Understanding and Mitigating Distribution Shifts in Volumetric Lung Nodule CAD Using a 3D Vision-Language Framework

As artificial intelligence becomes increasingly integrated into medical imaging practice, its robustness across heterogeneous real-world settings remains a major challenge. We quantified the effect of real-world distribution shifts on three-dimensional AI models for lung nodule analysis on CT and, motivated by these shifts, developed and evaluated MedStyle-3DG, an open-source 3D vision-language framework for domain generalization. This retrospective multicenter study assembled 2679 chest CTs acquired from 2010 to 2025, with institutional review approval and waiver of informed consent. The study was designed in two stages: first, to quantify the extent of performance degradation from in-distribution (ID) to out-of-distribution (OOD), under clinically realistic distribution shifts; and second, to test whether a domain generalization strategy can mitigate these gaps. Data were split into training, validation, test-ID, and test-OOD to model three shifts: exposure variations, device manufacturer, and geographic changes. We then proposed MedStyle-3DG, a 3D vision-language domain generalization framework combining feature statistics mixing, stochastic weight averaging, vision-language alignment, and a three-branch ensemble. OOD F1 score and the gaps between test-ID and test-OOD were the primary outcomes. The 3D ResNet50 baseline showed consistent ID-OOD degradation across all shifts, largest for exposure (F1, 0.630 vs 0.521; gap, 10.9 percentage points; P < .001). MedStyle-3DG improved exposure OOD F1 to 0.601 and reduced the gap to 7.7 percentage points, with similar improvements for manufacturer and geographic shifts. Across all three factors, MedStyle-3DG achieved state-of-the-art performance in mean OOD F1 (P < .001) and reduction of the F1 generalization gap (P < .041). Real-world protocol, vendor, and population shifts substantially degrade volumetric lung nodule CAD performance. MedStyle-3DG reduces generalization gaps and its code is available at  https://github.com/RafaelMedelean/MedCLIP-3DG .