PARProceedings of Anticancer Research2208-35452208-3553Bio-Byword Scientific Publishing Pty. Ltd.10.26689/par.v9i1.9462ArticleApplication of Artificial Neural Networks in Predicting Malignant Lung Nodules on Chest CT Scanshttps://artdesignp.com/journal/PAR/9/1/10.26689/par.v9i1.9462LiWenhui,YangYuping,LiangYixian,XuPengliang,ChenQiuqiang2025912025-02-13Objective: To explore a simple method for improving the diagnostic accuracy of malignant lung nodules based on imaging features of lung nodules. Methods: A retrospective analysis was conducted on the imaging data of 114 patients who underwent lung nodule surgery in the Thoracic Surgery Department of the First People’s Hospital of Huzhou from June to September 2024. Imaging features of lung nodules were summarized and trained using a BP neural network. Results: Training with the BP neural network increased the diagnostic accuracy for distinguishing between benign and malignant lung nodules based on imaging features from 84.2% (manual assessment) to 94.1%. Conclusion: Training with the BP neural network significantly improves the diagnostic accuracy of lung nodule malignancy based solely on imaging features.

Zhang J, 2023, Research on Semantic Segmentation Algorithm for Pulmonary Nodules Based on 3D U-Net, dissertation, Northwest A&F University.

Pei Q, Luo Y, Chen Y, et al., 2022, Artificial Intelligence in Clinical Applications for Lung Cancer: Diagnosis, Treatment and Prognosis. Clin Chem Lab Med, 60(12): 1974–1983. https://doi.org/10.1515/cclm-2022-0291

Yang X, Zhou Y, Chen F, et al., 2024, Construction and Practice of a Comprehensive Management Model for Pulmonary Nodules/Lung Cancer Based on “Internet+”. West China Medical Journal, 39(4): 613–618.

Chen Q, Zhu Q, Gu J, et al., 2023, Comparative Value of HRCT Targeted Scanning with Physiological Ventilation Assistance and Thin-Section CT Reconstruction in Diagnosing Pulmonary Ground-Glass Nodules. Chinese Journal of Medical Computer Imaging, 29(1): 26–31.

Zhao S, Meng L, Guo J, 2022, Construction of a Multimodal Combined Model Nomogram Based on CT Targeted Scanning for Evaluating Solitary Pulmonary Nodules. Radiologic Practice, 37(10): 6.

Feng X, Zhang M, Wu B, et al., 2020, Application Value of High-Resolution CT Combined with Serum CEA and NSE in Differential Diagnosis of Benign and Malignant Solitary Pulmonary Nodules. Chinese Journal of CT and MRI, 18(6): 3.

Liu J, Qian W, Li L, et al., 2024, Diagnostic Value of Imaging Features and Quantitative Parameters in Differentiating Benign and Malignant Pulmonary Nodules. Chinese Medical Engineering, 32(3): 25–30.

Fu Y, Xue P, Xiao T, et al., 2023, Semi-Supervised Adversarial Learning for Improving the Diagnosis of Pulmonary Nodules. IEEE J Biomed Health Inform, 27(1): 109–120. https://doi.org/10.1109/JBHI.2022.3216446

Li J, Song Z, Chen W, et al., 2024, Discussion on the Impact of Different Staining Modes on AI Diagnosis of Gastroscopy Tissue Pathology. Journal of Diagnostic Pathology, 31(9): 910–912.

Zhang J, Song Z, Wang S, et al., 2024, Research on a Classification Model Based on Pathological Data Features of Gastric Tumors. Journal of Instrumentation and Measurement, Online ahead of print.

Wang H, Guo M, Sun Y, et al., 2024, Research and Diagnostic Value of AI-Based Cytopathology Diagnostic System for Lung Cancer. Journal of PLA Medical College, 45(5): 463–468.

Hou N, Zhai W, Zheng J, 2024, Micro Knowledge through Macro Vision: Application Status and Challenges of AI in Digital Pathology of Renal Cancer. Chinese Journal of Cancer Prevention and Treatment, 16(2): 143–151.

Fan L, Song Z, Deng L, et al., 2024, Research Progress of AI in Pathological Diagnosis and Molecular Typing of Prostate Cancer. Journal of Naval Medical University, 45(9): 1141–1146.

Xie Y, 2023, Application of AI in Pathological Diagnosis and Prognosis of Metastatic Lymph Nodes in Hypopharyngeal Squamous Cell Carcinoma, dissertation, Shandong University.

Zhai T, Zhang M, Li D, 2024, Research Progress of AI in Imaging Diagnosis and Treatment of Breast Cancer. Journal of Molecular Imaging, 47(9): 1003–1006.

Chu X, Fu Y, Zheng S, et al., 2024, Opportunities and Challenges of AI in Imaging of Primary Liver Cancer. Radiologic Practice, 39(9): 1244–1249.

Wu Y, Hao J, 2024, Research Progress of Radiomics in Diagnosis and Treatment of Gallbladder Cancer. International Journal of Medical Radiology, 47(5): 594–598.

Jia W, Wang L, 2024, Research Progress of AI in MRI of Pituitary Tumors. Magnetic Resonance Imaging, 15(9): 162–166.

Deng B, Huang W, Jiang Y, 2024, Advances in AI-Based Precision Diagnosis and Treatment of Lung Cancer Using CT Imaging. Chongqing Medicine, Online ahead of print.