乳腺癌病理完全缓解的临床预测:机器学习研究。

IF 3.4 2区 医学 Q2 ONCOLOGY
Chongwu He, Tenghua Yu, Liu Yang, Longbo He, Jin Zhu, Jing Chen
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引用次数: 0

摘要

背景:本研究旨在建立和验证机器学习模型,以预测乳腺癌患者新辅助治疗后的病理完全缓解(pCR)。方法:对1143例患者的临床和病理资料进行分析,包括年龄、性别、婚姻状况、组织学分级、T分期、N分期、诊断至治疗月数、分子亚型、对新辅助治疗的反应等变量。使用内部和外部数据集训练和验证了七个机器学习模型。使用多个指标评估模型性能,并进行可解释性分析以评估特征的重要性。结果:影响pCR的关键变量包括分级、N分期、从诊断到治疗的月数和分子亚型。朴素贝叶斯模型的准确率(0.746)、灵敏度(0.699)、特异性(0.808)、F1评分(0.759)均优于其他模型,是最有效的模型。内部和外部验证均证实了该模型的稳健预测能力。为临床使用开发了一个网络工具,帮助制定个性化的治疗计划。可解释性分析进一步阐明了特征对pCR预测的贡献,提高了临床适用性。结论:朴素贝叶斯模型为乳腺癌患者接受新辅助治疗的个性化治疗决策提供了一个强大的工具。通过准确预测pCR率,它使临床医生能够定制治疗策略,潜在地改善结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clinical prediction of pathological complete response in breast cancer: a machine learning study.

Background: This study aimed to develop and validate machine learning models to predict pathological complete response (pCR) after neoadjuvant therapy in patients with breast cancer patients.

Methods: Clinical and pathological data from 1143 patients were analyzed, encompassing variables such as age, gender, marital status, histologic grade, T stage, N stage, months from diagnosis to treatment, molecular subtype, and response to neoadjuvant therapy. Seven machine learning models were trained and validated using both internal and external datasets. Model performance was evaluated using multiple metrics, and interpretability analysis was conducted to assess feature importance.

Results: Key variables influencing pCR included grade, N stage, months from diagnosis to treatment, and molecular subtype. The Naive Bayes model emerged as the most effective, with accuracy (0.746), sensitivity (0.699), specificity (0.808), and F1 score (0.759) surpassing other models. Both internal and external validation confirmed the model's robust predictive power. A web tool was developed for clinical use, aiding in personalized treatment planning. Interpretability analysis further elucidated the contribution of features to pCR prediction, enhancing clinical applicability.

Conclusion: The Naive Bayes model provides a robust tool for personalized treatment decisions in patients with breast cancer undergoing neoadjuvant therapy. By accurately predicting pCR rates, it enables clinicians to tailor treatment strategies, potentially improving outcomes.

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来源期刊
BMC Cancer
BMC Cancer 医学-肿瘤学
CiteScore
6.00
自引率
2.60%
发文量
1204
审稿时长
6.8 months
期刊介绍: BMC Cancer is an open access, peer-reviewed journal that considers articles on all aspects of cancer research, including the pathophysiology, prevention, diagnosis and treatment of cancers. The journal welcomes submissions concerning molecular and cellular biology, genetics, epidemiology, and clinical trials.
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