Gavriel Christos G, Dimitriou Neofytos, Brieu Nicolas, Nearchou Ines P, Arandjelović Ognjen, Schmidt Günter, Harrison David J, Caie Peter D
School of Medicine, University of St Andrews, St Andrews KY16 9TF, UK.
School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK.
Cancers (Basel). 2021 Apr 1;13(7):1624. doi: 10.3390/cancers13071624.
The clinical staging and prognosis of muscle-invasive bladder cancer (MIBC) routinely includes the assessment of patient tissue samples by a pathologist. Recent studies corroborate the importance of image analysis in identifying and quantifying immunological markers from tissue samples that can provide further insight into patient prognosis. In this paper, we apply multiplex immunofluorescence to MIBC tissue sections to capture whole-slide images and quantify potential prognostic markers related to lymphocytes, macrophages, tumour buds, and PD-L1. We propose a machine-learning-based approach for the prediction of 5 year prognosis with different combinations of image, clinical, and spatial features. An ensemble model comprising several functionally different models successfully stratifies MIBC patients into two risk groups with high statistical significance ( value < 1×10-5). Critical to improving MIBC survival rates, our method correctly classifies 71.4% of the patients who succumb to MIBC, which is significantly more than the 28.6% of the current clinical gold standard, the TNM staging system.
肌层浸润性膀胱癌（MIBC）的临床分期和预后通常包括病理学家对患者组织样本的评估。最近的研究证实了图像分析在识别和量化组织样本中的免疫标记物方面的重要性，这些标记物可以为患者预后提供进一步的见解。在本文中，我们将多重免疫荧光应用于MIBC组织切片，以捕获全切片图像并量化与淋巴细胞、巨噬细胞、肿瘤芽和PD-L1相关的潜在预后标志物。我们提出了一种基于机器学习的方法，用于通过图像、临床和空间特征的不同组合预测5年预后。一个由几个功能不同的模型组成的集成模型成功地将MIBC患者分为两个风险组，具有高度统计学意义（值<1×10-5）。对于提高MIBC生存率至关重要的是，我们的方法正确分类了71.4%死于MIBC的患者，这明显高于当前临床金标准TNM分期系统的28.6%。
