Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy
| Autor: | Ana P. Kiess, Zhi Cheng, Kousuke Sakaue, Utsunomiya Kazuki, Laura Burns, M.R. Bowers, Todd McNutt, Scott P. Robertson, Harry Quon, Chen Hu, Xuan Hui, Brandi R. Page, Shinya Sugiyama, John Wong, Joseph Moore, Minoru Nakatsugawa, M. Muse, A. Choflet |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Larynx
Oncology Cart lcsh:Medical physics. Medical radiology. Nuclear medicine medicine.medical_specialty medicine.medical_treatment lcsh:R895-920 lcsh:RC254-282 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Quality of life Weight loss Internal medicine medicine Radiology Nuclear Medicine and imaging business.industry Head and neck cancer Area under the curve medicine.disease lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens Head and Neck Cancer Radiation therapy medicine.anatomical_structure 030220 oncology & carcinogenesis Informatics medicine.symptom business |
| Zdroj: | Advances in Radiation Oncology, Vol 3, Iss 3, Pp 346-355 (2018) Advances in Radiation Oncology |
| ISSN: | 2452-1094 |
| Popis: | Objective We 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. Results Among 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. Conclusions We 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. |
| Databáze: | OpenAIRE |
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