The potential of GPT-4 advanced data analysis for radiomics-based machine learning models

Abstract

Abstract Background This study aimed to explore the potential of the Advanced Data Analytics (ADA) package of GPT-4 to autonomously develop machine learning models (MLMs) for predicting glioma molecular types using radiomics from MRI. Methods Radiomic features were extracted from preoperative MRI of n = 615 newly diagnosed glioma patients to predict glioma molecular types (IDH-wildtype vs IDH-mutant 1p19q-codeleted vs IDH-mutant 1p19q-non-codeleted) with a multiclass ML approach. Specifically, ADA was used to autonomously develop an ML pipeline and benchmark performance against an established handcrafted model using various MRI normalization methods (N4, Zscore, and WhiteStripe). External validation was performed on 2 public glioma datasets D2 (n = 160) and D3 (n = 410). Results GPT-4 achieved the highest accuracy of 0.820 (95% CI = 0.819-0.821) on the D3 dataset with N4/WS normalization, significantly outperforming the benchmark model’s accuracy of 0.678 (95% CI = 0.677-0.680) (P < .001). Class-wise analysis showed performance variations across different glioma types. In the IDH-wildtype group, GPT-4 had a recall of 0.997 (95% CI = 0.997-0.997), surpassing the benchmark’s 0.742 (95% CI = 0.740-0.743). For the IDH-mut 1p/19q-non-codel group, GPT-4’s recall was 0.275 (95% CI = 0.272-0.279), lower than the benchmark’s 0.426 (95% CI = 0.423-0.430). In the IDH-mut 1p/19q-codel group, GPT-4’s recall was 0.199 (95% CI = 0.191-0.206), below the benchmark’s 0.730 (95% CI = 0.721-0.738). On the D2 dataset, GPT-4’s accuracy was significantly lower (P < .001) than the benchmark’s, with N4/WS achieving 0.668 (95% CI = 0.666-0.671) compared with 0.719 (95% CI = 0.717-0.722) (P < .001). Class-wise analysis revealed the same pattern as observed in D3. Conclusions GPT-4 can autonomously develop radiomics-based MLMs, achieving performance comparable to handcrafted MLMs. However, its poorer class-wise performance due to unbalanced datasets shows limitations in handling complete end-to-end ML pipelines.

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