Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
Eur Radiol. 2020 Jun;30(6):3558-3566. doi: 10.1007/s00330-020-06666-3. Epub 2020 Feb 14.
(1) To assess the methodological quality of radiomics studies investigating histological subtypes, therapy response, and survival in patients with renal cell carcinoma (RCC) and (2) to determine the risk of bias in these radiomics studies.
In this systematic review, literature published since 2000 on radiomics in RCC was included and assessed for methodological quality using the Radiomics Quality Score. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool and a meta-analysis of radiomics studies focusing on differentiating between angiomyolipoma without visible fat and RCC was performed.
Fifty-seven studies investigating the use of radiomics in renal cancer were identified, including 4590 patients in total. The average Radiomics Quality Score was 3.41 (9.4% of total) with good inter-rater agreement (ICC 0.96, 95% CI 0.93-0.98). Three studies validated results with an independent dataset, one used a publically available validation dataset. None of the studies shared the code, images, or regions of interest. The meta-analysis showed moderate heterogeneity among the included studies and an odds ratio of 6.24 (95% CI 4.27-9.12; p < 0.001) for the differentiation of angiomyolipoma without visible fat from RCC.
Radiomics algorithms show promise for answering clinical questions where subjective interpretation is challenging or not established. However, the generalizability of findings to prospective cohorts needs to be demonstrated in future trials for progression towards clinical translation. Improved sharing of methods including code and images could facilitate independent validation of radiomics signatures.
• Studies achieved an average Radiomics Quality Score of 10.8%. Common reasons for low Radiomics Quality Scores were unvalidated results, retrospective study design, absence of open science, and insufficient control for multiple comparisons. • A previous training phase allowed reaching almost perfect inter-rater agreement in the application of the Radiomics Quality Score. • Meta-analysis of radiomics studies distinguishing angiomyolipoma without visible fat from renal cell carcinoma show moderate diagnostic odds ratios of 6.24 and moderate methodological diversity.
（1）评估研究肾细胞癌（RCC）组织亚型、治疗反应和生存的放射组学研究的方法学质量，（2）确定这些放射组学研究的偏倚风险。
在这项系统评价中，纳入了 2000 年以来发表的关于 RCC 放射组学的文献，并使用放射组学质量评分（Radiomics Quality Score）评估其方法学质量。使用诊断准确性研究质量评估工具（Quality Assessment of Diagnostic Accuracy Studies tool）评估偏倚风险，并对重点关注区分无可见脂肪的血管平滑肌脂肪瘤和 RCC 的放射组学研究进行荟萃分析。
共确定了 57 项研究，共纳入了 4590 例患者。平均放射组学质量评分为 3.41 分（总分的 9.4%），组内一致性较好（ICC 0.96，95%CI 0.93-0.98）。有 3 项研究使用独立数据集进行了结果验证，有 1 项研究使用了公共验证数据集。没有一项研究共享代码、图像或感兴趣区。荟萃分析显示纳入研究存在中度异质性，无可见脂肪的血管平滑肌脂肪瘤与 RCC 的鉴别诊断比值比为 6.24（95%CI 4.27-9.12；p<0.001）。
放射组学算法在回答主观解释具有挑战性或尚未确定的临床问题方面具有很大的潜力。然而，需要在未来的试验中证明发现对前瞻性队列的推广，以推进向临床转化。改进方法（包括代码和图像）的共享可以促进放射组学特征的独立验证。
（1）研究达到了平均 10.8%的放射组学质量评分。评分较低的常见原因包括未验证的结果、回顾性研究设计、缺乏开放科学以及未充分控制多重比较。（2）应用放射组学质量评分前的培训阶段，可实现几乎完美的组内一致性。（3）对区分无可见脂肪的血管平滑肌脂肪瘤和肾细胞癌的放射组学研究进行荟萃分析，显示出 6.24 的中等诊断比值比和中等方法多样性。
