ArticleAdvances in radiation oncology
Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy.
Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Prognostic models for radiation-induced complications after radiotherapy in head and neck cancer patients.The Cochrane database of systematic reviews · 2025Pooled it
- Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist.Frontiers in oncology · 2021Pooled it
- Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer.European journal of nuclear medicine and molecular imaging · 2022Trial
- Differentiating Pressure Ulcer Risk Levels through Interpretable Classification Models Based on Readily Measurable Indicators.Healthcare (Basel, Switzerland) · 2024Article
- Artificial intelligence to predict outcomes of head and neck radiotherapy.Clinical and translational radiation oncology · 2023Article
- CART model to classify the drought status of diverse tomato genotypes by VPD, air temperature, and leaf-air temperature difference.Scientific reports · 2023Article
- Models for Studying the Distribution of Ticks and Tick-Borne Diseases in Animals: A Systematic Review and a Meta-Analysis with a Focus on Africa.Pathogens (Basel, Switzerland) · 2021Review
- Patient Risk Factor Profiles Associated With the Timing of Goals-of-Care Consultation Before Death: A Classification and Regression Tree Analysis.The American journal of hospice & palliative care · 2020Observational
- Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree.Frontiers in genetics · 2020Article
- Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands.Radiation oncology (London, England) · 2019Article
- Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling.BJR open · 2019Review
- Phosphoinositide 3-kinase-delta could be a biomarker for eosinophilic nasal polyps.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
18 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe explore whether a knowledge-discovery approach building a Classification and Regression Tree (CART) prediction model for weight loss (WL) in head and neck cancer (HNC) patients treated with radiation therapy (RT) is feasible. METHODS AND MATERIALS: HNC patients from 2007 to 2015 were identified from a prospectively collected database Oncospace. Two prediction models at different time points were developed to predict weight loss ≥5 kg at 3 months post-RT by CART algorithm: (1) during RT planning using patient demographic, delineated dose data, planning target volume-organs at risk shape relationships data and (2) at the end of treatment (EOT) using additional on-treatment toxicities and quality of life data.
resultsAmong 391 patients identified, WL predictors during RT planning were International Classification of Diseases diagnosis; dose to masticatory and superior constrictor muscles, larynx, and parotid; and age. At EOT, patient-reported oral intake, diagnosis, N stage, nausea, pain, dose to larynx, parotid, and low-dose planning target volume-larynx distance were significant predictive factors. The area under the curve during RT and EOT was 0.773 and 0.821, respectively.
conclusionsWe demonstrate the feasibility and potential value of an informatics infrastructure that has facilitated insight into the prediction of WL using the CART algorithm. The prediction accuracy significantly improved with the inclusion of additional treatment-related data and has the potential to be leveraged as a strategy to develop a learning health system.
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Registered trials
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