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Yu Y, Guo H, Zhang M, Hou F, Yang S, Huang C, Duan L, Wang H. Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma. Cancer Imaging 2024; 24:59. [PMID: 38720384 PMCID: PMC11077743 DOI: 10.1186/s40644-024-00705-8] [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: 12/23/2023] [Accepted: 04/27/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND To develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression. METHODS We retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis. RESULTS For the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was - 0.03 and cumulative risk rates were calculated. DATA CONCLUSION To assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.
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Affiliation(s)
- Yuan Yu
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Hongwei Guo
- Department of Operation Center, Women and Children's Hospital, Qingdao University, Shandong, China
| | - Meng Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Shifeng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Chencui Huang
- Department of Research Collaboration, Research and Development (R&D) center, Beijing Deepwise & League of Philosophy Doctor (PHD) Technology Co., Ltd, Beijing, China
| | - Lisha Duan
- Department of Radiology, The Third Hospital of Hebei Medical University, Hebei, China.
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, China.
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De Angelis R, Casale R, Coquelet N, Ikhlef S, Mokhtari A, Simoni P, Bali MA. The impact of radiomics in the management of soft tissue sarcoma. Discov Oncol 2024; 15:62. [PMID: 38441726 PMCID: PMC10914656 DOI: 10.1007/s12672-024-00908-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/23/2024] [Indexed: 03/08/2024] Open
Abstract
INTRODUCTION Soft tissue sarcomas (STSs) are rare malignancies. Pre-therapeutic tumour grading and assessment are crucial in making treatment decisions. Radiomics is a high-throughput method for analysing imaging data, providing quantitative information beyond expert assessment. This review highlights the role of radiomic texture analysis in STSs evaluation. MATERIALS AND METHODS We conducted a systematic review according to the Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was conducted in PubMed/MEDLINE and Scopus using the search terms: 'radiomics [All Fields] AND ("soft tissue sarcoma" [All Fields] OR "soft tissue sarcomas" [All Fields])'. Only original articles, referring to humans, were included. RESULTS A preliminary search conducted on PubMed/MEDLINE and Scopus provided 74 and 93 studies respectively. Based on the previously described criteria, 49 papers were selected, with a publication range from July 2015 to June 2023. The main domains of interest were risk stratification, histological grading prediction, technical feasibility/reproductive aspects, treatment response. CONCLUSIONS With an increasing interest over the last years, the use of radiomics appears to have potential for assessing STSs from initial diagnosis to predicting treatment response. However, additional and extensive research is necessary to validate the effectiveness of radiomics parameters and to integrate them into a comprehensive decision support system.
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Affiliation(s)
- Riccardo De Angelis
- Institut Jules Bordet, Anderlecht, Belgium
- Université Libre de Bruxelles, Brussels, Belgium
| | - Roberto Casale
- Institut Jules Bordet, Anderlecht, Belgium.
- Université Libre de Bruxelles, Brussels, Belgium.
| | | | - Samia Ikhlef
- Institut Jules Bordet, Anderlecht, Belgium
- Université Libre de Bruxelles, Brussels, Belgium
| | - Ayoub Mokhtari
- Institut Jules Bordet, Anderlecht, Belgium.
- Université Libre de Bruxelles, Brussels, Belgium.
| | - Paolo Simoni
- Université Libre de Bruxelles, Brussels, Belgium
| | - Maria Antonietta Bali
- Institut Jules Bordet, Anderlecht, Belgium
- Université Libre de Bruxelles, Brussels, Belgium
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Osipov A, Nikolic O, Gertych A, Parker S, Hendifar A, Singh P, Filippova D, Dagliyan G, Ferrone CR, Zheng L, Moore JH, Tourtellotte W, Van Eyk JE, Theodorescu D. The Molecular Twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients. NATURE CANCER 2024; 5:299-314. [PMID: 38253803 PMCID: PMC10899109 DOI: 10.1038/s43018-023-00697-7] [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: 08/31/2022] [Accepted: 11/30/2023] [Indexed: 01/24/2024]
Abstract
Contemporary analyses focused on a limited number of clinical and molecular biomarkers have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma. Here we describe a precision medicine platform known as the Molecular Twin consisting of advanced machine-learning models and use it to analyze a dataset of 6,363 clinical and multi-omic molecular features from patients with resected pancreatic ductal adenocarcinoma to accurately predict disease survival (DS). We show that a full multi-omic model predicts DS with the highest accuracy and that plasma protein is the top single-omic predictor of DS. A parsimonious model learning only 589 multi-omic features demonstrated similar predictive performance as the full multi-omic model. Our platform enables discovery of parsimonious biomarker panels and performance assessment of outcome prediction models learning from resource-intensive panels. This approach has considerable potential to impact clinical care and democratize precision cancer medicine worldwide.
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Affiliation(s)
- Arsen Osipov
- Department of Medicine (Medical Oncology), Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Oncology, Pancreatic Cancer Precision Medicine Center of Excellence, Johns Hopkins University, Baltimore, MD, USA
| | | | - Arkadiusz Gertych
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sarah Parker
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Biomedical Sciences and Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Andrew Hendifar
- Department of Medicine (Medical Oncology), Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | | | - Grant Dagliyan
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Cristina R Ferrone
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Lei Zheng
- Department of Oncology, Pancreatic Cancer Precision Medicine Center of Excellence, Johns Hopkins University, Baltimore, MD, USA
| | - Jason H Moore
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Warren Tourtellotte
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jennifer E Van Eyk
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Biomedical Sciences and Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dan Theodorescu
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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Zhang X, Peng J, Ji G, Li T, Li B, Xiong H. Research status and progress of radiomics in bone and soft tissue tumors: A review. Medicine (Baltimore) 2023; 102:e36196. [PMID: 38013345 PMCID: PMC10681559 DOI: 10.1097/md.0000000000036198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 10/27/2023] [Indexed: 11/29/2023] Open
Abstract
Bone and soft tissue tumors are diverse, accompanying by complex histological components and significantly divergent biological behaviors. It is a challenge to address the demand for qualitative imaging as traditional imaging is restricted to the detection of anatomical structures and aberrant signals. With the improvement of digitalization in hospitals and medical centers, the introduction of electronic medical records and easier access to large amounts of information coupled with the improved computational power, traditional medicine has evolved into the combination of human brain, minimal data, and artificial intelligence. Scholars are committed to mining deeper levels of imaging data, and radiomics is worthy of promotion. Radiomics extracts subvisual quantitative features, analyzes them based on medical images, and quantifies tumor heterogeneity by outlining the region of interest and modeling. Two observers separately examined PubMed, Web of Science and CNKI to find existing studies, case reports, and clinical guidelines about research status and progress of radiomics in bone and soft tissue tumors from January 2010 to February 2023. When evaluating the literature, factors such as patient age, medical history, and severity of the condition will be considered. This narrative review summarizes the application and progress of radiomics in bone and soft tissue tumors.
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Affiliation(s)
- Xiaohan Zhang
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Jie Peng
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Guanghai Ji
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Tian Li
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Bo Li
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Hao Xiong
- Department of Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
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Lee S, Jung JY, Nam Y, Jung CK, Lee SY, Lee J, Shin SH, Chung YG. Diagnosis of Marginal Infiltration in Soft Tissue Sarcoma by Radiomics Approach Using T2-Weighted Dixon Sequence. J Magn Reson Imaging 2023; 57:752-760. [PMID: 35808915 DOI: 10.1002/jmri.28331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 06/14/2022] [Accepted: 06/16/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Determination of preoperative soft tissue sarcoma (STS) margin is crucial for patient prognosis. PURPOSE To evaluate diagnostic performance of radiomics model using T2-weighted Dixon sequence for infiltration degree of STS margin. STUDY TYPE Retrospective. POPULATION Seventy-two STS patients consisted of training (n = 58) and test (n = 14) sets. FIELD STRENGTH/SEQUENCE A 3.0 T; T2-weighted Dixon images. ASSESSMENT Pathologic result of marginal infiltration in STS (circumscribed margin; n = 27, group 1, focally infiltrative margin; n = 31, group 2-A, diffusely infiltrative margin; n = 14, group 2-B) was the reference standard. Radiomic volume and shape (VS) and other (T2) features were extracted from entire tumor volume and margin, respectively. Twelve radiomics models were generated using four combinations of classifier algorithms (R, SR, LR, LSR) and three different inputs (VS, T2, VS + T2 [VST2] features) to differentiate the three groups. Three radiologists (reader 1, 2, 3) analyzed the marginal infiltration with 6-scale confidence score. STATISTICAL TESTS Area under the receiver operating characteristic curve (AUC) and concordance rate. RESULTS Averaged AUCs of R, SR, LR, LSR models were 0.438, 0.466, 0.438, 0.466 using VS features, 0.596, 0.584, 0.814, 0.815 using T2 features, and 0.581, 0.587, 0.821, 0.821 using VST2 features, respectively. The LR and LSR models constructed with T2 or VST2 features showed higher AUC and concordance rate compared to radiologists' analysis (AUC; 0.730, 0.675, 0.706, concordance rate; 0.46, 0.43, 0.47 in reader 1, 2, 3). DATA CONCLUSION Radiomics model constructed with features from tumor margin on T2-weighted Dixon sequence is a promising method for differentiating infiltration degree of STS margin. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Seungeun Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Joon-Yong Jung
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yoonho Nam
- Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea
| | - Chan-Kwon Jung
- Department of Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - So-Yeon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jooyeon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.,Department of Applied Statistics, Hanyang University, Seoul, Republic of Korea
| | - Seung-Han Shin
- Department of Orthopedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yang-Guk Chung
- Department of Orthopedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Crombé A, Matcuk GR, Fadli D, Sambri A, Patel DB, Paioli A, Kind M, Spinnato P. Role of Imaging in Initial Prognostication of Locally Advanced Soft Tissue Sarcomas. Acad Radiol 2023; 30:322-340. [PMID: 35534392 DOI: 10.1016/j.acra.2022.04.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/21/2022] [Accepted: 04/06/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Although imaging is central in the initial staging of patients with soft tissue sarcomas (STS), it remains underused and few radiological features are currently used in practice for prognostication and to help guide the best therapeutic strategy. Yet, several prognostic qualitative and quantitative characteristics from magnetic resonance imaging (MRI) and positron emission tomography (PET) have been identified over these last decades. OBJECTIVE After an overview of the current validated prognostic features based on baseline imaging and their integration into prognostic tools, such as nomograms used by clinicians, the aim of this review is to summarize more complex and innovative MRI, PET, and radiomics features, and to highlight their role to predict indirectly (through histologic grade) or directly the patients' outcomes.
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Affiliation(s)
- Amandine Crombé
- Department of Diagnostic and Interventional Oncological Imaging, Institut Bergonié, Regional Comprehensive Cancer of Nouvelle-Aquitaine, 229, cours de l'Argonne, F-33076, Bordeaux, France; Department of musculoskeletal imaging, Pellegrin University Hospital, 2, place Amélie Raba-Léon, F-33000, Bordeaux, France; Models in Oncology (MONC) Team, INRIA Bordeaux Sud-Ouest, CNRS UMR 5251, Institut de Mathématiques de Bordeaux & Bordeaux University, 351 cours de la libération, F-33400 Talence, France.
| | - George R Matcuk
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California
| | - David Fadli
- Department of musculoskeletal imaging, Pellegrin University Hospital, 2, place Amélie Raba-Léon, F-33000, Bordeaux, France
| | - Andrea Sambri
- Alma Mater Studiorum, University of Bologna, Bologna, Italy; IRCCS Policlinico di Sant'Orsola, Bologna, Italy
| | - Dakshesh B Patel
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Anna Paioli
- Osteoncology Unit, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Michele Kind
- Department of Diagnostic and Interventional Oncological Imaging, Institut Bergonié, Regional Comprehensive Cancer of Nouvelle-Aquitaine, 229, cours de l'Argonne, F-33076, Bordeaux, France
| | - Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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Thammasorn P, Chaovalitwongse WA, Hippe DS, Wootton LS, Ford EC, Spraker MB, Combs SE, Peeken JC, Nyflot MJ. Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:586-600. [PMID: 33690126 DOI: 10.1109/tnnls.2021.3059635] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Multi-view classification with limited sample size and data augmentation is a very common machine learning (ML) problem in medicine. With limited data, a triplet network approach for two-stage representation learning has been proposed. However, effective training and verifying the features from the representation network for their suitability in subsequent classifiers are still unsolved problems. Although typical distance-based metrics for the training capture the overall class separability of the features, the performance according to these metrics does not always lead to an optimal classification. Consequently, an exhaustive tuning with all feature-classifier combinations is required to search for the best end result. To overcome this challenge, we developed a novel nearest-neighbor (NN) validation strategy based on the triplet metric. This strategy is supported by a theoretical foundation to provide the best selection of the features with a lower bound of the highest end performance. The proposed strategy is a transparent approach to identify whether to improve the features or the classifier. This avoids the need for repeated tuning. Our evaluations on real-world medical imaging tasks (i.e., radiation therapy delivery error prediction and sarcoma survival prediction) show that our strategy is superior to other common deep representation learning baselines [i.e., autoencoder (AE) and softmax]. The strategy addresses the issue of feature's interpretability which enables more holistic feature creation such that the medical experts can focus on specifying relevant data as opposed to tedious feature engineering.
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8
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Crombé A, Bertolo F, Fadli D, Kind M, Le Loarer F, Perret R, Chaire V, Spinnato P, Lucchesi C, Italiano A. Distinct patterns of the natural evolution of soft tissue sarcomas on pre-treatment MRIs captured with delta-radiomics correlate with gene expression profiles. Eur Radiol 2023; 33:1205-1218. [PMID: 36029343 DOI: 10.1007/s00330-022-09104-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 07/26/2022] [Accepted: 08/08/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVES Radiomics of soft tissue sarcomas (STS) is assumed to correlate with histologic and molecular tumor features, but radiogenomics analyses are lacking. Our aim was to identify if distinct patterns of natural evolution of STS obtained from consecutive pre-treatment MRIs are associated with differential gene expression (DGE) profiling in a pathway analysis. METHODS All patients with newly diagnosed STS treated in a curative intent in our sarcoma reference center between 2008 and 2019 and with two available pre-treatment contrast-enhanced MRIs were included in this retrospective study. Radiomics features (RFs) were extracted from fat-sat contrast-enhanced T1-weighted imaging. Log ratio and relative change in RFs were calculated and used to determine grouping of samples based on a consensus hierarchical clustering. DGE and oncogenesis pathway analysis were performed in the delta-radiomics groups identified in order to detect associations between delta-radiomics patterns and transcriptomics features of STS. Secondarily, the prognostic value of the delta-radiomics groups was investigated. RESULTS Sixty-three patients were included (median age: 63 years, interquartile range: 52.5-70). The consensus clustering identified 3 reliable delta-radiomics patient groups (A, B, and C). On imaging, group B patients were characterized by increase in tumor heterogeneity, necrotic signal, infiltrative margins, peritumoral edema, and peritumoral enhancement before the treatment start (p value range: 0.0019-0.0244), and, molecularly, by downregulation of natural killer cell-mediated cytotoxicity genes and upregulation of Hedgehog and Hippo signaling pathways. Group A patients were characterized by morphological stability of pre-treatment MRI traits and no local relapse (log-rank p = 0.0277). CONCLUSIONS This study highlights radiomics and transcriptomics convergence in STS. Proliferation and immune response inhibition were hyper-activated in the STS that were the most evolving on consecutive imaging. KEY POINTS • Three consensual and stable delta-radiomics clusters were identified and captured the natural patterns of morphological evolution of STS on pre-treatment MRIs. • These 3 patterns were explainable and correlated with different well-known semantic radiological features with an ascending gradient of pejorative characteristics from the A group to C group to B group. • Gene expression profiling stressed distinct patterns of up/downregulated oncogenetic pathways in STS from B group in keeping with its most aggressive radiological evolution.
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Affiliation(s)
- Amandine Crombé
- Department of Oncologic Imaging, Institut Bergonié, Comprehensive Cancer Center, F-33076, Bordeaux, France. .,Models in Oncology (MONC) Team, INRIA Bordeaux Sud-Ouest, CNRS UMR 5251 & Bordeaux University, F-33400, Talence, France. .,Department of Musculoskeletal Imaging, Pellegrin University Hospital, 2, place Amélie Raba Léon, F-33000, Bordeaux, France.
| | - Frédéric Bertolo
- Bioinformatics Department, Institut Bergonié, Comprehensive Cancer Center, F-33076, Bordeaux, France
| | - David Fadli
- Department of Musculoskeletal Imaging, Pellegrin University Hospital, 2, place Amélie Raba Léon, F-33000, Bordeaux, France
| | - Michèle Kind
- Department of Oncologic Imaging, Institut Bergonié, Comprehensive Cancer Center, F-33076, Bordeaux, France
| | - François Le Loarer
- Department of Pathology, Institut Bergonié, Comprehensive Cancer Center, F-33076, Bordeaux, France
| | - Raul Perret
- Department of Pathology, Institut Bergonié, Comprehensive Cancer Center, F-33076, Bordeaux, France
| | - Vanessa Chaire
- Department of Pathology, Institut Bergonié, Comprehensive Cancer Center, F-33076, Bordeaux, France
| | - Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, 40136, Bologna, Italy
| | - Carlo Lucchesi
- Bioinformatics Department, Institut Bergonié, Comprehensive Cancer Center, F-33076, Bordeaux, France
| | - Antoine Italiano
- Department of Medical Oncology, Institut Bergonié, Comprehensive Cancer Center, F-33076, Bordeaux, France
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Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors. LIFE (BASEL, SWITZERLAND) 2022; 13:life13010024. [PMID: 36675973 PMCID: PMC9866715 DOI: 10.3390/life13010024] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/08/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.
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Zhang Y, Zhao H, Liu Y, Zeng M, Zhang J, Hao D. Diagnostic Performance of Dynamic Contrast-Enhanced MRI and 18F-FDG PET/CT for Evaluation of Soft Tissue Tumors and Correlation with Pathology Parameters. Acad Radiol 2022; 29:1842-1851. [PMID: 35396157 DOI: 10.1016/j.acra.2022.03.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/05/2022] [Accepted: 03/06/2022] [Indexed: 01/26/2023]
Abstract
RATIONALE AND OBJECTIVES To assess the diagnostic performance of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and fluorine-18-fluorodeoxyglucose (18F-FDG) positron-emission tomography/computed tomography (PET/CT) parameters in evaluating the biological behavior of soft tissue tumors. MATERIALS AND METHODS We retrospectively analyzed DCE-MRI and 18F-FDG PET/CT parameters in 78 patients with pathology-confirmed soft tissue tumors. A total of 78 patients had undergone DCE-MRI examination, while 24 patients with malignant soft tissue tumor had undergone 18F-FDG PET/CT examination. Microvessel density (MVD) and the Ki-67 labeling index (LI) were detected using immunohistochemistry. Differences in parameters (Ktrans, Kep, Ve, MVD, and Ki-67 LI) between benign and malignant tumors were compared. Differences in parameters (Ktrans, Kep, Ve, MVD, and SUVmax) between high- and low-proliferation malignant tumors (grouped by Ki-67 LI) were compared. Correlation of the DCE-MRI and 18F-FDG PET/CT parameters with MVD and Ki-67 LI was analyzed. RESULTS Only the Ktrans, Kep, MVD, and Ki-67 LI differed significantly between the benign and malignant soft tissue tumors (all p < 0.001). Only Kep (p = 0.033) and SUVmax (p = 0.001) differed significantly between high- and low-proliferation malignant soft tissue tumors. Ktrans, Kep, and SUVmax correlated positively with MVD (r = 0.805, 0.778, 0.730, respectively; all p < 0.001), and with Ki-67 LI (r = 0.721, 0.685, 0.655, respectively; all p < 0.001). CONCLUSION DCE-MRI and 18F-FDG PET/CT parameters indicate soft tissue tumor biological behavior and can be used to differentiate between benign and malignant soft tissue tumors and between high- and low-proliferation malignant soft tissue tumors.
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Affiliation(s)
- Yu Zhang
- Department of Nuclear Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
| | - Haijing Zhao
- Department of Abdominal Ultrasound, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yayi Liu
- Department of Radiology, Qingdao Women and Children's Hospital, Qingdao, China
| | - Manqin Zeng
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Jun Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Dapeng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
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11
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Gurusamy VM, Al-Hammadi N, Caparrotti P, Divakar SR, Hammoud RW, Shaikh G. Real-world clinical outcomes with daily image-guided IMRT in extremity soft tissue sarcomas. Cancer Treat Res Commun 2022; 33:100655. [PMID: 36356354 DOI: 10.1016/j.ctarc.2022.100655] [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: 09/22/2022] [Revised: 10/24/2022] [Accepted: 10/25/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE We report the clinical outcomes of patients with soft tissue sarcomas (STS) arising in extremities treated with image-guided intensity modulated radiotherapy (IG-IMRT) at our institute. Local control of the tumors treated with RT was the primary end point of this study. Analyzing overall survival and long-term toxicities were the secondary objectives. METHODS AND MATERIALS The database of the patients with STS who received wide local excision and IG-IMRT at our institution from January 2012 to December 2020 was reviewed. Radiation was offered either preoperatively or postoperatively as part of multi-modality treatment. RESULTS Thirty-three consecutive patients were identified and included for analysis. Twenty-eight patients (84.8%) received postoperative adjuvant radiotherapy. Dedicated MRI simulation studies were performed in 31 patients (93.9%) in the treatment position. RapidArc IMRT technique was used in 31 patients (93.9%). A total of 2954 images were acquired during 991 treatment sessions. Errors exceeding 1 mm in the x, y and z directions were corrected online before the treatment. With a median follow-up of 36 months, two patients (6.1%) developed local recurrence. The 3-year local control was 90.9% (95% CI, 0.76 - 0.98), and the 5-year overall survival was 71.7% (95% CI, 0.44 - 0.88). One patient (3.03%) sustained a pathological fracture during the follow-up period. CONCLUSION Our results showed that IMRT with daily imaging offered excellent local control with acceptable long-term toxicity, as well as being feasible and practical to implement in our routine clinical practice.
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Affiliation(s)
- Venkada Manickam Gurusamy
- Department of Radiation Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar.
| | - Noora Al-Hammadi
- Department of Radiation Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Palmira Caparrotti
- Department of Radiation Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Saju Raveendran Divakar
- Department of Radiation Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Rabih Wafiq Hammoud
- Department of Radiation Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Ghazia Shaikh
- Department of Radiation Oncology, National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
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Arthur A, Johnston EW, Winfield JM, Blackledge MD, Jones RL, Huang PH, Messiou C. Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We? Front Oncol 2022; 12:892620. [PMID: 35847882 PMCID: PMC9286756 DOI: 10.3389/fonc.2022.892620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 05/31/2022] [Indexed: 12/13/2022] Open
Abstract
A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver “virtual biopsies” within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.
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Affiliation(s)
- Amani Arthur
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Edward W. Johnston
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Jessica M. Winfield
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Matthew D. Blackledge
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Robin L. Jones
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- Division of Clinical Studies, The Institute of Cancer Research, London, United Kingdom
| | - Paul H. Huang
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton, United Kingdom
- *Correspondence: Paul H. Huang, ; Christina Messiou,
| | - Christina Messiou
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- *Correspondence: Paul H. Huang, ; Christina Messiou,
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13
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Soft Tissue Sarcomas: The Role of Quantitative MRI in Treatment Response Evaluation. Acad Radiol 2022; 29:1065-1084. [PMID: 34548230 DOI: 10.1016/j.acra.2021.08.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/29/2021] [Accepted: 08/12/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Although curative surgery remains the cornerstone of the therapeutic strategy in patients with soft tissue sarcomas (STS), neoadjuvant radiotherapy and chemotherapy (NART and NACT, respectively) are increasingly used to improve operability, surgical margins and patient outcome. The best imaging modality for locoregional assessment of STS is MRI but these tumors are mostly evaluated in a qualitative manner. OBJECTIVE After an overview of the current standard of care regarding treatment for patients with locally advanced STS, this review aims to summarize the principles and limitations of (i) the current methods used to evaluate response to neoadjuvant treatment in clinical practice and clinical trials in STS (RECIST 1.1 and modified Choi criteria), (ii) quantitative MRI sequences (i.e., diffusion weighted imaging and dynamic contrast enhanced MRI), and (iii) texture analyses and (delta-) radiomics.
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Liang HY, Yang SF, Zou HM, Hou F, Duan LS, Huang CC, Xu JX, Liu SL, Hao DP, Wang HX. Deep Learning Radiomics Nomogram to Predict Lung Metastasis in Soft-Tissue Sarcoma: A Multi-Center Study. Front Oncol 2022; 12:897676. [PMID: 35814362 PMCID: PMC9265249 DOI: 10.3389/fonc.2022.897676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives To build and evaluate a deep learning radiomics nomogram (DLRN) for preoperative prediction of lung metastasis (LM) status in patients with soft tissue sarcoma (STS). Methods In total, 242 patients with STS (training set, n=116; external validation set, n=126) who underwent magnetic resonance imaging were retrospectively enrolled in this study. We identified independent predictors for LM-status and evaluated their performance. The minimum redundancy maximum relevance (mRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm were adopted to screen radiomics features. Logistic regression, decision tree, random forest, support vector machine (SVM), and adaptive boosting classifiers were compared for their ability to predict LM. To overcome the imbalanced distribution of the LM data, we retrained each machine-learning classifier using the synthetic minority over-sampling technique (SMOTE). A DLRN combining the independent clinical predictors with the best performing radiomics prediction signature (mRMR+LASSO+SVM+SMOTE) was established. Area under the receiver operating characteristics curve (AUC), calibration curves, and decision curve analysis (DCA) were used to assess the performance and clinical applicability of the models. Result Comparisons of the AUC values applied to the external validation set revealed that the DLRN model (AUC=0.833) showed better prediction performance than the clinical model (AUC=0.664) and radiomics model (AUC=0.799). The calibration curves indicated good calibration efficiency and the DCA showed the DLRN model to have greater clinical applicability than the other two models. Conclusion The DLRN was shown to be an accurate and efficient tool for LM-status prediction in STS.
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Affiliation(s)
- Hao-yu Liang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Shi-feng Yang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Hong-mei Zou
- Department of Radiology, The Third People’s Hospital of Qingdao, Qingdao, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Li-sha Duan
- Department of Radiology, The Third Hospital of Hebei Medical University, Shijiazhuang, China
| | - Chen-cui Huang
- Department of Research Collaboration, Research and Development (R&D) Center, Beijing Deepwise & League of Philosophy Doctor (PHD) Technology Co., Ltd, Beijing, China
| | - Jing-xu Xu
- Department of Research Collaboration, Research and Development (R&D) Center, Beijing Deepwise & League of Philosophy Doctor (PHD) Technology Co., Ltd, Beijing, China
| | - Shun-li Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Shun-li Liu, ; Da-peng Hao, ; He-xiang Wang,
| | - Da-peng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Shun-li Liu, ; Da-peng Hao, ; He-xiang Wang,
| | - He-xiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Shun-li Liu, ; Da-peng Hao, ; He-xiang Wang,
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Radiomic features as biomarkers of soft tissue paediatric sarcomas: preliminary results of a PET/MR study. Radiol Oncol 2022; 56:138-141. [PMID: 35344641 PMCID: PMC9122292 DOI: 10.2478/raon-2022-0013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/04/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Pediatric soft tissue sarcomas are rare tumors with rhabdomyosarcoma being the most frequent histotype. Diagnostic imaging plays a significant role in the evaluation of this type of tumors. Thus, aim of this study was to assess the prognostic and diagnostic value of radiomic features extracted from axial T2w images of the primary lesion in children with soft tissue sarcomas examined by PET/MR for staging. METHODS Using an open source software, each lesion was segmented and 33 radiomic features then extracted. Factor and logistic regression analyses were applied to select highly correlating features and evaluate their prognostic role, respectively. Differences in radiomic, demographics, metabolic, and laboratory variables according to tumor grade and histotype were investigated by the Students' and Chi-square tests. In case of differences the diagnostic value of the variable/s was assessed by receiver operating curves. RESULTS Eighteen children (11 female; mean age 7.8 ± 4.6-year-old) matched the inclusion criteria. The factor analysis allowed the selection of five highly correlating features which, according to regression analysis, did not influence the outcome (p > 0.05, each). The feature lmc1 was significantly higher in low grade lesions (p = 0.045) and showed 70.4% accuracy in classifying high grade tumors while the feature variance was significantly lower in rhabdomyosarcomas (p = 0.008) and showed 83.3% accuracy for this histotype. CONCLUSIONS In conclusion, our preliminary results suggest that specific radiomic features may act as biomarkers of pediatric soft tissue sarcoma grade and histotype.
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16
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Radiomics of Musculoskeletal Sarcomas: A Narrative Review. J Imaging 2022; 8:jimaging8020045. [PMID: 35200747 PMCID: PMC8876222 DOI: 10.3390/jimaging8020045] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 01/31/2022] [Accepted: 02/10/2022] [Indexed: 12/23/2022] Open
Abstract
Bone and soft-tissue primary malignant tumors or sarcomas are a large, diverse group of mesenchymal-derived malignancies. They represent a model for intra- and intertumoral heterogeneities, making them particularly suitable for radiomics analyses. Radiomic features offer information on cancer phenotype as well as the tumor microenvironment which, combined with other pertinent data such as genomics and proteomics and correlated with outcomes data, can produce accurate, robust, evidence-based, clinical-decision support systems. Our purpose in this narrative review is to offer an overview of radiomics studies dealing with Magnetic Resonance Imaging (MRI)-based radiomics models of bone and soft-tissue sarcomas that could help distinguish different histotypes, low-grade from high-grade sarcomas, predict response to multimodality therapy, and thus better tailor patients’ treatments and finally improve their survivals. Although showing promising results, interobserver segmentation variability, feature reproducibility, and model validation are three main challenges of radiomics that need to be addressed in order to translate radiomics studies to clinical applications. These efforts, together with a better knowledge and application of the “Radiomics Quality Score” and Image Biomarker Standardization Initiative reporting guidelines, could improve the quality of sarcoma radiomics studies and facilitate radiomics towards clinical translation.
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Zhao W, Huang X, Wang G, Guo J. PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma. Cancer Imaging 2022; 22:7. [PMID: 35022071 PMCID: PMC8756708 DOI: 10.1186/s40644-021-00438-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 12/05/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Various fusion strategies (feature-level fusion, matrix-level fusion, and image-level fusion) were used to fuse PET and MR images, which might lead to different feature values and classification performance. The purpose of this study was to measure the classification capability of features extracted using various PET/MR fusion methods in a dataset of soft-tissue sarcoma (STS). METHODS The retrospective dataset included 51 patients with histologically proven STS. All patients had pre-treatment PET and MR images. The image-level fusion was conducted using discrete wavelet transformation (DWT). During the DWT process, the MR weight was set as 0.1, 0.2, 0.3, 0.4, …, 0.9. And the corresponding PET weight was set as 1- (MR weight). The fused PET/MR images was generated using the inverse DWT. The matrix-level fusion was conducted by fusing the feature calculation matrix during the feature extracting process. The feature-level fusion was conducted by concatenating and averaging the features. We measured the predictive performance of features using univariate analysis and multivariable analysis. The univariate analysis included the Mann-Whitney U test and receiver operating characteristic (ROC) analysis. The multivariable analysis was used to develop the signatures by jointing the maximum relevance minimum redundancy method and multivariable logistic regression. The area under the ROC curve (AUC) value was calculated to evaluate the classification performance. RESULTS By using the univariate analysis, the features extracted using image-level fusion method showed the optimal classification performance. For the multivariable analysis, the signatures developed using the image-level fusion-based features showed the best performance. For the T1/PET image-level fusion, the signature developed using the MR weight of 0.1 showed the optimal performance (0.9524(95% confidence interval (CI), 0.8413-0.9999)). For the T2/PET image-level fusion, the signature developed using the MR weight of 0.3 showed the optimal performance (0.9048(95%CI, 0.7356-0.9999)). CONCLUSIONS For the fusion of PET/MR images in patients with STS, the signatures developed using the image-level fusion-based features showed the optimal classification performance than the signatures developed using the feature-level fusion and matrix-level fusion-based features, as well as the single modality features. The image-level fusion method was more recommended to fuse PET/MR images in future radiomics studies.
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Affiliation(s)
- Wenzhe Zhao
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Xin Huang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Geliang Wang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China
| | - Jianxin Guo
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi province, PR China.
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18
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Ericsson-Szecsenyi R, Zhang G, Redler G, Feygelman V, Rosenberg S, Latifi K, Ceberg C, Moros EG. Robustness Assessment of Images From a 0.35T Scanner of an Integrated MRI-Linac: Characterization of Radiomics Features in Phantom and Patient Data. Technol Cancer Res Treat 2022; 21:15330338221099113. [PMID: 35521966 PMCID: PMC9083059 DOI: 10.1177/15330338221099113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Purpose: Radiomics entails the extraction of quantitative imaging biomarkers (or radiomics features) hypothesized to provide additional pathophysiological and/or clinical information compared to qualitative visual observation and interpretation. This retrospective study explores the variability of radiomics features extracted from images acquired with the 0.35 T scanner of an integrated MRI-Linac. We hypothesized we would be able to identify features with high repeatability and reproducibility over various imaging conditions using phantom and patient imaging studies. We also compared findings from the literature relevant to our results. Methods: Eleven scans of a Magphan® RT phantom over 13 months and 11 scans of a ViewRay Daily QA phantom over 11 days constituted the phantom data. Patient datasets included 50 images from ten anonymized stereotactic body radiation therapy (SBRT) pancreatic cancer patients (50 Gy in 5 fractions). A True Fast Imaging with Steady-State Free Precession (TRUFI) pulse sequence was selected, using a voxel resolution of 1.5 mm × 1.5 mm × 1.5 mm and 1.5 mm × 1.5 mm × 3.0 mm for phantom and patient data, respectively. A total of 1087 shape-based, first, second, and higher order features were extracted followed by robustness analysis. Robustness was assessed with the Coefficient of Variation (CoV < 5%). Results: We identified 130 robust features across the datasets. Robust features were found within each category, except for 2 second-order sub-groups, namely, Gray Level Size Zone Matrix (GLSZM) and Neighborhood Gray Tone Difference Matrix (NGTDM). Additionally, several robust features agreed with findings from other stability assessments or predictive performance studies in the literature. Conclusion: We verified the stability of the 0.35 T scanner of an integrated MRI-Linac for longitudinal radiomics phantom studies and identified robust features over various imaging conditions. We conclude that phantom measurements can be used to identify robust radiomics features. More stability assessment research is warranted.
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Affiliation(s)
| | - Geoffrey Zhang
- Radiation Oncology Department, 25301H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Gage Redler
- Radiation Oncology Department, 25301H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Vladimir Feygelman
- Radiation Oncology Department, 25301H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Stephen Rosenberg
- Radiation Oncology Department, 25301H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Kujtim Latifi
- Radiation Oncology Department, 25301H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Crister Ceberg
- Department of Medical Radiation Physics, Clinical Sciences, 5193Lund University, Lund, Sweden
| | - Eduardo G Moros
- Radiation Oncology Department, 25301H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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Williams TL, Saadat LV, Gonen M, Wei A, Do RKG, Simpson AL. Radiomics in surgical oncology: applications and challenges. Comput Assist Surg (Abingdon) 2021; 26:85-96. [PMID: 34902259 DOI: 10.1080/24699322.2021.1994014] [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: 10/19/2022] Open
Abstract
Surgery is a curative treatment option for many patients with malignant tumors. Increased attention has focused on the combination of surgery with chemotherapy, as multimodality treatment has been associated with promising results in certain cancer types. Despite these data, there remains clinical equipoise on optimal timing and patient selection for neoadjuvant or adjuvant strategies. Radiomics, an emerging field involving the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and contribute to the advance of personalized therapy by helping predict tumor behavior and response to therapy. This review analyzes and summarizes studies that use radiomics with machine learning in patients who have received neoadjuvant and/or adjuvant chemotherapy to predict prognosis, recurrence, survival, and therapeutic response for various cancer types. While studies in both neoadjuvant and adjuvant settings demonstrate above average performance on ability to predict progression-free and overall survival, there remain many challenges and limitations to widespread implementation of this technology. The lack of standardization of common practices to analyze radiomics, limited data sharing, and absence of auto-segmentation have hindered the inclusion and rapid adoption of radiomics in prospective, clinical studies.
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Affiliation(s)
- Travis L Williams
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lily V Saadat
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mithat Gonen
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alice Wei
- Department of Surgery - Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber L Simpson
- School of Computing, Queen's University, Kingston, ON, Canada.,Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
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Evaluation of two modalities of perioperative treatment in the management of extremity and truncal soft tissue sarcomas: neoadjuvant concurrent chemoradiotherapy and sequential treatment. Strahlenther Onkol 2021; 197:1051-1062. [PMID: 34673991 DOI: 10.1007/s00066-021-01847-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 08/17/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE Patients with locally advanced grade 2-3 extremity/truncal soft tissue sarcomas (STS) are at high risk of recurrence. The objective of this study was to assess the efficacy and feasibility of neoadjuvant concurrent chemoradiotherapy (cCRT) in selected grade 2-3 patients with limb or trunk wall STS, and to compare this schedule to a sequential approach combining neoadjuvant chemotherapy and adjuvant radiotherapy. METHODS We retrospectively included patients who underwent neoadjuvant cCRT at two comprehensive cancer centers from 1992-2016. We then compared these results to those of patients treated with preoperative chemotherapy and postoperative radiotherapy from a third comprehensive cancer center with a propensity score matched analysis. RESULTS A total of 53 patients were treated by neoadjuvant cCRT; 58 patients could be matched with 29 patients in each treatment group after propensity score matching. Disease-free survival and overall survival at 5 years were 54.9 and 63.5%, respectively with neoadjuvant cCRT, with no significant difference when compared to the sequential treatment group. R0 resection rate was higher (90.9 vs 44.8%, p < 0.01) in the cCRT group than in the sequential treatment group during a shorter therapeutic sequence (118 vs 210.5 days, p < 0.01), with no impact on the surgical procedure or postoperative complications. CONCLUSION cCRT is feasible with acceptable immediate and late toxicities. It could facilitate surgery by increasing the R0 resection rate and improve patient compliance by shortening the therapeutic sequence.
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21
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Peeken JC, Asadpour R, Specht K, Chen EY, Klymenko O, Akinkuoroye V, Hippe DS, Spraker MB, Schaub SK, Dapper H, Knebel C, Mayr NA, Gersing AS, Woodruff HC, Lambin P, Nyflot MJ, Combs SE. MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy. Radiother Oncol 2021; 164:73-82. [PMID: 34506832 DOI: 10.1016/j.radonc.2021.08.023] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/15/2021] [Accepted: 08/27/2021] [Indexed: 02/09/2023]
Abstract
PURPOSE In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment ("delta-radiomics") may be able to predict the pathological complete response (pCR). METHODS MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort. RESULTS The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression. CONCLUSION This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.
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Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum, München, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany; Department of Radiation Oncology, University of Washington, Seattle, United States; Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, The Netherlands.
| | - Rebecca Asadpour
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Katja Specht
- Institute of Pathology, Technical University of Munich, Germany
| | - Eleanor Y Chen
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States
| | - Olena Klymenko
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Victor Akinkuoroye
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Daniel S Hippe
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States
| | - Matthew B Spraker
- Department of Radiation Oncology, Washington University in St. Louis, United States
| | - Stephanie K Schaub
- Department of Radiation Oncology, University of Washington, Seattle, United States
| | - Hendrik Dapper
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Carolin Knebel
- Department of Orthopedics and Sports Orthopedics, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Nina A Mayr
- Department of Radiation Oncology, University of Washington, Seattle, United States
| | - Alexandra S Gersing
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany
| | - Henry C Woodruff
- Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, The Netherlands; Department of Radiology and Nuclear Imaging, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, The Netherlands; Department of Radiology and Nuclear Imaging, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands
| | - Matthew J Nyflot
- Department of Radiation Oncology, University of Washington, Seattle, United States; Department of Radiology, University of Washington, Seattle, United States
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum, München, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
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22
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Crombé A, Cousin S, Spalato-Ceruso M, Le Loarer F, Toulmonde M, Michot A, Kind M, Stoeckle E, Italiano A. Implementing a Machine Learning Strategy to Predict Pathologic Response in Patients With Soft Tissue Sarcomas Treated With Neoadjuvant Chemotherapy. JCO Clin Cancer Inform 2021; 5:958-972. [PMID: 34524884 DOI: 10.1200/cci.21.00062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
PURPOSE Neoadjuvant chemotherapy (NAC) has been increasingly used in patients with locally advanced high-risk soft tissue sarcomas in the past decade, but definition and prognostic impact of a good histologic response (GHR) are lacking. Our aim was to investigate which histologic feature from the post-NAC surgical specimen independently correlated with metastatic relapse-free survival (MFS) in combination with clinical, radiologic, and pathologic features using a machine learning approach. METHODS This retrospective study included 175 consecutive patients (median age: 59 years, 75 women) with resectable disease, treated with anthracycline-based NAC between 1989 and 2015 in our sarcoma reference center, and with quantitative histopathologic analysis of the surgical specimen. The outcome of interest was the MFS. A multimodel, multivariate survival analysis was used to define GHR. The added prognostic value of GHR was investigated through the comparisons with the standard model (including histologic grade, size, and depth) and SARCULATOR nomogram using concordance indices (c-index) and Monte-Carlo cross-validation. RESULTS Seventy-two patients (72 of 175, 41.1%) had a metastatic relapse. Stepwise Cox regression, random survival forests, and least absolute shrinkage and selection operator-penalized Cox regression all converged toward the same definition for GHR, ie, < 5% stainable tumor cells. The five-year MFS probability was 1 (95% CI, 1 to 1) in patients with GHR versus 0.73 (95% CI, 0.65 to 0.81) in patients without GHR (log-rank P = .0122). The final prognostic model incorporating the GHR was significantly better than the standard model and SARCULATOR (average c-index in testing sets = 0.72 [95% CI, 0.61 to 0.82] v 0.57 [95% CI, 0.44 to 0.70] and 0.54 [95% CI, 0.45 to 0.64], respectively; P = .0414 and .0091). CONCLUSION Histologic response to NAC improves the prediction of MFS in patients with soft tissue sarcoma and represents a possible end point in future studies exploring innovative regimens in the neoadjuvant setting.
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Affiliation(s)
- Amandine Crombé
- Department of Oncological Imaging, Institut Bergonié, Bordeaux, France.,Mathematical Modeling for Oncology Team, Inria Bordeaux Sud-Ouest, Talence, France.,Bordeaux University, Bordeaux, France
| | - Sophie Cousin
- Early Phase Trials and Sarcoma Units, Department of Medical Oncology, Institut Bergonié, Bordeaux, France
| | - Mariella Spalato-Ceruso
- Early Phase Trials and Sarcoma Units, Department of Medical Oncology, Institut Bergonié, Bordeaux, France
| | - François Le Loarer
- Bordeaux University, Bordeaux, France.,Department of Pathology, Institut Bergonié, Bordeaux, France
| | - Maud Toulmonde
- Early Phase Trials and Sarcoma Units, Department of Medical Oncology, Institut Bergonié, Bordeaux, France
| | - Audrey Michot
- Bordeaux University, Bordeaux, France.,Department of Oncologic Surgery, Institut Bergonié, Bordeaux, France
| | - Michèle Kind
- Department of Oncological Imaging, Institut Bergonié, Bordeaux, France
| | - Eberhard Stoeckle
- Department of Oncologic Surgery, Institut Bergonié, Bordeaux, France
| | - Antoine Italiano
- Bordeaux University, Bordeaux, France.,Early Phase Trials and Sarcoma Units, Department of Medical Oncology, Institut Bergonié, Bordeaux, France
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23
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Nardone V, Boldrini L, Grassi R, Franceschini D, Morelli I, Becherini C, Loi M, Greto D, Desideri I. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers (Basel) 2021; 13:cancers13143590. [PMID: 34298803 PMCID: PMC8303203 DOI: 10.3390/cancers13143590] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary This review based on a literature search aims at showing the impact of Texture Analysis in the prediction of response to neoadjuvant radiotherapy and/or chemoradiotherapy. The manuscript explores radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma and rectal cancer in order to shed a light in the setting of neoadjuvant radiotherapy that can be used to tailor the best subsequent therapeutical strategy. Abstract Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Luca Boldrini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Davide Franceschini
- Radiotherapy and Radiosurgery Department, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Milan, Italy;
| | - Ilaria Morelli
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
- Correspondence: ; Tel.: +39-055-7947719
| | - Carlotta Becherini
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
| | - Mauro Loi
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Daniela Greto
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
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24
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Subhawong TK, Feister K, Sweet K, Alperin N, Kwon D, Rosenberg A, Trent J, Wilky BA. MRI Volumetrics and Image Texture Analysis in Assessing Systemic Treatment Response in Extra-Abdominal Desmoid Fibromatosis. Radiol Imaging Cancer 2021; 3:e210016. [PMID: 34213370 DOI: 10.1148/rycan.2021210016] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To determine whether MRI volumetric and image texture analysis correlates with treatment-induced biologic changes in desmoid fibromatosis (DF) earlier than conventional response criteria. Materials and Methods This retrospective study included 27 patients with histologically proven extra-abdominal DF who were managed with active surveillance or systemic therapy (from 2004 to 2016). MRI volumetric and image texture parameters were derived from manual tumor segmentations, and tumor signal intensity was normalized to muscle. Results were compared with objective response rates based on Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, World Health Organization (WHO) lesion response, volumetrics, and MRI-modified Choi criteria. Correlation coefficients (r) between image texture features and maximum tumor diameters were obtained by using a meta-analysis approach. Results The 27 included patients (mean age, 39 years; 74% women) were followed for an average of 4 years, comprising 207 distinct time-point assessments. The mean baseline tumor maximum diameter was 7.9 cm (range, 3.4-15.2 cm). Partial response (PR) rates as best response were 37%, 44%, 70%, and 81% by RECIST, WHO, volumetrics, and MRI-modified Choi criteria, respectively. Among the 10 tumors showing RECIST PR, a preceding MRI-modified Choi PR was observed in 70% (seven of 10), on average 1.3 years earlier. Multiple image texture parameters showed associations with objective measurements of tumor diameter including mean tumor-to-muscle signal ratio (r = 0.51; P = .004), median tumor-to-muscle signal ratio (r = 0.52; P = .003), energy (r = 0.48; P < .001), run entropy (r = 0.32, P = .04), and gray-level nonuniformity (r = 0.54; P ≤ .001). Conclusion Volumetric signal and image texture assessment allows more comprehensive analysis of DF biologic change and may permit early prediction of DF behavior and therapeutic response. Keywords: MR Imaging, Soft Tissues/Skin, Neoplasms-Primary © RSNA, 2021.
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Affiliation(s)
- Ty K Subhawong
- From the Departments of Radiology (T.K.S., N.A.), Pathology (A.R.), and Medicine-Medical Oncology (J.T., B.A.W.), Sylvester Comprehensive Cancer Center and the University of Miami Miller School of Medicine/Jackson Memorial Hospital, 1611 NW 12th Ave, JMH WW 279, Miami, FL 33136; Department of Radiology, University of Miami Miller School of Medicine, Miami, Fla (K.F., K.S.); and Department of Public Health Sciences, Sylvester Biostatistics and Bioinformatics Shared Resource, University of Miami Miller School of Medicine, Miami, Fla (D.K.)
| | - Katharina Feister
- From the Departments of Radiology (T.K.S., N.A.), Pathology (A.R.), and Medicine-Medical Oncology (J.T., B.A.W.), Sylvester Comprehensive Cancer Center and the University of Miami Miller School of Medicine/Jackson Memorial Hospital, 1611 NW 12th Ave, JMH WW 279, Miami, FL 33136; Department of Radiology, University of Miami Miller School of Medicine, Miami, Fla (K.F., K.S.); and Department of Public Health Sciences, Sylvester Biostatistics and Bioinformatics Shared Resource, University of Miami Miller School of Medicine, Miami, Fla (D.K.)
| | - Kevin Sweet
- From the Departments of Radiology (T.K.S., N.A.), Pathology (A.R.), and Medicine-Medical Oncology (J.T., B.A.W.), Sylvester Comprehensive Cancer Center and the University of Miami Miller School of Medicine/Jackson Memorial Hospital, 1611 NW 12th Ave, JMH WW 279, Miami, FL 33136; Department of Radiology, University of Miami Miller School of Medicine, Miami, Fla (K.F., K.S.); and Department of Public Health Sciences, Sylvester Biostatistics and Bioinformatics Shared Resource, University of Miami Miller School of Medicine, Miami, Fla (D.K.)
| | - Noam Alperin
- From the Departments of Radiology (T.K.S., N.A.), Pathology (A.R.), and Medicine-Medical Oncology (J.T., B.A.W.), Sylvester Comprehensive Cancer Center and the University of Miami Miller School of Medicine/Jackson Memorial Hospital, 1611 NW 12th Ave, JMH WW 279, Miami, FL 33136; Department of Radiology, University of Miami Miller School of Medicine, Miami, Fla (K.F., K.S.); and Department of Public Health Sciences, Sylvester Biostatistics and Bioinformatics Shared Resource, University of Miami Miller School of Medicine, Miami, Fla (D.K.)
| | - Deukwoo Kwon
- From the Departments of Radiology (T.K.S., N.A.), Pathology (A.R.), and Medicine-Medical Oncology (J.T., B.A.W.), Sylvester Comprehensive Cancer Center and the University of Miami Miller School of Medicine/Jackson Memorial Hospital, 1611 NW 12th Ave, JMH WW 279, Miami, FL 33136; Department of Radiology, University of Miami Miller School of Medicine, Miami, Fla (K.F., K.S.); and Department of Public Health Sciences, Sylvester Biostatistics and Bioinformatics Shared Resource, University of Miami Miller School of Medicine, Miami, Fla (D.K.)
| | - Andrew Rosenberg
- From the Departments of Radiology (T.K.S., N.A.), Pathology (A.R.), and Medicine-Medical Oncology (J.T., B.A.W.), Sylvester Comprehensive Cancer Center and the University of Miami Miller School of Medicine/Jackson Memorial Hospital, 1611 NW 12th Ave, JMH WW 279, Miami, FL 33136; Department of Radiology, University of Miami Miller School of Medicine, Miami, Fla (K.F., K.S.); and Department of Public Health Sciences, Sylvester Biostatistics and Bioinformatics Shared Resource, University of Miami Miller School of Medicine, Miami, Fla (D.K.)
| | - Jonathan Trent
- From the Departments of Radiology (T.K.S., N.A.), Pathology (A.R.), and Medicine-Medical Oncology (J.T., B.A.W.), Sylvester Comprehensive Cancer Center and the University of Miami Miller School of Medicine/Jackson Memorial Hospital, 1611 NW 12th Ave, JMH WW 279, Miami, FL 33136; Department of Radiology, University of Miami Miller School of Medicine, Miami, Fla (K.F., K.S.); and Department of Public Health Sciences, Sylvester Biostatistics and Bioinformatics Shared Resource, University of Miami Miller School of Medicine, Miami, Fla (D.K.)
| | - Breelyn A Wilky
- From the Departments of Radiology (T.K.S., N.A.), Pathology (A.R.), and Medicine-Medical Oncology (J.T., B.A.W.), Sylvester Comprehensive Cancer Center and the University of Miami Miller School of Medicine/Jackson Memorial Hospital, 1611 NW 12th Ave, JMH WW 279, Miami, FL 33136; Department of Radiology, University of Miami Miller School of Medicine, Miami, Fla (K.F., K.S.); and Department of Public Health Sciences, Sylvester Biostatistics and Bioinformatics Shared Resource, University of Miami Miller School of Medicine, Miami, Fla (D.K.)
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25
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Navarro F, Dapper H, Asadpour R, Knebel C, Spraker MB, Schwarze V, Schaub SK, Mayr NA, Specht K, Woodruff HC, Lambin P, Gersing AS, Nyflot MJ, Menze BH, Combs SE, Peeken JC. Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging. Cancers (Basel) 2021; 13:2866. [PMID: 34201251 PMCID: PMC8227009 DOI: 10.3390/cancers13122866] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/27/2021] [Accepted: 06/02/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. METHODS Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. RESULTS The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. CONCLUSIONS MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.
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Affiliation(s)
- Fernando Navarro
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (F.N.); (H.D.); (R.A.); (S.E.C.)
- Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, 85748 Garching, Germany;
- TranslaTUM—Central Institute for Translational Cancer Research, Einsteinstraße 25, 81675 Munich, Germany
| | - Hendrik Dapper
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (F.N.); (H.D.); (R.A.); (S.E.C.)
| | - Rebecca Asadpour
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (F.N.); (H.D.); (R.A.); (S.E.C.)
| | - Carolin Knebel
- Department of Orthopedics and Sports Orthopedics, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany;
| | - Matthew B. Spraker
- Department of Radiation Oncology, Washington University in St. Louis, 4511 Forest Park Ave, St. Louis, MO 63108, USA;
| | - Vincent Schwarze
- Department of Radiology, Grosshadern Campus, Ludwig-Maximilians-University Munich, Marchioninistraße 15, 81377 Munich, Germany; (V.S.); (A.S.G.)
| | - Stephanie K. Schaub
- Department of Radiation Oncology, University of Washington, 1959 NE Pacific St, 356043, Seattle, WA 98195, USA; (S.K.S.); (N.A.M.); (M.J.N.)
| | - Nina A. Mayr
- Department of Radiation Oncology, University of Washington, 1959 NE Pacific St, 356043, Seattle, WA 98195, USA; (S.K.S.); (N.A.M.); (M.J.N.)
| | - Katja Specht
- Department of Pathology, Technical University of Munich (TUM), Trogerstr. 18, 81675 Munich, Germany;
| | - Henry C. Woodruff
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Imaging, GROW—School for Oncology and Developmental Biology, P. Debyelaan 25, 6229 HX Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands; (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Imaging, GROW—School for Oncology and Developmental Biology, P. Debyelaan 25, 6229 HX Maastricht, The Netherlands
| | - Alexandra S. Gersing
- Department of Radiology, Grosshadern Campus, Ludwig-Maximilians-University Munich, Marchioninistraße 15, 81377 Munich, Germany; (V.S.); (A.S.G.)
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany
| | - Matthew J. Nyflot
- Department of Radiation Oncology, University of Washington, 1959 NE Pacific St, 356043, Seattle, WA 98195, USA; (S.K.S.); (N.A.M.); (M.J.N.)
- Department of Radiology, University of Washington, 4245 Roosevelt Way NE, Seattle, WA 98105, USA
| | - Bjoern H. Menze
- Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, 85748 Garching, Germany;
- Department for Quantitative Biomedicine, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Stephanie E. Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (F.N.); (H.D.); (R.A.); (S.E.C.)
- Department for Quantitative Biomedicine, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Ingolstaedter Landstr. 1, 85764 Munich, Germany
| | - Jan C. Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (F.N.); (H.D.); (R.A.); (S.E.C.)
- Department of Radiology and Nuclear Imaging, GROW—School for Oncology and Developmental Biology, P. Debyelaan 25, 6229 HX Maastricht, The Netherlands
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Ingolstaedter Landstr. 1, 85764 Munich, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site, 85764 Munich, Germany
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26
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Gitto S, Cuocolo R, Albano D, Morelli F, Pescatori LC, Messina C, Imbriaco M, Sconfienza LM. CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies. Insights Imaging 2021; 12:68. [PMID: 34076740 PMCID: PMC8172744 DOI: 10.1186/s13244-021-01008-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/05/2021] [Indexed: 02/07/2023] Open
Abstract
Background Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability. Results Out of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.
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Affiliation(s)
- Salvatore Gitto
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.
| | - Renato Cuocolo
- Dipartimento di Medicina Clinica e Chirurgia, Università degli Studi di Napoli "Federico II", Naples, Italy.,Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Palermo, Italy
| | | | - Lorenzo Carlo Pescatori
- Assistance Publique - Hôpitaux de Paris (AP-HP), Service d'Imagerie Médicale, CHU Henri Mondor, Créteil, France
| | - Carmelo Messina
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.,IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Massimo Imbriaco
- Dipartimento di Scienze Biomediche Avanzate, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Luca Maria Sconfienza
- Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Via Riccardo Galeazzi 4, 20161, Milan, Italy.,IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
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27
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Yang Y, Ma X, Wang Y, Ding X. Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest. Updates Surg 2021; 74:355-365. [PMID: 34003477 DOI: 10.1007/s13304-021-01074-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 04/29/2021] [Indexed: 02/05/2023]
Abstract
Many researches have applied machine learning methods to find associations between radiomic features and clinical outcomes. Random survival forests (RSF), as an accurate classifier, sort all candidate variables as the rank of importance values. There was no study concerning on finding radiomic predictors in patients with extremity and trunk wall soft-tissue sarcomas using RSF. This study aimed to determine associations between radiomic features and overall survival (OS) by RSF analysis. To identify radiomic features with important values by RSF analysis, construct predictive models for OS incorporating clinical characteristics, and evaluate models' performance with different method. We collected clinical characteristics and radiomic features extracted from plain and contrast-enhanced computed tomography (CT) from 353 patients with extremity and trunk wall soft-tissue sarcomas treated with surgical resection. All radiomic features were analyzed by Cox proportional hazard (CPH) and followed RSF analysis. The association between radiomics-predicted risks and OS was assessed by Kaplan-Meier analysis. All clinical features were screened by CPH analysis. Prognostic clinical and radiomic parameters were fitted into RSF and CPH integrative models for OS in the training cohort, respectively. The concordance indexes (C-index) and Brier scores of both two models were evaluated in both training and testing cohorts. The model with better predictive performance was interpreted with nomogram and calibration plots. Among all 86 radiomic features, there were three variables selected with high importance values. The RSF on these three features distinguished patients with high predicted risks from patients with low predicted risks for OS in the training set (P < 0.001) using Kaplan-Meier analysis. Age, lymph node involvement and grade were incorporated into the combined models for OS (P < 0.05). The C-indexes in both two integrative models fluctuated above 0.80 whose Brier scores maintained less than 15.0 in the training and testing datasets. The RSF model performed little advantages over the CPH model that the calibration curve of the RSF model showed favorable agreement between predicted and actual survival probabilities for the 3-year and 5-year survival prediction. The multimodality RSF model including clinical and radiomic characteristics conducted high capacity in prediction of OS which might assist individualized therapeutic regimens. Level III, prognostic study.
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Affiliation(s)
- Yuhan Yang
- West China School of Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, Sichuan, China
| | - Xuelei Ma
- State Key Laboratory of Biotherapy, Department of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Guoxue Road, Chengdu, 610041, China.
| | - Yixi Wang
- West China School of Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, Sichuan, China
| | - Xinyan Ding
- West China School of Medicine, Sichuan University, No.17 People's South Road, Chengdu, 610041, Sichuan, China
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Pattira B. Editorial for "Radiomics Nomograms Based on Non-enhanced MRI and Clinical Risk Factors for the Differentiation of Chondrosarcoma from Enchondroma". J Magn Reson Imaging 2021; 54:1324-1325. [PMID: 33957006 DOI: 10.1002/jmri.27670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 04/16/2021] [Indexed: 11/07/2022] Open
Affiliation(s)
- Boonsri Pattira
- Department of Radiology, Prince of Songkla University, Hat Yai, Songkhla, 90110, Thailand
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Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors. Eur Radiol 2021; 31:8522-8535. [PMID: 33893534 DOI: 10.1007/s00330-021-07914-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/18/2021] [Indexed: 12/11/2022]
Abstract
OBJECTIVES Our purpose was to differentiate between malignant from benign soft tissue neoplasms using a combination of MRI-based radiomics metrics and machine learning. METHODS Our retrospective study identified 128 histologically diagnosed benign (n = 36) and malignant (n = 92) soft tissue lesions. 3D ROIs were manually drawn on 1 sequence of interest and co-registered to other sequences obtained during the same study. One thousand seven hundred eight radiomics features were extracted from each ROI. Univariate analyses with supportive ROC analyses were conducted to evaluate the discriminative power of predictive models constructed using Real Adaptive Boosting (Adaboost) and Random Forest (RF) machine learning approaches. RESULTS Univariate analyses demonstrated that 36.89% of individual radiomics varied significantly between benign and malignant lesions at the p ≤ 0.05 level. Adaboost and RF performed similarly well, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81), respectively, after 10-fold cross-validation. Restricting the machine learning models to only sequences extracted from T2FS and STIR sequences maintained comparable performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84), respectively. CONCLUSION Machine learning decision classifiers constructed from MRI-based radiomics features show promising ability to preoperatively discriminate between benign and malignant soft tissue masses. Our approach maintains applicability even when the dataset is restricted to T2FS and STIR fluid-sensitive sequences, which may bolster practicality in clinical application scenarios by eliminating the need for complex co-registrations for multisequence analysis. KEY POINTS • Predictive models constructed from MRI-based radiomics data and machine learning-augmented approaches yielded good discriminative power to correctly classify benign and malignant lesions on preoperative scans, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81) for Real Adaptive Boosting (Adaboost) and Random Forest (RF), respectively. • Restricting the models to only use metrics extracted from T2 fat-saturated (T2FS) and Short-Tau Inversion Recovery (STIR) sequences yielded similar performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84) for Adaboost and RF, respectively. • Radiomics-based machine learning decision classifiers constructed from multicentric data more closely mimic the real-world practice environment and warrant additional validation ahead of prospective implementation into clinical workflows.
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Peeken JC, Neumann J, Asadpour R, Leonhardt Y, Moreira JR, Hippe DS, Klymenko O, Foreman SC, von Schacky CE, Spraker MB, Schaub SK, Dapper H, Knebel C, Mayr NA, Woodruff HC, Lambin P, Nyflot MJ, Gersing AS, Combs SE. Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics. Cancers (Basel) 2021; 13:1929. [PMID: 33923697 PMCID: PMC8073388 DOI: 10.3390/cancers13081929] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/13/2021] [Accepted: 04/13/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients' risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). METHODS Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. RESULTS ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. CONCLUSIONS T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.
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Affiliation(s)
- Jan C. Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (R.A.); (O.K.); (H.D.); (S.E.C.)
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, 85764 München, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6200 MD Maastricht, The Netherlands; (H.C.W.); (P.L.)
| | - Jan Neumann
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Rebecca Asadpour
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (R.A.); (O.K.); (H.D.); (S.E.C.)
| | - Yannik Leonhardt
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Joao R. Moreira
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Daniel S. Hippe
- Department of Radiation Oncology, University of Washington, Seattle, WA 98195, USA; (D.S.H.); (S.K.S.); (N.A.M.); (M.J.N.)
| | - Olena Klymenko
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (R.A.); (O.K.); (H.D.); (S.E.C.)
| | - Sarah C. Foreman
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Claudio E. von Schacky
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Matthew B. Spraker
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO 63110, USA;
| | - Stephanie K. Schaub
- Department of Radiation Oncology, University of Washington, Seattle, WA 98195, USA; (D.S.H.); (S.K.S.); (N.A.M.); (M.J.N.)
| | - Hendrik Dapper
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (R.A.); (O.K.); (H.D.); (S.E.C.)
| | - Carolin Knebel
- Department of Orthopedics and Sports Orthopedics, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany;
| | - Nina A. Mayr
- Department of Radiation Oncology, University of Washington, Seattle, WA 98195, USA; (D.S.H.); (S.K.S.); (N.A.M.); (M.J.N.)
| | - Henry C. Woodruff
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6200 MD Maastricht, The Netherlands; (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Imaging, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre, 6229 HX Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University, 6200 MD Maastricht, The Netherlands; (H.C.W.); (P.L.)
- Department of Radiology and Nuclear Imaging, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre, 6229 HX Maastricht, The Netherlands
| | - Matthew J. Nyflot
- Department of Radiation Oncology, University of Washington, Seattle, WA 98195, USA; (D.S.H.); (S.K.S.); (N.A.M.); (M.J.N.)
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Alexandra S. Gersing
- Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), 81675 Munich, Germany; (J.N.); (Y.L.); (J.R.M.); (S.C.F.); (C.E.v.S.); (A.S.G.)
| | - Stephanie E. Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; (R.A.); (O.K.); (H.D.); (S.E.C.)
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, 85764 München, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
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Hong JH, Jung JY, Jo A, Nam Y, Pak S, Lee SY, Park H, Lee SE, Kim S. Development and Validation of a Radiomics Model for Differentiating Bone Islands and Osteoblastic Bone Metastases at Abdominal CT. Radiology 2021; 299:626-632. [PMID: 33787335 DOI: 10.1148/radiol.2021203783] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Background It is important to diagnose sclerotic bone lesions in order to determine treatment strategy. Purpose To evaluate the diagnostic performance of a CT radiomics-based machine learning model for differentiating bone islands and osteoblastic bone metastases. Materials and Methods In this retrospective study, patients who underwent contrast-enhanced abdominal CT and were diagnosed with a bone island or osteoblastic metastasis between 2015 to 2019 at either of two different institutions were included: institution 1 for the training set and institution 2 for the external test set. Radiomics features were extracted. The random forest (RF) model was built using 10 selected features, and subsequent 10-fold cross-validation was performed. In the test phase, the RF model was tested with an external test set. Three radiologists reviewed the CT images for the test set. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated for the models and each of the three radiologists. The AUCs of the radiomics model and radiologists were compared. Results A total of 177 patients (89 with a bone island and 88 with metastasis; mean age, 66 years ± 12 [standard deviation]; 111 men) were in the training set, and 64 (23 with a bone island and 41 with metastasis; mean age, 69 years ± 14; 59 men) were in the test set. Radiomics features (n = 1218) were extracted. The average AUC of the RF model from 10-fold cross-validation was 0.89 (sensitivity, 85% [75 of 88 patients]; specificity, 82% [73 of 89 patients]; and accuracy, 84% [148 of 177 patients]). In the test set, the AUC of the trained RF model was 0.96 (sensitivity, 80% [33 of 41 patients]; specificity, 96% [22 of 23 patients]; and accuracy, 86% [55 of 64 patients]). The AUCs for the three readers were 0.95 (95% CI: 0.90, 1.00), 0.96 (95% CI: 0.90, 1.00), and 0.88 (95% CI: 0.80, 0.96). The AUC of radiomics model was higher than that of only reader 3 (0.96 vs 0.88, respectively; P = .03). Conclusion A CT radiomics-based random forest model was proven useful for differentiating bone islands from osteoblastic metastases and showed better diagnostic performance compared with an inexperienced radiologist. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Vannier in this issue.
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Affiliation(s)
- Ji Hyun Hong
- From the Department of Radiology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea (J.H.H.); Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea (J.Y.J., A.J., S.Y.L., H.P., S.E.L., S.K.); Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea (Y.N.); and Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea (S.P.)
| | - Joon-Yong Jung
- From the Department of Radiology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea (J.H.H.); Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea (J.Y.J., A.J., S.Y.L., H.P., S.E.L., S.K.); Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea (Y.N.); and Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea (S.P.)
| | - Aram Jo
- From the Department of Radiology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea (J.H.H.); Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea (J.Y.J., A.J., S.Y.L., H.P., S.E.L., S.K.); Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea (Y.N.); and Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea (S.P.)
| | - Yoonho Nam
- From the Department of Radiology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea (J.H.H.); Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea (J.Y.J., A.J., S.Y.L., H.P., S.E.L., S.K.); Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea (Y.N.); and Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea (S.P.)
| | - Seongyong Pak
- From the Department of Radiology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea (J.H.H.); Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea (J.Y.J., A.J., S.Y.L., H.P., S.E.L., S.K.); Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea (Y.N.); and Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea (S.P.)
| | - So-Yeon Lee
- From the Department of Radiology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea (J.H.H.); Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea (J.Y.J., A.J., S.Y.L., H.P., S.E.L., S.K.); Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea (Y.N.); and Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea (S.P.)
| | - Hyerim Park
- From the Department of Radiology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea (J.H.H.); Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea (J.Y.J., A.J., S.Y.L., H.P., S.E.L., S.K.); Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea (Y.N.); and Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea (S.P.)
| | - Seung Eun Lee
- From the Department of Radiology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea (J.H.H.); Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea (J.Y.J., A.J., S.Y.L., H.P., S.E.L., S.K.); Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea (Y.N.); and Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea (S.P.)
| | - Sanghee Kim
- From the Department of Radiology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea (J.H.H.); Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea (J.Y.J., A.J., S.Y.L., H.P., S.E.L., S.K.); Division of Biomedical Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, Republic of Korea (Y.N.); and Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea (S.P.)
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Igrec J, Fuchsjäger MH. Imaging of Bone Sarcomas and Soft-Tissue Sarcomas. ROFO-FORTSCHR RONTG 2021; 193:1171-1182. [PMID: 33772487 DOI: 10.1055/a-1401-0215] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND In the diagnosis of bone and soft-tissue sarcomas, the continuous advancement of various imaging modalities has improved the detection of small lesions, surgical planning, assessment of chemotherapeutic effects, and, importantly, guidance for surgery or biopsy. METHOD This review was composed based on a PubMed literature search for the terms "bone sarcoma," "bone cancer" and "soft tissue sarcoma," "imaging," "magnetic resonance imaging", "computed tomography", "ultrasound", "radiography", and "radiomics" covering the publication period 2005-2020. RESULTS AND CONCLUSION As discussed in this review, radiography, ultrasound, CT, and MRI all play key roles in the imaging evaluation of bone and soft-tissue sarcomas. In daily practice, advanced MRI techniques complement standard MRI but remain underused, as they are considered time-consuming, technically challenging, and not reliable enough to replace biopsy and histology. PET/MRI and radiomics have shown promise regarding the imaging of sarcomas in the future. KEY POINTS · Radiographs remain crucial in diagnostic imaging algorithms for sarcomas.. · US is an initial imaging study for the evaluation of superficial soft-tissue tumors.. · The role of CT continues to evolve as new techniques emerge.. · MRI allows the noninvasive evaluation of soft-tissue, osseous, and articular structures.. · Machine learning methods could improve personalized selection of therapy for patients with sarcoma.. CITATION FORMAT · Igrec J, Fuchsjäger MH. Imaging of Bone and Soft-Tissue Sarcomas. Fortschr Röntgenstr 2021; 193: 1171 - 1182.
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Affiliation(s)
- Jasminka Igrec
- Division of General Radiological Diagnostics, Department of Radiology, Hospital of the Federal State of Styria and University Hospital Graz, Austria
| | - Michael H Fuchsjäger
- Division of General Radiological Diagnostics, Department of Radiology, Hospital of the Federal State of Styria and University Hospital Graz, Austria
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Yan R, Hao D, Li J, Liu J, Hou F, Chen H, Duan L, Huang C, Wang H, Yu T. Magnetic Resonance Imaging-Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two-Center Study. J Magn Reson Imaging 2021; 53:1683-1696. [PMID: 33604955 DOI: 10.1002/jmri.27532] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/13/2021] [Accepted: 01/15/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed. PURPOSE To develop and test an magnetic resonance imaging (MRI)-based radiomics nomogram for predicting the grade of STS (low-grade vs. high grade). STUDY TYPE Retrospective POPULATION: One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N = 109; external validation set, N = 71). FIELD STRENGTH/SEQUENCE Unenhanced T1-weighted (T1WI) and fat-suppressed T2-weighted images (FS-T2WI) were acquired at 1.5 T and 3.0 T. ASSESSMENT Clinical-MRI characteristics included age, gender, tumor-node-metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression-free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS-T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS-T1, RS-FST2, and RS-Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors. STATISTICAL TESTS Clinical-MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS-T1 model, RS-FST2 model, and RS-Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS-Combined model had AUCs of 0.916 (95%CI, 0.866-0.966, training set) and 0.879 (95%CI, 0.791-0.967, external validation set), and demonstrated good calibration and good clinical utility. DATA CONCLUSION The proposed noninvasive MRI-based radiomics models showed good performance in differentiating low-grade from high-grade STSs. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Ruixin Yan
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266003, China
| | - Dapeng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266003, China
| | - Jie Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266003, China
| | - Jihua Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266003, China
| | - Feng Hou
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266003, China
| | - Haisong Chen
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266003, China
| | - Lisha Duan
- Department of CT/MRI, The Third Hospital of Hebei Medical University, Shi jiazhuang, Hebei, 050051, China
| | - Chencui Huang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, 100080, China
| | - Hexiang Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266003, China
| | - Tengbo Yu
- Department of Sports Medicine, the Affiliated Hospital of Qingdao University, QingDao, Shandong, 266003, China
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Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients. Cancers (Basel) 2021; 13:cancers13040786. [PMID: 33668646 PMCID: PMC7917758 DOI: 10.3390/cancers13040786] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 02/07/2021] [Accepted: 02/09/2021] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Determination of human papillomavirus (HPV) status for oropharyngeal cancer patients depicts a essential diagnostic factor and is important for treatment decisions. Current histological methods are invasive, time consuming and costly. We tested the ability of deep learning models for HPV status testing based on routinely acquired diagnostic CT images. A network trained for sports video clip classification was modified and then fine tuned for HPV status prediction. In this way, very basic information about image structures is induced into the model before training is started, while still allowing for exploitation of full 3D information in the CT images. Usage of this approach helps the network to cope with a small number of training examples and makes it more robust. For comparison, two other models were trained, one not relying on a pre-training task and another one pre-trained on 2D Data. The pre-trained video model preformed best. Abstract Infection with the human papillomavirus (HPV) has been identified as a major risk factor for oropharyngeal cancer (OPC). HPV-related OPCs have been shown to be more radiosensitive and to have a reduced risk for cancer related death. Hence, the histological determination of HPV status of cancer patients depicts an essential diagnostic factor. We investigated the ability of deep learning models for imaging based HPV status detection. To overcome the problem of small medical datasets, we used a transfer learning approach. A 3D convolutional network pre-trained on sports video clips was fine-tuned, such that full 3D information in the CT images could be exploited. The video pre-trained model was able to differentiate HPV-positive from HPV-negative cases, with an area under the receiver operating characteristic curve (AUC) of 0.81 for an external test set. In comparison to a 3D convolutional neural network (CNN) trained from scratch and a 2D architecture pre-trained on ImageNet, the video pre-trained model performed best. Deep learning models are capable of CT image-based HPV status determination. Video based pre-training has the ability to improve training for 3D medical data, but further studies are needed for verification.
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Peeken JC, Shouman MA, Kroenke M, Rauscher I, Maurer T, Gschwend JE, Eiber M, Combs SE. A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients. Eur J Nucl Med Mol Imaging 2020; 47:2968-2977. [PMID: 32468251 PMCID: PMC7680305 DOI: 10.1007/s00259-020-04864-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 05/07/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)-positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs). METHODS Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling. RESULTS Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node-specific decision curve analysis, there was a clinical net benefit above LN short diameter. CONCLUSION The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features.
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Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany.
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Neuherberg, Germany.
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany.
| | - Mohamed A Shouman
- Department of Radiation Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
| | - Markus Kroenke
- Department of Nuclear Medicine, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Munich, Germany
- Institute for Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Munich, Germany
| | - Isabel Rauscher
- Department of Nuclear Medicine, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Munich, Germany
| | - Tobias Maurer
- Institute for Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Munich, Germany
- Department of Urology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Munich, Germany
| | - Jürgen E Gschwend
- Department of Urology and Martini-Klinik, University Hamburg-Eppendorf, Hamburg, Germany
| | - Matthias Eiber
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
- Department of Nuclear Medicine, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
- Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Neuherberg, Germany
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
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Duan C, Chaovalitwongse WA, Bai F, Hippe DS, Wang S, Thammasorn P, Pierce LA, Liu X, You J, Miyaoka RS, Vesselle HJ, Kinahan PE, Rengan R, Zeng J, Bowen SR. Sensitivity analysis of FDG PET tumor voxel cluster radiomics and dosimetry for predicting mid-chemoradiation regional response of locally advanced lung cancer. Phys Med Biol 2020; 65:205007. [PMID: 33027064 PMCID: PMC7593986 DOI: 10.1088/1361-6560/abb0c7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We investigated the sensitivity of regional tumor response prediction to variability in voxel clustering techniques, imaging features, and machine learning algorithms in 25 patients with locally advanced non-small cell lung cancer (LA-NSCLC) enrolled on the FLARE-RT clinical trial. Metabolic tumor volumes (MTV) from pre-chemoradiation (PETpre) and mid-chemoradiation fluorodeoxyglucose-positron emission tomography (FDG PET) images (PETmid) were subdivided into K-means or hierarchical voxel clusters by standardized uptake values (SUV) and 3D-positions. MTV cluster separability was evaluated by CH index, and morphologic changes were captured by Dice similarity and centroid Euclidean distance. PETpre conventional features included SUVmean, MTV/MTV cluster size, and mean radiation dose. PETpre radiomics consisted of 41 intensity histogram and 3D texture features (PET Oncology Radiomics Test Suite) extracted from MTV or MTV clusters. Machine learning models (multiple linear regression, support vector regression, logistic regression, support vector machines) of conventional features or radiomic features were constructed to predict PETmid response. Leave-one-out-cross-validated root-mean-squared-error (RMSE) for continuous response regression (ΔSUVmean) and area-under-receiver-operating-characteristic-curve (AUC) for binary response classification were calculated. K-means MTV 2-clusters (MTVhi, MTVlo) achieved maximum CH index separability (Friedman p < 0.001). Between PETpre and PETmid, MTV cluster pairs overlapped (Dice 0.70-0.87) and migrated 0.6-1.1 cm. PETmid ΔSUVmean response prediction was superior in MTV and MTVlo (RMSE = 0.17-0.21) compared to MTVhi (RMSE = 0.42-0.52, Friedman p < 0.001). PETmid ΔSUVmean response class prediction performance trended higher in MTVlo (AUC = 0.83-0.88) compared to MTVhi (AUC = 0.44-0.58, Friedman p = 0.052). Models were more sensitive to MTV/MTV cluster regions (Friedman p = 0.026) than feature sets/algorithms (Wilcoxon signed-rank p = 0.36). Top-ranked radiomic features included GLZSM-LZHGE (large-zone-high-SUV), GTSDM-CP (cluster-prominence), GTSDM-CS (cluster-shade) and NGTDM-CNT (contrast). Top-ranked features were consistent between MTVhi and MTVlo cluster pairs but varied between MTVhi-MTVlo clusters, reflecting distinct regional radiomic phenotypes. Variability in tumor voxel cluster response prediction can inform robust radiomic target definition for risk-adaptive chemoradiation in patients with LA-NSCLC. FLARE-RT trial: NCT02773238.
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Affiliation(s)
- Chunyan Duan
- Department of Mechanical Engineering, Tongji University School of Mechanical Engineering, Shanghai China
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - W. Art Chaovalitwongse
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Fangyun Bai
- Department of Management Science and Engineering, Tongji University School of Economics and Management, Shanghai China
- Department of Industrial, Manufacturing, & Systems Engineering, University of Texas at Arlington College of Engineering, Arlington, TX
| | - Daniel S. Hippe
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Shouyi Wang
- Department of Industrial, Manufacturing, & Systems Engineering, University of Texas at Arlington College of Engineering, Arlington, TX
| | - Phawis Thammasorn
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Larry A. Pierce
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Xiao Liu
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Jianxin You
- Department of Management Science and Engineering, Tongji University School of Economics and Management, Shanghai China
| | - Robert S. Miyaoka
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Hubert J. Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Paul E. Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - Stephen R. Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
- Department of Radiology, University of Washington School of Medicine, Seattle WA
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Crombé A, Kind M, Fadli D, Le Loarer F, Italiano A, Buy X, Saut O. Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients. Sci Rep 2020; 10:15496. [PMID: 32968131 PMCID: PMC7511974 DOI: 10.1038/s41598-020-72535-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 08/24/2020] [Indexed: 12/12/2022] Open
Abstract
Intensity harmonization techniques (IHT) are mandatory to homogenize multicentric MRIs before any quantitative analysis because signal intensities (SI) do not have standardized units. Radiomics combine quantification of tumors' radiological phenotype with machine-learning to improve predictive models, such as metastastic-relapse-free survival (MFS) for sarcoma patients. We post-processed the initial T2-weighted-imaging of 70 sarcoma patients by using 5 IHTs and extracting 45 radiomics features (RFs), namely: classical standardization (IHTstd), standardization per adipose tissue SIs (IHTfat), histogram-matching with a patient histogram (IHTHM.1), with the average histogram of the population (IHTHM.All) and plus ComBat method (IHTHM.All.C), which provided 5 radiomics datasets in addition to the original radiomics dataset without IHT (No-IHT). We found that using IHTs significantly influenced all RFs values (p-values: < 0.0001-0.02). Unsupervised clustering performed on each radiomics dataset showed that only clusters from the No-IHT, IHTstd, IHTHM.All, and IHTHM.All.C datasets significantly correlated with MFS in multivariate Cox models (p = 0.02, 0.007, 0.004 and 0.02, respectively). We built radiomics-based supervised models to predict metastatic relapse at 2-years with a training set of 50 patients. The models performances varied markedly depending on the IHT in the validation set (range of AUROC from 0.688 with IHTstd to 0.823 with IHTHM.1). Hence, the use of intensity harmonization and the related technique should be carefully detailed in radiomics post-processing pipelines as it can profoundly affect the reproducibility of analyses.
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Affiliation(s)
- Amandine Crombé
- Department of Radiology, Institut Bergonie, 33000, Bordeaux, France. .,Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, Université de Bordeaux, 33405, Talence, France. .,University of Bordeaux, 33000, Bordeaux, France. .,Department of Diagnostic and Interventional Radiology, Institut Bergonié, Comprehensive Cancer Center of Nouvelle-Aquitaine, 229 cours de l'Argonne, 33000, Bordeaux, France.
| | - Michèle Kind
- Department of Radiology, Institut Bergonie, 33000, Bordeaux, France
| | - David Fadli
- Department of Radiology, Institut Bergonie, 33000, Bordeaux, France
| | - François Le Loarer
- University of Bordeaux, 33000, Bordeaux, France.,Department of Pathology, Institut Bergonie, 33000, Bordeaux, France
| | - Antoine Italiano
- University of Bordeaux, 33000, Bordeaux, France.,Department of Medical Oncology, Institut Bergonie, 33000, Bordeaux, France
| | - Xavier Buy
- Department of Radiology, Institut Bergonie, 33000, Bordeaux, France
| | - Olivier Saut
- Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, Université de Bordeaux, 33405, Talence, France.,University of Bordeaux, 33000, Bordeaux, France
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38
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Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186425] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Lung cancer accounts for the largest amount of deaths worldwide with respect to the other oncological pathologies. To guarantee the most effective cure to patients for such aggressive tumours, radiomics is increasing as a novel and promising research field that aims at extracting knowledge from data in terms of quantitative measures that are computed from diagnostic images, with prognostic and predictive ends. This knowledge could be used to optimize current treatments and to maximize their efficacy. To this end, we hereby study the use of such quantitative biomarkers computed from CT images of patients affected by Non-Small Cell Lung Cancer to predict Overall Survival. The main contributions of this work are two: first, we consider different volumes of interest for the same patient to find out whether the volume surrounding the visible lesions can provide useful information; second, we introduce 3D Local Binary Patterns, which are texture measures scarcely explored in radiomics. As further validation, we show that the proposed signature outperforms not only the features automatically computed by a deep learning-based approach, but also another signature at the state-of-the-art using other handcrafted features.
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Crombé A, Fadli D, Italiano A, Saut O, Buy X, Kind M. Systematic review of sarcomas radiomics studies: Bridging the gap between concepts and clinical applications? Eur J Radiol 2020; 132:109283. [PMID: 32980727 DOI: 10.1016/j.ejrad.2020.109283] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 08/29/2020] [Accepted: 09/08/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Sarcomas are a model for intra- and inter-tumoral heterogeneities making them particularly suitable for radiomics analyses. Our purposes were to review the aims, methods and results of radiomics studies involving sarcomas METHODS: Pubmed and Web of Sciences databases were searched for radiomics or textural studies involving bone, soft-tissues and visceral sarcomas until June 2020. Two radiologists evaluated their objectives, results and quality of their methods, imaging pre-processing and machine-learning workflow helped by the items of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2), Image Biomarker Standardization Initiative (IBSI) and 'Radiomics Quality Score' (RQS). Statistical analyses included inter-reader agreements, correlations between methodological assessments, scientometrics indices, and their changes over years, and between RQS, number of patients and models performance. RESULTS Fifty-two studies were included involving: soft-tissue sarcomas (29/52, 55.8 %), bone sarcomas (15/52, 28.8 %), gynecological sarcomas (6/52, 11.5 %) and mixed sarcomas (2/52, 3.8 %), mostly imaged with MRI (36/52, 69.2 %), for a total of distinct patients. Median RQS was 4.5 (28.4 % of the maximum, range: -7 - 17). Performances of predictive models and number of patients negatively correlated (p = 0.027). None of the studies detailed all the items from the IBSI guidelines. There was a significant increase in studies' impact factors since the establishing of the RQS in 2017 (p = 0.038). CONCLUSION Although showing promising results, further efforts are needed to make sarcoma radiomics studies reproducible with an acceptable level of evidence. A better knowledge of the RQS and IBSI reporting guidelines could improve the quality of sarcoma radiomics studies and accelerate clinical applications.
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Affiliation(s)
- Amandine Crombé
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France; Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, Université De Bordeaux, F-33405, Talence, France; University of Bordeaux, F-33000, Bordeaux, France.
| | - David Fadli
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
| | - Antoine Italiano
- University of Bordeaux, F-33000, Bordeaux, France; Department of Medical Oncology, Institut Bergonie, F-33000, Bordeaux, France
| | - Olivier Saut
- Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251, Université De Bordeaux, F-33405, Talence, France
| | - Xavier Buy
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
| | - Michèle Kind
- Department of Radiology, Institut Bergonie, F-33000, Bordeaux, France
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Holbrook MD, Blocker SJ, Mowery YM, Badea A, Qi Y, Xu ES, Kirsch DG, Johnson GA, Badea CT. MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice. Tomography 2020; 6:23-33. [PMID: 32280747 PMCID: PMC7138523 DOI: 10.18383/j.tom.2019.00021] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Small-animal imaging is an essential tool that provides noninvasive, longitudinal insight into novel cancer therapies. However, considerable variability in image analysis techniques can lead to inconsistent results. We have developed quantitative imaging for application in the preclinical arm of a coclinical trial by using a genetically engineered mouse model of soft tissue sarcoma. Magnetic resonance imaging (MRI) images were acquired 1 day before and 1 week after radiation therapy. After the second MRI, the primary tumor was surgically removed by amputating the tumor-bearing hind limb, and mice were followed for up to 6 months. An automatic analysis pipeline was used for multicontrast MRI data using a convolutional neural network for tumor segmentation followed by radiomics analysis. We then calculated radiomics features for the tumor, the peritumoral area, and the 2 combined. The first radiomics analysis focused on features most indicative of radiation therapy effects; the second radiomics analysis looked for features that might predict primary tumor recurrence. The segmentation results indicated that Dice scores were similar when using multicontrast versus single T2-weighted data (0.863 vs 0.861). One week post RT, larger tumor volumes were measured, and radiomics analysis showed greater heterogeneity. In the tumor and peritumoral area, radiomics features were predictive of primary tumor recurrence (AUC: 0.79). We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies.
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Affiliation(s)
- M. D. Holbrook
- Departments of Radiology, Center for In Vivo Microscopy; and
| | - S. J. Blocker
- Departments of Radiology, Center for In Vivo Microscopy; and
| | - Y. M. Mowery
- Radiation Oncology, Duke University Medical Center, Durham, NC
| | - A. Badea
- Departments of Radiology, Center for In Vivo Microscopy; and
| | - Y. Qi
- Departments of Radiology, Center for In Vivo Microscopy; and
| | - E. S. Xu
- Radiation Oncology, Duke University Medical Center, Durham, NC
| | - D. G. Kirsch
- Radiation Oncology, Duke University Medical Center, Durham, NC
| | - G. A. Johnson
- Departments of Radiology, Center for In Vivo Microscopy; and
| | - C. T. Badea
- Departments of Radiology, Center for In Vivo Microscopy; and
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Crombé A, Fadli D, Buy X, Italiano A, Saut O, Kind M. High-Grade Soft-Tissue Sarcomas: Can Optimizing Dynamic Contrast-Enhanced MRI Postprocessing Improve Prognostic Radiomics Models? J Magn Reson Imaging 2020; 52:282-297. [PMID: 31922323 DOI: 10.1002/jmri.27040] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 12/07/2019] [Accepted: 12/11/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Heterogeneity on pretreatment dynamic contrast-enhanced (DCE)-MRI of sarcomas may be prognostic, but the best technique to capture this characteristic remains unknown. PURPOSE To investigate the best method to extract prognostic data from baseline DCE-MRI. STUDY TYPE Retrospective, single-center. POPULATION Fifty consecutive uniformly-treated adults with nonmetastatic high-grade sarcomas. FIELD STRENGTH/SEQUENCE 1.5T; T2 -weighted-imaging, fat-suppressed fast spoiled gradient echo DCE-MRI. ASSESSMENT Ninety-two radiomics features (RFs) were extracted at each DCE-MRI phase (11, from t = 0-88 sec). Relative changes in RFs (rRFs) since the acquisition baseline were calculated (11 × 92 rRFs). Curves of rRF as function of time postinjection were integrated (92 integrated-rRFs [irRFs]). Ktrans and area under the time-intensity curve at 88-sec parametric maps were computed and 2 × 92 parametric-RFs (pRFs) were extracted. Five DCE-MRI-based radiomics models were built on: an RFs subset (32 sec, 64 sec, 88 sec); all rRFs; all irRFs; and all pRFs. Two models were elaborated as reference, on: conventional radiological features; and T2 -WI RFs. STATISTICAL TESTS A common machine-learning approach was applied to radiomics models. Features with P < 0.05 at univariate analysis were entered in a LASSO-penalized Cox regression including bootstrapped 10-fold cross-validation. The resulting radiomics scores (RScores) were dichotomized per their median and entered in multivariate Cox models for predicting metastatic relapse-free survival. Models were compared with integrative area under the curve (AUC) and concordance index. RESULTS Only dichotomized RScores from models based on rRFs subset, all rRFS and irRFS correlated with prognostic (P = 0.0107-0.0377). The models including all rRFs and irRFs had the highest c-index (0.83), followed by the radiological model. The radiological model had the highest integrative AUC (0.87), followed by models including all rRFs and irRFs. The radiological and full rRFs models were significantly better than the T2 -based radiomics model (P = 0.02). DATA CONCLUSION The initial DCE-MRI of STS contains prognostic information. It seems more relevant to make predictions on rRFs instead of pRFs. Evidence Level: 3 Technical Efficacy: 3 J. Magn. Reson. Imaging 2020;52:282-297.
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Affiliation(s)
- Amandine Crombé
- Department of Radiology, Institut Bergonie, Bordeaux, France.,Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251 & Université de Bordeaux, Talence, France.,University of Bordeaux, Bordeaux, France
| | - David Fadli
- Department of Radiology, Institut Bergonie, Bordeaux, France
| | - Xavier Buy
- Department of Radiology, Institut Bergonie, Bordeaux, France
| | - Antoine Italiano
- University of Bordeaux, Bordeaux, France.,Department of Medical Oncology, Institut Bergonie, Bordeaux, France
| | - Olivier Saut
- Modelisation in Oncology (MOnc) Team, INRIA Bordeaux-Sud-Ouest, CNRS UMR 5251 & Université de Bordeaux, Talence, France.,University of Bordeaux, Bordeaux, France
| | - Michèle Kind
- Department of Radiology, Institut Bergonie, Bordeaux, France
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Peeken JC, Wiestler B, Combs SE. Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application. Recent Results Cancer Res 2020; 216:773-794. [PMID: 32594406 DOI: 10.1007/978-3-030-42618-7_24] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Medical imaging plays an imminent role in today's radiation oncology workflow. Predominantly based on semantic image analysis, malignant tumors are diagnosed, staged, and therapy decisions are made. The field of "radiomics" promises to extract complementary, objective information from medical images. In radiomics, predefined quantitative features including intensity statistics, texture, shape, or filtering techniques are combined into statistical or machine learning models to predict clinical or biological outcomes. Alternatively, deep neural networks can directly analyze medical images and provide predictions. A large number of research studies could demonstrate that radiomics prediction models may provide significant benefits in the radiation oncology workflow including diagnostics, tumor characterization, target volume segmentation, prognostic stratification, and prediction of therapy response or treatment-related toxicities. This chapter provides an overview of techniques within the radiomics toolbox, potential clinical application, and current limitations. A literature overview of four selected malignant entities including non-small cell lung cancer, head and neck squamous cell carcinomas, soft tissue sarcomas, and gliomas is given.
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Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany.
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany.
- Deutsches Konsortium Für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany.
| | - Benedikt Wiestler
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany
- Department of Radiation Sciences (DRS), Institute of Radiation Medicine (IRM), Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
- Deutsches Konsortium Für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
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Local recurrence of soft tissue sarcoma: a radiomic analysis. Radiol Oncol 2019; 53:300-306. [PMID: 31553702 PMCID: PMC6765164 DOI: 10.2478/raon-2019-0041] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 07/25/2019] [Indexed: 12/13/2022] Open
Abstract
Background To perform a radiomics analysis in local recurrence (LR) surveillance of limb soft tissue sarcoma (STS) Patients and methods This is a sub-study of a prospective multicenter study with Institutional Review Board approval supported by ESSR (European Society of Musculoskeletal Radiology). radiomics analysis was done on fast spin echo axial T1w, T2w fat saturated and post-contrast T1w (T1wGd) 1.5T MRI images of consecutively recruited patients between March 2016 and September 2018. Results N = 11 adult patients (6 men and 5 women; mean age 57.8 ± 17.8) underwent MRI to exclude STS LR: a total of 33 follow-up events were evaluated. A total of 198 data-sets per patients of both pathological and normal tissue were analyzed. Four radiomics features were significantly correlated to tumor size (p < 0.02) and four radiomics features were correlated with grading (p < 0.05). ROC analysis showed an AUC between 0.71 (95%CI: 0.55-0.87) for T1w and 0.96 (95%CI: 0.87-1.00) for post-contrast T1w. Conclusions radiomics features allow to differentiate normal tissue from pathological tissue in MRI surveillance of local recurrence of STS. radiomics in STS evaluation is useful not only for detection purposes but also for lesion characterization.
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Peeken JC, Spraker MB, Knebel C, Dapper H, Pfeiffer D, Devecka M, Thamer A, Shouman MA, Ott A, von Eisenhart-Rothe R, Nüsslin F, Mayr NA, Nyflot MJ, Combs SE. Tumor grading of soft tissue sarcomas using MRI-based radiomics. EBioMedicine 2019; 48:332-340. [PMID: 31522983 PMCID: PMC6838361 DOI: 10.1016/j.ebiom.2019.08.059] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 08/13/2019] [Accepted: 08/24/2019] [Indexed: 12/13/2022] Open
Abstract
Background Treatment decisions for multimodal therapy in soft tissue sarcoma (STS) patients greatly depend on the differentiation between low-grade and high-grade tumors. We developed MRI-based radiomics grading models for the differentiation between low-grade (G1) and high-grade (G2/G3) STS. Methods The study was registered at ClinicalTrials.gov (number NCT03798795). Contrast-enhanced T1-weighted fat saturated (T1FSGd), fat-saturated T2-weighted (T2FS) MRI sequences, and tumor grading following the French Federation of Cancer Centers Sarcoma Group obtained from pre-therapeutic biopsies were gathered from two independent retrospective patient cohorts. Volumes of interest were manually segmented. After preprocessing, 1394 radiomics features were extracted from each sequence. Features unstable in 21 independent multiple-segmentations were excluded. Least absolute shrinkage and selection operator models were developed using nested cross-validation on a training patient cohort (122 patients). The influence of ComBatHarmonization was assessed for correction of batch effects. Findings Three radiomic models based on T2FS, T1FSGd and a combined model achieved predictive performances with an area under the receiver operator characteristic curve (AUC) of 0.78, 0.69, and 0.76 on the independent validation set (103 patients), respectively. The T2FS-based model showed the best reproducibility. The radiomics model involving T1FSGd-based features achieved significant patient stratification. Combining the T2FS radiomic model into a nomogram with clinical staging improved prognostic performance and the clinical net benefit above clinical staging alone. Interpretation MRI-based radiomics tumor grading models effectively classify low-grade and high-grade soft tissue sarcomas. Fund The authors received support by the medical faculty of the Technical University of Munich and the German Cancer Consortium.
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Affiliation(s)
- Jan C Peeken
- Department of Radiation Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany.
| | - Matthew B Spraker
- Department of Radiation Oncology, University of Washington, 1959 NE Pacific St, Box 356043, Seattle, WA 98195, United States of America
| | - Carolin Knebel
- Department of Orthopaedic Surgery, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 München, Germany
| | - Hendrik Dapper
- Department of Radiation Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany
| | - Daniela Pfeiffer
- Department of Radiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany
| | - Michal Devecka
- Department of Radiation Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany
| | - Ahmed Thamer
- Department of Radiation Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany
| | - Mohamed A Shouman
- Department of Radiation Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany
| | - Armin Ott
- Institute of Medical Informatics, Statistics and Epidemiology, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany
| | - Rüdiger von Eisenhart-Rothe
- Department of Orthopaedic Surgery, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 München, Germany
| | - Fridtjof Nüsslin
- Department of Radiation Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany
| | - Nina A Mayr
- Department of Radiation Oncology, University of Washington, 1959 NE Pacific St, Box 356043, Seattle, WA 98195, United States of America
| | - Matthew J Nyflot
- Department of Radiation Oncology, University of Washington, 1959 NE Pacific St, Box 356043, Seattle, WA 98195, United States of America; Department of Radiology, University of Washington, Seattle, WA, United States of America
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstaedter Landstrasse 1, 85764 Neuherberg, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany
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Hughes P, Miranda R, Doyle AJ. MRI imaging of soft tissue tumours of the foot and ankle. Insights Imaging 2019; 10:60. [PMID: 31161474 PMCID: PMC6546775 DOI: 10.1186/s13244-019-0749-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 05/13/2019] [Indexed: 02/07/2023] Open
Abstract
The majority of soft tissue lesions in the foot and ankle are benign. The aim of this review is to provide the reader with a comprehensive overview of the magnetic resonance imaging (MRI) characteristics of the most common benign and malignant soft tissue neoplasms which occur around the foot and ankle. This should enable the reader to formulate a reasonable differential diagnosis and, most importantly, to recognise those rare aggressive lesions that require further assessment and tissue biopsy.
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Affiliation(s)
- Peter Hughes
- Department of Radiology, Auckland City Hospital, Auckland, New Zealand.
| | - Rhian Miranda
- Department of Radiology, Auckland City Hospital, Auckland, New Zealand
| | - Anthony J Doyle
- Department of Radiology, Auckland City Hospital, Auckland, New Zealand
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