基于多区域动态对比增强磁共振成像的综合系统,用于预测乳腺癌腋窝淋巴结对新辅助化疗的病理完全反应:多中心研究。

IF 9.7 1区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
EBioMedicine Pub Date : 2024-09-01 Epub Date: 2024-08-26 DOI:10.1016/j.ebiom.2024.105311
Ziyin Li, Jing Gao, Heng Zhou, Xianglin Li, Tiantian Zheng, Fan Lin, Xiaodong Wang, Tongpeng Chu, Qi Wang, Simin Wang, Kun Cao, Yun Liang, Feng Zhao, Haizhu Xie, Cong Xu, Haicheng Zhang, Qingliang Niu, Heng Ma, Ning Mao
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引用次数: 0

摘要

背景:准确评估乳腺癌患者腋窝淋巴结(ALN)对新辅助化疗(NAC)的反应具有重要价值。本研究旨在开发一种人工智能系统,利用多区域动态对比增强磁共振成像(DCE-MRI)和临床病理特征预测乳腺癌新辅助化疗后腋窝病理完全反应(pCR):本研究纳入了2018年5月至2023年12月期间来自中国6个医疗中心的回顾性和前瞻性数据集。建立了一个基于深度学习的全自动集成系统(FAIS-DL),依次进行肿瘤和ALN分割以及腋窝pCR预测。使用接收者操作特征曲线下面积(AUC)、准确性、灵敏度和特异性评估了FAIS-DL的预测性能。对45名患者进行了RNA测序分析,以探索FAIS-DL的生物学基础:共评估了 1145 名患者(平均年龄为 50 岁 ±10 [SD])。在这些患者中,506 人进入训练和验证集(腋窝 pCR 率为 40.3%),127 人进入内部测试集(腋窝 pCR 率为 37.8%),414 人进入外部集合测试集(腋窝 pCR 率为 48.8%),98 人进入前瞻性测试集(腋窝 pCR 率为 43.9%)。在预测腋窝 pCR 方面,FAIS-DL 在内部测试集、外部集合测试集和前瞻性测试集中的 AUC 分别达到了 0.95、0.93 和 0.94,也明显高于临床模型和基于单区域 DCE-MRI 的深度学习模型(均为 P):FAIS-DL在预测腋窝pCR方面表现令人满意,可指导临床实践中乳腺癌患者个性化治疗方案的制定:本研究得到了国家自然科学基金(82371933)、山东省自然科学基金(ZR2021MH120)、泰山学者和青年专家项目(tsqn202211378)、中国医药教育协会重点项目(2022KTM030)、中国博士后科学基金(314730)和北京市博士后科研基金(2023-zz-012)的资助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiregional dynamic contrast-enhanced MRI-based integrated system for predicting pathological complete response of axillary lymph node to neoadjuvant chemotherapy in breast cancer: multicentre study.

Background: The accurate evaluation of axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer holds great value. This study aimed to develop an artificial intelligence system utilising multiregional dynamic contrast-enhanced MRI (DCE-MRI) and clinicopathological characteristics to predict axillary pathological complete response (pCR) after NAC in breast cancer.

Methods: This study included retrospective and prospective datasets from six medical centres in China between May 2018 and December 2023. A fully automated integrated system based on deep learning (FAIS-DL) was built to perform tumour and ALN segmentation and axillary pCR prediction sequentially. The predictive performance of FAIS-DL was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RNA sequencing analysis were conducted on 45 patients to explore the biological basis of FAIS-DL.

Findings: 1145 patients (mean age, 50 years ±10 [SD]) were evaluated. Among these patients, 506 were in the training and validation sets (axillary pCR rate of 40.3%), 127 in the internal test set (axillary pCR rate of 37.8%), 414 in the pooled external test set (axillary pCR rate of 48.8%), and 98 in the prospective test set (axillary pCR rate of 43.9%). For predicting axillary pCR, FAIS-DL achieved AUCs of 0.95, 0.93, and 0.94 in the internal test set, pooled external test set, and prospective test set, respectively, which were also significantly higher than those of the clinical model and deep learning models based on single-regional DCE-MRI (all P < 0.05, DeLong test). In the pooled external and prospective test sets, the FAIS-DL decreased the unnecessary axillary lymph node dissection rate from 47.9% to 6.8%, and increased the benefit rate from 52.2% to 86.5%. RNA sequencing analysis revealed that high FAIS-DL scores were associated with the upregulation of immune-mediated genes and pathways.

Interpretation: FAIS-DL has demonstrated satisfactory performance in predicting axillary pCR, which may guide the formulation of personalised treatment regimens for patients with breast cancer in clinical practice.

Funding: This study was supported by the National Natural Science Foundation of China (82371933), National Natural Science Foundation of Shandong Province of China (ZR2021MH120), Mount Taishan Scholars and Young Experts Program (tsqn202211378), Key Projects of China Medicine Education Association (2022KTM030), China Postdoctoral Science Foundation (314730), and Beijing Postdoctoral Research Foundation (2023-zz-012).

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来源期刊
EBioMedicine
EBioMedicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
17.70
自引率
0.90%
发文量
579
审稿时长
5 weeks
期刊介绍: eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.
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