Radiomics and Machine Learning With Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas

Abstract

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 (T1WI) with 550 msec repetition time (TR); 18 msec echo time (TE), 312 × 312 matrix, fat‐suppressed fast spin‐echo T2WI 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.

Magnetic Resonance Imaging‐Based Radiomics Nomogram for Prediction of the Histopathological Grade of Soft Tissue Sarcomas: A Two‐Center Study

“…Corino et al [34] reported that a radiomics classi er based on apparent diffusion coe cients in 19 patients could be used to distinguish grade II from III STS. Xiang et al [35] found that quantitative MRI-based histogram parameters can differentiate the grade of STS, Zhang et al [17] demonstrated that FS-T2WI-based radiomics could be used to predict the histopathological grade of STS in a study with a small cohort of 35 patients, and Wang et al [18] found that radiomics signature-based machine-learning classi ers can distinguish low-grade from high-grade STS. Nevertheless, these studies had small sample cohorts and two of them lacked a validation set, which potentially lead to the problem of over tting.…”

“…Thus, MRI-based radiomics would be broadly applicable to patients with sarcomas. Radiomics has been successfully applied in prediction of the histologic grade, local recurrence or distant metastasis, overall survival, and response to neoadjuvant therapy in patients with STSs [17][18][19][20][21][22]. Most previous reports de ned high-grade STS as grades II and III, while we de ne high-grade STS as grade III based on recently published studies [6,22].…”

Application of Nomogram Based on Radiomics Label in Prediction of Treatment Scheme for Lumbar Disc Herniation

“…The biomarkers included in the model cover different biological scales from molecular to phenotypic [15] . Radiomics in the application of skeletal muscle system is usually in terms of bone tumors, such as bone disease diagnosis and differential diagnosis of tumor [16,17] , prediction [18] of tumor complications, the prognosis of tumor treatment pathologic grading [19][20] and tumor, a small study applies beside the osteoporosis [21] , vertebral abscess [22] , temporo-mandibular joint osteoarthritis [23] , postoperative infection and in ammation [24] , and so on. Few radiomics studies have been conducted on LHD.…”

Tumor grade in soft-tissue sarcoma

“…There are small number of recent studies which have predicted grade of soft-tissue sarcoma using texture analysis. [ 22 – 24 ] They performed texture analysis based on only ADC maps or fat suppressed T2-weighted images or T1- and T2-weighted images. We used T1- and T2-weighted images and fat-suppressed CE T1-weighted images for texture analysis.…”

“…[ 10 ] Texture analysis has been studied to predict tumor stage and survival in various cancers, [ 12 – 16 ] differentiate benign from malignant lesions in breast, [ 17 , 18 ] and to predict treatment response in various other cancers. [ 19 – 21 ] To our knowledge, however, there have been a small number of studies [ 22 – 24 ] investigating whether texture analysis based on 3T preoperative MRI can predict the grade of soft-tissue sarcomas.…”