Work

AI-enhanced cancer radiotherapy quality assessment: utilizing daily linac performance, radiomics, dosimetrics, and planning complexity

Year: 2025

Type: article

Abstract: Objective: This study aimed to develop and validate an Informer- Convolutional Neural Network (CNN) model to predict the gamma passing rate (GPR) for patient-specific quality assurance in volumetric modulated arc therapy (VMAT), enhancing treatment safety and efficacy by integrating multiple data sources. Methods: Analyzing 465 VMAT treatment plans covering head & neck, chest, and abdomen, the study extracted data from 31 complexity indicators, 123 radiomics features, and 123 dosimetrics indices, along with daily linac performance data including 141 key performance indicators. A hybrid Informer-CNN architecture was used to handle both temporal and non-temporal data for predicting GPR. Results: The Informer-CNN model demonstrated superior predictive performance over traditional models like Convolutional Neural Networks (CNN), Long Short-Term Memory(LSTM), and Informer. Specifically, in the validation set, the model achieved a mean absolute error (MAE) of 0.0273 and a root mean square error (RMSE) of 0.0360 using the 3%/3mm criterion. In the test set, the MAE was 0.0327 and the RMSE was 0.0468. The model also showed high classification performance with AUC scores of 0.97 and 0.95 in test and validation sets, respectively. Conclusion: The developed Informer-CNN model significantly enhances the prediction accuracy and classification of gamma passing rates in VMAT treatment plans. It facilitates early integration of daily accelerator performance data, improving the assessment and verification of treatment plans for better patient-specific quality assurance.

Source: Frontiers in Oncology

DOI: 10.3389/fonc.2025.1503188

Authors: Jia Deng, Yaolin Zhao, Dengdian Huang, Qingju Zhang, Ye Hong, Xiangyang Wu

Institution: Xi'an Jiaotong University

Language: English

Topic: Advanced Radiotherapy Techniques

Subfield: Radiation

Field: Physics and Astronomy

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801