Trial reportBMC oral health2022
A prediction model for xerostomia in locoregionally advanced nasopharyngeal carcinoma patients receiving radical radiotherapy.
Trial report in BMC oral health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models for radiation-induced complications after radiotherapy in head and neck cancer patients.The Cochrane database of systematic reviews · 2025Pooled it
- High-Recall Biomedical Language Models for Radiation Oncology Evidence Synthesis: An Integrated Systematic Review of Prediction Models for Radiotherapy-Induced Complications in Nasopharyngeal Carcinoma.Life (Basel, Switzerland) · 2026Review
- A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion.BMC medical imaging · 2025Article
- Knowledge, attitude, and practice of cancer patients regarding radiotherapy and radiation protection.Frontiers in oncology · 2025Article
- Research Hotspots and Trends in Acupuncture for Cancer:A bibliometric analysis from 2004 to 2024.Journal of multidisciplinary healthcare · 2025Article
- Using real-world electronic health record data to predict the development of 12 cancer-related symptoms in the context of multimorbidity.JAMIA open · 2024Article
- Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review.JMIR cancer · 2024Review
- Predictive value of delta radiomics in xerostomia after chemoradiotherapy in patients with stage III-IV nasopharyngeal carcinoma.Radiation oncology (London, England) · 2024Article
- Neural network and spline-based regression for the prediction of salivary hypofunction in patients undergoing radiation therapy.Radiation oncology (London, England) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study was to evaluate the predictors of xerostomia and Grade 3 xerostomia in locoregionally advanced nasopharyngeal carcinoma (NPC) patients receiving radical radiotherapy and establish prediction models for xerostomia and Grade 3 xerostomia based on the predictors.
methodsTotally, 365 patients with locoregionally advanced NPC who underwent radical radiotherapy were randomly divided into the training set (n = 255) and the testing set (n = 110) at a ratio of 7:3. All variables were included in the least absolute shrinkage and selection operator regression to screen out the potential predictors for xerostomia as well as the Grade 3 xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy. The random forest (RF), a decision tree classifier (DTC), and extreme-gradient boosting (XGB) models were constructed. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC) and accuracy were analyzed to evaluate the predictive performance of the models.
resultsIn the RF model for predicting xerostomia, the sensitivity was 1.000 (95%CI 1.000-1.000), the PPV was 0.990 (95%CI 0.975-1.000), the NPV was 1.000 (95%CI 1.000-1.000), the AUC was 0.999 (95%CI 0.997-1.000) and the accuracy was 0.992 (95%CI 0.981-1.000) in the training set. The sensitivity was 0.933 (95%CI 0.880-0.985), the PPV was 0.933 (95%CI 0.880-0.985), and the AUC was 0.915 (95%CI 0.860-0.970) in the testing set. Hypertension, age, total radiotherapy dose, dose at 50% of the left parotid volume, mean dose to right parotid gland, mean dose to oral cavity, and course of induction chemotherapy were important variables associated with the risk of xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy. The AUC of DTC model for predicting xerostomia was 0.769 (95%CI 0.666-0.872) in the testing set. The AUC of the XGB model for predicting xerostomia was 0.834 (0.753-0.916) in the testing set. The RF model showed the good predictive ability with the AUC of 0.986 (95%CI 0.972-1.000) in the training set, and 0.766 (95%CI 0.626-0.905) in the testing set for identifying patients who at high risk of Grade 3 xerostomia in those with high risk of xerostomia.
conclusionsAn RF model for predicting xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy and an RF model for predicting Grade 3 xerostomia in those with high risk of xerostomia showed good predictive ability.
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.