目的:系统评价影像组学和深度学习(DL)在软组织肿瘤良恶性鉴别诊断中的应用价值。
方法:对截至2023年12月11日发表的利用影像组学和DL方法诊断STT的研究进行了系统评价。使用Radiomics质量评分(RQS)2.0系统和诊断准确性研究质量评估2(QUADAS-2)工具评估方法学质量和偏倚风险。分别。使用双变量随机效应模型来计算汇总的敏感性和特异性。为了确定导致异质性的因素,进行荟萃回归和亚组分析以评估以下协变量:诊断模式,感兴趣的区域/体积,影像学检查,研究设计,和病理类型。Deeks漏斗图的不对称性用于评估发表偏倚。
结果:共纳入21项研究,涉及3866名患者,13项使用独立测试/验证集的研究包括在定量统计分析中。平均RQS为21.31,评级者之间达成了实质性或近乎完美的协议。联合的敏感性和特异性分别为0.84(95%CI:0.76-0.89)和0.88(95%CI:0.69-0.96),分别。Meta回归和亚组分析显示,研究设计和感兴趣区域/体积是影响研究异质性的显著因素(P<0.05)。未观察到发表偏倚。
结论:影像组学和DL可以准确区分良性和恶性STT。未来的研究应该专注于提高研究设计的严谨性,进行多中心前瞻性验证,放大DL模型的可解释性,并整合多模式数据以提高软组织肿瘤评估的诊断准确性和临床实用性。
OBJECTIVE: To systematically evaluate the application value of radiomics and deep learning (DL) in the differential diagnosis of benign and malignant soft tissue tumors (STTs).
METHODS: A systematic
review was conducted on studies published up to December 11, 2023, that utilized radiomics and DL methods for the diagnosis of STTs. 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.
RESULTS: A total of 21 studies involving 3866 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.
CONCLUSIONS: Radiomics and DL can accurately distinguish between benign and malignant STTs. Future research should concentrate on enhancing the rigor of study designs, conducting multicenter prospective validations, amplifying the interpretability of DL models, and integrating multimodal data to elevate the diagnostic accuracy and clinical utility of soft tissue tumor assessments.