Solorzano Leslie, Robertson Stephanie, Acs Balazs, Hartman Johan, Rantalainen Mattias
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
Heliyon. 2024 Jun 14;10(12):e32892. doi: 10.1016/j.heliyon.2024.e32892. eCollection 2024 Jun 30.
Accurate detection of invasive breast cancer (IC) can provide decision support to pathologists as well as improve downstream computational analyses, where detection of IC is a first step. Tissue containing IC is characterized by the presence of specific morphological features, which can be learned by convolutional neural networks (CNN). Here, we compare the use of a single CNN model versus an ensemble of several base models with the same CNN architecture, and we evaluate prediction performance as well as variability across ensemble based model predictions. Two in-house datasets comprising 587 whole slide images (WSI) are used to train an ensemble of ten InceptionV3 models whose consensus is used to determine the presence of IC. A novel visualisation strategy was developed to communicate ensemble agreement spatially. Performance was evaluated in an internal test set with 118 WSIs, and in an additional external dataset (TCGA breast cancer) with 157 WSI. We observed that the ensemble-based strategy outperformed the single CNN-model alternative with respect to accuracy on tile level in 89 % of all WSIs in the test set. The overall accuracy was 0.92 (DICE coefficient, 0.90) for the ensemble model, and 0.85 (DICE coefficient, 0.83) for the single CNN alternative in the internal test set. For TCGA the ensemble outperformed the single CNN in 96.8 % of the WSI, with an accuracy of 0.87 (DICE coefficient 0.89), the single model provides an accuracy of 0.75 (DICE coefficient 0.78). The results suggest that an ensemble-based modeling strategy for breast cancer invasive cancer detection consistently outperforms the conventional single model alternative. Furthermore, visualisation of the ensemble agreement and confusion areas provide direct visual interpretation of the results. High performing cancer detection can provide decision support in the routine pathology setting as well as facilitate downstream computational analyses.
准确检测浸润性乳腺癌（IC）可为病理学家提供决策支持，并改善下游的计算分析，其中IC的检测是第一步。含有IC的组织具有特定的形态学特征，卷积神经网络（CNN）可以学习这些特征。在此，我们比较了使用单个CNN模型与使用具有相同CNN架构的多个基础模型的集成模型，并且我们评估了预测性能以及基于集成模型预测的变异性。使用包含587张全切片图像（WSI）的两个内部数据集来训练由十个InceptionV3模型组成的集成模型，其一致性用于确定IC的存在。开发了一种新颖的可视化策略来在空间上传达集成模型的一致性。在具有118个WSI的内部测试集以及具有157个WSI的另一个外部数据集（TCGA乳腺癌）中评估性能。我们观察到，在测试集中89%的所有WSI中，基于集成模型的策略在切片水平上的准确性优于单个CNN模型。在内部测试集中，集成模型的总体准确率为0.92（DICE系数，0.90），单个CNN模型的准确率为0.85（DICE系数，0.83）。对于TCGA，集成模型在96.8%的WSI中优于单个CNN模型，准确率为0.87（DICE系数0.89），单个模型的准确率为0.75（DICE系数0.78）。结果表明，基于集成模型的乳腺癌浸润癌检测建模策略始终优于传统的单个模型。此外，集成模型的一致性和混淆区域的可视化提供了对结果的直接视觉解释。高性能的癌症检测可以在常规病理环境中提供决策支持，并促进下游的计算分析。
