Examining longitudinal markers of bladder cancer recurrence through a semiautonomous machine learning system for quantifying specimen atypia from urine cytology.

Urine cytology (UC) is generally considered the primary approach for screening for recurrence of bladder cancer. However, it is currently unclear how best to use cytological exams themselves for the assessment and early detection of recurrence, beyond identifying a positive finding which requires more invasive methods to confirm recurrence and decide on therapeutic options. As screening programs are frequent, and can be burdensome, finding quantitative means to reduce this burden for patients, cytopathologists and urologists is an important endeavor and can improve both the efficiency and reliability of findings. Additionally, identifying ways to risk-stratify patients is crucial for improving quality of life while reducing the risk of future recurrence or progression of the cancer. In this study, we leveraged a computational machine learning tool, AutoParis-X, to extract imaging features from UC exams longitudinally to study the predictive potential of urine cytology for assessing recurrence risk. This study examined how the significance of imaging predictors changes over time before and after surgery to determine which predictors and time periods are most relevant for assessing recurrence risk. Results indicate that imaging predictors extracted using AutoParis-X can predict recurrence as well or better than traditional cytological / histological assessments alone and that the predictiveness of these features is variable across time, with key differences in overall specimen atypia identified immediately before tumor recurrence. Further research will clarify how computational methods can be effectively utilized in high volume screening programs to improve recurrence detection and complement traditional modes of assessment. This study used AutoParis-X, a machine learning tool, to extract imaging features from urine cytology exams to predict recurrence risk in bladder cancer patients. The results demonstrate that quantitative features of urine specimen atypia can predict recurrence as well or better than traditional cytological/histological assessments alone and can potentially complement traditional methods of assessment in screening programs pending further development and validation of computational methods which leverage multiple longitudinal cytology exams.

尿液细胞学检查（UC）通常被认为是筛查膀胱癌复发的主要方法。然而，目前除了识别出阳性结果（这需要更具侵入性的方法来确认复发并决定治疗方案）之外，尚不清楚如何最好地利用细胞学检查本身来评估和早期发现复发。由于筛查项目频繁且可能是繁重的，寻找定量方法来减轻患者、细胞病理学家和泌尿科医生的负担是一项重要的工作，并且可以提高检查结果的效率和可靠性。此外，确定对患者进行风险分层的方法对于提高生活质量同时降低癌症未来复发或进展的风险至关重要。在这项研究中，我们利用一种计算机机器学习工具AutoParis - X，纵向从尿液细胞学检查中提取影像特征，以研究尿液细胞学在评估复发风险方面的预测潜力。这项研究考察了影像预测因子的重要性在手术前后如何随时间变化，以确定哪些预测因子和时间段与评估复发风险最为相关。结果表明，使用AutoParis - X提取的影像预测因子可以像单独的传统细胞学/组织学评估一样好甚至更好地预测复发，并且这些特征的预测能力随时间变化，在肿瘤复发前即刻识别出的整体标本异型性方面存在关键差异。进一步的研究将阐明如何在大量筛查项目中有效利用计算方法来提高复发检测并补充传统的评估模式。 这项研究使用一种机器学习工具AutoParis - X从尿液细胞学检查中提取影像特征，以预测膀胱癌患者的复发风险。结果表明，尿液标本异型性的定量特征可以像单独的传统细胞学/组织学评估一样好甚至更好地预测复发，并且在利用多次纵向细胞学检查的计算方法得到进一步开发和验证之前，可能在筛查项目中补充传统的评估方法。