MIL与聚合：基于图学习评估患者级生存预测策略
MIL vs. Aggregation: Evaluating Patient-Level Survival Prediction Strategies Using Graph-Based Learning
AI总结：
本研究比较了基于图神经网络的WSI级与患者级生存预测策略，发现基于多示例学习（MIL）选择最具代表性的切片能有效提高预测准确性。
摘要：
肿瘤学家通常依赖包括全切片图像（WSIs）在内的多种数据来指导治疗决策，以期获得最佳的患者预后。然而，由于肿瘤异质性、患者个体差异以及分析WSIs的复杂性，预测癌症患者的预后是一项极具挑战性的任务。这些图像极为庞大，包含数十亿像素，直接处理计算成本高昂，需要专门的方法来提取相关信息。此外，来自同一患者的多张WSIs可能捕捉不同的肿瘤区域，其中一些比另一些包含更多信息。这引出了一个根本性问题：我们应该使用所有WSIs来表征患者，还是应该为预后识别最具代表性的切片？我们的工作旨在通过比较在WSI和患者层面预测生存的各种策略来回答这个问题。前者将每张WSI视为独立样本，模仿其他研究中采用的策略；而后者包含的方法要么聚合多张WSIs的预测结果，要么使用多示例学习（MIL）自动识别最相关的切片。此外，我们在这些策略下评估了不同的图神经网络架构。我们使用MMIST-ccRCC数据集进行实验，该数据集包含透明细胞肾细胞癌（ccRCC）患者。结果表明，基于MIL的选择提高了准确性，表明选择最具代表性的切片有利于生存预测。
英文摘要：
Oncologists often rely on a multitude of data, including whole-slide images (WSIs), to guide therapeutic decisions, aiming for the best patient outcome. However, predicting the prognosis of cancer patients can be a challenging task due to tumor heterogeneity and intra-patient variability, and the complexity of analyzing WSIs. These images are extremely large, containing billions of pixels, making direct processing computationally expensive and requiring specialized methods to extract relevant information. Additionally, multiple WSIs from the same patient may capture different tumor regions, some being more informative than others. This raises a fundamental question: Should we use all WSIs to characterize the patient, or should we identify the most representative slide for prognosis? Our work seeks to answer this question by performing a comparison of various strategies for predicting survival at the WSI and patient level. The former treats each WSI as an independent sample, mimicking the strategy adopted in other works, while the latter comprises methods to either aggregate the predictions of the several WSIs or automatically identify the most relevant slide using multiple-instance learning (MIL). Additionally, we evaluate different Graph Neural Networks architectures under these strategies. We conduct our experiments using the MMIST-ccRCC dataset, which comprises patients with clear cell renal cell carcinoma (ccRCC). Our results show that MIL-based selection improves accuracy, suggesting that choosing the most representative slide benefits survival prediction.
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