MRI‐Based Computer‐Aided Diagnostic Model to Predict Tumor Grading and Clinical Outcomes in Patients With Soft Tissue Sarcoma

• Published in: Journal of magnetic resonance imaging Vol. 56; no. 6; pp. 1733 - 1745
• Main Authors: Yang, Yuhan, Zhou, Yin, Zhou, Chen, Zhang, Xuemei, Ma, Xuelei
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2022; Wiley Subscription Services, Inc
• Subjects: Adult; Artificial neural networks; computer‐aided diagnosis; Deep learning; Diagnostic systems; Feature maps; Female; Humans; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Neoplasm Grading; Neural networks; Nomograms; Patients; Quality; Radiomics; Retrospective Studies; Sarcoma; Sarcoma - diagnostic imaging; Sarcoma - pathology; Segmentation; Signatures; Soft Tissue Neoplasms - diagnostic imaging; Soft Tissue Neoplasms - pathology; Soft tissue sarcoma; Soft tissues; Statistical analysis; Statistical tests; Support vector machines; Survival; Training; tumor grading; Tumors; deep learning; magnetic resonance imaging; computer-aided diagnosis; soft tissue sarcoma; tumor grading
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.28160

Background MRI acts as a potential resource for exploration and interpretation to identify tumor characterization by advanced computer‐aided diagnostic (CAD) methods. Purpose To evaluate and validate the performance of MRI‐based CAD models for identifying low‐grade and high‐grade soft tissue sarcoma (STS) and for investigating survival prognostication. Study Type Retrospective. Subjects A total of 540 patients (295 male/female: 295/245, median age: 42 years) with STSs. Field Sequence 5‐T MRI with T1WI sequence and fat‐suppressed T2‐weighted (T2FS) sequence. Assessment Manual regions of interests (ROIs) were delineated for generation of radiomic features. Automatic segmentation and pretrained convolutional neural networks (CNNs) were performed for deep learning (DL) analysis. The last fully connected layer at the top of CNNs was removed, and the global max pooling was added to transform feature maps to numeric values. Tumor grade was determined on histological specimens. Statistical Tests The support vector machine was adopted as the classifier for all MRI‐based models. The DL signature was derived from the DL‐MRI model with the highest area under the curve (AUC). The significant clinical variables, tumor location and size, integrated with radiomics and DL signatures were ready for construction of clinical‐MRI nomogram to identify tumor grading. The prognostic value of clinical variables and these MRI‐based signatures for overall survival (OS) was evaluated via Cox proportional hazard. Results The clinical‐MRI differentiation nomogram represented an AUC of 0.870 in the training cohort, and an AUC of 0.855, accuracy of 79.01%, sensitivity of 79.03%, and specificity of 78.95% in the validation cohort. The prognostic model showed good performance for OS with 3‐year C‐index of 0.681 and 0.642 and 5‐year C‐index of 0.722 and 0.676 in the training and validation cohorts. Data Conclusion MRI‐based CAD nomogram represents effective abilities in classification of low‐grade and high‐grade STSs. The MRI‐based prognostic model yields favorable preoperative capacities to identify long‐term survivals for STSs. Evidence Level 3 Technical Efficacy Stage 4