Abstract 5414: Automated detection of high-grade urothelial carcinoma from urine cytology slides using attention-based deep learning

Abstract

Abstract Urine cytology has long been an effective and non-invasive test for the detection of bladder urothelial carcinomas (UC) routinely performed in cases of unexplained hematuria or for monitoring patients with UC. In cytopathology practice, urine cytology specimens are examined manually with a light microscope to identify morphologic features associated with different diagnostic categories based on the Paris System (TPS) for Reporting Urinary Cytology. Specifically, the diagnosis of high-grade urothelial carcinoma (HGUC) requires the identification of > 5-10 cells with a nuclear/cytoplasm ratio of 0.7 or greater and hyperchromasia together with coarse chromatin or irregular nuclear membranes. However, the task of identifying HGUC involves a substantial degree of manual review and is often associated with intra-and inter-observer variability. To address this, we have designed an accurate and efficient deep learning system capable of automatically distinguishing between HGUC and non-HGUC using digitized cytology slides. Our model has been developed using a retrospective cohort of 158 digitized urine ThinPrep cytology slides consisting of HGUC (n=98) and negative for HGUC (n=60). The model was then prospectively validated on a cohort of 105 urine cytology slides that were also independently reviewed prospectively in a blinded manner by a cytopathologist and cytotechnologist. Our system uses Otsu’s method for automatic image thresholding followed by dividing images into non-overlapping tiles of 500 × 500 pixels at the highest magnification. Subsequently, we use a pre-trained ResNet50 model to extract features which are used for training our attention-based multiple instance learning framework. For the training task, our retrospective cohort (158 slides) has been divided into 10 different splits each consisting of training (70%), validation (15%), and testing (15%) sets. The training and validation sets were used for the model training and optimalization, respectively, while the testing set was used for assessing the performance. This process yielded 10 different models with an average Area Under the ROC Curve (AUC) of 0.80 in the testing set. The best performing model had an AUC of 0.90 and an accuracy of 0.88. This model was subsequently validated prospectively in an independent testing cohort with 105 slides. In the prospective testing cohort, the model was able to accurately distinguish between HGUC and non-HGUC with an AUC of 0.83, accuracy of 0.76, sensitivity of 0.89, and specificity of 0.62. Additionally, our system can detect slide regions with high attention score for HGUC which are enriched in atypical urothelial cells. These findings show that our system can be utilized to assist cytopathologists in assessing urine cytology slides and to detect regions with high-diagnostic relevance for further assessment which is expected to reduce the time needed for manual review. Citation Format: Mohamed Omar, David Kim, Luigi Marchionni, Momin T. Siddiqui. Automated detection of high-grade urothelial carcinoma from urine cytology slides using attention-based deep learning. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5414.