Diagnostic Performance of Radiomics and Deep Learning to Identify Benign and Malignant Soft Tissue Tumors: A Systematic Review and Meta-analysis

Abstract

Rationale and ObjectivesTo systematically evaluate the application value of radiomics and deep learning in the differential diagnosis of benign and malignant soft tissue tumors. Materials and MethodsA systematic review was conducted on studies published up to December 11, 2023, that utilized radiomics and deep learning methods for the diagnosis of soft tissue tumors. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS) 2.0 system and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, respectively. A bivariate random-effects model was used to calculate the summarized sensitivity and specificity. To identify factors contributing to heterogeneity, meta-regression and subgroup analyses were performed to assess the following covariates: diagnostic modality, region/volume of interest, imaging examination, study design, and pathology type. The asymmetry of Deeks' funnel plot was used to assess publication bias. ResultsA total of 21 studies involving 3,866 patients were included, with 13 studies using independent test/validation sets included in the quantitative statistical analysis. The average RQS was 21.31, with substantial or near-perfect inter-rater agreement. The combined sensitivity and specificity were 0.84 (95% CI: 0.76-0.89) and 0.88 (95% CI: 0.69-0.96), respectively. Meta-regression and subgroup analyses showed that study design and the region/volume of interest were significant factors affecting study heterogeneity (P<0.05). No publication bias was observed. ConclusionRadiomics and deep learning can accurately distinguish between benign and malignant soft tissue tumors. Future research should concentrate on enhancing the rigor of study designs, conducting multicenter prospective validations, amplifying the interpretability of deep learning models, and integrating multimodal data to elevate the diagnostic accuracy and clinical utility of soft tissue tumor assessments.