Radiomics and Machine Learning With Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas.
Background: Preoperative prediction of the grade of soft tissue sarcomas (STSs) is important because of its effect on treatment planning.Purpose: To assess the value of radiomics features in distinguishing histological grades of STSs.Study Type: Retrospective.Population: In all, 113 patients with pa...
| Publicado en: | Journal of Magnetic Resonance Imaging Vol. 51; no. 3; pp. 791 - 798 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
Mar2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141660621&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141660621 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10531807 O63 jtl: Journal of Magnetic Resonance Imaging issn: 10531807 maglogo: Y pubinfo: dt: Mar2020 vid: 51 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 141660621 141660621 NLM31486565 141660621 10.1002/jmri.26901 NLM31486565 141660621 ppf: 791 ppct: 7 formats: tig: atl: Radiomics and Machine Learning With Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas. aug: au: Wang, Hexiang Chen, Haisong Duan, Shaofeng Hao, Dapeng Liu, Jihua affil: Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China sug: subj: Sarcoma Soft Tissue Neoplasms Retrospective Design Human Magnetic Resonance Imaging Comparative Studies Multicenter Studies Evaluation Research Validation Studies Clinical Assessment Tools Scales ab: Background: Preoperative prediction of the grade of soft tissue sarcomas (STSs) is important because of its effect on treatment planning.Purpose: To assess the value of radiomics features in distinguishing histological grades of STSs.Study Type: Retrospective.Population: In all, 113 patients with pathology-confirmed low-grade (grade I), intermediate-grade (grade II), or high-grade (grade III) soft tissue sarcoma were collected.Field Strength/sequence: The 3.0T axial T1 -weighted imaging (T1 WI) with 550 msec repetition time (TR); 18 msec echo time (TE), 312 × 312 matrix, fat-suppressed fast spin-echo T2 WI with 4291 msec TR, 85 msec TE, 312 × 312 matrix.Assessment: Multiple machine-learning methods were trained to establish classification models for predicting STS grades. Eighty STS patients (18 low-grade [grade I]; 62 high-grade [grades II-III]) were enrolled in the primary set and we tested the model with a validation set with 33 patients (7 low-grade, 26 high-grade).Statistical Tests: 1) Student's t-tests were applied for continuous variables and the χ2 test were applied for categorical variables between low-grade STS and high-grade STS groups. 2) For feature subset selection, either no subset selection or recursive feature elimination was performed. This technology was combined with random forest and support vector machine-learning methods. Finally, to overcome the disparity in the frequencies of the STS grades, each machine-learning model was trained i) without subsampling, ii) with the synthetic minority oversampling technique, and iii) with random oversampling examples, for a total of 12 combinations of machine-learning algorithms that were assessed, trained, and tested in the validation cohort.Results: The best classification model for the prediction of STS grade was a combination of features selected by recursive feature elimination and random forest classification algorithms with a synthetic minority oversampling technique, which had an area under the curve of 0.9615 (95% confidence interval 0.8944-1.0) in the validation set.Data Conclusion: Radiomics feature-based machine-learning methods are useful for distinguishing STS grades.Level Of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:791-797. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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