组织学分级和分子亚型在确定个性化或精准医学中具有重要参考价值，作为代表浸润性乳腺癌（IBC）生物行为的重要预后指标。 为了评估一个结合分子亚型（MS）信息的IBC分级的两阶段深度学习框架，使用DCE-MRI。 在第一阶段，开发了一种名为IOS2-DA的创新神经网络，其中包括一个具有池化层（DA）的稠密浮动空间金字塔池化块和具有双核心挤压特征的内核-八方块（IOS2）。 该方法侧重于IBC分级的影像表现，使用一种新颖的F1得分损失函数进行初步预测。 在第二阶段，引入了一个MS注意分支，通过Kullback-Leibler散度对IOS2-DA提取的整合深度向量进行微调。 通过集成学习进行肿瘤分级预测的三个MRI造影后系列对分类值进行分析，将MS引导信息与初步结果进行加权，经由接受者操作特征分析被定量评估。DeLong检验用于测量统计学显著性（P < 0.05）。在准确度（0.927），精确度（0.942），AUC（0.927，95％置信区间：[0.908，0.946]）和F1分数（0.930）方面，以分子亚型引导的IOS2-DA显著优于单一的IOS2-DA。梯度加权类激活图显示从IOS2-DA提取的特征表示与肿瘤区域一致。IOS2-DA阐释了其在非侵入性肿瘤分级预测中的潜力。关于MS和组织学分级之间的相关性，它在将相关临床生物标志物应用于增强IBC分级的诊断效能方面展现出显着的临床前景。因此，DCE-MRI倾向于成为对乳腺生物行为和癌症预后进行全面术前评估的可行成像方法。 版权所有 © 2023。 Elsevier B.V.出版。

Histological grade and molecular subtype have presented valuable references in assigning personalized or precision medicine as the significant prognostic indicators representing biological behaviors of invasive breast cancer (IBC). To evaluate a two-stage deep learning framework for IBC grading that incorporates with molecular-subtype (MS) information using DCE-MRI.In Stage I, an innovative neural network called IOS2-DA is developed, which includes a dense atrous-spatial pyramid pooling block with a pooling layer (DA) and inception-octconved blocks with double kernel squeeze-and-excitations (IOS2). This method focuses on the imaging manifestation of IBC grades and performs preliminary prediction using a novel class F1-score loss function. In Stage II, a MS attention branch is introduced to fine-tune the integrated deep vectors from IOS2-DA via Kullback-Leibler divergence. The MS-guided information is weighted with preliminary results to obtain classification values, which are analyzed by ensemble learning for tumor grade prediction on three MRI post-contrast series. Objective assessment is quantitatively evaluated by receiver operating characteristic curve analysis. DeLong test is applied to measure statistical significance (P < 0.05).The molecular-subtype guided IOS2-DA performs significantly better than the single IOS2-DA in terms of accuracy (0.927), precision (0.942), AUC (0.927, 95% CI: [0.908, 0.946]), and F1-score (0.930). The gradient-weighted class activation maps show that the feature representations extracted from IOS2-DA are consistent with tumor areas.IOS2-DA elucidates its potential in non-invasive tumor grade prediction. With respect to the correlation between MS and histological grade, it exhibits remarkable clinical prospects in the application of relevant clinical biomarkers to enhance the diagnostic effectiveness of IBC grading. Therefore, DCE-MRI tends to be a feasible imaging modality for the thorough preoperative assessment of breast biological behavior and carcinoma prognosis.Copyright © 2023. Published by Elsevier B.V.