Enhanced dose prediction for head and neck cancer artificial intelligence-driven radiotherapy based on transfer learning with limited training data

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Hui-Ju Wang, Austen Maniscalco, David Sher, Mu-Han Lin, Steve Jiang, Dan Nguyen
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引用次数: 0

Abstract

Purpose

Training deep learning dose prediction models for the latest cutting-edge radiotherapy techniques, such as AI-based nodal radiotherapy (AINRT) and Daily Adaptive AI-based nodal radiotherapy (DA-AINRT), is challenging due to limited data. This study aims to investigate the impact of transfer learning on the predictive performance of an existing clinical dose prediction model and its potential to enhance emerging radiotherapy approaches for head and neck cancer patients.

Method

We evaluated the impact and benefits of transfer learning by fine-tuning a Hierarchically Densely Connected U-net on both AINRT and DA-AINRT patient datasets, creating ModelAINRT (Study 1) and ModelDA-AINRT (Study 2). These models were compared against pretrained and baseline models trained from scratch. In Study 3, both fine-tuned models were tested using DA-AINRT patients' final adaptive sessions to assess ModelAINRT ’s effectiveness on DA-AINRT patients, given that the primary difference is planning target volume (PTV) sizes between AINRT and DA-AINRT.

Result

Studies 1 and 2 revealed that the transfer learning model accurately predicted the mean dose within 0.71% and 0.86% of the prescription dose on the test data. This outperformed the pretrained and baseline models, which showed PTV mean dose prediction errors of 2.29% and 1.1% in Study 1, and 2.38% and 2.86% in Study 2 (P < 0.05). Additionally, Study 3 demonstrated significant improvements in PTV dose prediction error with ModelDA-AINRT, with a mean dose difference of 0.86% ± 0.73% versus 2.26% ± 1.65% (P < 0.05). This emphasizes the importance of training models for specific patient cohorts to achieve optimal outcomes.

Conclusion

Applying transfer learning to dose prediction models significantly improves prediction accuracy for PTV while maintaining similar dose performance in predicting organ-at-risk (OAR) dose compared to pretrained and baseline models. This approach enhances dose prediction models for novel radiotherapy methods with limited training data.

Abstract Image

基于有限训练数据迁移学习的头颈癌人工智能放疗剂量预测
目的:由于数据有限,为最新的尖端放疗技术(如AI-based淋巴结放疗(AINRT)和Daily Adaptive AI-based淋巴结放疗(DA-AINRT))训练深度学习剂量预测模型具有挑战性。本研究旨在探讨迁移学习对现有临床剂量预测模型预测性能的影响及其对头颈癌患者新放疗方法的潜力。方法:我们通过在AINRT和DA-AINRT患者数据集上微调分层密集连接的U-net来评估迁移学习的影响和益处,创建ModelAINRT(研究1)和ModelDA-AINRT(研究2)。将这些模型与从头训练的预训练模型和基线模型进行比较。在研究3中,考虑到AINRT和DA-AINRT之间的主要区别是计划靶体积(PTV)大小,两种微调模型都使用DA-AINRT患者的最终适应阶段进行测试,以评估ModelAINRT对DA-AINRT患者的有效性。结果:研究1和研究2显示迁移学习模型准确预测处方剂量在试验数据0.71%和0.86%范围内的平均剂量。研究1的PTV平均剂量预测误差为2.29%和1.1%,研究2的PTV平均剂量预测误差为2.38%和2.86% (P DA-AINRT),平均剂量差为0.86%±0.73%,而研究2为2.26%±1.65% (P结论:将迁移学习应用于剂量预测模型显著提高了PTV的预测精度,同时在预测器官危险(OAR)剂量方面保持了与预训练模型和基线模型相似的剂量性能。该方法增强了训练数据有限的新型放射治疗方法的剂量预测模型。
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来源期刊
CiteScore
3.60
自引率
19.00%
发文量
331
审稿时长
3 months
期刊介绍: Journal of Applied Clinical Medical Physics is an international Open Access publication dedicated to clinical medical physics. JACMP welcomes original contributions dealing with all aspects of medical physics from scientists working in the clinical medical physics around the world. JACMP accepts only online submission. JACMP will publish: -Original Contributions: Peer-reviewed, investigations that represent new and significant contributions to the field. Recommended word count: up to 7500. -Review Articles: Reviews of major areas or sub-areas in the field of clinical medical physics. These articles may be of any length and are peer reviewed. -Technical Notes: These should be no longer than 3000 words, including key references. -Letters to the Editor: Comments on papers published in JACMP or on any other matters of interest to clinical medical physics. These should not be more than 1250 (including the literature) and their publication is only based on the decision of the editor, who occasionally asks experts on the merit of the contents. -Book Reviews: The editorial office solicits Book Reviews. -Announcements of Forthcoming Meetings: The Editor may provide notice of forthcoming meetings, course offerings, and other events relevant to clinical medical physics. -Parallel Opposed Editorial: We welcome topics relevant to clinical practice and medical physics profession. The contents can be controversial debate or opposed aspects of an issue. One author argues for the position and the other against. Each side of the debate contains an opening statement up to 800 words, followed by a rebuttal up to 500 words. Readers interested in participating in this series should contact the moderator with a proposed title and a short description of the topic
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