基于abvs的放射组学早期预测乳腺癌患者新辅助化疗的疗效。

IF 3.3 4区 医学 Q2 ONCOLOGY
Wei Jiang, Xiaofei Deng, Ting Zhu, Jing Fang, Jinyao Li
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引用次数: 0

摘要

背景:新辅助化疗(NAC)在乳腺癌(BC)治疗中起着重要作用;然而,其疗效因患者而异。目前的评估方法可能导致延迟治疗改变,传统的成像方式往往产生不准确的结果。放射组学是医学影像学的一个新兴领域,为改进肿瘤特征和个性化医疗提供了潜力。然而,它在早期和准确预测NAC反应中的应用仍未得到充分研究。目的:本研究旨在开发一种基于自动乳腺体积扫描仪(ABVS)的放射组学模型,以促进早期发现次优NAC反应,最终促进BC患者的个性化治疗方法。方法:本回顾性研究纳入248例接受NAC治疗的BC患者。遵循标准指南,并根据治疗结果将患者分为反应者或无反应者。在NAC之前和期间获得ABVS图像,并使用PyRadiomics工具包提取放射组学特征。评估了观察者间一致性和分层特征选择。三种机器学习分类器,逻辑回归,支持向量机和随机森林,使用三次重复的五倍交叉验证进行训练和验证。基于鉴别、校准和临床效用对模型性能进行综合评估。结果:248例BC患者中,157例(63.3%)有应答,91例(36.7%)无应答。放射组学特征选择显示了7个NAC前特征和6个NAC后特征,NAC后特征的权重高于NAC前特征(最小>0.05)(最大)。结论:基于ABVS的放射组学模型能有效预测BC患者的次优NAC反应,早期NAC后分类器在鉴别和临床应用方面优于NAC前分类器。它可以加强个性化治疗,改善BC治疗的患者预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers.

ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers.

ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers.

ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers.

Background: Neoadjuvant chemotherapy (NAC) plays a significant role in breast cancer (BC) management; however, its efficacy varies among patients. Current evaluation methods may lead to delayed treatment alterations, and traditional imaging modalities often yield inaccurate results. Radiomics, an emerging field in medical imaging, offers potential for improved tumor characterization and personalized medicine. Nevertheless, its application in early and accurately predicting NAC response remains underinvestigated.

Objective: This study aims to develop an automated breast volume scanner (ABVS)-based radiomics model to facilitate early detection of suboptimal NAC response, ultimately promoting personalized therapeutic approaches for BC patients.

Methods: This retrospective study involved 248 BC patients receiving NAC. Standard guidelines were followed, and patients were classified as responders or non-responders based on treatment outcomes. ABVS images were obtained before and during NAC, and radiomics features were extracted using the PyRadiomics toolkit. Inter-observer consistency and hierarchical feature selection were assessed. Three machine learning classifiers, logistic regression, support vector machine, and random forest, were trained and validated using a five-fold cross-validation with three repetitions. Model performance was comprehensively evaluated based on discrimination, calibration, and clinical utility.

Results: Of the 248 BC patients, 157 (63.3%) were responders, and 91 (36.7%) were non-responders. Radiomics feature selection revealed 7 pre-NAC and 6 post-NAC ABVS features, with higher weights for post-NAC features (min >0.05) than pre-NAC (max <0.03). The three post-NAC classifiers demonstrated AUCs of approximately 0.9, indicating excellent discrimination. DCA curves revealed a substantial net benefit when the threshold probability exceeded 40%. Conversely, the three pre-NAC classifiers had AUCs between 0.7 and 0.8, suggesting moderate discrimination and limited clinical utility based on their DCA curves.

Conclusion: The ABVS-based radiomics model effectively predicted suboptimal NAC responses in BC patients, with early post-NAC classifiers outperforming pre-NAC classifiers in discrimination and clinical utility. It could enhance personalized treatment and improve patient outcomes in BC management.

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来源期刊
CiteScore
4.10
自引率
0.00%
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审稿时长
16 weeks
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