ArticleJournal of applied clinical medical physics2025
Prediction of lung dose-volume parameters of the patients with esophageal cancer undergoing radiotherapy based on artificial neural network.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Prediction of lung dose-volume parameters of the patients with esophageal cancer undergoing radiotherapy based on artificial neural network.Journal of applied clinical medical physics · 2025Article
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Abstract
objectiveConsidering the complex and nonlinear relationship between characteristic parameters of tumors (location and morphology) and lung dose-volume parameters of patients with esophageal cancer (EPC) undergoing intensity-modulated radiation therapy (IMRT), the model identification method of artificial neural network (ANN) was adopted to build the model to predict lung dose-volume parameters under different characteristic parameters of tumors. The goal is to enhance the efficiency and quality of radiotherapy planning and reduce the risk of radiation-induced lung injury (RILI) by providing pre-planning dose predictions.
methods1). In the previous research work, a retrospective analysis was done on 103 cases of patients with EPC who were treated by radiotherapy, in which a linear regression model was employed and had proved the association between tumor morphology [including tumor relative length (L) and "tumor axial cross-sectional area" (S)] and lung dose-volume parameters (including V
results1). Quadratic regression models were applied to analyze the relationship of P with V
conclusionThe ANN model developed in this study can effectively predict lung dose-volume parameters for patients with EPC undergoing IMRT. It provides pre-planning guidance that enhances the efficiency and quality of radiotherapy planning and reducing the risk of RILI.
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