Clinical applications of deep learning in distinguishing benign from malignant pulmonary nodules in computed tomography scans

Yongzhong Xu; Yunxin Li; Feng Wang; Yafei Zhang; Delong Huang

doi:10.5114/aoms/202835

Clinical applications of deep learning in distinguishing benign from malignant pulmonary nodules in computed tomography scans

Arch Med Sci. 2025 May 18;22(1):12-26. doi: 10.5114/aoms/202835. eCollection 2026 Jan.

Authors

Yongzhong Xu¹, Yunxin Li¹, Feng Wang¹, Yafei Zhang¹, Delong Huang¹

Affiliation

¹ Imaging Department, Yantaishan Hospital, Yantai, Shandong, China.

Abstract

Early diagnosis is crucial for improving the prognosis of lung cancer, one of the leading causes of cancer-related deaths. Lung cancer includes small cell lung cancer (SCLC, ~15% of cases) and non-small cell lung cancer (NSCLC, ~80-85%). Prognosis depends on the stage at diagnosis: the 5-year survival rate is 65% for localized NSCLC but only 9% for distant-stage disease. Radiologists face challenges distinguishing benign from malignant pulmonary nodules on computed tomography scans. This review explores deep learning (DL) methods, including multi-view convolutional neural networks (CNNs) and 3D models for nodule segmentation, emphasizing volumetric assessments for malignancy prediction. CNNs effectively analyze CT data, achieving 94.2% sensitivity with 1.0 false positives per scan in lung nodule detection. DL enhances diagnostic accuracy, reduces radiologist workload, and enables earlier lung cancer detection. Further research is needed to improve model adaptability across diverse clinical settings.

Keywords: malignant tumor; morphological detection; pathology; radiology and oncology.