Radiomics and Machine Learning Differentiate Soft-Tissue Lipoma and Liposarcoma Better than Musculoskeletal Radiologists.
Distinguishing lipoma from liposarcoma is challenging on conventional MRI examination. In case of uncertain diagnosis following MRI, further invasive procedure (percutaneous biopsy or surgery) is often required to allow for diagnosis based on histopathological examination. Radiomics and machine lear...
| Published in: | Sarcoma pp. 1 - 11 |
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| Main Authors: | , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
1/7/2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141095009&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141095009 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1357714X 57R jtl: Sarcoma issn: 1357714X maglogo: Y pubinfo: dt: 1/7/2020 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 141095009 141095009 141095009 10.1155/2020/7163453 141095009 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics and Machine Learning Differentiate Soft-Tissue Lipoma and Liposarcoma Better than Musculoskeletal Radiologists. aug: au: Malinauskaite, Ieva Hofmeister, Jeremy Burgermeister, Simon Neroladaki, Angeliki Hamard, Marion Montet, Xavier Boudabbous, Sana affil: Geneva University Hospital, Diagnosis Department, Radiology Division, Rue Gabrielle-Perret-Gentil 4, 1211 Geneva 4, Switzerland sug: subj: Lipoma Diagnosis Liposarcoma Diagnosis Machine Learning Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Methods Preoperative Period Sensitivity and Specificity Radiologists Human Retrospective Design Histological Techniques Methods Systems Analysis ROC Curve McNemar's Test Liposarcoma Lipoma Surgery Soft Tissue Neoplasms Surgery Musculoskeletal System ab: Distinguishing lipoma from liposarcoma is challenging on conventional MRI examination. In case of uncertain diagnosis following MRI, further invasive procedure (percutaneous biopsy or surgery) is often required to allow for diagnosis based on histopathological examination. Radiomics and machine learning allow for several types of pathologies encountered on radiological images to be automatically and reliably distinguished. The aim of the study was to assess the contribution of radiomics and machine learning in the differentiation between soft-tissue lipoma and liposarcoma on preoperative MRI and to assess the diagnostic accuracy of a machine-learning model compared to musculoskeletal radiologists. 86 radiomics features were retrospectively extracted from volume-of-interest on T1-weighted spin-echo 1.5 and 3.0 Tesla MRI of 38 soft-tissue tumors (24 lipomas and 14 liposarcomas, based on histopathological diagnosis). These radiomics features were then used to train a machine-learning classifier to distinguish lipoma and liposarcoma. The generalization performance of the machine-learning model was assessed using Monte-Carlo cross-validation and receiver operating characteristic curve analysis (ROC-AUC). Finally, the performance of the machine-learning model was compared to the accuracy of three specialized musculoskeletal radiologists using the McNemar test. Machine-learning classifier accurately distinguished lipoma and liposarcoma, with a ROC-AUC of 0.926. Notably, it performed better than the three specialized musculoskeletal radiologists reviewing the same patients, who achieved ROC-AUC of 0.685, 0.805, and 0.785. Despite being developed on few cases, the trained machine-learning classifier accurately distinguishes lipoma and liposarcoma on preoperative MRI, with better performance than specialized musculoskeletal radiologists. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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