DataSheet_2_The predictive value of radiomics-based machine learning for peritoneal metastasis in gastric cancer patients: a systematic review and meta-analysis.docx

BackgroundFor patients with gastric cancer (GC), effective preoperative identification of peritoneal metastasis (PM) remains a severe challenge in clinical practice. Regrettably, effective early identification tools are still lacking up to now. With the popularization and application of radiomics method in tumor management, some researchers try to introduce it into the early identification of PM in patients with GC. However, due to the complexity of radiomics, the value of radiomics method in the early identification of PM in GC patients remains controversial. Therefore, this systematic review was conducted to explore the feasibility of radiomics in the early identification of PM in GC patients.
MethodsPubMed, Cochrane, Embase and the Web of Science were comprehensively and systematically searched up to 25 July, 2022 (CRD42022350512). The quality of the included studies was assessed using the radiomics quality score (RQS). To discuss the superiority in diagnostic accuracy of radiomics-based machine learning, a subgroup analysis was performed by machine learning (ML) based on clinical features, radiomics features, and radiomics + clinical features.
ResultsFinally, 11 eligible original studies covering 78 models were included in this systematic review. According to the meta-analysis, the radiomics + clinical features model had a c-index of 0.919 (95% CI: 0.871-0.969), pooled sensitivity and specificity of 0.90 (0.83-0.94) and 0.87 (0.78-0.92), respectively, in the training set, and a c- index of 0.910 (95% CI: 0.886-0.934), pooled sensitivity and specificity of 0.78 (0.71-0.84) and 0.83 (0.74-0.89), respectively, in the validation set.
ConclusionsThe ML methods based on radiomics + clinical features had satisfactory accuracy for the early diagnosis of PM in GC patients, and can be used as an auxiliary diagnostic tool for clinicians. However, the lack of guidelines for the proper operation of radiomics has led to the diversification of radiomics methods, which seems to limit the development of radiomics. Even so, the clinical application value of radiomics cannot be ignored. The standardization of radiomics research is required in the future for the wider application of radiomics by developing intelligent tools of radiomics.
Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=350512, identifier CRD42022350512.

背景 针对胃癌（gastric cancer, GC）患者，术前有效鉴别腹膜转移（peritoneal metastasis, PM）仍是临床实践中的重大难题。遗憾的是，截至目前仍缺乏可靠的早期筛查工具。随着放射组学（radiomics）方法在肿瘤诊疗领域的普及与应用，部分研究者尝试将其引入胃癌患者腹膜转移的早期鉴别工作中。然而，由于放射组学本身的复杂性，其在胃癌患者腹膜转移早期鉴别中的应用价值仍存在争议。因此，本系统综述旨在探讨放射组学在胃癌患者腹膜转移早期鉴别中的可行性。

方法 本研究于2022年7月25日前全面系统检索PubMed、Cochrane、Embase及Web of Science数据库，系统综述注册编号为CRD42022350512。采用放射组学质量评分（radiomics quality score, RQS）对纳入研究的方法学质量进行评价。为探讨基于放射组学的机器学习（machine learning, ML）模型在诊断效能上的优势，本研究依据临床特征、放射组学特征以及放射组学+临床特征三类建模方案，开展机器学习亚组分析。

结果 本系统综述最终纳入11项符合纳入标准的原始研究，共涉及78个预测模型。荟萃分析结果显示，在训练集当中，放射组学+临床特征融合模型的C指数（c-index）为0.919（95%置信区间：0.871~0.969），合并灵敏度与特异度分别为0.90（0.83~0.94）与0.87（0.78~0.92）；在验证集当中，该模型的C指数为0.910（95%置信区间：0.886~0.934），合并灵敏度与特异度分别为0.78（0.71~0.84）与0.83（0.74~0.89）。

结论 基于放射组学与临床特征融合的机器学习模型，在胃癌患者腹膜转移的早期诊断中展现出优异的诊断效能，可作为临床医师的辅助诊断工具。然而，目前缺乏放射组学规范操作的相关指南，导致放射组学方法的应用呈现多样化趋势，一定程度上限制了放射组学的发展。即便如此，放射组学的临床应用价值仍不容忽视。未来需推动放射组学研究的标准化进程，通过开发放射组学智能工具，以实现其更广泛的临床应用。

系统综述注册信息 https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=350512，注册编号：CRD42022350512。