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。

IF 2.3 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Quantitative Imaging in Medicine and Surgery Pub Date : 2025-06-06 Epub Date: 2025-05-21 DOI:10.21037/qims-24-2440
Wenbiao Zhang, Huiyun Ma, Ying Zhu, Wenjing Gou, Baocong Liu, Qiong Li, Shuangjiang Li
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引用次数: 0

摘要

背景:胸部薄层计算机断层扫描(TS-CT)有可能为预测同步多发原发肺癌(SMPLC)的淋巴结转移(LNM)提供证据。本研究旨在开发并验证一种新的基于ct的多参数决策树算法(CT-DTA)模型,该模型能够准确评估SMPLC术前LNM的风险。方法:最终纳入中山大学肿瘤中心(sysuc)、中山大学第一附属医院(FAH-SYSU)和四川省人民医院(SPPH)手术切除的SMPLC患者235例。我们最初检索了训练队列(来自SYSUCC的139例)中所有ct衍生的定量指标,并选择具有统计学意义的指标建立DTA模型。在验证队列(来自FAH-SYSU和SPPH的96例患者)中,进一步从外部验证CT-DTA模型对LNM发生的判别能力。此外,还对整个队列的不同亚组进行了CT-DTA模型的性能评估。结果:胸部TS-CT测量的5个关键定量协变量构成了7叶节点的CT-DTA模型,实部长轴直径是LNM最主要的危险因素。该CT-DTA模型获得了令人满意的预测精度,在训练组(0.905;结论:CT-DTA模型可作为一种无创、用户友好、实用的风险预测工具,帮助手术切除SMPLC的治疗决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Quantitative Imaging in Medicine and Surgery
Quantitative Imaging in Medicine and Surgery Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.20
自引率
17.90%
发文量
252
期刊介绍: Information not localized
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