Evidence map›Paper›PMID 42415304›Full record

ArticleJournal of applied clinical medical physics2026

Feasibility study of three-dimensional dose prediction of esophageal cancer radiotherapy based on CGAN.

Qichao Hu, Yanrui Li, Youyi Tao, Yanxia Wang, Xudong Kong

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Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Qichao HuDepartment of Radiation Oncology, Donghai County People's Hospital affiliated to Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, China.
Yanrui LiDepartment of Radiation Oncology, Donghai County People's Hospital affiliated to Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, China.
Youyi TaoDepartment of Radiation Oncology, Donghai County People's Hospital affiliated to Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, China.
Yanxia WangDepartment of Radiation Oncology, Donghai County People's Hospital affiliated to Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, China.
Xudong KongMedical Physics in Radiation Oncology, Department of Radiation Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China.

Funding

Lianyungang Association for Science and Technology Soft Science Project LYGKXLXKT042
6 · The paper itself

Abstract

objectiveTo evaluate the feasibility of a Conditional Generative Adversarial Network (CGAN)-based model (2UDCNN-AT-GAN) for predicting 3D radiotherapy dose distributions in esophageal cancer, and to assess its potential utility as a guidance tool for Intensity-Modulated Radiotherapy (IMRT) plan design.

methodsA CGAN-based model was trained on 95 esophageal cancer IMRT plans and tested on 15 plans. The model integrated a dual-path encoder-decoder, attention-gated fusion, and Dose-volume histogram (DVH)-based conditioning. Predicted dose distributions were compared with manual clinical plans using DVH metrics, plan evaluation parameters, and error analysis.

resultsAll predicted plans met clinical constraints (100% qualification rate). Predicted and manual plans identified concordant near-limit organs at risk (OARs) cases. This consistency mirrored the critical OAR constraints that govern target dose distribution. The mean prediction error was 2.88 ± 2.88% with target errors of 1.00 ± 1.00%. The homogeneity index (0.08 ± 0.02) matched that of manual plans, while the conformity index (CI) was slightly lower (p = 0.0034). The mean Dice Similarity Coefficient (DSC) was 0.916.

conclusionIn this feasibility study, the 2UDCNN-AT-GAN model generated preliminary clinically acceptable dose predictions that met basic clinical constraints and showed spatial similarity to manual plans. A systematic ablation study confirmed that the integrated dual-path and attention-gated architecture outperforms individual components, establishing proof-of-concept feasibility for esophageal IMRT planning guidance. However, systematic target underdosage and the single-institution scope limit its current role to a preliminary planning reference.

Indexed as

Esophageal NeoplasmsGenerative Adversarial NetworksOrgans at RiskRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedFeasibility StudiesHumansRadiotherapy Dosageesophageal cancergenerative adversarial networksradiotherapythree‐dimensional dose prediction

Identifiers

PMID42415304
PMCPMC13341968

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