Patient-Level Cross-validated nnU-Net for Multiclass Segmentation of Lung Parenchyma and Solid Adenocarcinoma on...

Automated segmentation of lung parenchyma and solid lung adenocarcinoma on thoracic computed tomography (CT) is needed for reproducible quantitative imaging, radiomics extraction, and treatment planning-related research. However, tumor segmentation remains challenging because of small lesion size, irregular morphology, partial-volume effects, and boundary ambiguity near vessels, pleura, atelectasis, and mediastinal structures. This study evaluated a single-input-channel multiclass nnU-Net framework for joint lung parenchyma and solid adenocarcinoma segmentation in pre-treatment CT examinations from 150 patients with pathologically confirmed solid lung adenocarcinoma. The thoracic CT volume was the only model input. Reference segmentation maps encoded three mutually exclusive classes: background (label 0), lung parenchyma (label 1), and tumor (label 2). The model was trained using the 3D full-resolution nnU-Net configuration and evaluated using patient-level fivefold cross-validation. Performance was quantified using the Dice similarity coefficient (DSC), intersection-over-union (IoU), 95th percentile Hausdorff distance (HD95), relative volume difference (RVD), and volume similarity (VS). Lung segmentation was highly accurate and stable, with median DSC > 0.97, IoU > 0.95, HD95 < 5 mm, and VS > 0.99 across folds. Tumor segmentation achieved moderate overlap performance, with median DSC values of approximately 0.70-0.78 and median IoU values of approximately 0.54-0.64, but substantial HD95 variability indicated residual boundary-localization failures in challenging cases. The single-channel multiclass framework is promising as a segmentation backbone for quantitative CT analysis; however, external validation, direct comparison with alternative models, and improved false-positive suppression are required before direct clinical use.