ArticleJournal of applied clinical medical physics2022
The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer.
Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 21 citations in OpenAlex.
- AI-assisted VMAT planning incorporating deep learning-based dose prediction for head and neck cancer: feasibility of quality standardization and human intervention for irregular cases.Journal of applied clinical medical physics · 2026Article
- Generalizability of deep learning-based dose conversion model in proton beam therapy.Journal of applied clinical medical physics · 2026Article
- A tandem reinforcement learning framework for localized prostate cancer treatment planning and machine parameter optimization.Medical physics · 2026Article
- Recent advancements in the application of artificial intelligence-based approaches for screening, diagnosis, prognosis and treatment of cervical cancer.Oncology reviews · 2026Review
- Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review.Visual computing for industry, biomedicine, and art · 2025Review
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning-powered radiotherapy dose prediction: clinical insights from 622 patients across multiple sites tumor at a single institution.Radiation oncology (London, England) · 2025Article
- Enhancing stereotactic ablative boost radiotherapy dose prediction for bulky lung cancer: A multi-scale dilated network approach with scale-balanced structure loss.Journal of applied clinical medical physics · 2025Article
- A new deep learning model for predicting IMRT dose distributions for lung cancer with dose masks.Frontiers in oncology · 2025Article
- Dose prediction for cervical cancer in radiotherapy based on the beam channel generative adversarial network.Heliyon · 2024Article
- Application and progress of artificial intelligence in radiation therapy dose prediction.Clinical and translational radiation oncology · 2024Review
- Exploring the impact of network depth on 3D U-Net-based dose prediction for cervical cancer radiotherapy.Frontiers in oncology · 2024Article
- Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers.Medical physics · 2023Article
- A dual deep neural network for auto-delineation in cervical cancer radiotherapy with clinical validation.Radiation oncology (London, England) · 2022Article
- The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer.Journal of applied clinical medical physics · 2022Article
- Direct Dose Prediction With Deep Learning for Postoperative Cervical Cancer Underwent Volumetric Modulated Arc Therapy.Technology in cancer research & treatmentArticle
- A Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy.Technology in cancer research & treatmentArticle
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo develop a 3D-Unet dose prediction model to predict the three-dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explore the feasibility of model generalization.
methodsOne hundred and seventeen cases of cervical cancer and 20 cases of endometrial cancer treated with VMAT were used for the model training, validation, and test. The prescribed dose was 50.4 Gy in 28 fractions. Eight independent channels of contoured structures were input to the model, and the dose distribution was used as the output of the model. The 3D-Unet prediction model was trained and validated on the training set (n = 86) and validation set (n = 11), respectively. Then the model was tested on the test set (n = 20) of cervical cancer and endometrial cancer, respectively. The results between clinical dose distribution and predicted dose distribution were compared in the following aspects: (a) the mean absolute error (MAE) within the body, (b) the Dice similarity coefficients (DSCs) under different isodose volumes, (c) the dosimetric indexes including the mean dose (D
resultsThe model can accurately predict the dose distribution of the VMAT plan for cervical cancer and endometrial cancer. The overall average MAE and maximum MAE for cervical cancer were 2.43 ± 3.17% and 3.16 ± 4.01% of the prescribed dose, respectively, and for endometrial cancer were 2.70 ± 3.54% and 3.85 ± 3.11%. The average DSCs under different isodose volumes is above 0.9. The predicted dosimetric indexes and DVHs are equivalent to the clinical dose for both cervical cancer and endometrial cancer, and there is no statistically significant difference.
conclusionA 3D-Unet dose prediction model was developed for VMAT of cervical cancer, which can predict the dose distribution accurately for cervical cancer. The model can also be generalized for endometrial cancer with good performance.
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