Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models

Bin Xie, Mingda Mo, Haidong Cui, Yijie Dong, Hongping Yin, Zhe Lu

Objectives: Lung cancer is one of the most prevalent cancers worldwide. Accurately determining lung cancer subtypes and identifying high-risk patients are helpful for individualized treatment and follow-up. Our study aimed to establish an effective model for subtype classification and overall survival (OS) prediction in patients with lung cancer. Methods: Histopathological images, clinical data, and genetic information of lung adenocarcinoma and lung squamous cell carcinoma cases were downloaded from The Cancer Genome Atlas. An influencing factor system was optimized based on the nuclear, clinical, and genetic features. Four machine-learning models—light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), random forest (RF), and adaptive boosting (AdaBoost)—and three deep-learning models—multilayer perceptron (MLP), TabNet, and convolutional neural network (CNN)—were employed for subtype classification and OS prediction. The performance of the models was comprehensively evaluated. Results: XGBoost exhibited the highest area under the curve (AUC) value of 0.9821 in subtype classification, whereas RF exhibited the highest AUC values of 0.9134, 0.8706, and 0.8765 in predicting OS at 1, 2, and 3 years, respectively. Conclusions: Our study was the first to incorporate the characteristics of nuclei and the genetic information of patients to predict the subtypes and OS of patients with lung cancer. The combination of different factors and the usage of artificial intelligence methods achieved a small breakthrough in the results of previous studies regarding AUC values.