ArticleCancer medicine2021
Using deep learning to identify bladder cancers with FGFR-activating mutations from histology images.
Article in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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27 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Toward comprehensive cellular characterization of H&E slides.Journal of pathology informatics · 2026Article
- The outcome prediction method of football matches by the quantum neural network based on deep learning.Scientific reports · 2025Article
- An efficient graph attention framework enhances bladder cancer prediction.Scientific reports · 2025Article
- Predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer via interpretable multimodal deep learning.NPJ digital medicine · 2025Article
- Deep learning-based lymph node metastasis status predicts prognosis from muscle-invasive bladder cancer histopathology.World journal of urology · 2025Article
- AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer.Nature communications · 2024Article
- FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.Molecular diversity · 2024Article
- Targeting FGFR for cancer therapy.Journal of hematology & oncology · 2024Review
- Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.Nature communications · 2024Article
- FGFR-targeted therapeutics: clinical activity, mechanisms of resistance and new directions.Nature reviews. Clinical oncology · 2024Review
- Enhancing recurrence risk prediction for bladder cancer using multi-sequence MRI radiomics.Insights into imaging · 2024Article
- MAMILNet: advancing precision oncology with multi-scale attentional multi-instance learning for whole slide image analysis.Frontiers in oncology · 2024Article
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
- Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review.Cancers · 2023Review
- Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement.Diagnostics (Basel, Switzerland) · 2023Review
- Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.Scientific reports · 2023Article
- Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images.BMC bioinformatics · 2023Article
- Multimodal investigation of bladder cancer data based on computed tomography, whole slide imaging, and transcriptomics.Quantitative imaging in medicine and surgery · 2023Article
- Global research trends of the application of artificial intelligence in bladder cancer since the 21st century: a bibliometric analysis.Frontiers in oncology · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn 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.
methodsThis study analyzed genomic profiles and H&E-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.
resultsThis 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).
conclusionTIL 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.
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