Context-Dependent Effects of Lung Segmentation Preprocessing on Radiology-Report-Derived Pneumonia Classification in Chest Radiographs: A Comparative Study Across Acquisition Settings and Backbone Architectures

Lung segmentation is often used to restrict chest radiograph classifiers to pulmonary anatomy, but its observed effects may vary across acquisition settings, mask sources, and model configurations. We evaluated radiology-report-derived pneumonia classification on MIMIC-CXR-JPG across two frontal views, five input conditions (raw, MedSAM3 mask/crop, and CheXMask mask/crop), and seven backbones using patient-grouped 5-fold cross-validation with a 15% outer holdout. In anteroposterior (AP) radiographs, RAD-DINO with MedSAM3 cropping achieved the numerically highest AUROC of 0.8153 (95% CI, 0.7984–0.8316). In posteroanterior (PA) radiographs, raw EVA-X-Base achieved the numerically highest AUROC of 0.8261 (95% CI, 0.8048–0.8462). Across backbones, crop-versus-raw AUROC differences were small and heterogeneous, with none remaining statistically significant after multiplicity adjustment; equivalence or noninferiority was not formally assessed. Hard masking showed larger mean AUROC reductions in PA radiographs (−0.0141 for MedSAM3 and −0.0142 for CheXMask), with four backbone-specific PA masking contrasts remaining significant after Holm adjustment. These findings do not support a universal benefit of lung-focused preprocessing within the internally validated MIMIC-CXR-JPG cohort. Cross-backbone differences were interpreted descriptively under the shared training protocol, and the generalizability of these preprocessing effects to independent institutions and imaging populations remains to be established.