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 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 performed survival analyses and explored the association between our scoring system and seven histopathological features, as well as 126 molecular signatures. Through our analysis, we identified two distinct risk groups with varying prognoses, reflecting inherent differences in histopathological and molecular subtypes. The hazard ratio for overall mortality was 1.46 (95% CI 1.05-2.02; z: 2.23; p=0.03). Furthermore, we observed an association between our novel digital biomarker and the squamous phenotype, subtypes of miRNA, mRNA, long non-coding RNA, DNA hypomethylation, as well as 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 the use of 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.
Open the ArticleSmoke exposure is associated with bladder cancer (BC). However, little is known whether the histologic changes of BC can predict the smoke exposure status. Given this knowledge gap, the current study investigated the potential association between histology images and smoke exposure status. A total of 483 whole slide histology images of 285 unique cases of BC was available from multiple centers for BC diagnosis. A deep learning model was developed to predict the smoke exposure status and externally validated on BC cases. The development set consisted of 66 cases from two centers. The external validation consisted of 94 cases from remaining centers for patients who either never smoked cigarettes or were active smokers at the time of diagnosis. The threshold for binary categorization was fixed to the median confidence score (65) of the development set. On external validation, AUC was used to assess the randomness of predicted smoke status, we utilized latent feature presentation to determine common histologic patterns for smoke exposure status, and mixed effect logistic regression models determined the parameter independency from BC grade, gender, time to diagnosis and age at diagnosis. We used 2,000-times bootstrap resampling to estimate the 95% Confidence Interval (CI) on the external validation set. The results showed an AUC of 0.67 (95% CI: 0.58–0.76), indicating non-randomness of model classification, with a specificity of 51.2% and sensitivity of 82.2%. Multivariate analyses revealed that our model provided an independent predictor for smoke exposure status derived from histology images, with an odds ratio of 1.710 (95% CI: 1.148–2.54). Common histologic patterns of BC were found in active or never smokers. In conclusion, deep learning reveals histopathologic features of BC that are predictive of smoke exposure, and therefore may provide valuable information regarding smoke exposure status.
Open the ArticleBesides grading, deep learning could improve expert consensus to predict prostate cancer (PCa) recurrence. We developed a novel PCa recurrence prediction system based on artificial intelligence (AI). We validated it using multi-institutional and international datasets comprising 2,647 PCa patients with at least a 10-year follow-up. Survival analyses were performed and goodness-of-fit of multivariate models was evaluated using partial likelihood ratio tests, Akaike's test, or Bayesian information criteria to determine the superiority of our system over existing grading systems. Comprehensive survival analyses demonstrated the effectiveness of our AI-system in categorizing PCa into four distinct risk groups. The system was independent and superior to the existing five grade groups for malignancies. A high consensus level was observed among five blinded genitourinary pathology experts in ranking images according to our prediction system. Therefore, AI may help develop an accurate and clinically interpretable PCa recurrence prediction system, facilitating informed decision-making for PCa patients.
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