ArticleFrontiers in oncology2021
Deep Learning on Enhanced CT Images Can Predict the Muscular Invasiveness of Bladder Cancer.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.World journal of urology · 2026Pooled it
- Application Value of Radiomics-Based Machine Learning for Preoperative Risk Stratification of Bladder Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Diagnostic performance of artificial intelligence in detecting bladder carcinoma.World journal of urology · 2025Pooled it
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- Graph Neural Network-Based Multi-Scale Whole Slide Image Fusion for pT Staging of Muscle-Invasive Bladder Cancer.Cancer science · 2026Article
- An explainable machine learning model for predicting bladder tumor aecurrence risk.Frontiers in oncology · 2026Article
- Innovative AI model for bladder cancer diagnosis.Discover oncology · 2025Article
- A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.Abdominal radiology (New York) · 2025Review
- Breaking barriers: noninvasive AI model for BRAFInternational journal of computer assisted radiology and surgery · 2025Article
- Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study.Scientific reports · 2025Article
- A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.BMC medical research methodology · 2025Article
- Artificial Intelligence-Based Classification and Segmentation of Bladder Cancer in Cystoscope Images.Cancers · 2024Article
- The accuracy and quality of image-based artificial intelligence for muscle-invasive bladder cancer prediction.Insights into imaging · 2024Review
- Diagnosis of skull-base invasion by nasopharyngeal tumors on CT with a deep-learning approach.Japanese journal of radiology · 2024Article
- Predicting preoperative muscle invasion status for bladder cancer using computed tomography-based radiomics nomogram.BMC medical imaging · 2024Article
- Bladder Cancer and Artificial Intelligence: Emerging Applications.The Urologic clinics of North America · 2024Review
- Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer.Heliyon · 2024Article
- SMMF: a self-attention-based multi-parametric MRI feature fusion framework for the diagnosis of bladder cancer grading.Frontiers in oncology · 2024Article
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
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12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical treatment decision making of bladder cancer (BCa) relies on the absence or presence of muscle invasion and tumor staging. Deep learning (DL) is a novel technique in image analysis, but its potential for evaluating the muscular invasiveness of bladder cancer remains unclear. The purpose of this study was to develop and validate a DL model based on computed tomography (CT) images for prediction of muscle-invasive status of BCa.
methodsA total of 441 BCa patients were retrospectively enrolled from two centers and were divided into development (n=183), tuning (n=110), internal validation (n=73) and external validation (n=75) cohorts. The model was built based on nephrographic phase images of preoperative CT urography. Receiver operating characteristic (ROC) curves were performed and the area under the ROC curve (AUC) for discrimination between muscle-invasive BCa and non-muscle-invasive BCa was calculated. The performance of the model was evaluated and compared with that of the subjective assessment by two radiologists.
resultsThe DL model exhibited relatively good performance in all cohorts [AUC: 0.861 in the internal validation cohort, 0.791 in the external validation cohort] and outperformed the two radiologists. The model yielded a sensitivity of 0.733, a specificity of 0.810 in the internal validation cohort and a sensitivity of 0.710 and a specificity of 0.773 in the external validation cohort.
conclusionThe proposed DL model based on CT images exhibited relatively good prediction ability of muscle-invasive status of BCa preoperatively, which may improve individual treatment of BCa.
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