Abstract 1073: AI-assisted high-dimensional cytological single cell analysis distinguishes normal and malignant urine samples using cell morphology images with high accuracy at the single cell level

Abstract

Abstract Background: Urothelial cancer (UCC) is the 4th most common malignancy in men. The current clinical problem in UCC management is the high interobserver variability of tissue UCC grading and the low sensitivity of urine cytology for early detection of recurrences and progression. We hypothesize that assessing urine samples and UCC tissues with detailed quantitative image analysis based on artificial intelligence has strong potential in both urine-based early detection of tumor recurrence and progression as well as tissue-based pathological grading. Methods: Here, we utilized the Deepcell® Human Foundation Model (HFM), a self-supervised deep learning model, to assess a cohort of urine samples from 54 normal healthy cases, 19 atypical cases, and 4 UCC cases. High resolution images of label-free single cells were captured and analyzed using the HFM to identify distinct morphological characteristics of cells from different urine origins. The urine samples were loaded onto a single-use microfluidic cartridge as part of the Deepcell® REM-I platform and brightfield images of single cells were captured in real-time. For data analysis, images of all cells run through the REM-I instrument are automatically sent to the Deepcell® Axon Suite, which provides an analysis platform to visualize cell images from specific runs, generate UMAPs of the embedding space, and run additional deep neural network models on samples in silico. Results: High-dimensional analysis of 115 deep learning and morphometric features reveals 12 morphotypes among the pooled urine samples, as represented by morphology UMAP plots. Morphology analysis reveals distinct morphotypes in normal compared to pathological urine samples. Confusion matrix showed deep learning model performance in classifying atypical versus UCC with 89.1% (Atypical (n = 8642), UCC (n = 12637)). This indicates high model performance accuracy for AI prediction of atypical versus UCC samples at the single cell level. Conclusions: The Deepcell® REM-I platform performs well with urine samples and identified numerous cell clusters among normal and pathological urines. AI-assisted high-dimensional morphology analysis distinguishes atypical urine samples from UCC cases with 89.1% accuracy at the single cell level. Future applications of this study include early detection or monitoring of UCC cases using non-invasive patient samples such as urine. Citation Format: Thierry P. van den Bosch, Geert J.L.H. van Leenders, Siamala Sinnadurai, Zhouyang Lian, Ryan Carelli, Matthew Nakaki, Vivian Lu, Manisha Ray, Janifer Cruz, Kelly R. Miller, Mahyar Salek, Maddison Masaeli, Peter J. van der Spek. AI-assisted high-dimensional cytological single cell analysis distinguishes normal and malignant urine samples using cell morphology images with high accuracy at the single cell level [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1073.