Guo Jianming, Chen Baihui, Cao Hongda, Dai Quan, Qin Ling, Zhang Jinfeng, Zhang Youxue, Zhang Huanyu, Sui Yuan, Chen Tianyu, Yang Dongxu, Gong Xue, Li Dalin
Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150000, Harbin, China.
School of Computer, Beihang University, 100191, Beijing, China.
NPJ Precis Oncol. 2024 Sep 5;8(1):189. doi: 10.1038/s41698-024-00678-8.
Pathological complete response (pCR) serves as a critical measure of the success of neoadjuvant chemotherapy (NAC) in breast cancer, directly influencing subsequent therapeutic decisions. With the continuous advancement of artificial intelligence, methods for early and accurate prediction of pCR are being extensively explored. In this study, we propose a cross-modal multi-pathway automated prediction model that integrates temporal and spatial information. This model fuses digital pathology images from biopsy specimens and multi-temporal ultrasound (US) images to predict pCR status early in NAC. The model demonstrates exceptional predictive efficacy. Our findings lay the foundation for developing personalized treatment paradigms based on individual responses. This approach has the potential to become a critical auxiliary tool for the early prediction of NAC response in breast cancer patients.
病理完全缓解（pCR）是衡量乳腺癌新辅助化疗（NAC）成功与否的关键指标，直接影响后续治疗决策。随着人工智能的不断发展，早期准确预测pCR的方法正在被广泛探索。在本研究中，我们提出了一种整合时空信息的跨模态多途径自动预测模型。该模型融合活检标本的数字病理图像和多时间点超声（US）图像，以在NAC早期预测pCR状态。该模型显示出卓越的预测效能。我们的研究结果为基于个体反应制定个性化治疗模式奠定了基础。这种方法有可能成为乳腺癌患者NAC反应早期预测的关键辅助工具。
