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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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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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Yao Y, Zhao Y, Lu L, Zhao Y, Lin X, Xia J, Zheng X, Shen Y, Cai Z, Li Y, Yang Z, Lin D. Prediction of histopathologic grades of myxofibrosarcoma with radiomics based on magnetic resonance imaging. J Cancer Res Clin Oncol 2023; 149:10169-10179. [PMID: 37264266 DOI: 10.1007/s00432-023-04939-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 05/24/2023] [Indexed: 06/03/2023]
Abstract
PURPOSE To develop a radiomics-based model from preoperative magnetic resonance imaging (MRI) for predicting the histopathological grades of myxofibrosarcoma. METHODS This retrospective study included 54 patients. The tumors were classified into high-grade and low-grade myxofibrosarcoma. The tumor size, signal intensity heterogeneity, margin, and surrounding tissue were evaluated on MRI. Using the least absolute shrinkage and selection operator (LASSO) algorithms, 1037 radiomics features were obtained from fat-suppressed T2-weighted images (T2WI), and a radiomics signature was established. Using multivariable logistic regression analysis, three models were built to predict the histopathologic grade of myxofibrosarcoma. A radiomics nomogram represents the integrative model. The three models' performance was evaluated using the receiver operating characteristics (ROC) and calibration curves. RESULTS The high-grade myxofibrosarcoma had greater depth (P = 0.027), more frequent heterogeneous signal intensity at T2WI (P = 0.015), and tail sign (P = 0.014) than the low-grade tumor. The area under curve (AUC) of these conventional MRI features models was 0.648, 0.656, and 0.668, respectively. Seven radiomic features were selected by LASSO to construct the radiomics signature model, with an AUC of 0.791. The AUC of the integrative model based on radiomics signature and conventional MRI features was 0.875. The integrative model's calibration curve and insignificant Hosmer-Lemeshow test statistic (P = 0.606) revealed good calibration. CONCLUSION An integrative model using radiomics signature and three conventional MRI features can preoperatively predict low- or high-grade myxofibrosarcoma.
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Affiliation(s)
- Yubin Yao
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Yan Zhao
- Central Laboratory, Clinical Research Center, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Liejing Lu
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China
| | - Yongqiang Zhao
- Department of Pathology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Xiaokun Lin
- Department of Radiology, The First People's Hospital of Jiexi, No. 7 Dangxiao Road, Jieyang, 515400, People's Republic of China
| | - Jianfeng Xia
- Department of Radiology, The First People's Hospital of Qinzhou, No. 47 Qianjin Road, Qinzhou, 535000, People's Republic of China
| | - Xufeng Zheng
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Yi Shen
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Zonghuan Cai
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China
| | - Yangkang Li
- Department of Radiology, Cancer Hospital, Shantou University Medical College, No. 7 Raoping Road, Shantou, 515041, People's Republic of China
| | - Zehong Yang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou, 510120, People's Republic of China
| | - Daiying Lin
- Department of Radiology, Shantou Central Hospital, No. 114 Waima Road, Shantou, 515031, People's Republic of China.
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Bazzocchi A, Guglielmi G, Aparisi Gómez MP. Sarcoma Imaging Surveillance. Magn Reson Imaging Clin N Am 2023; 31:193-214. [PMID: 37019546 DOI: 10.1016/j.mric.2023.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Soft tissue sarcomas (STS) are a heterogeneous group of solid tumors. There are many histologic subtypes. The prognosis after treatment may be estimated by the analysis of the type of tumor, grade, depth, size at diagnosis, and age of the patient. These type of sarcomas most commonly metastasize to the lungs and may have a relatively high rate of local recurrence, depending on the histologic type and surgical margins. Patients with recurrence have a poorer prognosis. The surveillance of patients with STS is therefore extremely important. This review analyzes the role of MR imaging and US in detecting local recurrence.
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Affiliation(s)
- Alberto Bazzocchi
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Via G. C. Pupilli 1, Bologna 40136, Italy.
| | - Giuseppe Guglielmi
- Department of Radiology, Hospital San Giovanni Rotondo, Italy; Department of Radiology, University of Foggia, Viale Luigi Pinto 1, Foggia 71100, Italy
| | - Maria Pilar Aparisi Gómez
- Department of Radiology, Auckland City Hospital, 2 Park Road, Grafton, Auckland 1023, New Zealand; Department of Radiology, IMSKE, Calle Suiza, 11, Valencia 46024, Spain
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Giraudo C, Fichera G, Del Fiore P, Mocellin S, Brunello A, Rastrelli M, Stramare R. Tumor cellularity beyond the visible in soft tissue sarcomas: Results of an ADC-based, single center, and preliminary radiomics study. Front Oncol 2022; 12:879553. [DOI: 10.3389/fonc.2022.879553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeSoft tissue sarcomas represent approximately 1% of all malignancies, and diagnostic radiology plays a significant role in the overall management of this rare group of tumors. Recently, quantitative imaging and, in particular, radiomics demonstrated to provide significant novel information, for instance, in terms of prognosis and grading. The aim of this study was to evaluate the prognostic role of radiomic variables extracted from apparent diffusion coefficient (ADC) maps collected at diagnosis in patients with soft tissue sarcomas in terms of overall survival and metastatic spread as well as to assess the relationship between radiomics and the tumor grade.MethodsPatients with histologically proven soft tissue sarcomas treated in our tertiary center from 2016 to 2019 who underwent an Magnetic Resonance (MR) scan at diagnosis including diffusion-weighted imaging were included in this retrospective institution review board–approved study. Each primary lesion was segmented using the b50 images; the volumetric region of interest was then applied on the ADC map. A total of 33 radiomic features were extracted, and highly correlating features were selected by factor analysis. In the case of feature/s showing statistically significant results, the diagnostic accuracy was computed. The Spearman correlation coefficient was used to evaluate the relationship between the tumor grade and radiomic features selected by factor analysis. All analyses were performed applying p<0.05 as a significant level.ResultsA total of 36 patients matched the inclusion criteria (15 women; mean age 58.9 ± 15 years old). The most frequent histotype was myxofibrosarcoma (16.6%), and most of the patients were affected by high-grade lesions (77.7%). Seven patients had pulmonary metastases, and, altogether, eight were deceased. Only the feature Imc1 turned out to be a predictor of metastatic spread (p=0.045 after Bonferroni correction) with 76.7% accuracy. The value -0.16 showed 73.3% sensitivity and 71.4% specificity, and patients with metastases showed lower values (mean Imc1 of metastatic patients -0.31). None of the examined variables was a predictor of the overall outcome (p>0.05, each). A moderate statistically significant correlation emerged only between Imc1 and the tumor grade (r=0.457, p=0.005).ConclusionsIn conclusion, the radiomic feature Imc1 acts as a predictor of metastatic spread in patients with soft tissue sarcomas and correlates with the tumor grade.
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Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Prospective clinical research of radiomics and deep learning in oncology: A translational review. Crit Rev Oncol Hematol 2022; 179:103823. [PMID: 36152912 DOI: 10.1016/j.critrevonc.2022.103823] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/13/2022] [Accepted: 09/20/2022] [Indexed: 10/31/2022] Open
Abstract
Radiomics and deep learning (DL) hold transformative promise and substantial and significant advances in oncology; however, most methods have been tested in retrospective or simulated settings. There is considerable interest in the biomarker validation, clinical utility, and methodological robustness of these studies and their deployment in real-world settings. This review summarizes the characteristics of studies, the level of prospective validation, and the overview of research on different clinical endpoints. The discussion of methodological robustness shows the potential for independent external replication of prospectively reported results. These in-depth analyses further describe the barriers limiting the translation of radiomics and DL into primary care options and provide specific recommendations regarding clinical deployment. Finally, we propose solutions for integrating novel approaches into the treatment environment to unravel the critical process of translating AI models into the clinical routine and explore strategies to improve personalized medicine.
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Affiliation(s)
- Xingping Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China; Department of New Networks, Peng Cheng Laboratory, Shenzhen 518000, China
| | - Yanchun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China; Department of New Networks, Peng Cheng Laboratory, Shenzhen 518000, China; Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, VIC 8001, Australia.
| | - Guijuan Zhang
- Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110189, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
| | - Liefa Liao
- School of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330000, China; School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
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Sedaghat S, Ravesh MS, Sedaghat M, Meschede J, Jansen O, Both M. Does the primary soft-tissue sarcoma configuration predict configuration of recurrent tumors on magnetic resonance imaging? Acta Radiol 2022; 63:642-651. [PMID: 33853376 DOI: 10.1177/02841851211008381] [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: 11/16/2022]
Abstract
BACKGROUND Soft-tissue sarcomas (STS) are rare malignancies of the soft tissue. PURPOSE To assess whether the magnetic resonance imaging (MRI) configuration of primary STS can predict the configuration of a recurring tumor and whether the MRI configuration of multiple recurrences differs in one and the same patient. MATERIAL AND METHODS Thirty-nine patients with histologically proven recurrent STS were included in this retrospective study and underwent pre- and post-treatment MRI. Three main configurations of primary and recurrent tumors were identified: polycyclic/multilobulated; ovoid/nodular; and streaky. RESULTS Sixty recurrent lesions were detected: 34 ovoid/nodular; 15 polycyclic/multilobulated; and 11 streaky. Five recurrences were multifocal and eight were bifocal. Of 39 patients, 28 (71.8%) presented one recurrence within the MRI follow-up period (P = 0.006); in 10 patients (25.6%), up to three different configurations of recurring STS were identified in one patient. Recurrences of polycyclic/multilobulated primaries were mostly ovoid/nodular (48%; P = 0.003) or polycyclic/multilobulated (37%; P = 0.014), and recurring ovoid/nodular STS significantly most often showed the same configuration as the primary tumor (85%; P < 0.001). Primary STS with a streaky configuration recurred in all three configurations in roughly equal proportions. Homogeneity/heterogeneity and tumor borders are significantly associated with the configuration of recurrences. CONCLUSION Primary STS configuration may help predict recurrent tumor configuration when the primary STS had a polycyclic/multilobulated or ovoid/nodular configuration. However, recurrent STS configuration can also differ from primary STS configuration, especially when the primary STS had a streaky configuration, rendering recurrent STS difficult to predict. Different configurations of recurrent STS in one and the same patient are common.
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Affiliation(s)
- Sam Sedaghat
- Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany
- Institute of Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Bergmannsheil, Bochum, Germany
| | - Mona Salehi Ravesh
- Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany
| | - Maya Sedaghat
- Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany
- Institute of Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Bergmannsheil, Bochum, Germany
| | - Jens Meschede
- Institute of Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Bergmannsheil, Bochum, Germany
- Department for Radiology and Neuroradiology, Klinikum Dortmund, Germany
| | - Olav Jansen
- Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany
| | - Marcus Both
- Department for Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Campus Kiel, Germany
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Bone and Soft Tissue Tumors. Radiol Clin North Am 2022; 60:339-358. [DOI: 10.1016/j.rcl.2021.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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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: 16] [Impact Index Per Article: 8.0] [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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Bignotti B, Rossi F, Signori A, Solari N, Spina B, Martinoli C, Tagliafico AS. Magnetic Resonance Imaging or Ultrasound in Localized Intermediate- or High-Risk Soft Tissue Tumors of the Extremities (MUSTT): Final Results of a Prospective Comparative Trial. Diagnostics (Basel) 2022; 12:diagnostics12020411. [PMID: 35204502 PMCID: PMC8871362 DOI: 10.3390/diagnostics12020411] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 01/27/2023] Open
Abstract
Objectives: To report final results of the MUSTT trial, which has been designed to independently compare magnetic resonance imaging (MRI) and ultrasound (US) for local recurrences of non-metastatic patients operated for malignant soft tissue tumors (STT). Methods: Magnetic resonance imaging or ultrasound in soft tissue tumors (MUSTT) is a prospective monocentric study recruiting asymptomatic, non-metastatic patients operated on for localized soft tissue sarcomas between 2015 and April. Eligible patients had MRI and physician-performed ultrasound (US) with an independent interpretation of imaging. Outcome measures were compared using ROC analysis and the X2 test. An analysis of all patients was performed on a per-follow-up event basis. Results: A total of n = 51 patients who met the inclusion criteria agreed to participate. Among them, n = 8 were lost to follow-up, n = 6 had US and MRI acquired after a time frame > 7 days and were therefore excluded. Complete data available for 37 patients with 232 MRI and 232 US scan were finally considered (men/women: 18/20; age range, 18–84 years). Recurrences within 5 years occurred in 10/37 patients (27%). ROC analysis comparing US and MRI showed an AUC with 95% confidence intervals of 0.909 (0.832 to 0.981) for US and 0.966 (0.939 to 0.989) for MRI with Prob > X2 = 0. Conclusions: Each of these tests detected local recurrences with suitable accuracy. MRI did not result clearly superior to US in terms of diagnostic accuracy, but US showed some false positive or negative results.
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Affiliation(s)
- Bianca Bignotti
- Ospedale Policlinico San Martino, 16132 Genoa, Italy; (B.B.); (N.S.); (B.S.)
- Department of Experimental Medicine (DIMES), University of Genoa, 16132 Genoa, Italy;
| | - Federica Rossi
- Department of Experimental Medicine (DIMES), University of Genoa, 16132 Genoa, Italy;
- Ospedale Santa Corona, Pietra Ligure, 17027 Genoa, Italy
| | - Alessio Signori
- Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy;
| | - Nicola Solari
- Ospedale Policlinico San Martino, 16132 Genoa, Italy; (B.B.); (N.S.); (B.S.)
| | - Bruno Spina
- Ospedale Policlinico San Martino, 16132 Genoa, Italy; (B.B.); (N.S.); (B.S.)
| | - Carlo Martinoli
- Ospedale Policlinico San Martino, 16132 Genoa, Italy; (B.B.); (N.S.); (B.S.)
- Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy;
- Correspondence: (C.M.); or (A.S.T.)
| | - Alberto Stefano Tagliafico
- Ospedale Policlinico San Martino, 16132 Genoa, Italy; (B.B.); (N.S.); (B.S.)
- Department of Health Sciences (DISSAL), University of Genoa, 16132 Genoa, Italy;
- Correspondence: (C.M.); or (A.S.T.)
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Kim M, Jee WH, Lee Y, Hong JH, Jung CK, Chung YG, Lee SY. Tumor Margin Infiltration in Soft Tissue Sarcomas: Prediction Using 3T MRI Texture Analysis. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2022; 83:112-126. [PMID: 36237350 PMCID: PMC9238208 DOI: 10.3348/jksr.2021.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/27/2021] [Accepted: 05/11/2021] [Indexed: 11/15/2022]
Affiliation(s)
- Minji Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Radiology, Uijeongbu St. Mary's hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Won-Hee Jee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Youngjun Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Department of Radiology, Uijeongbu St. Mary's hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Ji Hyun Hong
- Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Chan Kwon Jung
- Department of Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yang-Guk Chung
- Departments of Orthopedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - So-Yeon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study. Eur Radiol 2021; 32:793-805. [PMID: 34448928 DOI: 10.1007/s00330-021-08221-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 06/19/2021] [Accepted: 07/15/2021] [Indexed: 02/01/2023]
Abstract
OBJECTIVES To evaluate the performance of a deep learning radiomic nomogram (DLRN) model at predicting tumor relapse in patients with soft tissue sarcomas (STS) who underwent surgical resection. METHODS In total, 282 patients who underwent MRI and resection for STS at three independent centers were retrospectively enrolled. In addition, 113 of the 282 patients received additional contrast-enhanced MRI scans. We separated the participants into a development cohort and an external test cohort. The development cohort consisted of patients from one center and the external test cohort consisted of patients from two other centers. Two MRI-based DLRNs for prediction of tumor relapse after resection of STS were established. We universally tested the DLRNs and compared them with other prediction models constructed by using widespread adopted predictors (i.e., staging systems and Ki67) instead of radiomics features. RESULTS The DLRN1 model incorporated plain MRI-based radiomics signature into the clinical data, and the DLRN2 model integrated radiomics signature extracted from plain and contrast-enhanced MRI with the clinical predictors. Across both study sets, the two MRI-based DLRNs had relatively better prognostic capability (C index ≥ 0.721 and median AUC ≥ 0.746; p < 0.05 compared with most other models and predictors) and less opportunity for prediction error (integrated Brier score ≤ 0.159). The decision curve analysis indicates that the DLRNs have greater benefits than staging systems, Ki67, and other models. We selected appropriate cutoff values for the DLRNs to divide STS recurrence into three risk strata (low, medium, and high) and calculated those groups' cumulative risk rates. CONCLUSION The DLRNs were shown to be a reliable and externally validated tool for predicting STS recurrence by comparing with other prediction models. KEY POINTS • The prediction of a high recurrence rate of STS before emergence of local recurrence can help to determine whether more active treatment should be implemented. • Two MRI-based DLRNs for prediction of tumor relapse were shown to be a reliable and externally validated tool for predicting STS recurrence. • We used the DLRNs to divide STS recurrence into three risk strata (low, medium, and high) to facilitate more targeted postoperative management in the clinic.
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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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Dionisio FCF, Oliveira LS, Hernandes MDA, Engel EE, de Azevedo-Marques PM, Nogueira-Barbosa MH. Manual versus semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging: evaluation of similarity and comparison of segmentation times. Radiol Bras 2021; 54:155-164. [PMID: 34108762 PMCID: PMC8177681 DOI: 10.1590/0100-3984.2020.0028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Objective To evaluate the degree of similarity between manual and semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging (MRI). Materials and Methods This was a retrospective study of 15 MRI examinations of patients with histopathologically confirmed soft-tissue sarcomas acquired before therapeutic intervention. Manual and semiautomatic segmentations were performed by three radiologists, working independently, using the software 3D Slicer. The Dice similarity coefficient (DSC) and the Hausdorff distance were calculated in order to evaluate the similarity between manual and semiautomatic segmentation. To compare the two modalities in terms of the tumor volumes obtained, we also calculated descriptive statistics and intraclass correlation coefficients (ICCs). Results In the comparison between manual and semiautomatic segmentation, the DSC values ranged from 0.871 to 0.973. The comparison of the volumes segmented by the two modalities resulted in ICCs between 0.9927 and 0.9990. The DSC values ranged from 0.849 to 0.979 for intraobserver variability and from 0.741 to 0.972 for interobserver variability. There was no significant difference between the semiautomatic and manual modalities in terms of the segmentation times (p > 0.05). Conclusion There appears to be a high degree of similarity between manual and semiautomatic segmentation, with no significant difference between the two modalities in terms of the time required for segmentation.
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Affiliation(s)
| | - Larissa Santos Oliveira
- Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP), Ribeirão Preto, SP, Brazil
| | - Mateus de Andrade Hernandes
- Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP), Ribeirão Preto, SP, Brazil
| | - Edgard Eduard Engel
- Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (HCFMRP-USP), Ribeirão Preto, SP, Brazil
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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: 37] [Impact Index Per Article: 12.3] [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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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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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: 38] [Impact Index Per Article: 12.7] [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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Noebauer-Huhmann IM, Chaudhary SR, Papakonstantinou O, Panotopoulos J, Weber MA, Lalam RK, Albtoush OM, Fueger BJ, Szomolanyi P, Grieser T, Bloem JL. Soft Tissue Sarcoma Follow-up Imaging: Strategies to Distinguish Post-treatment Changes from Recurrence. Semin Musculoskelet Radiol 2020; 24:627-644. [PMID: 33307581 DOI: 10.1055/s-0040-1721464] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Soft tissue sarcomas encompass multiple entities with differing recurrence rates and follow-up intervals. The detection of recurrences and their differentiation from post-therapeutic changes is therefore complex, with a central role for the clinical radiologist. This article describes approved recommendations. Prerequisite is a precise knowledge of the current clinical management and surgical techniques. We review recurrence rates and treatment modalities. An adequate imaging technique is paramount, and comparison with previous imaging is highly recommended. We describe time-dependent therapy-related complications on magnetic resonance imaging compared with the spectrum of regular post-therapeutic changes. Early complications such as seromas, hematomas, and infections, late complications such as edema and fibrosis, and inflammatory pseudotumors are elucidated. The appearance of recurrences and radiation-associated sarcomas is contrasted with these changes. This systematic approach in follow-up imaging of soft tissue sarcoma patients will facilitate the differentiation of post-therapeutic changes from recurrences.
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Affiliation(s)
- Iris-M Noebauer-Huhmann
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Snehansh R Chaudhary
- Department of Clinical Radiology, Aintree University Hospitals NHS Foundation Trust, Liverpool, United Kingdom
| | | | - Joannis Panotopoulos
- Department of Orthopedics and Trauma-Surgery, Medical University of Vienna, Vienna, Austria
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Rostock, Germany
| | - Radhesh K Lalam
- Department of Radiology, Robert Jones and Agnes Hunt Orthopaedic Hospital, Oswestry, United Kingdom
| | - Omar M Albtoush
- Department of Radiology, University of Jordan, and Department of Radiology, King Hussein Cancer Center, Amman, Jordan
| | - Barbara J Fueger
- Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Pavol Szomolanyi
- High Field MR Center, Department of Biomedical Imaging and Image Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Thomas Grieser
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Augsburg, Germany
| | - Johan L Bloem
- Department of radiology, Leiden University Medical Center, Leiden, The Netherlands
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Tagliafico AS, Piana M, Schenone D, Lai R, Massone AM, Houssami N. Overview of radiomics in breast cancer diagnosis and prognostication. Breast 2019; 49:74-80. [PMID: 31739125 PMCID: PMC7375670 DOI: 10.1016/j.breast.2019.10.018] [Citation(s) in RCA: 135] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 10/29/2019] [Accepted: 10/30/2019] [Indexed: 12/11/2022] Open
Abstract
Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity and suboptimal positive predictive power of radiology screening and diagnostic approaches, respectively; b) invasiveness of biopsy with discomfort for women undergoing diagnostic tests; c) long turnaround time for recall tests. In the screening setting, radiology sensitivity is suboptimal, and when a suspicious lesion is detected and a biopsy is recommended, the positive predictive value of radiology is modest. Recent technological advances in medical imaging, especially in the field of artificial intelligence applied to image analysis, hold promise in addressing clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. Radiomics include feature extraction from clinical images; these features are related to tumor size, shape, intensity, and texture, collectively providing comprehensive tumor characterization, the so-called radiomics signature of the tumor. Radiomics is based on the hypothesis that extracted quantitative data derives from mechanisms occurring at genetic and molecular levels. In this article we focus on the role and potential of radiomics in breast cancer diagnosis and prognostication.
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Affiliation(s)
- Alberto Stefano Tagliafico
- Department of Health Sciences, University of Genoa, Genoa, Italy; Ospedale Policlinico San Martino, Genoa, Italy.
| | - Michele Piana
- Dipartimento di Matematica, Università di Genova, Genova, Italy; CNR - SPIN, Genova, Italy
| | | | | | - Anna Maria Massone
- Dipartimento di Matematica, Università di Genova, Genova, Italy; CNR - SPIN, Genova, Italy
| | - Nehmat Houssami
- Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, NSW, Australia
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