دورية أكاديمية
Automated Robust Planning for IMPT in Oropharyngeal Cancer Patients Using Machine Learning.
| العنوان: | Automated Robust Planning for IMPT in Oropharyngeal Cancer Patients Using Machine Learning. |
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| المؤلفون: | van Bruggen IG; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands. Electronic address: i.g.van.bruggen@umcg.nl., Huiskes M; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands., de Vette SPM; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands., Holmström M; RaySearch Laboratories, Stockholm, Sweden., Langendijk JA; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands., Both S; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands., Kierkels RGJ; Radiotherapiegroep, Deventer, the Netherlands., Korevaar EW; Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands. |
| المصدر: | International journal of radiation oncology, biology, physics [Int J Radiat Oncol Biol Phys] 2023 Apr 01; Vol. 115 (5), pp. 1283-1290. Date of Electronic Publication: 2022 Dec 16. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier, Inc Country of Publication: United States NLM ID: 7603616 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-355X (Electronic) Linking ISSN: 03603016 NLM ISO Abbreviation: Int J Radiat Oncol Biol Phys Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: New York, NY : Elsevier, Inc Original Publication: Elmsford, N. Y., Pergamon Press. |
| مواضيع طبية MeSH: | Proton Therapy*/methods , Oropharyngeal Neoplasms*/diagnostic imaging , Oropharyngeal Neoplasms*/radiotherapy , Radiotherapy, Intensity-Modulated*/methods , Xerostomia*, Humans ; Radiotherapy Planning, Computer-Assisted/methods ; Radiotherapy Dosage ; Organs at Risk/diagnostic imaging |
| مستخلص: | Purpose: The aim of this study was to evaluate an automated treatment planning method for robustly optimized intensity modulated proton therapy (IMPT) plans for oropharyngeal carcinoma patients and to compare the results with manually optimized robust IMPT plans. Methods and Materials: An atlas regression forest-based machine learning (ML) model for dose prediction was trained on CT scans, contours, and dose distributions of robust IMPT plans of 88 oropharyngeal cancer (OPC) patients. The ML model was combined with a robust voxel and dose volume histogram-based dose mimicking optimization algorithm for 21 perturbed scenarios to generate a machine-deliverable plan from the predicted dose distribution. Machine learning optimization (MLO) configuration was performed using a cross-validation approach with 3 × 8 tuning patients and comprised of adjustments to the mimicking optimization, to generate higher-quality MLO plans. Independent testing of the MLO algorithm was performed with another 25 patients. Plan quality of clinical and MLO plans was evaluated by the clinical target volume (D98% voxel-wise minimum dose >94%), organ at risk (OAR) doses, and the normal tissue complication probability (NTCP) (sum (Σ) of grade-2 and grade-3 dysphagia and xerostomia). Results: Adequate robust target coverage was achieved in 24/25 clinical plans and in 23/25 MLO plans in the primary clinical target volume (CTV). In the elective CTV, 22/25 clinical plans and 24/25 MLO plans passed the robust target coverage evaluation threshold. The MLO average Σgrade 2 and Σgrade 3 NTCPs were comparable to the clinical plans (Σgrade 2 NTCPs: clinical 47.5% vs MLO 48.4%, Σgrade 3 NTCPs: clinical 11.9% vs MLO 12.3%). Significant increases in OAR average doses in MLO plans were found in the pharynx constrictor muscles (163 cGy, P = .002) and cervical esophagus (265 cGy, P = .002). The MLO plans were created within 45 minutes. Conclusion: This study showed that automated MLO planning can generate robustly optimized MLO plans with quality comparable to clinical plans in OPC patients. (Copyright © 2022. Published by Elsevier Inc.) |
| تواريخ الأحداث: | Date Created: 20221219 Date Completed: 20230320 Latest Revision: 20230511 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1016/j.ijrobp.2022.12.004 |
| PMID: | 36535432 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1879-355X |
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| DOI: | 10.1016/j.ijrobp.2022.12.004 |