Muscle-invasive bladder cancer (MIBC) is a highly heterogeneous and costly disease with significant morbidity and mortality. Understanding tumor histopathology leads to tailored therapies and improved outcomes. In this study, we employed a weakly supervised learning and neural architecture search to develop a data-driven scoring system. This system aimed to capture prognostic histopathological patterns observed in H&E-stained whole-slide images. We constructed and externally validated our scoring system using multi-institutional datasets with 653 whole-slide images. Additionally, we explored the association between our scoring system, seven histopathological features, and 126 molecular signatures. Through our analysis, we identified two distinct risk groups with varying prognoses, reflecting inherent differences in histopathological and molecular subtypes. The adjusted hazard ratio for overall mortality was 1.46 (95% CI 1.05–2.02; z: 2.23;p= 0.03), thus identifying two prognostic subgroups in high-grade MIBC. Furthermore, we observed an association between our novel digital biomarker and the squamous phenotype, subtypes of miRNA, mRNA, long non-coding RNA, DNA hypomethylation, and several gene mutations, including FGFR3 in MIBC. Our findings underscore the risk of confounding bias when reducing the complex biological and clinical behavior of tumors to a single mutation. Histopathological changes can only be fully captured through comprehensive multi-omics profiles. The introduction of our scoring system has the potential to enhance daily clinical decision making for MIBC. It facilitates shared decision making by offering comprehensive and precise risk stratification, treatment planning, and cost-effective preselection for expensive molecular characterization.
肌层浸润性膀胱癌是一种高度异质性且治疗成本高昂的疾病，具有显著的发病率和死亡率。理解肿瘤组织病理学特征有助于制定个体化治疗方案并改善预后。本研究采用弱监督学习和神经架构搜索技术，开发了一种数据驱动的评分系统。该系统旨在捕捉苏木精-伊红染色全切片图像中观察到的预后组织病理学模式。我们使用包含653张全切片图像的多机构数据集构建并外部验证了该评分系统。此外，我们还探讨了评分系统与七种组织病理学特征及126种分子标志物之间的关联。通过分析，我们识别出两个具有不同预后的风险组，反映了组织病理学和分子亚型的内在差异。总体死亡率的校正风险比为1.46（95% CI 1.05–2.02；z值：2.23；p=0.03），从而在高分级肌层浸润性膀胱癌中确定了两个预后亚组。进一步研究发现，我们提出的新型数字生物标志物与鳞状表型、miRNA亚型、mRNA亚型、长链非编码RNA亚型、DNA低甲基化以及包括FGFR3在内的多个基因突变存在关联。研究结果提示，将肿瘤复杂的生物学和临床行为简化为单一突变可能导致混杂偏倚风险。只有通过全面的多组学分析才能完整捕捉组织病理学变化。本评分系统的引入有望优化肌层浸润性膀胱癌的日常临床决策，通过提供全面精准的风险分层、治疗规划以及对昂贵分子检测的成本效益预筛选，促进医患共同决策。
肿瘤(癌症)患者之家