specificity. The clinical context should dictate the choice of biomarker(s) used to address different clinical questions such as diagnosis, monitoring disease activity or monitoring response to treatment. In the future, in addition to known serum biomarkers, it seems fruitful to further explore a possible role of imaging, exhaled air and even biopsy-related biomarkers in sarcoidosis to guide clinical management.
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Affiliation(s)
- Sophie C van der Mark
- Department of Pulmonology, ILD Center of Excellence, St. Antonius Hospital, Koekoekslaan 1, 3435 CM, Nieuwegein, The Netherlands; Division of Heart and Lungs, University Medical Center, Utrecht, The Netherlands
| | - Vikaash W S Bajnath
- Department of Pulmonology, ILD Center of Excellence, St. Antonius Hospital, Koekoekslaan 1, 3435 CM, Nieuwegein, The Netherlands
| | - Marcel Veltkamp
- Department of Pulmonology, ILD Center of Excellence, St. Antonius Hospital, Koekoekslaan 1, 3435 CM, Nieuwegein, The Netherlands; Division of Heart and Lungs, University Medical Center, Utrecht, The Netherlands.
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12
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Beaudoin AM, Ho JK, Lam A, Thijs V. Radiomics Studies on Ischemic Stroke and Carotid Atherosclerotic Disease: A Reporting Quality Assessment. Can Assoc Radiol J 2024:8465371241234545. [PMID: 38420881 DOI: 10.1177/08465371241234545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
Objective: To assess the reporting quality of radiomics studies on ischemic stroke, intracranial and carotid atherosclerotic disease using the Image Biomarker Standardization Initiative (IBSI) reporting guidelines with the aim of finding avenues of improvement for future publications. Method: PubMed database was searched to identify relevant radiomics studies. Of 560 articles, 41 original research articles were included in this analysis. Based on IBSI radiomics reporting guidelines, checklists for CT-based and MRI-based studies were created to allow a structured and comprehensive evaluation of each study's adherence to these guidelines. Results: The main topics covered by the included radiomics studies were ischemic stroke, intracranial artery disease, and carotid atherosclerotic disease. The reporting checklist median score was 17/40 for the 20 CT-based radiomics studies and 22.5/50 for the 20 MRI-based studies. Basic items like imaging modality, region of interest, and image biomarker set utilized were included in all studies. However, details regarding image acquisition and reconstruction, post-acquisition image processing, and image biomarkers computation were inconsistently detailed across studies. Conclusion: The overall reporting quality of the included radiomics studies was suboptimal. These findings underscore a pressing need for improved reporting practices in radiomics research, to ensure validation and reproducibility of results. Our study provides insights into current reporting standards and highlights specific areas where adherence to IBSI guidelines could be significantly improved.
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Affiliation(s)
- Ann-Marie Beaudoin
- Université de Sherbrooke, Sherbrooke, QC, Canada
- The Florey, Heidelberg, VIC, Australia
| | - Jan Kee Ho
- The Florey, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
| | | | - Vincent Thijs
- The Florey, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
- Department of Medicine, University of Melbourne, Heidelberg, VIC, Australia
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13
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Tominaga M, Yamazaki M, Umezu H, Sugino H, Fuzawa Y, Yagi T, Ishikawa H. Prognostic Value and Pathological Correlation of Peritumoral Radiomics in Surgically Resected Non-Small Cell Lung Cancer. Acad Radiol 2024:S1076-6332(24)00055-2. [PMID: 38402002 DOI: 10.1016/j.acra.2024.01.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/24/2024] [Accepted: 01/24/2024] [Indexed: 02/26/2024]
Abstract
RATIONALE AND OBJECTIVES To determine the additional value of peritumoral radiomics in predicting overall survival (OS) in surgically resected non-small cell lung cancer (NSCLC) and its correlation with pathological findings. METHODS A total of 526 patients with surgically resected NSCLC were included (191 training, 160 internal validation, and 175 external validation cohorts). CT images were used to segment the gross tumor volume (GTV) and peritumoral volume (PTV) within distances of 3, 6, 9 mm from the tumor boundary (PTV3, PTV6, and PTV9), and radiomic features were extracted. Four prognostic models for OS (GTV, GTV + PTV3, GTV + PTV6, and GTV + PTV9) were constructed using the training cohort. The prognostic ability and feature importance were evaluated using the validation cohorts. Pathological findings were compared between the two patient groups (n = 30 for each) having the top 30 and bottom 30 values of the most important peritumoral feature. RESULTS The GTV+ PTV3 models exhibited the highest predictive ability, which was higher than that of the GTV model in the internal validation cohort (C-index: 0.666 vs. 0.616, P = 0.027) and external validation cohort (C-index: 0.705 vs. 0.656, P = 0.048). The most important feature was GLDM_Dependence_Entropy, extracted from PTV3. High peritumoral GLDM_Dependence_Entropy was associated with a high proportion of invasive histological types, tumor spread through air spaces, and tumor-infiltrating lymphocytes (all P < 0.05). CONCLUSION The GTV and PTV3 combination demonstrated a higher prognostic ability, compared to GTV alone. Peritumoral radiomic features may be associated with various pathological prognostic factors.
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Affiliation(s)
- Masaki Tominaga
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Motohiko Yamazaki
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
| | - Hajime Umezu
- Division of Pathology, Niigata University Medical and Dental Hospital, Niigata, Japan
| | - Hideaki Sugino
- Division of Pathology, Niigata University Medical and Dental Hospital, Niigata, Japan
| | - Yuma Fuzawa
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takuya Yagi
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hiroyuki Ishikawa
- Department of Radiology and Radiation Oncology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
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Hassan J, Saeed SM, Deka L, Uddin MJ, Das DB. Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges. Pharmaceutics 2024; 16:260. [PMID: 38399314 PMCID: PMC10892549 DOI: 10.3390/pharmaceutics16020260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.
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Affiliation(s)
- Jasmin Hassan
- Drug Delivery & Therapeutics Lab, Dhaka 1212, Bangladesh; (J.H.); (S.M.S.)
| | | | - Lipika Deka
- Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK;
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Diganta B. Das
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK
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15
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Zou Y, Mao Q, Zhao Z, Zhou X, Pan Y, Zuo Z, Zhang W. Intratumoural and peritumoural CT-based radiomics for diagnosing lepidic-predominant adenocarcinoma in patients with pure ground-glass nodules: a machine learning approach. Clin Radiol 2024; 79:e211-e218. [PMID: 38044199 DOI: 10.1016/j.crad.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 10/10/2023] [Accepted: 11/06/2023] [Indexed: 12/05/2023]
Abstract
AIM To develop and validate a diagnostic model utilising machine-learning algorithms that differentiates lepidic predominant adenocarcinoma (LPA) from other pathological subtypes in patients with pure ground-glass nodules (pGGNs). MATERIALS AND METHODS This bicentric study was conducted across two medical centres and included 151 patients diagnosed with lung adenocarcinoma based on histopathological confirmation of pGGNs. The training cohort consisted of 99 patients from Institution 1, while the test cohort included 52 patients from Institution 2. Radiomics features were extracted from both tumours and the 2 mm peritumoural parenchyma. The tumoural and peritumoural radiomics were designated as Modeltumoural and Modelperitumoural, respectively. The diagnostic efficacy of various models was evaluated through the receiver operating characteristic (ROC) curve analysis. Subsequently, a machine-learning-based prediction model that combined Modeltumoural, Modelperitumoural, and Modelclinical-radiological was developed to differentiate LPA from other pathological subtypes in patients with pGGNs. RESULTS Modeltumoural achieved area under the curve (AUC) values of 0.762 and 0.783 in the training and validation sets, respectively. Modelperitumoural attained AUCs of 0.742 and 0.667, and Modelclinical-radiological generated an AUC of 0.727 and 0.739 in the training and validation sets, respectively. Among the machine-learning models evaluated, gradient boosting machines demonstrated the best diagnostic efficacy, with accuracy, AUC, F1 score, and log loss values of 0.885, 0.956, 0.943, and 0.260, respectively. CONCLUSION The combined model based on machine learning that incorporated tumour and peritumoural parenchyma, as well as clinical and imaging characteristics, may offer benefits in assessing the pathological subtype of pGGNs.
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Affiliation(s)
- Y Zou
- Department of Radiology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, 545006, China; Guangxi Key Clinical Specialties of Medical Imaging, Liuzhou, 545006, China; Liuzhou Key Laboratory of Molecular Imaging, Liuzhou, 545006, China
| | - Q Mao
- Department of Radiology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, 545006, China; Guangxi Key Clinical Specialties of Medical Imaging, Liuzhou, 545006, China; Liuzhou Key Laboratory of Molecular Imaging, Liuzhou, 545006, China
| | - Z Zhao
- Department of Radiology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, 545006, China; Guangxi Key Clinical Specialties of Medical Imaging, Liuzhou, 545006, China; Liuzhou Key Laboratory of Molecular Imaging, Liuzhou, 545006, China
| | - X Zhou
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, 411000, China
| | - Y Pan
- Department of Radiology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, 545006, China; Guangxi Key Clinical Specialties of Medical Imaging, Liuzhou, 545006, China; Liuzhou Key Laboratory of Molecular Imaging, Liuzhou, 545006, China
| | - Z Zuo
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, 411000, China
| | - W Zhang
- Department of Radiology, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, 545006, China; Guangxi Key Clinical Specialties of Medical Imaging, Liuzhou, 545006, China; Liuzhou Key Laboratory of Molecular Imaging, Liuzhou, 545006, China.
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16
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Dia AK, Ebrahimpour L, Yolchuyeva S, Tonneau M, Lamaze FC, Orain M, Coulombe F, Malo J, Belkaid W, Routy B, Joubert P, Després P, Manem VSK. The Cross-Scale Association between Pathomics and Radiomics Features in Immunotherapy-Treated NSCLC Patients: A Preliminary Study. Cancers (Basel) 2024; 16:348. [PMID: 38254838 PMCID: PMC10813866 DOI: 10.3390/cancers16020348] [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: 10/17/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Recent advances in cancer biomarker development have led to a surge of distinct data modalities, such as medical imaging and histopathology. To develop predictive immunotherapy biomarkers, these modalities are leveraged independently, despite their orthogonality. This study aims to explore the cross-scale association between radiological scans and digitalized pathology images for immunotherapy-treated non-small cell lung cancer (NSCLC) patients. METHODS This study involves 36 NSCLC patients who were treated with immunotherapy and for whom both radiology and pathology images were available. A total of 851 and 260 features were extracted from CT scans and cell density maps of histology images at different resolutions. We investigated the radiopathomics relationship and their association with clinical and biological endpoints. We used the Kolmogorov-Smirnov (KS) method to test the differences between the distributions of correlation coefficients with the two imaging modality features. Unsupervised clustering was done to identify which imaging modality captures poor and good survival patients. RESULTS Our results demonstrated a significant correlation between cell density pathomics and radiomics features. Furthermore, we also found a varying distribution of correlation values between imaging-derived features and clinical endpoints. The KS test revealed that the two imaging feature distributions were different for PFS and CD8 counts, while similar for OS. In addition, clustering analysis resulted in significant differences in the two clusters generated from the radiomics and pathomics features with respect to patient survival and CD8 counts. CONCLUSION The results of this study suggest a cross-scale association between CT scans and pathology H&E slides among ICI-treated patients. These relationships can be further explored to develop multimodal immunotherapy biomarkers to advance personalized lung cancer care.
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Affiliation(s)
- Abdou Khadir Dia
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
| | - Leyla Ebrahimpour
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
- Department of Physics, Laval University, Quebec City, QC G1V 0A6, Canada
- Centre de Recherche du CHU de Québec-Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Sevinj Yolchuyeva
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
- Centre de Recherche du CHU de Québec-Université Laval, Quebec City, QC G1V 0A6, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Marion Tonneau
- Lille Faculty of Medicine, University of Lille, 59020 Lille, France
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Fabien C. Lamaze
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
| | - Michèle Orain
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
| | - Francois Coulombe
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
| | - Julie Malo
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Wiam Belkaid
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Bertrand Routy
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Philippe Joubert
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Philippe Després
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
- Department of Physics, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Venkata S. K. Manem
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
- Centre de Recherche du CHU de Québec-Université Laval, Quebec City, QC G1V 0A6, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, QC G1V 0A6, Canada
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Meng X, Xu H, Liang Y, Liang M, Song W, Zhou B, Shi J, Du M, Gao Y. Enhanced CT-based radiomics model to predict natural killer cell infiltration and clinical prognosis in non-small cell lung cancer. Front Immunol 2024; 14:1334886. [PMID: 38283362 PMCID: PMC10811188 DOI: 10.3389/fimmu.2023.1334886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 12/29/2023] [Indexed: 01/30/2024] Open
Abstract
Background Natural killer (NK) cells are crucial for tumor prognosis; however, their role in non-small-cell lung cancer (NSCLC) remains unclear. The current detection methods for NSCLC are inefficient and costly. Therefore, radiomics represent a promising alternative. Methods We analyzed the radiogenomics datasets to extract clinical, radiological, and transcriptome data. The effect of NK cells on the prognosis of NSCLC was assessed. Tumors were delineated using a 3D Slicer, and features were extracted using pyradiomics. A radiomics model was developed and validated using five-fold cross-validation. A nomogram model was constructed using the selected clinical variables and a radiomic score (RS). The CIBERSORTx database and gene set enrichment analysis were used to explore the correlations of NK cell infiltration and molecular mechanisms. Results Higher infiltration of NK cells was correlated with better overall survival (OS) (P = 0.002). The radiomic model showed an area under the curve of 0.731, with 0.726 post-validation. The RS differed significantly between high and low infiltration of NK cells (P < 0.01). The nomogram, using RS and clinical variables, effectively predicted 3-year OS. NK cell infiltration was correlated with the ICOS and BTLA genes (P < 0.001) and macrophage M0/M2 levels. The key pathways included TNF-α signaling via NF-κB and Wnt/β-catenin signaling. Conclusions Our radiomic model accurately predicted NK cell infiltration in NSCLC. Combined with clinical characteristics, it can predict the prognosis of patients with NSCLC. Bioinformatic analysis revealed the gene expression and pathways underlying NK cell infiltration in NSCLC.
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Affiliation(s)
- Xiangzhi Meng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haijun Xu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yicheng Liang
- Department of Thoracic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Mei Liang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weijian Song
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Boxuan Zhou
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianwei Shi
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Minjun Du
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yushun Gao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Lu J, Ji X, Liu X, Jiang Y, Li G, Fang P, Li W, Zuo A, Guo Z, Yang S, Ji Y, Lu D. Machine learning-based radiomics strategy for prediction of acquired EGFR T790M mutation following treatment with EGFR-TKI in NSCLC. Sci Rep 2024; 14:446. [PMID: 38172228 PMCID: PMC10764785 DOI: 10.1038/s41598-023-50984-7] [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: 10/15/2023] [Accepted: 12/28/2023] [Indexed: 01/05/2024] Open
Abstract
The epidermal growth factor receptor (EGFR) Thr790 Met (T790M) mutation is responsible for approximately half of the acquired resistance to EGFR-tyrosine kinase inhibitor (TKI) in non-small-cell lung cancer (NSCLC) patients. Identifying patients at diagnosis who are likely to develop this mutation after first- or second-generation EGFR-TKI treatment is crucial for better treatment outcomes. This study aims to develop and validate a radiomics-based machine learning (ML) approach to predict the T790M mutation in NSCLC patients at diagnosis. We collected retrospective data from 210 positive EGFR mutation NSCLC patients, extracting 1316 radiomics features from CT images. Using the LASSO algorithm, we selected 10 radiomics features and 2 clinical features most relevant to the mutations. We built models with 7 ML approaches and assessed their performance through the receiver operating characteristic (ROC) curve. The radiomics model and combined model, which integrated radiomics features and relevant clinical factors, achieved an area under the curve (AUC) of 0.80 (95% confidence interval [CI] 0.79-0.81) and 0.86 (0.87-0.88), respectively, in predicting the T790M mutation. Our study presents a convenient and noninvasive radiomics-based ML model for predicting this mutation at the time of diagnosis, aiding in targeted treatment planning for NSCLC patients with EGFR mutations.
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Affiliation(s)
- Jiameng Lu
- Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical Medicine, 16766 Jingshilu, Lixia, Jinan, 250014, Shandong, People's Republic of China
- School of Microelectronics, Shandong University, Jinan, 250100, Shandong, People's Republic of China
| | - Xiaoqing Ji
- Department of Nursing, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, 250014, Shandong, People's Republic of China
| | - Xinyi Liu
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Yunxiu Jiang
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Gang Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medicine Imaging, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, 250000, Shandong, China
| | - Ping Fang
- Department of Blood Transfusion, The First Affiliated Hospital of Shandong First Medical University and Shandong Province Qianfoshan Hospital, Jinan, 250014, Shandong, China
| | - Wei Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medicine Imaging, Shandong Lung Cancer Institute, Shandong Institute of Neuroimmunology, Jinan, 250000, Shandong, China
| | - Anli Zuo
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Zihan Guo
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Shuran Yang
- Graduate School of Shandong First Medical University, Jinan, 250000, Shandong, People's Republic of China
| | - Yanbo Ji
- Department of Nursing, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Jinan, 250014, Shandong, People's Republic of China
| | - Degan Lu
- Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical Medicine, 16766 Jingshilu, Lixia, Jinan, 250014, Shandong, People's Republic of China.
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Meng N, Feng P, Yu X, Wu Y, Fu F, Li Z, Luo Y, Tan H, Yuan J, Yang Y, Wang Z, Wang M. An [ 18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer. Eur Radiol 2024; 34:318-329. [PMID: 37530809 DOI: 10.1007/s00330-023-09978-2] [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: 11/14/2022] [Revised: 04/09/2023] [Accepted: 04/21/2023] [Indexed: 08/03/2023]
Abstract
OBJECTIVES To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC). METHODS A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA). RESULTS A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively. CONCLUSION The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC. CLINICAL RELEVANCE STATEMENT A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice. KEY POINTS • The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.
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Affiliation(s)
- Nan Meng
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, 7 Weiwu Road, Zhengzhou, 450000, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Biomedical Research Institute, Henan Academy of Science, Zhengzhou, China
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Pengyang Feng
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, 7 Weiwu Road, Zhengzhou, 450000, China
- Department of Medical Imaging, Henan University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Xuan Yu
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, 7 Weiwu Road, Zhengzhou, 450000, China
| | - Yaping Wu
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, 7 Weiwu Road, Zhengzhou, 450000, China
| | - Fangfang Fu
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, 7 Weiwu Road, Zhengzhou, 450000, China
| | - Ziqiang Li
- Department of Medical Imaging, Xinxiang Medical University People's Hospital & Henan Provincial People's Hospital, Zhengzhou, China
| | - Yu Luo
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, 7 Weiwu Road, Zhengzhou, 450000, China
| | - Hongna Tan
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, 7 Weiwu Road, Zhengzhou, 450000, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Yang Yang
- Beijing United Imaging Research Institute of Intelligent Imaging, United Imaging Healthcare Group, Beijing, China
| | - Zhe Wang
- Central Research Institute, United Imaging Healthcare Group, Shanghai, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, 7 Weiwu Road, Zhengzhou, 450000, China.
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Biomedical Research Institute, Henan Academy of Science, Zhengzhou, China.
- Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China.
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Sharma M, Kirby M, McCormack DG, Parraga G. Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity. Acad Radiol 2023:S1076-6332(23)00658-X. [PMID: 38161089 DOI: 10.1016/j.acra.2023.11.022] [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: 10/07/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 01/03/2024]
Abstract
RATIONALE AND OBJECTIVES Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DLCO) are at higher risk of developing COPD. It remains difficult to make clinical management decisions for such ex-smokers without other objective assessments consistent with COPD. Hence, our objective was to develop a machine-learning and CT texture-analysis pipeline to dichotomize ex-smokers with normal and abnormal DLCO (DLCO≥75%pred and DLCO<75%pred). MATERIALS AND METHODS In this retrospective study, 71 ex-smokers (50-85yrs) without COPD underwent spirometry, plethysmography, thoracic CT, and 3He MRI to generate ventilation defect percent (VDP) and apparent diffusion coefficients (ADC). PyRadiomics was utilized to extract 496 CT texture-features; Boruta and principal component analysis were used for feature selection and various models were investigated for classification. Machine-learning classifiers were evaluated using area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and F1-measure. RESULTS Of 71 ex-smokers without COPD, 29 with mildly abnormal DLCO had significantly different MRI ADC (p < .001), residual-volume to total-lung-capacity ratio (p = .003), St. George's Respiratory Questionnaire (p = .029), and six-minute-walk distance (6MWD) (p < .001), but similar relative area of the lung < -950 Hounsfield-units (RA950) (p = .9) compared to 42 ex-smokers with normal DLCO. Logistic-regression machine-learning mixed-model trained on selected texture-features achieved the best classification accuracy of 87%. All clinical and imaging measurements were outperformed by high-high-pass filter high-gray-level-run-emphasis texture-feature (AUC=0.81), which correlated with DLCO (ρ = -0.29, p = .02), MRI ADC (ρ = 0.23, p = .048), and 6MWD (ρ = -0.25, p = .02). CONCLUSION In ex-smokers with no CT evidence of emphysema, machine-learning models exclusively trained on CT texture-features accurately classified ex-smokers with abnormal diffusing capacity, outperforming conventional quantitative CT measurements.
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Affiliation(s)
- Maksym Sharma
- Robarts Research Institute, Western University, 1151 Richmond St N, London, N6A 5B7, Canada (M.S., G.P.); Department of Medical Biophysics, Western University, London, Canada (M.S., G.P.)
| | - Miranda Kirby
- Department of Physics, Toronto Metropolitan University, Toronto, Canada (M.K.)
| | | | - Grace Parraga
- Robarts Research Institute, Western University, 1151 Richmond St N, London, N6A 5B7, Canada (M.S., G.P.); Department of Medical Biophysics, Western University, London, Canada (M.S., G.P.); Division of Respirology, Department of Medicine (D.G.M., G.P.); School of Biomedical Engineering, Western University, London, Canada (G.P.).
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Xu H, Wu W, Zhao Y, Liu Z, Bao D, Li L, Lin M, Zhang Y, Zhao X, Luo D. Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma. Cancer Imaging 2023; 23:118. [PMID: 38098119 PMCID: PMC10722708 DOI: 10.1186/s40644-023-00629-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/19/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Postsurgical recurrence is of great concern for papillary thyroid carcinoma (PTC). We aim to investigate the value of computed tomography (CT)-based radiomics features and conventional clinical factors in predicting the recurrence of PTC. METHODS Two-hundred and eighty patients with PTC were retrospectively enrolled and divided into training and validation cohorts at a 6:4 ratio. Recurrence was defined as cytology/pathology-proven disease or morphological evidence of lesions on imaging examinations within 5 years after surgery. Radiomics features were extracted from manually segmented tumor on CT images and were then selected using four different feature selection methods sequentially. Multivariate logistic regression analysis was conducted to identify clinical features associated with recurrence. Radiomics, clinical, and combined models were constructed separately using logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and neural network (NN), respectively. Receiver operating characteristic analysis was performed to evaluate the model performance in predicting recurrence. A nomogram was established based on all relevant features, with its reliability and reproducibility verified using calibration curves and decision curve analysis (DCA). RESULTS Eighty-nine patients with PTC experienced recurrence. A total of 1218 radiomics features were extracted from each segmentation. Five radiomics and six clinical features were related to recurrence. Among the 4 radiomics models, the LR-based and SVM-based radiomics models outperformed the NN-based radiomics model (P = 0.032 and 0.026, respectively). Among the 4 clinical models, only the difference between the area under the curve (AUC) of the LR-based and NN-based clinical model was statistically significant (P = 0.035). The combined models had higher AUCs than the corresponding radiomics and clinical models based on the same classifier, although most differences were not statistically significant. In the validation cohort, the combined models based on the LR, SVM, KNN, and NN classifiers had AUCs of 0.746, 0.754, 0.669, and 0.711, respectively. However, the AUCs of these combined models had no significant differences (all P > 0.05). Calibration curves and DCA indicated that the nomogram have potential clinical utility. CONCLUSIONS The combined model may have potential for better prediction of PTC recurrence than radiomics and clinical models alone. Further testing with larger cohort may help reach statistical significance.
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Affiliation(s)
- Haijun Xu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Wenli Wu
- Medical Imaging Center, Liaocheng Tumor Hospital, Liaocheng, 252000, China
| | - Yanfeng Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Zhou Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Dan Bao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lin Li
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Meng Lin
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Ya Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Xinming Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Dehong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China.
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Zhu J, Qu Y, Lu M, Ma A, Mo J, Wen Z. CT-based radiomics for prediction of pulmonary haemorrhage after percutaneous CT-guided transthoracic lung biopsy of pulmonary nodules. Clin Radiol 2023; 78:e993-e1000. [PMID: 37726191 DOI: 10.1016/j.crad.2023.08.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/14/2023] [Accepted: 08/23/2023] [Indexed: 09/21/2023]
Abstract
AIM To evaluate the feasibility of intranodular and perinodular computed tomography (CT) radiomics features for predicting the occurrence of pulmonary haemorrhage after percutaneous CT-guided transthoracic lung biopsy (PCTLB) in pulmonary nodules. MATERIALS AND METHODS The data for 332 patients with pulmonary nodules who underwent PCTLB were reviewed retrospectively. Pulmonary haemorrhage after PCTLB was evaluated using CT (144 cases occurred). Radiomics features based on gross nodular (GNV) and perinodular volumes (PNV) were extracted from pre-biopsy CT images and features selection using least absolute shrinkage and selection operator (LASSO) regression, and three radiomics scores (rad-scores) were built. Rad-scores, clinical, and clinical-radiomic models were developed and evaluated to predict the occurrence of pulmonary haemorrhage. RESULTS Five, five, and six significant features were selected for prediction of pulmonary haemorrhage based on GNV, PNV, and GNV + PNV, respectively. Lesion depth was the only clinical characteristics related to pulmonary haemorrhage. Lesion depth and rad-score based on GNV, PNV, and GNV + PNV for predicting the pulmonary haemorrhage achieved areas under the curves (AUCs) of 0.656, 0.645, 0.651, and 0.635 in the validation group, respectively. Three clinical-radiomic models improved the AUCs to 0.743, 0.723, and 0.748. The performance of rad-score_GNV + PNV combined with lesion depth outperformed the clinical model (p=0.024) and the radiomics signature (p=0.038). In addition, the radiomics signatures were significantly associated with higher-grade pulmonary haemorrhage (p<0.05). CONCLUSIONS Radiomics features from intranodular and perinodular regions of pulmonary nodules have good predictive ability for pulmonary haemorrhage after PCTLB, which may provide additional predictive value for clinical practice.
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Affiliation(s)
- J Zhu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Y Qu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - M Lu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - A Ma
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - J Mo
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Z Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China.
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Abbas E, Fanni SC, Bandini C, Francischello R, Febi M, Aghakhanyan G, Ambrosini I, Faggioni L, Cioni D, Lencioni RA, Neri E. Delta-radiomics in cancer immunotherapy response prediction: A systematic review. Eur J Radiol Open 2023; 11:100511. [PMID: 37520768 PMCID: PMC10371799 DOI: 10.1016/j.ejro.2023.100511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 08/01/2023] Open
Abstract
Background The new immunotherapies have not only changed the oncological therapeutic approach but have also made it necessary to develop new imaging methods for assessing the response to treatment. Delta radiomics consists of the analysis of radiomic features variation between different medical images, usually before and after therapy. Purpose This review aims to evaluate the role of delta radiomics in the immunotherapy response assessment. Methods A systematic search was performed in PubMed, Scopus, and Web Of Science using "delta radiomics AND immunotherapy" as search terms. The included articles' methodological quality was measured using the Radiomics Quality Score (RQS) tool. Results Thirteen articles were finally included in the systematic review. Overall, the RQS of the included studies ranged from 4 to 17, with a mean RQS total of 11,15 ± 4,18 with a corresponding percentage of 30.98 ± 11.61 %. Eleven articles out of 13 performed imaging at multiple time points. All the included articles performed feature reduction. No study carried out prospective validation, decision curve analysis, or cost-effectiveness analysis. Conclusions Delta radiomics has been demonstrated useful in evaluating the response in oncologic patients undergoing immunotherapy. The overall quality was found law, due to the lack of prospective design and external validation. Thus, further efforts are needed to bring delta radiomics a step closer to clinical implementation.
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Affiliation(s)
- Engy Abbas
- The Joint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women’s College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9
| | | | - Claudio Bandini
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Roberto Francischello
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Maria Febi
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Gayane Aghakhanyan
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Ilaria Ambrosini
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Dania Cioni
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | | | - Emanuele Neri
- The Joint Department of Medical Imaging, University of Toronto, University Health Network, Sinai Health System, Women’s College Hospital, 610 University Ave, Toronto, ON, Canada M5G 2M9
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
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Tomassini S, Falcionelli N, Bruschi G, Sbrollini A, Marini N, Sernani P, Morettini M, Müller H, Dragoni AF, Burattini L. On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans. Comput Med Imaging Graph 2023; 110:102310. [PMID: 37979340 DOI: 10.1016/j.compmedimag.2023.102310] [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: 07/18/2023] [Revised: 10/25/2023] [Accepted: 11/03/2023] [Indexed: 11/20/2023]
Abstract
Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information.
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Affiliation(s)
- Selene Tomassini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Nicola Falcionelli
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Giulia Bruschi
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Agnese Sbrollini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Niccolò Marini
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
| | - Paolo Sernani
- Department of Law, University of Macerata (UNIMC), Macerata, Italy
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
| | - Aldo Franco Dragoni
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche (UNIVPM), Ancona, Italy.
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Xiong Z, Zhao W, Tian D, Zhang J, He Y, Qin D, Li Z. Invasiveness identification in pure ground-glass nodules: exploring the generalizability of radiomics based on external validation and stress testing. J Cancer Res Clin Oncol 2023; 149:12723-12735. [PMID: 37452850 DOI: 10.1007/s00432-023-05105-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 06/30/2023] [Indexed: 07/18/2023]
Abstract
PURPOSE This study aimed to apply external validation and stress tests to evaluate the generalizability of radiomics models built using various machine-learning methods for identifying the invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules (pGGNs). METHODS This retrospective study enrolled 495 patients (514 pGGNs) confirmed as lung adenocarcinomas by postoperative pathology from three centers. All nodules were included in the primary cohort (randomly divided into training and test cohorts), two external validation cohorts, and two stress test cohorts. Six machine-learning radiomics models were constructed in the training cohort using the optimal features. Performance of radiomics models and clinical models were compared in primary cohort and external validation cohorts. The stress tests included stratified performance evaluation and shifted performance evaluation and contrastive evaluation under three single-condition modification settings. The predictive performance was validated by area under curve (AUC) of receiver operating characteristic (ROC). RESULTS Of the six radiomics models, the best logistic regression (LR) model was able to maintain high differential diagnostic capability (AUC: 0.849 ± 0.049) and good stability (relative standard deviation, 5.719%), but it showed poorer performance (AUC = 0.835) than the clinical model (AUC = 0.862) in the external validation cohort E1. The stress tests suggested LR model had no significant difference in performance between subgroups after stratification and had good consistency in the predictions before and after the three transformations (Kappa = 0.960, 0.840, and 0.933, respectively; p < 0.05, all). CONCLUSION The rigorous testing procedure facilitates the selection of high-performance radiomics models with good clinical generalizability.
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Affiliation(s)
- Ziqi Xiong
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Xigang District, Dalian, 116011, China
| | - Wenjing Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Xigang District, Dalian, 116011, China
| | - Di Tian
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Xigang District, Dalian, 116011, China
| | - Jingyu Zhang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Xigang District, Dalian, 116011, China
| | - Yifan He
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Xigang District, Dalian, 116011, China
| | - Dongxue Qin
- Department of Radiology, The Second Hospital of Dalian Medical University, 467 Zhongshan Road, Shahekou District, Dalian, China
| | - Zhiyong Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, 222 Zhongshan Road, Xigang District, Dalian, 116011, China.
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Small C, Prior P, Nasief H, Zeitlin R, Saeed H, Paulson E, Morrow N, Rownd J, Erickson B, Bedi M. A general framework to develop a radiomic fingerprint for progression-free survival in cervical cancer. Brachytherapy 2023; 22:728-735. [PMID: 37574352 DOI: 10.1016/j.brachy.2023.06.004] [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: 02/27/2023] [Revised: 05/11/2023] [Accepted: 06/06/2023] [Indexed: 08/15/2023]
Abstract
PURPOSE Treatment of locally advanced cervical cancer patients includes chemoradiation followed by brachytherapy. Our aim is to develop a delta radiomics (DRF) model from MRI-based brachytherapy treatment and assess its association with progression free survival (PFS). MATERIALS AND METHODS A retrospective analysis of FIGO stage IB- IV cervical cancer patients between 2012 and 2018 who were treated with definitive chemoradiation followed by MRI-based intracavitary brachytherapy was performed. Clinical factors together with 18 radiomic features extracted from different radiomics matrices were analyzed. The delta radiomic features (DRFs) were extracted from MRI on the first and last brachytherapy fractions. Support Vector Machine (SVM) models were fitted to combinations of 2-3 DRFs found significant after Spearman correlation and Wilcoxon rank sum test statistics. Additional models were tested that included clinical factors together with DRFs. RESULTS A total of 39 patients were included in the analysis with a median patient age of 52 years. Progression occurred in 20% of patients (8/39). The significant DRFs using two DRF feature combinations was a model using auto correlation (AC) and sum variance (SV). The best performing three feature model combined mean, AC & SV. Additionally, the inclusion of FIGO stages with the 2- and 3 DRF combination model(s) improved performance compared to models with only DRFs. However, all the clinical factor + DRF models were not significantly different from one another (all AUCs were 0.77). CONCLUSIONS Our study shows promising evidence that radiomics metrics are associated with progression free survival in cervical cancer.
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Affiliation(s)
- Christina Small
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI.
| | - Phillip Prior
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Haidy Nasief
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Ross Zeitlin
- Department of Radiation Oncology, John H Stroger, Jr. Hospital of Cook County, Chicago, IL
| | - Hina Saeed
- Department of Radiation Oncology, Lynn Cancer Institute, Baptist Health South Florida, Boynton Beach, FL
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Natalya Morrow
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Jason Rownd
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Beth Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Meena Bedi
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
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Amorrortu R, Garcia M, Zhao Y, El Naqa I, Balagurunathan Y, Chen DT, Thieu T, Schabath MB, Rollison DE. Overview of approaches to estimate real-world disease progression in lung cancer. JNCI Cancer Spectr 2023; 7:pkad074. [PMID: 37738580 PMCID: PMC10637832 DOI: 10.1093/jncics/pkad074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/28/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023] Open
Abstract
BACKGROUND Randomized clinical trials of novel treatments for solid tumors normally measure disease progression using the Response Evaluation Criteria in Solid Tumors. However, novel, scalable approaches to estimate disease progression using real-world data are needed to advance cancer outcomes research. The purpose of this narrative review is to summarize examples from the existing literature on approaches to estimate real-world disease progression and their relative strengths and limitations, using lung cancer as a case study. METHODS A narrative literature review was conducted in PubMed to identify articles that used approaches to estimate real-world disease progression in lung cancer patients. Data abstracted included data source, approach used to estimate real-world progression, and comparison to a selected gold standard (if applicable). RESULTS A total of 40 articles were identified from 2008 to 2022. Five approaches to estimate real-world disease progression were identified including manual abstraction of medical records, natural language processing of clinical notes and/or radiology reports, treatment-based algorithms, changes in tumor volume, and delta radiomics-based approaches. The accuracy of these progression approaches were assessed using different methods, including correlations between real-world endpoints and overall survival for manual abstraction (Spearman rank ρ = 0.61-0.84) and area under the curve for natural language processing approaches (area under the curve = 0.86-0.96). CONCLUSIONS Real-world disease progression has been measured in several observational studies of lung cancer. However, comparing the accuracy of methods across studies is challenging, in part, because of the lack of a gold standard and the different methods used to evaluate accuracy. Concerted efforts are needed to define a gold standard and quality metrics for real-world data.
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Affiliation(s)
| | - Melany Garcia
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Yayi Zhao
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
| | | | - Dung-Tsa Chen
- Department of Biostatistics and Bionformatics, Moffitt Cancer Center, Tampa, FL, USA
| | - Thanh Thieu
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
| | - Matthew B Schabath
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Dana E Rollison
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
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Liang H, Zhang L, Zhao X, Rong J. The therapeutic potential of exosomes in lung cancer. Cell Oncol (Dordr) 2023; 46:1181-1212. [PMID: 37365450 DOI: 10.1007/s13402-023-00815-8] [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] [Accepted: 04/11/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Lung cancer (LC) is one of the most common malignancies globally. Besides early detection and surgical resection, there is currently no effective curative treatment for metastatic advanced LC. Exosomes are endogenous nano-extracellular vesicles produced by somatic cells that play an important role in the development and maintenance of normal physiology. Exosomes can carry proteins, peptides, lipids, nucleic acids, and various small molecules for intra- and intercellular material transport or signal transduction. LC cells can maintain their survival, proliferation, migration, invasion, and metastasis, by producing or interacting with exosomes. Basic and clinical data also show that exosomes can be used to suppress LC cell proliferation and viability, induce apoptosis, and enhance treatment sensitivity. Due to the high stability and target specificity, good biocompatibility, and low immunogenicity of exosomes, they show promise as vehicles of LC therapy. CONCLUSION We have written this comprehensive review to communicate the LC treatment potential of exosomes and their underlying molecular mechanisms. We found that overall, LC cells can exchange substances or crosstalk with themselves or various other cells in the surrounding TME or distant organs through exosomes. Through this, they can modulate their survival, proliferation, stemness, migration, and invasion, EMT, metastasis, and apoptotic resistance.
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Affiliation(s)
- Hongyuan Liang
- Department of Radiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Shenyang, 110004, China
| | - Lingyun Zhang
- Department of Medical Oncology, the First Hospital of China Medical University, No. 210, BaiTa Street, Hunnan District, Shenyang, 110001, People's Republic of China
| | - Xiangxuan Zhao
- Health Sciences Institute, China Medical University, No.77 Puhe Road, Shenyang North New Area, Shenyang, 110022, People's Republic of China.
| | - Jian Rong
- Department of Pediatrics, PICU, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Shenyang, Liaoning Province, 110004, People's Republic of China.
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Bianconi F, Salis R, Fravolini ML, Khan MU, Minestrini M, Filippi L, Marongiu A, Nuvoli S, Spanu A, Palumbo B. Performance Analysis of Six Semi-Automated Tumour Delineation Methods on [ 18F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) in Patients with Head and Neck Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:7952. [PMID: 37766009 PMCID: PMC10537871 DOI: 10.3390/s23187952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/01/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023]
Abstract
Background. Head and neck cancer (HNC) is the seventh most common neoplastic disorder at the global level. Contouring HNC lesions on [18F] Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) scans plays a fundamental role for diagnosis, risk assessment, radiotherapy planning and post-treatment evaluation. However, manual contouring is a lengthy and tedious procedure which requires significant effort from the clinician. Methods. We evaluated the performance of six hand-crafted, training-free methods (four threshold-based, two algorithm-based) for the semi-automated delineation of HNC lesions on FDG PET/CT. This study was carried out on a single-centre population of n=103 subjects, and the standard of reference was manual segmentation generated by nuclear medicine specialists. Figures of merit were the Sørensen-Dice coefficient (DSC) and relative volume difference (RVD). Results. Median DSC ranged between 0.595 and 0.792, median RVD between -22.0% and 87.4%. Click and draw and Nestle's methods achieved the best segmentation accuracy (median DSC, respectively, 0.792 ± 0.178 and 0.762 ± 0.107; median RVD, respectively, -21.6% ± 1270.8% and -32.7% ± 40.0%) and outperformed the other methods by a significant margin. Nestle's method also resulted in a lower dispersion of the data, hence showing stronger inter-patient stability. The accuracy of the two best methods was in agreement with the most recent state-of-the art results. Conclusions. Semi-automated PET delineation methods show potential to assist clinicians in the segmentation of HNC lesions on FDG PET/CT images, although manual refinement may sometimes be needed to obtain clinically acceptable ROIs.
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Affiliation(s)
- Francesco Bianconi
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06125 Perugia, Italy; (M.L.F.); (M.U.K.)
| | - Roberto Salis
- Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (R.S.); (A.M.); (S.N.)
| | - Mario Luca Fravolini
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06125 Perugia, Italy; (M.L.F.); (M.U.K.)
| | - Muhammad Usama Khan
- Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06125 Perugia, Italy; (M.L.F.); (M.U.K.)
| | - Matteo Minestrini
- Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy; (M.M.); (B.P.)
| | - Luca Filippi
- Policlinico Tor Vergata Hospital, Viale Oxford 81, 00133 Rome, Italy;
| | - Andrea Marongiu
- Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (R.S.); (A.M.); (S.N.)
| | - Susanna Nuvoli
- Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (R.S.); (A.M.); (S.N.)
| | - Angela Spanu
- Unit of Nuclear Medicine, Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy; (R.S.); (A.M.); (S.N.)
| | - Barbara Palumbo
- Section of Nuclear Medicine and Health Physics, Department of Medicine and Surgery, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy; (M.M.); (B.P.)
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Liang H, Zhang L, Rong J. Potential roles of exosomes in the initiation and metastatic progression of lung cancer. Biomed Pharmacother 2023; 165:115222. [PMID: 37549459 DOI: 10.1016/j.biopha.2023.115222] [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: 05/09/2023] [Revised: 07/17/2023] [Accepted: 07/23/2023] [Indexed: 08/09/2023] Open
Abstract
Lung cancer (LC) incidence and mortality continue to increase annually worldwide. LC is insidious and readily metastasizes and relapses. Except for its early diagnosis and surgical resection, there is no effective cure for advanced metastatic LC, and the prognosis remains dismal. Exosomes, a class of nano-sized extracellular vesicles produced by healthy or diseased cells, are coated with a bilayer lipid membrane and contain various functional molecules such as proteins, lipids, and nucleic acids. They can be used for intracellular or intercellular signaling or the transportation of biological substances. A growing body of evidence supports that exosomes play multiple crucial roles in the occurrence and metastatic progression of many malignancies, including LC. The elucidation of the potential roles of exosomes in the initiation, invasion, and metastasis of LC and their underlying molecular mechanisms may contribute to improved early diagnosis and treatment.
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Affiliation(s)
- Hongyuan Liang
- Department of Radiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Shenyang 110004, PR China
| | - Lingyun Zhang
- Department of Medical Oncology, the First Hospital of China Medical University, No. 210 Baita Street, Hunnan District, Shenyang 110001, PR China.
| | - Jian Rong
- Department of Pediatrics, PICU, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Shenyang 110004, PR China.
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Qiu J, Yan M, Wang H, Liu Z, Wang G, Wu X, Gao Q, Hu H, Chen J, Dai Y. Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study. Front Med (Lausanne) 2023; 10:1202486. [PMID: 37601775 PMCID: PMC10433756 DOI: 10.3389/fmed.2023.1202486] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/12/2023] [Indexed: 08/22/2023] Open
Abstract
Obstructive To develop and validate radiomics and machine learning models for identifying encrusted stents and compare their recognition performance with multiple metrics. Methods A total of 354 patients with ureteral stent placement were enrolled from two medical institutions and divided into the training cohort (n = 189), internal validation cohort (n = 81) and external validation cohort (n = 84). Based on features selected by Wilcoxon test, Spearman Correlation Analysis and least absolute shrinkage and selection operator (LASSO) regression algorithm, six machine learning models based on radiomics features were established with six classifiers (LR, DT, SVM, RF, XGBoost, KNN). After comparison with those models, the most robust model was selected. Considering its feature importance as radscore, the combined model and a nomogram were constructed by incorporating indwelling time. Accuracy, sensitivity, specificity, area under the curve (AUC), decision curve analysis (DCA) and calibration curve were used to evaluate the recognition performance of models. Results 1,409 radiomics features were extracted from 641 volumes of interest (VOIs) and 20 significant radiomics features were selected. Considering the superior performance (AUC 0.810, 95%CI, 0.722-0.888) in the external validation cohort, feature importance of XGBoost was used as a radscore, constructing a combined model and a nomogram with indwelling time. The accuracy, sensitivity, specificity and AUC of the combined model were 98, 100, 97.3% and 0.999 for the training cohort, 83.3, 80, 84.5% and 0.867 for the internal cohort and 78.2, 76.3, 78.8% and 0.820 for the external cohort, respectively. DCA indicates the favorable clinical utility of models. Conclusion Machine learning model based on radiomics features enables to identify ureteral stent encrustation with high accuracy.
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Affiliation(s)
- Junliang Qiu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Minbo Yan
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Haojie Wang
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Zicheng Liu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Guojie Wang
- Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Xianbo Wu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Qindong Gao
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Hongji Hu
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Junyong Chen
- Department of Urology, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, Guangdong, China
| | - Yingbo Dai
- Department of Urology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
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Chen J, Meng T, Xu J, Ooi JD, Eggenhuizen PJ, Liu W, Li F, Wu X, Sun J, Zhang H, Zhou YO, Luo H, Xiao X, Pei Y, Li W, Zhong Y. Development of a radiomics nomogram to predict the treatment resistance of Chinese MPO-AAV patients with lung involvement: a two-center study. Front Immunol 2023; 14:1084299. [PMID: 37503353 PMCID: PMC10369051 DOI: 10.3389/fimmu.2023.1084299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 06/19/2023] [Indexed: 07/29/2023] Open
Abstract
Background Previous studies from our group and other investigators have shown that lung involvement is one of the independent predictors for treatment resistance in patients with myeloperoxidase (MPO)-anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (MPO-AAV). However, it is unclear which image features of lung involvement can predict the therapeutic response in MPO-AAV patients, which is vital in decision-making for these patients. Our aim was to develop and validate a radiomics nomogram to predict treatment resistance of Chinese MPO-AAV patients based on low-dose multiple slices computed tomography (MSCT) of the involved lung with cohorts from two centers. Methods A total of 151 MPO-AAV patients with lung involvement (MPO-AAV-LI) from two centers were enrolled. Two different models (Model 1: radiomics signature; Model 2: radiomics nomogram) were built based on the clinical and MSCT data to predict the treatment resistance of MPO-AAV with lung involvement in training and test cohorts. The performance of the models was assessed using the area under the curve (AUC). The better model was further validated. A nomogram was constructed and evaluated by DCA and calibration curves, which further tested in all enrolled data and compared with the other model. Results Model 2 had a higher predicting ability than Model 1 both in training (AUC: 0.948 vs. 0.824; p = 0.039) and test cohorts (AUC: 0.913 vs. 0.898; p = 0.043). As a better model, Model 2 obtained an excellent predictive performance (AUC: 0.929; 95% CI: 0.827-1.000) in the validation cohort. The DCA curve demonstrated that Model 2 was clinically feasible. The calibration curves of Model 2 closely aligned with the true treatment resistance rate in the training (p = 0.28) and test sets (p = 0.70). In addition, the predictive performance of Model 2 (AUC: 0.929; 95% CI: 0.875-0.964) was superior to Model 1 (AUC: 0.862; 95% CI: 0.796-0.913) and serum creatinine (AUC: 0.867; 95% CI: 0.802-0.917) in all patients (all p< 0.05). Conclusion The radiomics nomogram (Model 2) is a useful, non-invasive tool for predicting the treatment resistance of MPO-AAV patients with lung involvement, which might aid in individualizing treatment decisions.
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Affiliation(s)
- Juan Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ting Meng
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jia Xu
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Joshua D. Ooi
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Centre for Inflammatory Diseases, Monash University, Clayton, VIC, Australia
| | | | - Wenguang Liu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Fang Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xueqin Wu
- Department of Nephrology, The third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jian Sun
- Department of Nephrology, The third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hao Zhang
- Department of Nephrology, The third Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ya-Ou Zhou
- Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, China
| | - Hui Luo
- Department of Rheumatology and Immunology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiangcheng Xiao
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yigang Pei
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wenzheng Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yong Zhong
- Department of Nephrology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Key Laboratory of Biological Nanotechnology of National Health Commission, Xiangya Hospital, Central South University, Changsha, Hunan, China
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Janik A, Torrente M, Costabello L, Calvo V, Walsh B, Camps C, Mohamed SK, Ortega AL, Nováček V, Massutí B, Minervini P, Campelo MRG, del Barco E, Bosch-Barrera J, Menasalvas E, Timilsina M, Provencio M. Machine Learning-Assisted Recurrence Prediction for Patients With Early-Stage Non-Small-Cell Lung Cancer. JCO Clin Cancer Inform 2023; 7:e2200062. [PMID: 37428988 PMCID: PMC10569772 DOI: 10.1200/cci.22.00062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 01/30/2023] [Accepted: 04/14/2023] [Indexed: 07/12/2023] Open
Abstract
PURPOSE Stratifying patients with cancer according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to use machine learning to estimate probability of relapse in patients with early-stage non-small-cell lung cancer (NSCLC)? MATERIALS AND METHODS For predicting relapse in 1,387 patients with early-stage (I-II) NSCLC from the Spanish Lung Cancer Group data (average age 65.7 years, female 24.8%, male 75.2%), we train tabular and graph machine learning models. We generate automatic explanations for the predictions of such models. For models trained on tabular data, we adopt SHapley Additive exPlanations local explanations to gauge how each patient feature contributes to the predicted outcome. We explain graph machine learning predictions with an example-based method that highlights influential past patients. RESULTS Machine learning models trained on tabular data exhibit a 76% accuracy for the random forest model at predicting relapse evaluated with a 10-fold cross-validation (the model was trained 10 times with different independent sets of patients in test, train, and validation sets, and the reported metrics are averaged over these 10 test sets). Graph machine learning reaches 68% accuracy over a held-out test set of 200 patients, calibrated on a held-out set of 100 patients. CONCLUSION Our results show that machine learning models trained on tabular and graph data can enable objective, personalized, and reproducible prediction of relapse and, therefore, disease outcome in patients with early-stage NSCLC. With further prospective and multisite validation, and additional radiological and molecular data, this prognostic model could potentially serve as a predictive decision support tool for deciding the use of adjuvant treatments in early-stage lung cancer.
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Affiliation(s)
| | - Maria Torrente
- Medical Oncology Department, Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain
| | | | - Virginia Calvo
- Medical Oncology Department, Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain
| | - Brian Walsh
- Data Science Institute, University of Galway, Galway, Ireland
- Insight Centre for Data Analytics, University of Galway, Galway, Ireland
| | | | - Sameh K. Mohamed
- Data Science Institute, University of Galway, Galway, Ireland
- Insight Centre for Data Analytics, University of Galway, Galway, Ireland
| | | | - Vít Nováček
- Data Science Institute, University of Galway, Galway, Ireland
- Insight Centre for Data Analytics, University of Galway, Galway, Ireland
- Faculty of Informatics, Masaryk University, Brno, Czech Republic
- Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | | | | | | | | | | | | | - Mohan Timilsina
- Data Science Institute, University of Galway, Galway, Ireland
- Insight Centre for Data Analytics, University of Galway, Galway, Ireland
| | - Mariano Provencio
- Medical Oncology Department, Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain
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Prencipe B, Delprete C, Garolla E, Corallo F, Gravina M, Natalicchio MI, Buongiorno D, Bevilacqua V, Altini N, Brunetti A. An Explainable Radiogenomic Framework to Predict Mutational Status of KRAS and EGFR in Lung Adenocarcinoma Patients. Bioengineering (Basel) 2023; 10:747. [PMID: 37508774 PMCID: PMC10376018 DOI: 10.3390/bioengineering10070747] [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: 06/05/2023] [Revised: 06/17/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
The complex pathobiology of lung cancer, and its spread worldwide, has prompted research studies that combine radiomic and genomic approaches. Indeed, the early identification of genetic alterations and driver mutations affecting the tumor is fundamental for correctly formulating the prognosis and therapeutic response. In this work, we propose a radiogenomic workflow to detect the presence of KRAS and EGFR mutations using radiomic features extracted from computed tomography images of patients affected by lung adenocarcinoma. To this aim, we investigated several feature selection algorithms to identify the most significant and uncorrelated sets of radiomic features and different classification models to reveal the mutational status. Then, we employed the SHAP (SHapley Additive exPlanations) technique to increase the understanding of the contribution given by specific radiomic features to the identification of the investigated mutations. Two cohorts of patients with lung adenocarcinoma were used for the study. The first one, obtained from the Cancer Imaging Archive (TCIA), consisted of 60 cases (25% EGFR, 23% KRAS); the second one, provided by the Azienda Ospedaliero-Universitaria 'Ospedali Riuniti' of Foggia, was composed of 55 cases (16% EGFR, 28% KRAS). The best-performing models proposed in our study achieved an AUC of 0.69 and 0.82 on the validation set for predicting the mutational status of EGFR and KRAS, respectively. The Multi-layer Perceptron model emerged as the top-performing model for both oncogenes, in some cases outperforming the state of the art. This study showed that radiomic features can be associated with EGFR and KRAS mutational status in patients with lung adenocarcinoma.
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Affiliation(s)
- Berardino Prencipe
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
| | - Claudia Delprete
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
| | - Emilio Garolla
- Department of Medical and Surgical Sciences, University of Foggia, Viale Pinto 1, 71122 Foggia, Italy
| | - Fabio Corallo
- Department of Medical and Surgical Sciences, University of Foggia, Viale Pinto 1, 71122 Foggia, Italy
| | - Matteo Gravina
- Department of Medical and Surgical Sciences, University of Foggia, Viale Pinto 1, 71122 Foggia, Italy
| | - Maria Iole Natalicchio
- Molecular Oncology and Pharmacogenomics Laboratory, University of Foggia, Viale Pinto 1, 71122 Foggia, Italy
| | - Domenico Buongiorno
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
- Apulian Bioengineering SRL, Via delle Violette 14, 70026 Modugno, Italy
| | - Vitoantonio Bevilacqua
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
- Apulian Bioengineering SRL, Via delle Violette 14, 70026 Modugno, Italy
| | - Nicola Altini
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
| | - Antonio Brunetti
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy
- Apulian Bioengineering SRL, Via delle Violette 14, 70026 Modugno, Italy
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Zhang Y, Feng W, Wu Z, Li W, Tao L, Liu X, Zhang F, Gao Y, Huang J, Guo X. Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1088. [PMID: 37374292 DOI: 10.3390/medicina59061088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 06/29/2023]
Abstract
Background and Objectives: Lung cancer remains a leading cause of cancer mortality worldwide. Accurately classifying benign pulmonary nodules and malignant ones is crucial for early diagnosis and improved patient outcomes. The purpose of this study is to explore the deep-learning model of ResNet combined with a convolutional block attention module (CBAM) for the differentiation between benign and malignant lung cancer, based on computed tomography (CT) images, morphological features, and clinical information. Methods and materials: In this study, 8241 CT slices containing pulmonary nodules were retrospectively included. A random sample comprising 20% (n = 1647) of the images was used as the test set, and the remaining data were used as the training set. ResNet combined CBAM (ResNet-CBAM) was used to establish classifiers on the basis of images, morphological features, and clinical information. Nonsubsampled dual-tree complex contourlet transform (NSDTCT) combined with SVM classifier (NSDTCT-SVM) was used as a comparative model. Results: The AUC and the accuracy of the CBAM-ResNet model were 0.940 and 0.867, respectively, in test set when there were only images as inputs. By combining the morphological features and clinical information, CBAM-ResNet shows better performance (AUC: 0.957, accuracy: 0.898). In comparison, a radiomic analysis using NSDTCT-SVM achieved AUC and accuracy values of 0.807 and 0.779, respectively. Conclusions: Our findings demonstrate that deep-learning models, combined with additional information, can enhance the classification performance of pulmonary nodules. This model can assist clinicians in accurately diagnosing pulmonary nodules in clinical practice.
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Affiliation(s)
- Yanfei Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Wei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Zhiyuan Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Weiming Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Lixin Tao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Xiangtong Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Feng Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
| | - Yan Gao
- Department of Nuclear Medicine, Xuanwu Hospital Capital Medical University, Beijing 100053, China
| | - Jian Huang
- School of Mathematical Sciences, University College Cork, T12 YN60 Cork, Ireland
| | - Xiuhua Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing 100069, China
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Patel RV, Yao S, Huang RY, Bi WL. Application of radiomics to meningiomas: A systematic review. Neuro Oncol 2023; 25:1166-1176. [PMID: 36723606 PMCID: PMC10237421 DOI: 10.1093/neuonc/noad028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Quantitative imaging analysis through radiomics is a powerful technology to non-invasively assess molecular correlates and guide clinical decision-making. There has been growing interest in image-based phenotyping for meningiomas given the complexities in management. METHODS We systematically reviewed meningioma radiomics analyses published in PubMed, Embase, and Web of Science until December 20, 2021. We compiled performance data and assessed publication quality using the radiomics quality score (RQS). RESULTS A total of 170 publications were grouped into 5 categories of radiomics applications to meningiomas: Tumor detection and segmentation (21%), classification across neurologic diseases (54%), grading (14%), feature correlation (3%), and prognostication (8%). A majority focused on technical model development (73%) versus clinical applications (27%), with increasing adoption of deep learning. Studies utilized either private institutional (50%) or public (49%) datasets, with only 68% using a validation dataset. For detection and segmentation, radiomic models had a mean accuracy of 93.1 ± 8.1% and a dice coefficient of 88.8 ± 7.9%. Meningioma classification had a mean accuracy of 95.2 ± 4.0%. Tumor grading had a mean area-under-the-curve (AUC) of 0.85 ± 0.08. Correlation with meningioma biological features had a mean AUC of 0.89 ± 0.07. Prognostication of the clinical course had a mean AUC of 0.83 ± 0.08. While clinical studies had a higher mean RQS compared to technical studies, quality was low overall with a mean RQS of 6.7 ± 5.9 (possible range -8 to 36). CONCLUSIONS There has been global growth in meningioma radiomics, driven by data accessibility and novel computational methodology. Translatability toward complex tasks such as prognostication requires studies that improve quality, develop comprehensive patient datasets, and engage in prospective trials.
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Affiliation(s)
- Ruchit V Patel
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Shun Yao
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Raymond Y Huang
- Division of Neuroradiology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Wenya Linda Bi
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
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Delasos L, Madabhushi A, Patil PD. Can Radiomics Bridge the Gap Between Immunotherapy and Precision Medicine in Lung Cancer? J Thorac Oncol 2023; 18:686-688. [PMID: 37210178 DOI: 10.1016/j.jtho.2023.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 05/22/2023]
Affiliation(s)
- Lukas Delasos
- Department of Hematology and Medical Oncology, Cleveland Clinic Taussig Cancer Center, Cleveland, Ohio
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University, Atlanta, Georgia; Georgia Institute of Technology, Atlanta, Georgia; Atlanta Veterans Affairs Medical Center, Atlanta, Georgia
| | - Pradnya D Patil
- Department of Hematology and Medical Oncology, Cleveland Clinic Taussig Cancer Center, Cleveland, Ohio.
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Lin JX, Wang FH, Wang ZK, Wang JB, Zheng CH, Li P, Huang CM, Xie JW. Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01637-2. [PMID: 37148481 DOI: 10.1007/s11547-023-01637-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 04/21/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVE The objective is to develop a mitotic prediction model and preoperative risk stratification nomogram for gastrointestinal stromal tumor (GIST) based on computed tomography (CT) radiomic features. METHODS A total of 267 GIST patients from 2009.07 to 2015.09 were retrospectively collected and randomly divided into (6:4) training cohort and validation cohort. The 2D-tumor region of interest was delineated from the portal-phase images on contrast-enhanced (CE)-CT, and radiomic features were extracted. Lasso regression method was used to select valuable features to establish a radiomic model for predicting mitotic index in GIST. Finally, the nomogram of preoperative risk stratification was constructed by combining the radiomic features and clinical risk factors. RESULTS Four radiomic features closely related to the level of mitosis were obtained, and a mitotic radiomic model was constructed. The area under the curve (AUC) of the radiomics signature model used to predict mitotic levels in training and validation cohorts (training cohort AUC = 0.752; 95% confidence interval [95%CI] 0.674-0.829; validation cohort AUC = 0.764; 95% CI 0.667-0.862). Finally, the preoperative risk stratification nomogram combining radiomic features was equivalent to the clinically recognized gold standard AUC (0.965 vs. 0.983) (p = 0.117). The Cox regression analysis found that the nomogram score was one of the independent risk factors for the long-term prognosis of the patients. CONCLUSION Preoperative CT radiomic features can effectively predict the level of mitosis in GIST, and combined with preoperative tumor size, accurate preoperative risk stratification can be performed to guide clinical decision-making and individualized treatment.
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Affiliation(s)
- Jian-Xian Lin
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Fu-Hai Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zu-Kai Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jia-Bin Wang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Chao-Hui Zheng
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Ping Li
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China
| | - Chang-Ming Huang
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China.
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China.
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
| | - Jian-Wei Xie
- Department of Gastric Surgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian Province, China.
- Fujian Provincial Minimally Invasive Medical Center, Fuzhou, China.
- Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, China.
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Zhang R, Shi J, Liu S, Chen B, Li W. Performance of radiomics models derived from different CT reconstruction parameters for lung cancer risk prediction. BMC Pulm Med 2023; 23:132. [PMID: 37081469 PMCID: PMC10116652 DOI: 10.1186/s12890-023-02366-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 02/21/2023] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND This study analysed the performance of radiomics features extracted from computed tomography (CT) images with different reconstruction parameters in differentiating malignant and benign pulmonary nodules. METHODS We evaluated routine chest CT images acquired from 148 participants with pulmonary nodules, which were pathologically diagnosed during surgery in West China Hospital, including a 5 mm unenhanced lung window, a 5 mm unenhanced mediastinal window, a 5 mm contrast-enhanced mediastinal window and a 1 mm unenhanced lung window. The pulmonary nodules were segmented, and 1409 radiomics features were extracted for each window. Then, we created 15 cohorts consisting of single windows or multiple windows. Univariate correlation analysis and principal component analysis were performed to select the features, and logistic regression analysis was performed to establish models for each cohort. The area under the curve (AUC) was applied to compare model performance. RESULTS There were 75 benign and 73 malignant pulmonary nodules, with mean diameters of 18.63 and 19.86 mm, respectively. For the single-window setting, the AUCs of the radiomics model from the 5 mm unenhanced lung window, 5 mm unenhanced mediastinal window, 5 mm contrast-enhanced mediastinal window and 1 mm unenhanced lung window were 0.771, 0.808, 0.750, and 0.771 in the training set and 0.711, 0.709, 0.684, and 0.674 in the test set, respectively. Regarding the multiple-window setting, the radiomics model based on all four windows showed an AUC of 0.825 in the training set and 0.743 in the test set. Statistically, the 15 models demonstrated comparable performances (P > 0.05). CONCLUSION A single chest CT window was acceptable in predicting the malignancy of pulmonary nodules, and additional windows did not statistically improve the performance of the radiomics models. In addition, slice thickness and contrast enhancement did not affect the diagnostic performance.
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Affiliation(s)
- Rui Zhang
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, 37 GuoXue Alley, Wuhou District, Chengdu, Sichuan Province, 610041, People's Republic of China
- Department of General Practice, West China Hospital, Sichuan University, Chengdu, People's Republic of China
| | - Jie Shi
- GE Healthcare, Shanghai, China
| | | | - Bojiang Chen
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, 37 GuoXue Alley, Wuhou District, Chengdu, Sichuan Province, 610041, People's Republic of China.
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, 37 GuoXue Alley, Wuhou District, Chengdu, Sichuan Province, 610041, People's Republic of China.
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Tan H, Wang Y, Jiang Y, Li H, You T, Fu T, Peng J, Tan Y, Lu R, Peng B, Huang W, Xiong F. A study on the differential of solid lung adenocarcinoma and tuberculous granuloma nodules in CT images by Radiomics machine learning. Sci Rep 2023; 13:5853. [PMID: 37041262 PMCID: PMC10090156 DOI: 10.1038/s41598-023-32979-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/05/2023] [Indexed: 04/13/2023] Open
Abstract
To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions.
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Affiliation(s)
- Huibin Tan
- Department of Radiology, The General Hospital of Central Theater Command, PLA, Wuhan, 430070, Hubei, China
| | - Ye Wang
- Department of Radiology, The General Hospital of Central Theater Command, PLA, Wuhan, 430070, Hubei, China
| | - Yuanliang Jiang
- Department of Radiology, The General Hospital of Central Theater Command, PLA, Wuhan, 430070, Hubei, China
| | - Hanhan Li
- Department of Radiology, The General Hospital of Central Theater Command, PLA, Wuhan, 430070, Hubei, China
| | - Tao You
- Department of Radiology, The General Hospital of Central Theater Command, PLA, Wuhan, 430070, Hubei, China
| | - Tingting Fu
- Department of Radiology, The General Hospital of Central Theater Command, PLA, Wuhan, 430070, Hubei, China
| | - Jiaheng Peng
- School of Computer Science and Information Engineering, Hubei University, Wuhan, 430062, Hubei, China
| | - Yuxi Tan
- Medical School, Hubei Minzu University, Enshi, 445000, Hubei, China
| | - Ran Lu
- Department of Radiology, The General Hospital of Central Theater Command, PLA, Wuhan, 430070, Hubei, China
| | - Biwen Peng
- School of Basic Medical Sciences, Wuhan University, Wuhan, 430060, Hubei, China
| | - Wencai Huang
- Department of Radiology, The General Hospital of Central Theater Command, PLA, Wuhan, 430070, Hubei, China.
| | - Fei Xiong
- Department of Radiology, The General Hospital of Central Theater Command, PLA, Wuhan, 430070, Hubei, China.
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Mayoral M, Pagano AM, Araujo-Filho JAB, Zheng J, Perez-Johnston R, Tan KS, Gibbs P, Fernandes Shepherd A, Rimner A, Simone II CB, Riely G, Huang J, Ginsberg MS. Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses. Lung Cancer 2023; 178:206-212. [PMID: 36871345 PMCID: PMC10544811 DOI: 10.1016/j.lungcan.2023.02.014] [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: 10/04/2022] [Revised: 01/14/2023] [Accepted: 02/19/2023] [Indexed: 02/23/2023]
Abstract
OBJECTIVES The aim of this study was to differentiate benign from malignant tumors in the anterior mediastinum based on computed tomography (CT) imaging characteristics, which could be useful in preoperative planning. Additionally, our secondary aim was to differentiate thymoma from thymic carcinoma, which could guide the use of neoadjuvant therapy. MATERIALS AND METHODS Patients referred for thymectomy were retrospectively selected from our database. Twenty-five conventional characteristics were evaluated by visual analysis, and 101 radiomic features were extracted from each CT. In the step of model training, we applied support vector machines to train classification models. Model performance was assessed using the area under the receiver operating curves (AUC). RESULTS Our final study sample comprised 239 patients, 59 (24.7 %) with benign mediastinal lesions and 180 (75.3 %) with malignant thymic tumors. Among the malignant masses, there were 140 (58.6 %) thymomas, 23 (9.6 %) thymic carcinomas, and 17 (7.1 %) non-thymic lesions. For the benign versus malignant differentiation, the model that integrated both conventional and radiomic features achieved the highest diagnostic performance (AUC = 0.715), in comparison to the conventional (AUC = 0.605) and radiomic-only (AUC = 0.678) models. Similarly, regarding thymoma versus thymic carcinoma differentiation, the model that integrated both conventional and radiomic features also achieved the highest diagnostic performance (AUC = 0.810), in comparison to the conventional (AUC = 0.558) and radiomic-only (AUC = 0.774) models. CONCLUSION CT-based conventional and radiomic features with machine learning analysis could be useful for predicting pathologic diagnoses of anterior mediastinal masses. The diagnostic performance was moderate for differentiating benign from malignant lesions and good for differentiating thymomas from thymic carcinomas. The best diagnostic performance was achieved when both conventional and radiomic features were integrated in the machine learning algorithms.
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Affiliation(s)
- Maria Mayoral
- Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Medical Imaging Department. Hospital Clinic of Barcelona, 170 Villarroel street, Barcelona 08036, Spain.
| | - Andrew M Pagano
- Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Jose Arimateia Batista Araujo-Filho
- Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Department of Radiology. Hospital Sirio-Libanes, 91 Dona Adma Jafet street, São Paulo 01308-050, Brazil
| | - Junting Zheng
- Department of Epidemiology and Biostatistics. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Rocio Perez-Johnston
- Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Kay See Tan
- Department of Epidemiology and Biostatistics. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Peter Gibbs
- Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Annemarie Fernandes Shepherd
- Department of Radiation Oncology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Andreas Rimner
- Department of Radiation Oncology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Charles B Simone II
- Department of Radiation Oncology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Gregory Riely
- Department of Surgery. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - James Huang
- Department of Surgery. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Michelle S Ginsberg
- Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
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42
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Jia L, Wu W, Hou G, Zhao J, Qiang Y, Zhang Y, Cai M. Residual neural network with mixed loss based on batch training technique for identification of EGFR mutation status in lung cancer. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-21. [PMID: 37362735 PMCID: PMC10020767 DOI: 10.1007/s11042-023-14876-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 11/11/2022] [Accepted: 02/06/2023] [Indexed: 06/28/2023]
Abstract
Epidermal growth factor receptor (EGFR) is the key to targeted therapy with tyrosine kinase inhibitors in lung cancer. Traditional identification of EGFR mutation status requires biopsy and sequence testing, which may not be suitable for certain groups who cannot perform biopsy. In this paper, using easily accessible and non-invasive CT images, the residual neural network (ResNet) with mixed loss based on batch training technique is proposed for identification of EGFR mutation status in lung cancer. In this model, the ResNet is regarded as the baseline for feature extraction to avoid the gradient disappearance. Besides, a new mixed loss based on the batch similarity and the cross entropy is proposed to guide the network to better learn the model parameters. The proposed mixed loss utilizes the similarity among batch samples to evaluate the distribution of training data, which can reduce the similarity of different classes and the difference of the same classes. In the experiments, VGG16Net, DenseNet, ResNet18, ResNet34 and ResNet50 models with the mixed loss are trained on the public CT dataset with 155 patients including EGFR mutation status from TCIA. The trained networks are employed to the collected preoperative CT dataset with 56 patients from the cooperative hospital for validating the efficiency of the proposed models. Experimental results show that the proposed models are more appropriate and effective on the lung cancer dataset for identifying the EGFR mutation status. In these models, the ResNet34 with mixed loss is optimal (accuracy = 81.58%, AUC = 0.8861, sensitivity = 80.02%, specificity = 82.90%).
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Affiliation(s)
- Liye Jia
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600 China
| | - Wei Wu
- Department of Physiology, Shanxi Medical University, Taiyuan, 030051 China
| | - Guojie Hou
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600 China
| | - Juanjuan Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600 China
| | - Yan Qiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600 China
| | - Yanan Zhang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600 China
| | - Meiling Cai
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600 China
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43
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Yao F, Shi W, Fang F, Lv MY, Xu M, Wu SY, Huang CL. Exosomal miR-196a-5p enhances radioresistance in lung cancer cells by downregulating NFKBIA. Kaohsiung J Med Sci 2023. [PMID: 36912495 DOI: 10.1002/kjm2.12673] [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: 10/26/2022] [Revised: 02/14/2023] [Accepted: 02/16/2023] [Indexed: 03/14/2023] Open
Abstract
Radiation therapy is recognized as an effective modality in the treatment of lung cancer, but radioresistance resulting from prolonged treatment reduces the chances of recovery. MicroRNAs (miRNAs) play a pivotal role in radiotherapy immunity. In this study, we aimed to investigate the mechanism by which miR-196a-5p affects radioresistance in lung cancer. The radioresistant lung cancer cell line A549R26-1 was established by radiation treatment. Cancer-associated fibroblasts (CAFs) and normal fibroblasts (NFs) were observed by microscopy, and the expression levels of CAF-specific marker proteins were detected by immunofluorescence. The shape of the exosomes was observed by electron microscopy. A CCK-8 assay was used to detect cell viability, while clone formation assays were used to detect cell proliferative capacity. Flow cytometry was performed to investigate apoptosis. The binding of miR-196a-5p and NFKBIA was predicted and further verified by the dual luciferase reporter experiment. qRT-PCR and western blotting were used to detect gene mRNA and protein levels. We found that exosomes secreted by CAFs could enhance lung cancer cell radioresistance. Moreover, miR-196a-5p potentially bound to NFKBIA, promoting malignant phenotypes in radioresistant cells. Furthermore, exosomal miR-196a-5p derived from CAFs increased radiotherapy immunity in lung cancer. Exosomal miR-196a-5p derived from CAFs enhanced radioresistance in lung cancer cells by downregulating NFKBIA, providing a new potential target for the treatment of lung cancer.
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Affiliation(s)
- Fei Yao
- Department of Oncology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Wei Shi
- Department of Oncology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Fang Fang
- Department of Oncology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Meng-Yu Lv
- Department of Oncology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Mei Xu
- Department of Oncology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Shan-Yan Wu
- Department of Oncology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Chun-Li Huang
- The First Clinical Faculty, Guangxi University of Chinese Medicine, Nanning, Guangxi Zhuang Autonomous Region, China
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44
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Radiomics-Based Machine Learning Model for Predicting Overall and Progression-Free Survival in Rare Cancer: A Case Study for Primary CNS Lymphoma Patients. Bioengineering (Basel) 2023; 10:bioengineering10030285. [PMID: 36978676 PMCID: PMC10045100 DOI: 10.3390/bioengineering10030285] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/15/2023] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
Primary Central Nervous System Lymphoma (PCNSL) is an aggressive neoplasm with a poor prognosis. Although therapeutic progresses have significantly improved Overall Survival (OS), a number of patients do not respond to HD–MTX-based chemotherapy (15–25%) or experience relapse (25–50%) after an initial response. The reasons underlying this poor response to therapy are unknown. Thus, there is an urgent need to develop improved predictive models for PCNSL. In this study, we investigated whether radiomics features can improve outcome prediction in patients with PCNSL. A total of 80 patients diagnosed with PCNSL were enrolled. A patient sub-group, with complete Magnetic Resonance Imaging (MRI) series, were selected for the stratification analysis. Following radiomics feature extraction and selection, different Machine Learning (ML) models were tested for OS and Progression-free Survival (PFS) prediction. To assess the stability of the selected features, images from 23 patients scanned at three different time points were used to compute the Interclass Correlation Coefficient (ICC) and to evaluate the reproducibility of each feature for both original and normalized images. Features extracted from Z-score normalized images were significantly more stable than those extracted from non-normalized images with an improvement of about 38% on average (p-value < 10−12). The area under the ROC curve (AUC) showed that radiomics-based prediction overcame prediction based on current clinical prognostic factors with an improvement of 23% for OS and 50% for PFS, respectively. These results indicate that radiomics features extracted from normalized MR images can improve prognosis stratification of PCNSL patients and pave the way for further study on its potential role to drive treatment choice.
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45
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Liu Y. Editorial: Imaging in non-small cell lung cancer. Front Oncol 2023; 13:1143204. [PMID: 36824140 PMCID: PMC9942925 DOI: 10.3389/fonc.2023.1143204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 01/31/2023] [Indexed: 02/10/2023] Open
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46
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Tan M, Ma W, Yang Y, Duan S, Jin L, Wu Y, Li M. Predictive value of peritumour radiomics in the diagnosis of benign and malignant pulmonary nodules with halo sign. Clin Radiol 2023; 78:e52-e62. [PMID: 36460488 DOI: 10.1016/j.crad.2022.09.130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/05/2022] [Accepted: 09/26/2022] [Indexed: 12/03/2022]
Abstract
AIM To evaluate peritumour radiomics in predicting benign and malignant pulmonary nodules with halo sign. MATERIALS AND METHODS In this retrospective study, 305 pulmonary nodules with halo sign (benign, 120; adenocarcinoma, 185) were collected. Manual segmentation was used to mark the gross tumour volume (GTV) and the peritumour volume (PTV) was established by uniform dilation (1 cm) of the tumour area in three dimensions. The GTV and PTV radiomic features were combined to produce the gross tumour and peritumour volume (GPTV). The minimum-redundancy maximum-relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) algorithm were used to eliminate redundant radiomic features. Predictive models combined with clinical features and radiomic signatures were established. Multivarible logistic regression analysis was used to establish the combined model and develop a nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of the model. RESULTS In the testing cohort, the area under the ROC curve (AUC) of the GTV, PTV, and GPTV radiomic models was 0.701 (95% CI: 0.589-0.814), 0.674 (95% CI: 0.557-0.791) and 0.755 (95% CI: 0.643-0.867), respectively. The AUC of the nomogram model based on clinical and GPTV radiomic signatures was 0.804 (95% CI: 0.707-0.901). CONCLUSION The nomogram model based on clinical and GPTV radiomic signatures can better predict benign and malignant pulmonary nodules with halo signs, demonstrating that the model has potential as a convenient and effective auxiliary diagnostic tool for radiologists.
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Affiliation(s)
- M Tan
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China; Department of Radiology, Chengdu Second People's Hospital, Chengdu, China
| | - W Ma
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China; Department of Radiology, Shanghai Chest Hospital, Shanghai, China
| | - Y Yang
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - S Duan
- GE Healthcare, Shanghai, China
| | - L Jin
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Y Wu
- Department of Thoracic Surgery, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
| | - M Li
- Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
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Guo Z, Yang J, Zhao L, Yuan J, Yu H. 3D SAACNet with GBM for the classification of benign and malignant lung nodules. Comput Biol Med 2023; 153:106532. [PMID: 36623436 DOI: 10.1016/j.compbiomed.2022.106532] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 12/15/2022] [Accepted: 12/31/2022] [Indexed: 01/05/2023]
Abstract
In view of the low diagnostic accuracy of the current classification methods of benign and malignant pulmonary nodules, this paper proposes a 3D segmentation attention network integrating asymmetric convolution (SAACNet) classification model combined with a gradient boosting machine (GBM). This can make full use of the spatial information of pulmonary nodules. First, the asymmetric convolution (AC) designed in SAACNet can not only strengthen feature extraction but also improve the network's robustness to object flip and rotation detection and improve network performance. Second, the segmentation attention network integrating AC (SAAC) block can effectively extract more fine-grained multiscale spatial information while adaptively recalibrating multidimensional channel attention weights. The SAACNet also uses a dual-path connection for feature reuse, where the model makes full use of features. In addition, this article makes the loss function pay more attention to difficult and misclassified samples by adding adjustment factors. Third, the GBM is used to splice the nodule size, originally cropped nodule pixels, and the depth features learned by SAACNet to improve the prediction accuracy of the overall model. A comprehensive ablation experiment is carried out on the public dataset LUNA16 and compared with other lung nodule classification models. The classification accuracy (ACC) is 95.18%, and the area under the curve (AUC) is 0.977. The results show that this method effectively improves the classification performance of pulmonary nodules. The proposed method has advantages in the classification of benign and malignant pulmonary nodules, and it can effectively assist radiologists in pulmonary nodule classification.
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Affiliation(s)
- Zhitao Guo
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
| | - Jikai Yang
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
| | - Linlin Zhao
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
| | - Jinli Yuan
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, 01854, USA.
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Giacobbe G, Granata V, Trovato P, Fusco R, Simonetti I, De Muzio F, Cutolo C, Palumbo P, Borgheresi A, Flammia F, Cozzi D, Gabelloni M, Grassi F, Miele V, Barile A, Giovagnoni A, Gandolfo N. Gender Medicine in Clinical Radiology Practice. J Pers Med 2023; 13:jpm13020223. [PMID: 36836457 PMCID: PMC9966684 DOI: 10.3390/jpm13020223] [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: 12/24/2022] [Revised: 01/18/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023] Open
Abstract
Gender Medicine is rapidly emerging as a branch of medicine that studies how many diseases common to men and women differ in terms of prevention, clinical manifestations, diagnostic-therapeutic approach, prognosis, and psychological and social impact. Nowadays, the presentation and identification of many pathological conditions pose unique diagnostic challenges. However, women have always been paradoxically underestimated in epidemiological studies, drug trials, as well as clinical trials, so many clinical conditions affecting the female population are often underestimated and/or delayed and may result in inadequate clinical management. Knowing and valuing these differences in healthcare, thus taking into account individual variability, will make it possible to ensure that each individual receives the best care through the personalization of therapies, the guarantee of diagnostic-therapeutic pathways declined according to gender, as well as through the promotion of gender-specific prevention initiatives. This article aims to assess potential gender differences in clinical-radiological practice extracted from the literature and their impact on health and healthcare. Indeed, in this context, radiomics and radiogenomics are rapidly emerging as new frontiers of imaging in precision medicine. The development of clinical practice support tools supported by artificial intelligence allows through quantitative analysis to characterize tissues noninvasively with the ultimate goal of extracting directly from images indications of disease aggressiveness, prognosis, and therapeutic response. The integration of quantitative data with gene expression and patient clinical data, with the help of structured reporting as well, will in the near future give rise to decision support models for clinical practice that will hopefully improve diagnostic accuracy and prognostic power as well as ensure a more advanced level of precision medicine.
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Affiliation(s)
- Giuliana Giacobbe
- General and Emergency Radiology Department, “Antonio Cardarelli” Hospital, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Piero Trovato
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Correspondence:
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy
| | - Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Federica Flammia
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Diletta Cozzi
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, 56126 Pisa, Italy
| | - Francesca Grassi
- Division of Radiology, “Università degli Studi della Campania Luigi Vanvitelli”, 80138 Naples, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Antonio Barile
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, 67100 L’Aquila, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
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Miranda J, Horvat N, Fonseca GM, Araujo-Filho JDAB, Fernandes MC, Charbel C, Chakraborty J, Coelho FF, Nomura CH, Herman P. Current status and future perspectives of radiomics in hepatocellular carcinoma. World J Gastroenterol 2023; 29:43-60. [PMID: 36683711 PMCID: PMC9850949 DOI: 10.3748/wjg.v29.i1.43] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/27/2022] [Accepted: 12/14/2022] [Indexed: 01/04/2023] Open
Abstract
Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-010, Brazil
| | - Natally Horvat
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | | | - Maria Clara Fernandes
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Charlotte Charbel
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | - Jayasree Chakraborty
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, United States
| | | | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
| | - Paulo Herman
- Department of Gastroenterology, University of Sao Paulo, Sao Paulo 05403-000, Brazil
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50
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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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