Generalizable FGFR prediction across tumor types using self supervised learning.

e15057 Background: Deep Learning models have demonstrated the ability to detect tumors, classify disease state, or infer genetic biomarkers from digital whole slide images (WSI) of Hematoxylin and Eosin (H&E)-stained tissue. Such models can be deployed to improve clinical practice or efficiently recruit for clinical trials. However, the data to train these models is often very limited and image capture protocols vary between labs. We propose to use Self Supervised Learning (SSL) on large unlabeled histopathology data sets to improve generalizability and predictive performance of deep learning models on unseen data. Methods: We pre-trained a Convolutional Neural Network (CNN) using SSL on 25,120 unlabeled, digital WSIs from various sources (multiple scanners, hospital systems, labs, diseases, tissue sites). We then fine-tuned the pre-trained CNN to infer the presence or absence of select FGFR alterations in Muscle Invasive Bladder Cancer (MIBC) from the digital WSI. We applied this model (FGFR SSL ) to WSIs of biopsies from other data sources and tumor types and compared with a model (FGFR TRAD ) that was trained exclusively on MIBC samples without self-supervised learning. Results: The FGFR SSL model achieved an Area Under ROC Curve (AUC = 0.80) on MIBC WSIs and maintained performance on images from an independent lab (AUC=0.82). We further show that the model generalizes to Non-Muscle Invasive Bladder Cancer (NMIBC) samples with (AUC = 0.76) and Pan-Tumor WSIs (AUC: 0.83). The FGFR TRAD model trained without SSL-based pre-training achieved an Area Under ROC Curve (AUC: 0.76) on MIBC WSIs was less generalizable, showing lower performance for independent data (AUC = 0.72), NMIBC samples (AUC = 0.72), and Pan-Tumor samples (AUC = 0.64). Conclusions: We leveraged SSL pretraining to improve the reliability and generalizability of AI-based models across multiple data sources. We also demonstrate our model’s ability to infer FGFR status across multiple solid tumor types after training on only MIBC samples, suggesting the cell morphology conferred by FGFR alteration in cancer may be shared across diverse tumor types. These models represent a means for efficiently screening patients for actionable clinical biomarkers in a robust manner to guide clinical decisions and inform drug development efforts. [Table: see text]