Construction and Validation of A Prediction Model for Recurrence and Metastasis of Elderly Lung Cancer Patients after Radical Surgery Based on Random Forest Algorithm

Abstract

Objective To explore the application value of random forest algorithm in the construction of recurrence and metastasis prediction model for elderly lung cancer patients after radical surgery. Methods A total of 150 elderly lung cancer patients who underwent radical surgery at the Second Affiliated Hospital of Air Force Medical University from January 2014 to January 2016 were selected as the modeling group. Random forest algorithm modeling was conducted on the candidate variables with P<0.05 in the univariate analysis results of the relevant factors in the modeling group to screen the factors related to postoperative recurrence and metastasis. The factors were ranked according to their importance. Another 70 elderly lung cancer patients who underwent radical surgery for lung cancer from February 2016 to February 2017 were selected as the validation group to validate the random forest graph model. Results During the follow-up period, 44 cases (29.33%) elderly patients with lung cancer undergoing radical surgery recurrence, 51 cases (34.00%) metastasis, and 20 cases (13.33%) had both recurrence and metastasis. Rank of importance of postoperative recurrence variables: mediastinal lymph node metastasis, number of lymph node dissection, T stage, N stage, degree of differentiation, pathological stage, age, lesion diameter. Rank of importance of postoperative metastasis variables: mediastinal lymph node metastasis, N stage, number of lymph node metastasis, number of lymph node dissection, T stage, degree of differentiation, postoperative chemoradiotherapy, pathological stage, age. The random forest model was used to select the characteristic variables. In the random forest model, the model built by the mediastinal lymph node metastasis, the number of lymph nodes dissected, the T stage and the N stage indexes had an efficacy of 0.904 in predicting postoperative recurrence. The predictive efficacy of the mediastinal lymph node metastasis, N stage, number of lymph node metastasis, number of lymph node dissection and T stage was 0.897. The externally validated random forest model predicted a postoperative recurrence efficacy of 0.905 and a postoperative metastasis efficacy of 0.910 for elderly lung cancer, which was in general agreement with the internal validation. Conclusion In this study, the construction of the random forest prediction model can predict the risk of recurrence and metastasis after radical resection of lung cancer in the elderly to a certain extent. It can provide clinical reference for surgical effect and postoperative adjuvant therapy.

Keywords

• radical surgery of lung cancer
• recurrence
• metastasis
• random forest algorithm
• lymph node metastasis