Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma—a systematic review and meta-analysis

Abstract

Objectives (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. Methods 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. Results 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. Conclusions 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. Key Points • 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.

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications

“…Furthermore, most researchers do not offer details regarding preprocessing steps and model parameters. Only a few authors made their code publicly available in a repository [40]. This lack of detailed information hampers the reproducibility of the results, makes it difficult to compare methods, and does not help in pushing this field forward.…”

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

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“…Therefore, the link between image tumor properties and tumor biology is not straightforward, correlation does not imply causation, and a statistical relationship between radiomics and genetic footprints or prognosis should be established [65]. Lastly, if dataset heterogeneity is not addressed for the conduction of future studies, bridging the translational leap from an experimental research tool to an essential clinically applicable diagnostic and predictive instrument will be challenging [3].…”

“…An increasing incidental detection of renal masses with cross-sectional imaging led to the diagnosis of more asymptomatic, small, and clinically localized renal masses. Small renal masses (SRMs), defined as ≤4 cm in diameter, account for more than 50% of all renal masses, approximately 10-30% of which result in benign histology [3,4]. The diagnosis of SRMs carries the risk of subjecting patients to unnecessary procedures and overtreating lesions that may not progress.…”