School of Medicine
Artificial Intelligence and Robotics | Biological Factors | Diagnosis | Female Urogenital Diseases and Pregnancy Complications | Male Urogenital Diseases | Neoplasms
BACKGROUND: In recent years, the fibroblast growth factor receptor (FGFR) pathway has been proven to be an important therapeutic target in bladder cancer. FGFR-targeted therapies are effective for patients with FGFR mutation, which can be discovered through genetic sequencing. However, genetic sequencing is not commonly performed at diagnosis, whereas a histologic assessment of the tumor is. We aim to computationally extract imaging biomarkers from existing tumor diagnostic slides in order to predict FGFR alterations in bladder cancer.
METHODS: This study analyzed genomic profiles and HandE-stained tumor diagnostic slides of bladder cancer cases from The Cancer Genome Atlas (n = 418 cases). A convolutional neural network (CNN) identified tumor-infiltrating lymphocytes (TIL). The percentage of the tissue containing TIL ("TIL percentage") was then used to predict FGFR activation status with a logistic regression model.
RESULTS: This predictive model could proficiently identify patients with any type of FGFR gene aberration using the CNN-based TIL percentage (sensitivity = 0.89, specificity = 0.42, AUROC = 0.76). A similar model which focused on predicting patients with only FGFR2/FGFR3 mutation was also found to be highly sensitive, but also specific (sensitivity = 0.82, specificity = 0.85, AUROC = 0.86).
CONCLUSION: TIL percentage is a computationally derived image biomarker from routine tumor histology that can predict whether a tumor has FGFR mutations. CNNs and other digital pathology methods may complement genome sequencing and provide earlier screening options for candidates of targeted therapies.
convolutional neural networks, deep learning, fibroblast growth factor receptors, tumor-infiltrating lymphocytes, urinary bladder neoplasms
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© 2021 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
DOI of Published Version
Velmahos CS, Badgeley M, Lo YC. Using deep learning to identify bladder cancers with FGFR-activating mutations from histology images. Cancer Med. 2021 Jul;10(14):4805-4813. doi: 10.1002/cam4.4044. Epub 2021 Jun 10. PMID: 34114376; PMCID: PMC8290253. Link to article on publisher's site
Velmahos CS, Badgeley M, Lo Y. (2021). Using deep learning to identify bladder cancers with FGFR-activating mutations from histology images. Open Access Publications by UMass Chan Authors. https://doi.org/10.1002/cam4.4044. Retrieved from https://escholarship.umassmed.edu/oapubs/4770
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This work is licensed under a Creative Commons Attribution 4.0 License.
Artificial Intelligence and Robotics Commons, Biological Factors Commons, Diagnosis Commons, Female Urogenital Diseases and Pregnancy Complications Commons, Male Urogenital Diseases Commons, Neoplasms Commons