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Updated: Jul 14, 2026
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
A novel computed tomography-based multi-parameter decision tree algorithm model for preoperatively predicting the
Wenbiao Zhang1, Huiyun Ma1, Ying Zhu2
1Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Background:
Chest thin-section computed tomography (TS-CT) has the potential to provide evidence for the prediction of lymph node metastasis (LNM) in synchronous multiple primary lung cancer (SMPLC). The present study aims to develop and validate a new CT-based multi-parametric decision tree algorithm (CT-DTA) model capable of accurate risk evaluation for LNM in SMPLC preoperatively.
Methods:
A total of 235 patients with surgically resected SMPLC from Sun Yat-Sen University Cancer Center (SYSUCC), the First Affiliated Hospital of Sun Yat-Sen University (FAH-SYSU) and Sichuan Provincial People's Hospital (SPPH) were finally included. We initially retrieved all the CT-derived quantitative signs in the training cohort (139 cases from SYSUCC) and selected those with statistical significance to build a DTA model. The discriminative power of CT-DTA model for the occurrence of LNM was further externally validated among the validation cohort (96 patients from FAH-SYSU and SPPH). In addition, the performance of CT-DTA model was also assessed across different subgroups of the entire cohort.
Results:
Five key quantitative covariables measured on chest TS-CT constituted a CT-DTA model with seven leaf nodes, and long-axis diameter of the solid portion was the most dominant risk contributor of LNM. This CT-DTA model gained a satisfactory predictive accuracy, revealed by an area under the curve >0.80 in both the training cohort (0.905; P<0.001) and the validation cohort (0.812; P<0.001). Moreover, our CT-DTA model was also exhaustively demonstrated to perform as an independent predictor for risk stratification of LNM in both the training cohort (odds ratio: 12.01; P=0.003) and the validation cohort (odds ratio: 8.11; P=0.033). Its potent performance for risk prediction still remained stable across nearly all of the subgroups stratified by clinicopathological characteristics.
Conclusions:
This CT-DTA model could serve as a noninvasive, user-friendly and practicable risk prediction tool to aid treatment decision-making in surgically resectable SMPLC.
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