ArticleFrontiers in microbiology2022
Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients.
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 14 citations in OpenAlex.
- Development and internal validation of a clinical nomogram for predicting major postoperative pulmonary complications following radical cystectomy: a retrospective cohort study.Translational andrology and urology · 2026Article
- Construction of a predictive model for radiation proctitis after radical radiotherapy for cervical cancer based on MRI radiomics.Abdominal radiology (New York) · 2026Article
- Deep learning and dual-radiomics model incorporating brachytherapy applicator type to predict radiation-induced acute rectal injury in cervical cancer patients.Physics and imaging in radiation oncology · 2026Article
- From Host-Microbiome Symbiosis to Clinical Translation: A Gut Microbiome Perspective on Radiation Enteritis.International journal of biological sciences · 2026Review
- Microbiome-Driven Risk Stratification and Biotherapeutics in Radiation Enteritis.Computational and structural biotechnology journal · 2026Review
- Radiomics and artificial intelligence in precision radiotherapy for cervical cancer: a narrative review.Frontiers in oncology · 2026Review
- Predicting hematologic toxicity in advanced cervical cancer patients using interpretable machine learning models based on radiomics and dosimetrics.BMC cancer · 2025Article
- Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review.Visual computing for industry, biomedicine, and art · 2025Review
- A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across Infection Types.Journal of cellular and molecular medicine · 2025Article
- Delta-radiomics analysis based on magnetic resonance imaging to identify radiation proctitis in patients with cervical cancer after radiotherapy.Frontiers in oncology · 2025Article
- Hepatic encephalopathy post-TIPS: Current status and prospects in predictive assessment.Computational and structural biotechnology journal · 2024Review
- Unravelling Quality of Life for Head and Neck Cancer Patients after VMAT Radiation Therapy: Insights from Toxicity, Dosimetry and Symptoms Correlation.Clinics and practice · 2024Article
- Pre-treatment T2-weighted magnetic resonance radiomics for prediction of loco-regional recurrence after image-guided adaptive brachytherapy for locally advanced cervical cancer.Journal of contemporary brachytherapy · 2024Article
- 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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Authors and funding
16 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Radiation proctitis is a common complication after radiotherapy for cervical cancer. Unlike simple radiation damage to other organs, radiation proctitis is a complex disease closely related to the microbiota. However, analysis of the gut microbiota is time-consuming and expensive. This study aims to mine rectal information using radiomics and incorporate it into a nomogram model for cheap and fast prediction of severe radiation proctitis prediction in postoperative cervical cancer patients. Methods: The severity of the patient's radiation proctitis was graded according to the RTOG/EORTC criteria. The toxicity grade of radiation proctitis over or equal to grade 2 was set as the model's target. A total of 178 patients with cervical cancer were divided into a training set ( Results: The radiomics model [AUC=0.6855(0.5174-0.8535)] showed better performance and more net benefit in the validation set than the non-radiomic model [AUC=0.6641(0.4904-0.8378)]. In particular, we applied SHapley Additive exPlanation (SHAP) method for the first time to a radiomics-based logistic regression model to further interpret the radiomic features from case-based and feature-based perspectives. The integrated radiomic model enables the first accurate quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients, addressing the limitations of the current qualitative assessment of the plan through dose-volume parameters only. Conclusion: We successfully developed and validated an integrated radiomic model containing rectal information. SHAP analysis of the model suggests that radiomic features have a supporting role in the quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients.
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