Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer

Journal of Thoracic Disease

article

Mingfei Geng¹  Mingsha Geng²  Rong Wei³  Mingwei Chen⁴ 

[1] Department of State-owned Assets Management, The First Affiliated Hospital of Xi’an Jiaotong University;Department of Information Management & Information Technology, The Second Affiliated Hospital of Xi’an Jiaotong University;Department of Information Management & Information Technology, The First Affiliated Hospital of Xi’an Jiaotong University;Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University

关键词: Computed tomography image (CT images);    lymph node metastasis;    artificial intelligence;    neural network;    prediction model;   

DOI  :  10.21037/jtd-22-1511

学科分类：呼吸医学

来源: Pioneer Bioscience Publishing Company

【 摘 要 】

Background: Computed tomography (CT) is important in the diagnosing of lung cancer. The combination of CT features and artificial intelligence algorithm have been used in the diagnosis of various lung diseases. However, limited studies focused on the relationship between the combination of CT features and artificial intelligence algorithm and lymph node metastasis in non-small cell lung cancer (NSCLC). This study developed an algorithm for lung cancer CT image segmentation based on an artificial neural network model and investigated the role of a nomogram model based on CT images for predicting lymph node metastasis in lung cancer. Methods: Wiener filtering and fuzzy enhancement were first used to suppress image noise and improve image contrast. Next, texture features and fractal features were extracted. In the third step, the artificial neural network model was trained and tested according to the best parameters of the network. Results: The area under the curve (AUC) of the constructed nomogram model on the training set and the test set were 0.859 (sensitivity, 0.810; specificity, 0.773) and 0.864 (sensitivity, 0.820; specificity, 0.753), respectively. The decision curve indicated that the model had good clinical application value. The lung cancer CT images contained 13 significant regional features of cancer. The best classification function obtained from training and testing data was Levenberg-Marquardt backpropagation. The sensitivity, specificity, and accuracy in the training stage could reach 98.4%, 100%, and 98.6%, respectively, and the corresponding indexes in the test stage reached 90.9%, 100%, and 95.1%, respectively. Conclusions: The image segmentation algorithm based on the artificial neural network model could extract CT lung cancer lesions efficiently and quasi-determinately, which could be used as an effective tool for radiologists to diagnose lung cancer. The nomogram model based on CT image features and related clinical indicators was an effective method for noninvasive prediction of lymph node metastasis in lung cancer.

【 授权许可】

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