Dataset Information

Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy.

ABSTRACT:

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.

SUBMITTER: Cheng Z

PROVIDER: S-EPMC6127872 | biostudies-literature | 2018 Jul-Sep

REPOSITORIES: biostudies-literature

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Publications

Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy.

Cheng Zhi Z Nakatsugawa Minoru M Hu Chen C Robertson Scott P SP Hui Xuan X Moore Joseph A JA Bowers Michael R MR Kiess Ana P AP Page Brandi R BR Burns Laura L Muse Mariah M Choflet Amanda A Sakaue Kousuke K Sugiyama Shinya S Utsunomiya Kazuki K Wong John W JW McNutt Todd R TR Quon Harry H

Advances in radiation oncology 20171207 3

<h4>Objective</h4>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.<h4>Methods and materials</h4>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 algorit ...[more]

PMID: 30197940

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Dataset Information

Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy.

Objective

Methods and materials

Results

Conclusions

Publications

Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy.

Similar Datasets

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