Abstract
Introduction: Transurethral resection followed by intravesical bacillus Calmette-Guerin (BCG) is the standard of care for high-risk non-muscle-invasive bladder cancer (HR-NMIBC) patients. However, 20% of HR-NMIBC patients progress to muscle-invasive or metastatic disease, which is associated with a high-risk of death. Early identification of patients who are unlikely to benefit from BCG treatment could benefit from an early radical cystectomy or bladder-sparing alternatives. Previously, we showed that molecular subtyping or HR-NMIBC identified BCG response subtypes (BRS 1/2/3). BRS3 tumors were associated with a poor response to BCG and a high-risk of progression. However, molecular subtyping relies on resource-intensive and costly sequencing methods such as next-generation sequencing, and microarray analysis. We employed deep learning (DL) to predict BRS3 vs BRS1/2 molecular subtypes from digitized histology slides. Methods: Haematoxylin and eosin (H&E)-stained slides were digitized, and quality control was performed. The areas where the RNA was extracted were annotated pixel-wise by a trained pathology assistant, using QuPath. Image tiles of 512 $\times$ 512 pixels at 10X, 20X, and 40X magnifications were extracted with a 25% overlap. For classification, BRS1 and BRS2 images were grouped, while BRS3 images were categorized separately. After applying Macenko normalization, tiles were split based on individual patients into a 75/25 training/validation ratio via stratified 4-fold cross-validation. Four DL models (DenseNet, Inception, ShuffleNet, ResNet) were trained to classify tiles into BRS3 and BRS1/2, with tile labels based on the patient's subtype. Patient-level classification was performed using majority voting from tile predictions. Model efficacy was gauged using the area under the ROC curve (AUC). Results: Out of 245 H&E slides, 45 were discarded after quality control. From 200 patients - with a 70:30 distribution between BRS1&2 and BRS3 The DenseNet model at 10x magnification was the best-performing model, achieving the highest AUC compared to all other models and magnifications. On the image tile level, the best DenseNet achieved an average AUC of 0.65 with a standard deviation of 0.03 across the four folds of the validation set. At the patient-level prediction, the best DenseNet reached an average AUC of 0.80 and a standard deviation of 0.05 on the validation set. Conclusion: Our study demonstrates that deep learning classifies BSR3 from BSR1 and 2 subtypes from H&E slides. This method holds potential to identify BRS3 HR-NMIBC patients who may benefit from alternative treatments than BCG. With further refinement, this deep learning strategy can become a key tool in histology, aiding in swift, affordable, and efficient subtype identification. Hence, this method has the potential to bridge the gap between molecular techniques and clinical practice. Future research should prioritize evaluating the model on an external test cohort and consider predicting additional molecular markers to fine-tune prediction outcomes.
Citation Format: Farbod Khoraminia, Flouros C de Jong, Farhan Akram, Geert Litjens, Maarten D.J. Jansen, Alberto Nakuama Gonzalez, Danique Lichtenburg, Andrew Stubbs, Nadieh Khalili, Tahlita C.M. Zuiverloon. Deep learning unveils molecular footprints in histology: predicting molecular subtypes from bladder cancer histology slides [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr B004.