Fusion model integrating multi-sequence MRI radiomics and habitat imaging for predicting pathological complete response in breast cancer treated with neoadjuvant therapy.

IF 3.5 2区 医学 Q2 ONCOLOGY
Shaojie Xu, Yushi Ying, Qilan Hu, Xingyin Li, Yulin Li, Hao Xiong, Yanyan Chen, Qing Ye, Xingrui Li, Yue Liu, Tao Ai, Yaying Du
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引用次数: 0

Abstract

Background: This study aimed to develop a predictive model integrating multi-sequence MRI radiomics, deep learning features, and habitat imaging to forecast pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant therapy (NAT).

Methods: A retrospective analysis included 203 breast cancer patients treated with NAT from May 2018 to January 2023. Patients were divided into training (n = 162) and test (n = 41) sets. Radiomics features were extracted from intratumoral and peritumoral regions in multi-sequence MRI (T2WI, DWI, and DCE-MRI) datasets. Habitat imaging was employed to analyze tumor subregions, characterizing heterogeneity within the tumor. We constructed and validated machine learning models, including a fusion model integrating all features, using Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves, decision curve analysis (DCA), and confusion matrices. Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analyses were performed for model interpretability.

Results: The fusion model achieved superior predictive performance compared to single-region models, with AUCs of 0.913 (95% CI: 0.770-1.000) in the test set. PR curve analysis showed improved precision-recall balance, while DCA indicated higher clinical benefit. Confusion matrix analysis confirmed the model's classification accuracy. SHAP revealed DCE_LLL_DependenceUniformity as the most critical feature for predicting pCR and PC72 for non-pCR. LIME provided patient-specific insights into feature contributions.

Conclusion: Integrating multi-dimensional MRI features with habitat imaging enhances pCR prediction in breast cancer. The fusion model offers a robust, non-invasive tool for guiding individualized treatment strategies while providing transparent interpretability through SHAP and LIME analyses.

结合多序列MRI放射组学和栖息地成像的融合模型预测乳腺癌新辅助治疗的病理完全缓解。
背景:本研究旨在建立一种综合多序列MRI放射组学、深度学习特征和栖息地成像的预测模型,以预测乳腺癌新辅助治疗(NAT)患者的病理完全缓解(pCR)。方法:回顾性分析2018年5月至2023年1月期间接受NAT治疗的203例乳腺癌患者。患者分为训练组(n = 162)和测试组(n = 41)。从多序列MRI (T2WI、DWI和DCE-MRI)数据集中提取肿瘤内和肿瘤周围区域的放射组学特征。栖息地成像用于分析肿瘤亚区,表征肿瘤内的异质性。我们构建并验证了机器学习模型,包括融合所有特征的融合模型,使用接收者工作特征(ROC)和精确召回率(PR)曲线,决策曲线分析(DCA)和混淆矩阵。对模型可解释性进行Shapley加性解释(SHAP)和局部可解释模型不可知论解释(LIME)分析。结果:与单区域模型相比,融合模型取得了更好的预测性能,测试集中的auc为0.913 (95% CI: 0.77 -1.000)。PR曲线分析显示精密度-召回率平衡改善,而DCA显示更高的临床效益。混淆矩阵分析证实了该模型的分类准确性。SHAP显示DCE_LLL_DependenceUniformity是预测pCR和非pCR的最关键特征。LIME提供了针对患者的特性贡献的见解。结论:将MRI的多维特征与栖息地成像相结合,可以增强乳腺癌的pCR预测能力。融合模型为指导个性化治疗策略提供了一个强大的、非侵入性的工具,同时通过SHAP和LIME分析提供了透明的可解释性。
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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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