ReviewFrontiers in oncology2022
The role of radiomics with machine learning in the prediction of muscle-invasive bladder cancer: A mini review.
Review in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Systems Biology and Multi-Omics Determinants of Response to Bladder-Preserving Trimodality Therapy in Muscle-Invasive Bladder Cancer.Life (Basel, Switzerland) · 2026Review
- Review
- Clinician-driven automated data preprocessing in nuclear medicine AI environments.European journal of nuclear medicine and molecular imaging · 2025Article
- A Machine Learning Model Based on Multi-Phase Contrast-enhanced CT for the Preoperative Prediction of the Muscle-Invasive Status of Bladder Cancer.Current medical imaging · 2025Article
- Assessing muscle invasion in bladder cancer via virtual biopsy: a study on quantitative parameters and classical radiomics features from dual-energy CT imaging.BMC medical imaging · 2024Article
- Predicting preoperative muscle invasion status for bladder cancer using computed tomography-based radiomics nomogram.BMC medical imaging · 2024Article
- Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
- Review
- Value of the application of computed tomography-based radiomics for preoperative prediction of unfavorable pathology in initial bladder cancer.Cancer medicine · 2023Article
- MRI-Based Radiomics in Bladder Cancer: A Systematic Review and Radiomics Quality Score Assessment.Diagnostics (Basel, Switzerland) · 2023Review
- Systematic radiomics analysis based on multiparameter MRI to preoperatively predict the expression of Ki67 and histological grade in patients with bladder cancer.The British journal of radiology · 2023Article
- Diffusion-weighted MRI to determine response and long-term clinical outcomes in muscle-invasive bladder cancer following neoadjuvant chemotherapy.Frontiers in oncology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bladder cancer is a common malignant tumor in the urinary system. Depending on whether bladder cancer invades muscle tissue, it is classified into non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). It is crucial to accurately diagnose the muscle invasion of bladder cancer for its clinical management. Although imaging modalities such as CT and multiparametric MRI play an important role in this regard, radiomics has shown great potential with the development and innovation of precision medicine. It features outstanding advantages such as non-invasive and high efficiency, and takes on important significance in tumor assessment and laor liberation. In this article, we provide an overview of radiomics in the prediction of muscle-invasive bladder cancer and reflect on its future trends and challenges.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.