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研究领域生物医学和临床科学瘤学和致癌症预测和预后标志物
结合多序MRI放射学和息地成像的融合模型,用于预测新辅助疗法治疗的乳腺癌的病理完整反应

结合多序MRI放射学和息地成像的融合模型,用于预测新辅助疗法治疗的乳腺癌的病理完整反应

Shaojie Xu ¹, Yushi Ying ¹, Qilan Hu ², Xingyin Li ³, Yulin Li ⁴, Hao Xiong ⁴, Yanyan Chen ⁴, Qing Ye ⁴, Xingrui Li ¹, Yue Liu ⁵, Tao Ai ⁶, Yaying Du ⁷

Shaojie Xu ¹, Yushi Ying ¹, Qilan Hu ²

¹Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China.
²Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China.
³Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People's Republic of China.
⁴Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China.
⁵Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China. liuyue@hust.edu.cn.
⁶Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China. aitao007@hotmail.com.
⁷Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China. yayingdu@hust.edu.cn.

⁰Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, People's Republic of China.

Cancer Imaging : the Official Publication of the International Cancer Imaging Society +

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2025年八月29日

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在PubMed上查看摘要

概括

此摘要是机器生成的。

一个结合MRI放射学,深度学习和息地成像的新模型准确地预测接受新辅助疗法 (NAT) 的乳腺癌患者的病理完整反应 (pCR). 这种融合模式为个性化治疗决策提供了一个非侵入性的工具.

科学领域

癌症学
放射学
人工智能

背景情况

接受新辅助疗法 (NAT) 的乳腺癌患者需要准确预测病理完整反应 (pCR).
目前的预测方法可能无法完全捕捉瘤异质性和治疗反应.
整合多模式成像数据可能会提高预测准确性.

研究的目的

在接受NAT的乳腺癌患者中开发和验证pCR的预测模型.
整合多序MRI放射学,深度学习功能和息地成像以提高预测.
评估模型的性能和可用于临床应用的解释性.

主要方法

对203名接受NAT治疗的乳腺癌患者进行了回顾性分析.
从多序MRI (T2WI,DWI,DCE-MRI) 和息地成像分析中提取放射性特征.
使用ROC,PR曲线,DCA和可解释性技术 (SHAP,LIME) 的融合机器学习模型的开发和验证.

主要成果

聚变模型在试验组中显示出优异的预测性能,AUC为0. 913.
与单个区域模型相比,观察到精确回忆平衡和更高的临床益处.
SHAP分析确定了影响pCR预测的关键放射性特征,提高了模型的可解释性.

结论

整合多维MRI特征和息地成像可以显著提高乳腺癌的pCR预测.
开发的融合模型是指导个性化的新辅助治疗策略的强大,非侵入性工具.
通过SHAP和LIME的模型解释性支持临床信任和应用.

关键词:

乳腺癌异质性磁共振新辅助疗法辐射学

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Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...

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