基于超声放射组学的低her2乳腺癌患者新辅助化疗疗效预测

IF 3.5 2区 医学 Q2 ONCOLOGY
Qing Peng, Ziyao Ji, Nan Xu, Zixian Dong, Tian Zhang, Mufei Ding, Le Qu, Yimo Liu, Jun Xie, Feng Jin, Bo Chen, Jiangdian Song, Ang Zheng
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引用次数: 0

摘要

背景:新辅助化疗(NAC)是治疗乳腺癌的重要治疗方法,但准确预测治疗反应仍然是一个重大的临床挑战。常规超声在评估肿瘤形态方面起着至关重要的作用,但缺乏定量捕获肿瘤内异质性的能力。超声放射组学提取高通量定量成像特征,为提高NAC反应预测提供了新方法。本研究旨在评估基于治疗前、治疗后及综合影像学特征的超声放射组学模型对低her2乳腺癌患者NAC反应的预测效果。方法:这项回顾性多中心研究纳入了2016年1月1日至2020年12月31日期间接受NAC治疗的359例her2低乳腺癌患者。从治疗前后的超声图像中提取了488个放射学特征。特征选择分两个阶段进行:首先,采用Pearson相关分析(阈值为0.65)去除高相关特征,减少冗余;然后,采用递归特征消除与交叉验证(RFECV)识别最优特征子集进行模型构建;数据集分为训练集(244例患者)和外部验证集(来自独立中心的115例患者)。通过受试者工作特征曲线下面积(AUC)、准确度、精密度、召回率和F1评分来评估模型的性能。结果:初步建立了3个模型:(1)预处理模型(AUC = 0.716),(2)处理后模型(AUC = 0.772),(3)处理前后联合模型(AUC = 0.762)。为了增强特征选择,采用递归特征消除交叉验证,得到了特征集减少的优化模型:(1)预处理模型(AUC = 0.746),(2)后处理模型(AUC = 0.712),(3)组合模型(AUC = 0.759)。结论:超声放射组学是预测低her2乳腺癌新辅助化疗反应的一种无创且有前景的方法。经过特征选择后,预处理模型的性能较为可靠。虽然联合模型并没有显著提高预测准确性,但其稳定的性能表明纵向超声成像可能有助于捕获治疗引起的表型变化。这些发现为个性化治疗决策提供了初步支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of neoadjuvant chemotherapy efficacy in patients with HER2-low breast cancer based on ultrasound radiomics.

Background: Neoadjuvant chemotherapy (NAC) is a crucial therapeutic approach for treating breast cancer, yet accurately predicting treatment response remains a significant clinical challenge. Conventional ultrasound plays a vital role in assessing tumor morphology but lacks the ability to quantitatively capture intratumoral heterogeneity. Ultrasound radiomics, which extracts high-throughput quantitative imaging features, offers a novel approach to enhance NAC response prediction. This study aims to evaluate the predictive efficacy of ultrasound radiomics models based on pre-treatment, post-treatment, and combined imaging features for assessing the NAC response in patients with HER2-low breast cancer.

Methods: This retrospective multicenter study included 359 patients with HER2-low breast cancer who underwent NAC between January 1, 2016, and December 31, 2020. A total of 488 radiomic features were extracted from pre- and post-treatment ultrasound images. Feature selection was conducted in two stages: first, Pearson correlation analysis (threshold: 0.65) was applied to remove highly correlated features and reduce redundancy; then, Recursive Feature Elimination with Cross-Validation (RFECV) was employed to identify the optimal feature subset for model construction. The dataset was divided into a training set (244 patients) and an external validation set (115 patients from independent centers). Model performance was assessed via the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score.

Results: Three models were initially developed: (1) a pre-treatment model (AUC = 0.716), (2) a post-treatment model (AUC = 0.772), and (3) a combined pre- and post-treatment model (AUC = 0.762).To enhance feature selection, Recursive Feature Elimination with Cross-Validation was applied, resulting in optimized models with reduced feature sets: (1) the pre-treatment model (AUC = 0.746), (2) the post-treatment model (AUC = 0.712), and (3) the combined model (AUC = 0.759).

Conclusions: Ultrasound radiomics is a non-invasive and promising approach for predicting response to neoadjuvant chemotherapy in HER2-low breast cancer. The pre-treatment model yielded reliable performance after feature selection. While the combined model did not substantially enhance predictive accuracy, its stable performance suggests that longitudinal ultrasound imaging may help capture treatment-induced phenotypic changes. These findings offer preliminary support for individualized therapeutic decision-making.

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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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