ArticleRadiation oncology (London, England)2018
Investigating rectal toxicity associated dosimetric features with deformable accumulated rectal surface dose maps for cervical cancer radiotherapy.
Article in Radiation oncology (London, England), 2018. 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.
- 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
- Accumulated dose on daily iCBCT for rectal cancer: Effects of inter-fraction bowel cavity motion volume.Journal of applied clinical medical physics · 2026Article
- A review of artificial intelligence in brachytherapy.Journal of applied clinical medical physics · 2025Review
- Article
- Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients' needs.Clinical and translational radiation oncology · 2024Review
- Article
- Improvement of accumulated dose distribution in combined cervical cancer radiotherapy with deep learning-based dose prediction.Frontiers in oncology · 2024Article
- Article
- Development and validation of radiomic signature for predicting overall survival in advanced-stage cervical cancer.Frontiers in nuclear medicine · 2023Article
- Current status and future developments in predicting outcomes in radiation oncology.The British journal of radiology · 2022Review
- [A discrimination model for differentiation of renal cell carcinoma from renal angiomyolipoma without visible fat: based on hierarchical fusion framework of multi-classifier].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022Article
- Prediction models for brachytherapy-induced rectal toxicity in patients with locally advanced pelvic cancers: a systematic review.Journal of contemporary brachytherapy · 2022Review
- Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer.Journal of personalized medicine · 2021Article
- Predicting Cervical Cancer Outcomes: Statistics, Images, and Machine Learning.Frontiers in artificial intelligence · 2021Review
- Rectum Protection by Rectal Gel Injection in Cervical Cancer Brachytherapy: A Dosimetric StudyFrontiers in oncology · 2021Article
- Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy.Frontiers in oncology · 2020Review
- [Prediction of rectal toxicity of radiotherapy for prostate cancer based on multi-modality feature and multi-classifiers].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2019Article
- Region-specific Multi-Omics Modeling for Predicting Acute Radiation-Induced Proctitis in Cervical Cancer Radiotherapy: A Retrospective Analysis.Technology in cancer research & treatmentArticle
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10 authors.
Funding
Abstract
backgroundBetter knowledge of the dose-toxicity relationship is essential for safe dose escalation to improve local control in cervical cancer radiotherapy. The conventional dose-toxicity model is based on the dose volume histogram, which is the parameter lacking spatial dose information. To overcome this limit, we explore a comprehensive rectal dose-toxicity model based on both dose volume histogram and dose map features for accurate radiation toxicity prediction.
methodsForty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively studied, including 12 with Grade ≥ 2 rectum toxicity and 30 patients with Grade 0-1 toxicity (non-toxicity patients). The cumulative equivalent 2-Gy rectal surface dose was deformably summed using the deformation vector fields obtained through a recent developed local topology preserved non-rigid point matching algorithm. The cumulative three-dimensional (3D) dose was flattened and mapped to a two-dimensional (2D) plane to obtain the rectum surface dose map (RSDM). The dose volume parameters (DVPs) were calculated from the 3D rectum surface, while the texture features and the dose geometric parameters (DGPs) were extracted from the 2D RSDM. Representative features further computed from DVPs, textures and DGPs by principle component analysis (PCA) and statistical analysis were respectively fed into a support vector machine equipped with a sequential feature selection procedure. The predictive powers of the representative features were compared with the GEC-ESTRO dosimetric parameters D
resultsSatisfactory predictive accuracy of sensitivity 74.75 and 84.75%, specificity 72.67 and 79.87%, and area under the receiver operating characteristic curve (AUC) 0.82 and 0.91 were respectively achieved by the PCA features and statistical significant features, which were superior to the D
conclusionsOur extensive experimental results have demonstrated the feasibility of the proposed scheme. A future large patient cohort study is still needed for model validation.
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