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Abstract：
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.
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.
摘要：
背景：多项研究表明，磁共振成像影像组学可以预测乳腺癌患者的生存率，但是潜在的生物基础仍然模糊不清。在这里，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.
方法：在这项多中心研究中，纳入1113例非转移性浸润性乳腺癌患者，并分为训练组(n=698)，验证队列(n=171)，和测试队列(n=244)。使用Cox比例风险深度神经网络DeepSurv构建RadiomicDeepSurvNet（RDeepNet）模型，用于预测个体复发风险。进行RNA测序以探索放射组学与肿瘤微环境之间的关联。进行相关性和方差分析，以检查具有不同治疗反应的患者和新辅助化疗后的影像组学变化。进一步分析了影像组学与表观遗传分子特征的关联和定量关系，以揭示影像组学的作用机制。
结果：RDeepNet模型显示与无复发生存期（RFS）显着相关（HR0.03，95％CI0.02-0.06，P<0.001），并且在1-，2-,和3年RFS，分别。在验证和测试队列中，RDeepNet模型还可以将患者分为高风险和低风险组，3年RFS的AUC分别为0.91和0.94，分别。放射学特征显示两个风险组之间的差异表达。此外，RDeepNet模型在不同分子亚型和采用不同治疗方案的患者人群中的普适性得到证实(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患者的影像学特征变化。重要的是,发现放射组学和长链非编码RNA(lncRNA)之间存在显著的相关性.通过基于深度学习的放射组学预测模型，发现关键的lncRNA是非侵入性定量的，在训练队列中AUC为0.79，在测试队列中AUC为0.77。
结论：这项研究表明，MRI的机器学习影像组学可以有效预测乳腺癌患者术后的RFS，并强调了使用放射组学对lncRNAs进行非侵入性定量的可行性，这表明影像组学在指导治疗决策方面的潜力。
方法：在这项多中心研究中，纳入1113例非转移性浸润性乳腺癌患者，并分为训练组(n=698)，验证队列(n=171)，和测试队列(n=244)。使用Cox比例风险深度神经网络DeepSurv构建RadiomicDeepSurvNet（RDeepNet）模型，用于预测个体复发风险。进行RNA测序以探索放射组学与肿瘤微环境之间的关联。进行相关性和方差分析，以检查具有不同治疗反应的患者和新辅助化疗后的影像组学变化。进一步分析了影像组学与表观遗传分子特征的关联和定量关系，以揭示影像组学的作用机制。
结果：RDeepNet模型显示与无复发生存期（RFS）显着相关（HR0.03，95％CI0.02-0.06，P<0.001），并且在1-，2-,和3年RFS，分别。在验证和测试队列中，RDeepNet模型还可以将患者分为高风险和低风险组，3年RFS的AUC分别为0.91和0.94，分别。放射学特征显示两个风险组之间的差异表达。此外，RDeepNet模型在不同分子亚型和采用不同治疗方案的患者人群中的普适性得到证实(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患者的影像学特征变化。重要的是,发现放射组学和长链非编码RNA(lncRNA)之间存在显著的相关性.通过基于深度学习的放射组学预测模型，发现关键的lncRNA是非侵入性定量的，在训练队列中AUC为0.79，在测试队列中AUC为0.77。
结论：这项研究表明，MRI的机器学习影像组学可以有效预测乳腺癌患者术后的RFS，并强调了使用放射组学对lncRNAs进行非侵入性定量的可行性，这表明影像组学在指导治疗决策方面的潜力。
