Training and Evaluation of a Knowledge-Based Model for Automated Treatment Planning of Multiple Brain Metastases: An Advanced Study

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Training and Evaluation of a Knowledge-Based Model for Automated Treatment Planning of Multiple Brain Metastases: An Advanced Study

September 12, 2021 Medicine and Medical Science 0

Aim: Due to its capacity to give steep dose gradients around targets as well as modest doses to essential structures, volumetric modulated arc therapy (VMAT) has been used to plan and treat numerous cranial lesion metastases utilising a single isocenter. When compared to employing a single isocenter for each lesion, VMAT treatment takes significantly less time. However, approaches to shorten treatment planning time for these patients while simultaneously standardising plan quality must be developed. In this paper, we show how to plan numerous cranial SRS cases using RapidPlan, a knowledge-based treatment (KBP) planning software.

Methods: A model was trained using 66 patient plans with 125 lesions (range 1-4, median 1). In addition, the model was tested on 10 previously treated cases that were picked at random. For target coverage and critical organ dose, the clinical plans were compared to RapidPlan plans.

Results: There was no significant difference in coverage to the target volume, gradient index (GI), conformance index (CI), or minimal dose to the target between the original clinical plan and the plan created by KBP. There was no discernible difference in dosages to the brainstem, brain, chiasm, eyes, optic nerves, and lenses when they were compared. The clinical plan had somewhat greater target dosage uniformity, but the difference was statistically insignificant.

Conclusion: This study shows that KBP may be taught and used effectively to help reduce treatment planning time while also standardising and improving treatment plan quality.

Author (S) Details

Vishruta A. Dumane
Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Tzu-Chi Tseng
Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Ren-Dih Sheu
Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Yeh-Chi Lo
Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Vishal Gupta
Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Audrey Saitta
Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Kenneth E. Rosenzweig
Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Sheryl Green
Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

View Book :- https://stm.bookpi.org/HMMS-V18/article/view/3250

 

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