基于形态学图的临床病理生物标志物联合多参数MRI对乳腺癌肿瘤浸润性淋巴细胞表达的预测价值。

IF 3.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Zhe Zhou, Zhanguo Sun, Fan Zhao, Yuan Jin, Yueqin Chen, Laimin Zhu, Yanji Guo, Weiwei Wang
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引用次数: 0

摘要

理由和目的:探讨临床病理特征和多参数磁共振成像(MRI)在预测乳腺癌肿瘤浸润性淋巴细胞(TIL)水平中的价值。材料和方法:共171例术前MRI诊断为浸润性导管癌的患者(2023-2025)。分析的重点是临床病理特征以及常规MRI特征和一系列定量参数。多元logistic回归分析确定了高、低TIL水平的独立预测因子。根据多变量logistic回归模型的结果,构造了一个nomogram。结果:Logistic回归分析发现组织学分级、D、D*、Ktrans和Kep是训练队列的独立因素。训练组和验证组的nomogram C-index分别为0.944和0.964。训练组nomogram模型的曲线下面积(AUC)为0.954(灵敏度85.1%,特异度91.1%,准确度87.4%),验证组为0.974(灵敏度96.7%,特异度92.1%,准确度92.6%),均显著高于相应队列的个体模型(Z=3.018-6.653,均为p。将临床病理特征与多参数MRI参数相结合可显著提高乳腺癌TIL水平的预测准确性。这种综合模型具有相当大的临床潜力,为个性化治疗策略提供强有力的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Value of Nomogram-Based Clinicopathological Biomarkers Combined with Multiparametric MRI for Tumour-Infiltrating Lymphocyte Expression in Breast Cancer.

Rationale and objectives: To investigate the value of clinicopathological features and multiparametric magnetic resonance imaging (MRI) in predicting tumour-infiltrating lymphocyte (TIL) levels in breast cancer.

Materials and methods: A total of 171 patients diagnosed with invasive ductal carcinoma who underwent preoperative MRI (2023-2025) were included. The analysis focused on the clinicopathological characteristics alongside conventional MRI features and a range of quantitative parameters. Multiple logistic regression analysis identified independent predictors of high and low TIL levels. A nomogram was constructed based on the multivariable logistic regression model results.

Results: Logistic regression analysis identified histological grade, D, D*, Ktrans, and Kep as independent factors in the training cohort. The nomogram's C-index was 0.944 in the training cohort and 0.964 in the validation cohort. The area under the curve (AUC) of the nomogram model was 0.954 (85.1% sensitivity, 91.1% specificity, and 87.4% accuracy) in the training cohort and 0.974 (96.7% sensitivity, 92.1% specificity, and 92.6% accuracy) in the validation cohort, both significantly higher than those of the individual models in the corresponding cohorts (Z=3.018-6.653, all P<0.05 and Z=2.546-5.668, all P<0.05).

Conclusion: Combining clinicopathological characteristics with multiparametric MRI parameters significantly improves prediction accuracy for TIL levels in breast cancer. This integrated model holds considerable clinical potential, providing robust support for personalised treatment strategies.

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来源期刊
Academic Radiology
Academic Radiology 医学-核医学
CiteScore
7.60
自引率
10.40%
发文量
432
审稿时长
18 days
期刊介绍: Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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