Development and application of an elastic net logistic regression model to investigate the impact of cardiac substructure dose on radiation-induced pericardial effusion in patients with NSCLC.

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Abstract

Typically, cardiac substructures are neither delineated nor analyzed during radiation treatment planning. Therefore, we developed a novel machine learning model to evaluate the impact of cardiac substructure dose for predicting radiation-induced pericardial effusion (PCE).

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Journal

Journal ID (iso-abbrev): Acta Oncol

Title: Acta oncologica (Stockholm, Sweden)

Publisher: Informa UK Limited

ISSN (Electronic): 1651-226X

ISSN (Print): 0284-186X

Publication date (Electronic): Oct 2020

Volume: 59

Issue: 10

Affiliations

[1 ] Department of Radiation Physics, The University of Texas-MD Anderson Cancer Center, Houston, TX, USA.

[2 ] Department of Imaging Physics, The University of Texas-MD Anderson Cancer Center, Houston, TX, USA.

[3 ] Department of Radiation Oncology, The University of Texas-MD Anderson Cancer Center, Houston, TX, USA.

[4 ] Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.

[5 ] Department of Head & Neck Surgery, The University of Texas-MD Anderson Cancer Center, Houston, TX, USA.

Article

DOI: 10.1080/0284186X.2020.1794034

PubMed ID: 32678696

SO-VID: 52bdbb6a-a635-4521-a388-42a3b01778cb

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