ArticleCancers2023
Feature Importance Analysis of a Deep Learning Model for Predicting Late Bladder Toxicity Occurrence in Uterine Cervical Cancer Patients.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Clinical challenges in the adaptation of AI predictive models in radiation oncology for gynaecological cancer: a systematic review by the radiation oncology-AI MITO group.La Radiologia medica · 2026Pooled it
- Development of a preoperative accuracy prediction model using machine learning for implant placement in static-guided surgery: retrospective observational study.International journal of implant dentistry · 2026Observational
- Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026Review
- Mapping coastal forest retreat using convolutional neural networks and different satellite imagery.PloS one · 2026Article
- Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review.Visual computing for industry, biomedicine, and art · 2025Review
- Article
- Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
(1) In this study, we developed a deep learning (DL) model that can be used to predict late bladder toxicity. (2) We collected data obtained from 281 uterine cervical cancer patients who underwent definitive radiation therapy. The DL model was trained using 16 features, including patient, tumor, treatment, and dose parameters, and its performance was compared with that of a multivariable logistic regression model using the following metrics: accuracy, prediction, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). In addition, permutation feature importance was calculated to interpret the DL model for each feature, and the lightweight DL model was designed to focus on the top five important features. (3) The DL model outperformed the multivariable logistic regression model on our dataset. It achieved an F1-score of 0.76 and an AUROC of 0.81, while the corresponding values for the multivariable logistic regression were 0.14 and 0.43, respectively. The DL model identified the doses for the most exposed 2 cc volume of the bladder (BD
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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.