基于梯度增强机的预测乳腺癌肿瘤浸润淋巴细胞比例模型的开发和验证。

IF 1.6 4区 医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
American journal of translational research Pub Date : 2025-07-25 eCollection Date: 2025-01-01 DOI:10.62347/PDEW5000
Xiaobin Zhang, Shulin Xian, Daolai Huang, Naihan Cui, Jiehua Li
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引用次数: 0

摘要

目的:建立并验证乳腺癌(BC)患者肿瘤浸润淋巴细胞(TIL)水平的多维评估模型。方法:回顾性研究广西医科大学第一附属医院于2021年1月至2024年12月收治的318例经MRI及手术病理证实的318个病变的BC患者。将患者随机分为训练组(n=228)和验证组(n=90),并根据免疫表型评估进一步分为低TIL组和高TIL组。采用多变量Logistic回归确定TILs水平的独立预测因子。建立了梯度增强机(GBM)模型和Logistic回归模型。采用受试者工作特征(ROC)曲线、校准曲线和决策曲线分析(DCA)评估模型的性能。在2025年1月至2025年5月期间入院的120例BC患者的外部验证队列用于验证GBM模型的预测准确性。结果Ki-67水平、内部增强模式、多灶性、表观扩散系数(ADC)值和中性粒细胞与淋巴细胞比值(NLR)是高TIL水平的独立预测因子。与训练集中的Logistic回归相比,GBM模型表现出更好的性能(AUC: 0.859 vs 0.724; P=0.014)。校正曲线显示两种模型的预测概率与观测概率吻合良好。DCA结果表明,GBM模型具有较高的临床应用价值。外部验证结果表明,GBM模型的AUC为0.784,标定曲线和DCA进一步证实了该模型具有良好的标定性和临床适用性。结论:基于gbm的多维模型可靠地预测BC患者TIL水平,支持预后评估,指导个性化治疗策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of a gradient boosting machine-based model for predicting tumor-infiltrating lymphocyte proportions in breast cancer.

Objective: To construct and validate a multidimensional model for evaluating tumor-infiltrating lymphocyte (TIL) levels in breast cancer (BC) patients.

Methods: This retrospective study included 318 BC patients with 318 lesions confirmed by MRI and surgical pathology in the First Affiliated Hospital of Guangxi Medical University from January 2021 to December 2024. The patients were randomly split into a training set (n=228) and a validation (n=90) set, and further divided into low and high TIL groups based on immunophenotype assessment. Multivariate Logistic regression was used to identify independent predictors of TILs levels. A gradient boosting machine (GBM) model and a Logistic regression model were built. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). An external validation cohort of 120 BC patients admitted between January 2025 and May 2025 was used to verify the predictive accuracy of the GBM model. Results Ki-67 level, internal enhancement pattern, multifocality, apparent diffusion coefficient (ADC) value, and neutrophil-to-lymphocyte ratio (NLR) were identified as independent predictors of high TIL levels. The GBM model demonstrated superior performance compared to the Logistic regression in the training set (AUC: 0.859 vs 0.724; P=0.014). Calibration curves indicated good agreement between predicted and observed probabilities in both models. DCA showed that the GBM model provided higher clinical utility. External validation yielded an AUC of 0.784 for the GBM model, with the calibration curve and DCA further confirming the model's good calibration and clinical applicability.

Conclusion: The GBM-based multidimensional model reliably predicts TIL levels in BC patients, supporting prognosis evaluation and guiding personalized treatment strategies.

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American journal of translational research
American journal of translational research ONCOLOGY-MEDICINE, RESEARCH & EXPERIMENTAL
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