Malignant mesothelioma is classified into three histological subtypes, Epithelioid, Sarcomatoid, and Biphasic according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Biphasic tumors display significant populations of both cell types. This subtyping is subjective and limited by current diagnostic guidelines and can differ even between expert thoracic pathologists when characterising the continuum of relative proportions of epithelioid and sarcomatoid components using a three class system. In this work, we develop a novel dual-task Graph Neural Network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score of all the cells in the sample. The proposed approach uses only core-level labels and frames the prediction task as a dual multiple instance learning (MIL) problem. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multi-centric test set from Mesobank, on which we demonstrate the predictive performance of our model. We validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score, finding that some of the morphological differences identified by our model match known differences used by pathologists. We further show that the model score is predictive of patient survival with a hazard ratio of 2.30. The code for the proposed approach, along with the dataset, is available at: https://github.com/measty/MesoGraph.

翻译：恶性间皮瘤根据肿瘤细胞中上皮样细胞与肉瘤样细胞的相对比例，分为三种组织学亚型：上皮样型、肉瘤样型及双相型。双相型肿瘤具有显著数量的两种细胞类型。这种亚型分类具有主观性，且受限于当前诊断指南，即使在专家胸科病理学家之间，使用三类系统对上皮样与肉瘤样成分连续相对比例进行表征时也可能存在分歧。本研究开发了一种新型双任务图神经网络架构，结合排序损失函数，构建能够对组织区域进行细胞级评分的模型。通过汇总样本中所有细胞的肉瘤样关联分数，该方法实现了肿瘤样本的定量分析。所提方法仅使用核心级标签，并将预测任务构建为双示例学习问题。组织以包含细胞级形态特征与区域特征的细胞图形式表示。我们采用来自Mesobank的多中心外部测试集，验证了模型的预测性能。通过分析根据预测分数划分的细胞典型形态特征，发现模型识别的部分形态差异与病理学家使用的已知差异相符。此外，模型分数对患者生存具有预测能力，风险比为2.30。所提方法代码及数据集开放获取于：https://github.com/measty/MesoGraph。