A novel predictor for dosimetry data of lung and the radiation pneumonitis incidence prior to SBRT in lung cancer patients.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Radiation Pneumonitis*/epidemiology ; Radiation Pneumonitis*/etiology ; Lung Neoplasms*/radiotherapy ; Radiosurgery*/adverse effects ; Radiosurgery*/methods ; Radiometry*; Humans ; Male ; Female ; Aged ; Middle Aged ; Incidence ; Lung/radiation effects ; Radiotherapy Dosage ; Aged, 80 and over ; Radiotherapy Planning, Computer-Assisted

Normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) in lung cancer patients with stereotactic body radiation therapy (SBRT), which based on dosimetric data from treatment planning, are limited to patients who have already received radiation therapy (RT). This study aims to identify a novel predictive factor for lung dose distribution and RP probability before devising actionable SBRT plans for lung cancer patients. A comprehensive correlation analysis was performed on the clinical and dose parameters of lung cancer patients who underwent SBRT. Linear regression models were utilized to analyze the dosimetric data of lungs. The performance of the regression models was evaluated using mean squared error (MSE) and the coefficient of determination (R ² ). Correlational analysis revealed that most clinical data exhibited weak correlations with dosimetric data. However, nearly all dosimetric variables showed "strong" or "very strong" correlations with each other, particularly concerning the mean dose of the ipsilateral lung (MI) and the other dosimetric parameters. Further study verified that the lung tumor ratio (LTR) was a significant predictor for MI, which could predict the incidence of RP. As a result, LTR can predict the probability of RP without the need to design an elaborate treatment plan. This study, as the first to offer a comprehensive correlation analysis of dose parameters, explored the specific relationships among them. Significantly, it identified LTR as a novel predictor for both dose parameters and the incidence of RP, without the need to design an elaborate treatment plan.
(© 2024. The Author(s).)

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cphcf-2022-146 The Research Foundation on Cutting-edge Cancer Supportive Care

Keywords: Lung cancer; Lung tumor ratio; Machine learning models; Radiation pneumonitis; Stereotactic body radiation therapy

Date Created: 20240811 Date Completed: 20240811 Latest Revision: 20240814

20240814

PMC11317486

10.1038/s41598-024-69293-8

39128912