Segmentation-Free 3D Deep Learning for CT-Based Chronic Rhinosinusitis Classification

Chronic rhinosinusitis (CRS) is routinely assessed with computed tomography (CT), but CT-based differentiation between CRS with nasal polyps (CRSwNP) and without nasal polyps (CRSsNP) remains challenging. We retrospectively evaluated a segmentation-free three-dimensional convolutional neural network (3D CNN) for three-class classification using an audited cohort of 776 adult CT examinations: 155 CRSwNP, 222 CRSsNP, and 399 endocrine orbitopathy (EO) non-CRS controls. The volumes were standardized to 150 × 150 × 150 voxels. Baseline, augmentation, and augmentation-plus-L2 configurations were compared exclusively by validation macro-F1 before the primary locked-test evaluation of the selected compact model; post hoc architecture and sensitivity analyses were subsequently performed on the same frozen test set and were considered exploratory. On the 155-patient held-out test set, the validation-selected compact model achieved an accuracy of 0.748 (95% CI 0.684–0.813), balanced accuracy of 0.683 (0.602–0.763), macro-F1 of 0.691 (0.607–0.770), and macro one-versus-rest AUC of 0.882 (0.841–0.919). Performance was strongest for EO-control discrimination (one-versus-rest AUC 0.959), whereas direct CRSsNP-versus-CRSwNP discrimination was more limited (AUC 0.704; 95% CI 0.574–0.827). The CRSwNP recall was 0.452, and 15 of 31 CRSwNP cases were classified as CRSsNP. Thus, the aggregate macro-AUC was influenced predominantly by the stronger separation of EO controls from CRS and should not be interpreted as uniformly strong CRS phenotype discrimination. In a post hoc architecture sensitivity analysis, a 3D ResNet-10 achieved a higher observed test macro-F1 of 0.742, but used approximately 206 times as many parameters as the compact model. The HiResCAM maps were technically valid in all test cases, although 38/155 maps (24.5%) peaked outside an intensity-derived patient envelope that was not a sinus segmentation mask. These findings provide a research-stage proof of concept for future studies on CRS subtype discrimination; however, potential demographic- and acquisition-related shortcuts, together with the absence of external validation, preclude any claim of clinical readiness.