Methodological issues in radiomics: impact on accuracy of MRI for predicting response to neoadjuvant chemotherapy in breast cancer
Abstract
Aim: To investigate whether methodological aspects may influence the performance of MRI-radiomic models to predict response to neoadjuvant treatment (NAT) in breast cancer (BC) patients.
Materials and methods: We conducted a systematic review until March 2023. A random-effects meta-analysis was performed to combine the area under the receiver operating characteristic curve (AUC) values. Publication bias was assessed using Egger's test and heterogeneity was estimated by I2. A meta-regression was conducted to investigate the impact of various factors, including scanner, features' number/transformation/type, pixel/voxel scaling, etc. RESULTS: Forty-two studies were included. The summary AUC was 0.77 (95% CI: 0.74-0.81). Substantial heterogeneity was observed (I2 = 81%) with no publication bias (p = 0.35). Radiomic model accuracy was influenced by the scanner vendor, with lower AUCs in studies using mixed scanner vendors (AUC; 95% CI: 0.70; 0.61-0.78) compared to studies including images obtained from the same scanner (AUC (95% CI): 0.83 (0.77-0.88), 0.74 (0.67-0.82), 0.83 (0.78-0.89) for three different vendors; vendors 1, 2, and 3, respectively; p-value = 0.03 for comparison with vendor 1). Feature type also seemed to have an impact on the AUC, with higher prediction accuracy observed for studies using 3D than 2D/2.5D images (AUC; 95% CI: 0.81; 0.78-0.85 and 0.73; 0.65-0.81, respectively, p-value = 0.03). Non-significant between-study heterogeneity was observed in the studies…
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