使用基于MRI的神经网络对乳腺癌新辅助治疗反应进行早期预测：来自ACRIN 6698试验和一项中国前瞻性队列研究的数据

Early prediction of neoadjuvant therapy response in breast cancer using MRI-based neural networks: data from the ACRIN 6698 trial and a prospective Chinese cohort.

作者信息

Du Siyao, Xie Wanfang, Gao Si, Zhao Ruimeng, Wang Huidong, Tian Jie, Liu Jiangang, Liu Zhenyu, Zhang Lina

机构信息

Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, Liaoning Province, China.

School of Engineering Medicine, Beihang University, Beijing, 100191, People's Republic of China.

出版信息

Breast Cancer Res. 2025 Apr 3;27(1):52. doi: 10.1186/s13058-025-02009-6.

DOI:10.1186/s13058-025-02009-6

PMID:40181457

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11969705/

Abstract

BACKGROUND

Early prediction of treatment response to neoadjuvant therapy (NAT) in breast cancer patients can facilitate timely adjustment of treatment regimens. We aimed to develop and validate a MRI-based enhanced self-attention network (MESN) for predicting pathological complete response (pCR) based on longitudinal images at the early stage of NAT.

METHODS

Two imaging datasets were utilized: a subset from the ACRIN 6698 trial (dataset A, n = 227) and a prospective collection from a Chinese hospital (dataset B, n = 245). These datasets were divided into three cohorts: an ACRIN 6698 training cohort (n = 153) from dataset A, an ACRIN 6698 test cohort (n = 74) from dataset A, and an external test cohort (n = 245) from dataset B. The proposed MESN allowed for the integration of multiple timepoint features and extraction of dynamic information from longitudinal MR images before and after early-NAT. We also constructed the Pre model based on pre-NAT MRI features. Clinicopathological characteristics were added to these image-based models to create integrated models (MESN-C and Pre-C), and their performance was evaluated and compared.

RESULTS

The MESN-C yielded area under the receiver operating characteristic curve (AUC) values of 0.944 (95% CI: 0.906 - 0.973), 0.903 (95%CI: 0.815 - 0.965), and 0.861 (95%CI: 0.811 - 0.906) in the ACRIN 6698 training, ACRIN 6698 test and external test cohorts, respectively, which were significantly higher than those of the clinical model (AUC: 0.720 [95%CI: 0.587 - 0.842], 0.738 [95%CI: 0.669 - 0.796] for the two test cohorts, respectively; p < 0.05) and Pre-C (AUC: 0.697 [95%CI: 0.554 - 0.819], 0.726 [95%CI: 0.666 - 0.797] for the two test cohorts, respectively; p < 0.05). High AUCs of the MESN-C maintained in the ACRIN 6698 standard (AUC = 0.853 [95%CI: 0.676 - 1.000]) and experimental (AUC = 0.905 [95%CI: 0.817 - 0.993]) subcohorts, and the interracial and external subcohort (AUC = 0.861 [95%CI: 0.811 - 0.906]). Moreover, the MESN-C increased the positive predictive value from 48.6 to 71.3% compared with Pre-C model, and maintained a high negative predictive value (80.4-86.7%).

CONCLUSION

The MESN-C using longitudinal multiparametric MRI after a short-term therapy achieved favorable performance for predicting pCR, which could facilitate timely adjustment of treatment regimens, increasing the rates of pCR and avoiding toxic effects.

TRIAL REGISTRATION

Trial registration at https://www.chictr.org.cn/ .

REGISTRATION NUMBER

ChiCTR2000038578, registered September 24, 2020.

摘要

背景

早期预测乳腺癌患者对新辅助治疗（NAT）的反应有助于及时调整治疗方案。我们旨在开发并验证一种基于磁共振成像（MRI）的增强自注意力网络（MESN），用于根据NAT早期的纵向图像预测病理完全缓解（pCR）。

方法

使用了两个成像数据集：ACRIN 6698试验的一个子集（数据集A，n = 227）和一家中国医院的前瞻性收集数据（数据集B，n = 245）。这些数据集被分为三个队列：来自数据集A的ACRIN 6698训练队列（n = 153）、来自数据集A的ACRIN 6698测试队列（n = 74）以及来自数据集B的外部测试队列（n = 245）。所提出的MESN允许整合多个时间点特征，并从早期NAT前后的纵向MR图像中提取动态信息。我们还基于NAT前的MRI特征构建了Pre模型。将临床病理特征添加到这些基于图像的模型中以创建整合模型（MESN-C和Pre-C），并对它们的性能进行评估和比较。

结果

MESN-C在ACRIN 6698训练队列、ACRIN 6698测试队列和外部测试队列中的受试者操作特征曲线下面积（AUC）值分别为0.944（95%CI：0.906 - 0.973）、0.903（95%CI：0.815 - 0.965）和0.861（95%CI：0.811 - 0.906），显著高于临床模型（两个测试队列的AUC分别为0.720 [95%CI：0.587 - 0.842]、0.738 [95%CI：0.669 - 0.796]；p < 0.05）和Pre-C（两个测试队列的AUC分别为0.697 [95%CI：0.554 - 0.819]、0.726 [95%CI：0.666 - 0.797]；p < 0.05）。MESN-C在ACRIN 6698标准亚组（AUC = 0.853 [95%CI：0.676 - 1.000]）、实验亚组（AUC = 0.905 [95%CI：0.817 - 0.993]）以及跨种族和外部亚组（AUC = 0.861 [95%CI：0.811 - 0.906]）中均保持较高的AUC值。此外，与Pre-C模型相比，MESN-C将阳性预测值从48.6%提高到了71.3%，并保持了较高的阴性预测值（80.4 - 86.7%）。