多项研究表明，磁共振成像放射组学可以预测乳腺癌患者的生存率，但潜在的生物学基础仍然不明确。在此，我们的目标是开发一个可解释的基于深度学习的网络，用于对复发风险进行分类并揭示潜在的生物学机制。在这项多中心研究中，纳入了 1113 名非转移性浸润性乳腺癌患者，并分为训练队列（n = 698 ）、验证队列（n = 171）和测试队列（n = 244）。 Radiomic DeepSurv Net (RDeepNet) 模型是使用 Cox 比例风险深度神经网络 DeepSurv 构建的，用于预测个体复发风险。进行 RNA 测序是为了探索放射组学和肿瘤微环境之间的关联。进行相关性和方差分析来检查不同治疗反应的患者和新辅助化疗后的放射组学变化。进一步分析放射组学和表观遗传分子特征的关联和定量关系，揭示放射组学的作用机制。RDeepNet模型显示与无复发生存（RFS）显着相关（HR 0.03，95% CI 0.02-0.06，P < 0.001） ），1 年、2 年和 3 年 RFS 的 AUC 分别为 0.98、0.94 和 0.92。在验证和测试队列中，RDeepNet 模型还可以将患者分为高风险组和低风险组，并证明 3 年 RFS 的 AUC 分别为 0.91 和 0.94。放射组学特征显示两个风险组之间的差异表达。此外，RDeepNet 模型的普适性在不同分子亚型和不同治疗方案的患者群体中得到了证实（所有 P < 0.001）。该研究还确定了具有不同治疗反应和新辅助化疗后的患者的放射组学特征的差异。重要的是，发现放射组学和长非编码 RNA (lncRNA) 之间存在显着相关性。发现一个关键的 lncRNA 可通过基于深度学习的放射组学预测模型进行无创量化，训练队列中的 AUC 为 0.79，测试队列中的 AUC 为 0.77。本研究表明 MRI 的机器学习放射组学可以有效预测患者手术后的 RFS与乳腺癌，并强调了使用放射组学对 lncRNA 无创定量的可行性，这表明放射组学在指导治疗决策方面的潜力。© 2023。作者。

Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms.In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics.The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort.This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.© 2023. The Author(s).