Affiliation:
1. National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
2. Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
Abstract
Abstract
Background and purpose The purpose of this study is to investigate the clinical application and assessment of deep learning (DL)-assisted automatic radiotherapy planning for lung cancer patients.Materials and methods We developed a deep learning model for predicting patient-specific dose that was trained and validated on a dataset of 235 lung cancer patients, and the model was integrated into clinical workflow to assist planners in generating treatment plans. We retrospectively selected and recovered additional 50 clinically treated lung cancer patients’ manual volumetric modulated arc therapy (VMAT) plans with different target volumes and different treatment patterns. Subsequently, automatic plans were generated for each of these patients. Both automatic and manual plans were subsequently compared in terms of overall plan quality metric (PQM), target coverage and homogeneity, organ at risk (OAR) sparing, monitor units (MUs), and planning time. Additionally, qualitative reviews of automatic and manual plans were implemented by four expert reviewers to assess the clinical applicability of DL-assisted automatic plans.Results The average PQM score was 40.7 ± 13.1 for manual plans and 40.8 ± 13.5 for automatic plans, and they had equivalent overall plan quality. The targets coverage and homogeneity of the automatic plans were considered equivalent or superior when compared to manual plans. Both plans had their own advantages in OAR sparing, such as better sparing of lung for manual plans and better sparing of heart for automatic plans. It is worth to note that the average planning time of automatic plans was reduced from 103.1 ± 18.5 minutes to 32.6 ± 5.3 minutes (P<0.001) and the MUs were reduced from 789.9 ± 234.3 to 692.5 ± 210.7 (P<0.001). In qualitative evaluation, automatic plans were deemed to be clinically acceptable for treatment in 88% of reviews (176/200), and all were accepted after fine tuning. Most expert reviews indicated a preference for equivalence between automatic and manual plans when making their selection.Conclusion The DL-assisted lung cancer plans demonstrated comparable or superior quality to manual plans, improved planning and treatment efficiency, and significantly reduced planning time and MUs. It has the potential to enhance the workflow of radiotherapy departments, ultimately providing tangible benefit to lung cancer patients.
Publisher
Research Square Platform LLC
Reference28 articles.
1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. vol 68, pg 394,. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries (2018). Ca-a Cancer Journal for Clinicians. 2020; 70:313-.
2. Automatic treatment planning for cervical cancer radiation therapy using direct three-dimensional patient anatomy match;Zhang D;J Appl Clin Med Phys,2022
3. Fully automated VMAT treatment planning for advanced-stage NSCLC patients;Della Gala G;Strahlenther Onkol,2017
4. Use of Simultaneous Radiation Boost Achieves High Control Rates in Patients With Non-Small-Cell Lung Cancer Who Are Not Candidates for Surgery or Conventional Chemoradiation;Swanick CW;Clin Lung Cancer,2015
5. Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations;Hussein M;Br J Radiol,2018