ArticleCancer imaging : the official publication of the International Cancer Imaging Society2023
CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Artificial intelligence advances in cystoscopy and imaging for bladder cancer: a narrative review.The Canadian journal of urology · 2026Review
- Interpretable machine learning model of contrast-enhanced CT radiomics for predicting post-BCG recurrence in high-grade non-muscle-invasive bladder cancer.World journal of urology · 2026Article
- Advancing Genitourinary Cancer Surgery: The Role of Artificial Intelligence and Robotics.Journal of clinical medicine · 2026Review
- Deep learning-based CT radiomics for ALK rearrangement status prediction in lung adenocarcinoma.BMC cancer · 2026Article
- A Novel Radiomics-based Interpretable Model for Bladder Cancer Grade Prediction Using White-Light Cystoscopy Images.European urology open science · 2026Article
- Deep learning radiomics based on multimodal MRI for preoperative prediction of N stage in tongue squamous cell carcinoma: a multicenter study.BMC oral health · 2026Article
- Interpretable machine learning using CT radiomics predicts pathological upgrading after secondary resection in non-muscle-invasive bladder cancer.BMC cancer · 2026Article
- A radiomics-driven machine learning model for predicting bladder cancer prognosis identifies genes associated with radiomic features.Clinical & experimental metastasis · 2025Article
- Recent Advances in Artificial Intelligence for Precision Diagnosis and Treatment of Bladder Cancer: A Review.Annals of surgical oncology · 2025Review
- Multi-machine learning model based on radiomics features to predict prognosis of muscle-invasive bladder cancer.BMC cancer · 2025Article
- A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.Abdominal radiology (New York) · 2025Review
- Novel deep learning radiomics nomogram-based multiparametric MRI for predicting the lymph node metastasis in rectal cancer: A dual-center study.Journal of cancer research and clinical oncology · 2024Article
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review.Cancers · 2024Review
- A stacking ensemble system for identifying the presence of histological variants in bladder carcinoma: a multicenter study.Frontiers in oncology · 2024Article
- LLM-BCgrading: Large language model-based Chinese medical long text classification for bladder cancer grade prediction.Digital healthArticle
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively.
methodsWe retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).
resultsThe support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature.
conclusionThe proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.
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