传统的手工制作放射癌学特征是从磁共振成像(MRI)的肿瘤中提取的，一直被认为是浅显且低级的。近年来，深度学习技术的最新进展表明，从肿瘤图像自动提取的高阶深度放射癌学特征可以更有效地捕获肿瘤的异质性。我们假设基于MRI的深度放射癌表型与乳腺癌肿瘤的分子表型存在显著关联。我们的目标是从MRI中确定深度放射学特征(DRFs)，评估它们在预测乳腺癌(Breast Cancer，BC)临床特征中的意义，并探讨它们与多层次基因组因素的关联。 为了提取4,096个DRFs，我们构建了一个去噪自编码器，从110个BC患者的MRI中回溯提取。这些DRFs经过可视化和聚类处理。线性混合效应模型用于测试它们与同一患者mRNA表达谱中提取的多层次基因组特征(风险基因、基因签名和生物通路活性)之间的关联。使用“最小绝对收缩和选择算子”模型来识别每种临床特征(肿瘤大小(T)、淋巴结转移(N)、雌激素受体(ER)、孕激素受体(PR)和人表皮生长因子受体2(HER2)状态)的最有预测意义的DRFs。 与之前的研究提供的87个BC患者的36个传统放射癌特征(CRFs)进行比较。超过1,000个DRFs与风险基因、基因签名和生物通路活性相关(adjust P-值< 0.05)。 DRFs在使用DRFs进行预测时表现更好，可预测T、N、ER、PR和HER2状态(AUC > 0.9)。这些DRFs表现出显著的患者分层能力，并与相关的生物学和临床特征相关。作为对比，只有8个风险基因与CRFs相关。在预测临床特征方面，RFs表现不及DRFs。 基于深度学习的MRI特征在预测BC临床特征方面表现更好，且与GFs的关联更显著，这比传统的半自动MRI特征更好。我们的放射基因组方法为确定基于MRI的成像签名铺平了潜在途径，以发现调节特定肿瘤表型的遗传机制，并可能加速新型成像模式的转化为个性化医学。©2023年，作者(们)。

It has been believed that traditional handcrafted radiomic features extracted from magnetic resonance imaging (MRI) of tumors are normally shallow and low-ordered. Recent advancement in deep learning technology shows that the high-order deep radiomic features extracted automatically from tumor images can capture tumor heterogeneity in a more efficient way. We hypothesize that MRI-based deep radiomic phenotypes have significant associations with molecular profiles of breast cancer tumors. We aim to identify deep radiomic features (DRFs) from MRI, evaluate their significance in predicting breast cancer (BC) clinical characteristics and explore their associations with multi-level genomic factors.A denoising autoencoder was built to retrospectively extract 4,096 DRFs from 110 BC patients' MRI. Visualization and clustering were applied to these DRFs. Linear Mixed Effect models were used to test their associations with multi-level genomic features (GFs) (risk genes, gene signatures, and biological pathway activities) extracted from the same patients' mRNA expression profile. A Least Absolute Shrinkage and Selection Operator model was used to identify the most predictive DRFs for each clinical characteristic (tumor size (T), lymph node metastasis (N), estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status).Thirty-six conventional radiomic features (CRFs) for 87 of the 110 BC patients provided by a previous study were used for comparison. More than 1,000 DRFs were associated with the risk genes, gene signatures, and biological pathways activities (adjusted P-value < 0.05). DRFs produced better performance in predicting T, N, ER, PR, and HER2 status (AUC > 0.9) using DRFs. These DRFs showed significant powers of stratifying patients, linking to relevant biological and clinical characteristics. As a contrast, only eight risk genes were associated with CRFs. The RFs performed worse in predicting clinical characteristics than DRFs.The deep learning-based auto MRI features perform better in predicting BC clinical characteristics, which are more significantly associated with GFs than traditional semi-auto MRI features. Our radiogenomic approach for identifying MRI-based imaging signatures may pave potential pathways for the discovery of genetic mechanisms regulating specific tumor phenotypes and may enable a more rapid innovation of novel imaging modalities, hence accelerating their translation to personalized medicine.© 2023. The Author(s).