{"title":"一种新的基于计算机断层扫描的多参数决策树算法模型,用于术前预测可手术切除的同步多发原发性肺癌淋巴结转移的风险。","authors":"Wenbiao Zhang, Huiyun Ma, Ying Zhu, Wenjing Gou, Baocong Liu, Qiong Li, Shuangjiang Li","doi":"10.21037/qims-24-2440","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"4972-4994"},"PeriodicalIF":2.3000,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209643/pdf/","citationCount":"0","resultStr":"{\"title\":\"A novel computed tomography-based multi-parameter decision tree algorithm model for preoperatively predicting the risk of lymph node metastasis in surgically resectable synchronous multiple primary lung cancer.\",\"authors\":\"Wenbiao Zhang, Huiyun Ma, Ying Zhu, Wenjing Gou, Baocong Liu, Qiong Li, Shuangjiang Li\",\"doi\":\"10.21037/qims-24-2440\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":54267,\"journal\":{\"name\":\"Quantitative Imaging in Medicine and Surgery\",\"volume\":\"15 6\",\"pages\":\"4972-4994\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209643/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quantitative Imaging in Medicine and Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.21037/qims-24-2440\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/5/21 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Imaging in Medicine and Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/qims-24-2440","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/21 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
A novel computed tomography-based multi-parameter decision tree algorithm model for preoperatively predicting the risk of lymph node metastasis in surgically resectable synchronous multiple primary lung cancer.
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.