Efficacious patient-specific QA for Vertebra SBRT using a high-resolution detector array SRS MapCHECK: AAPM TG-218 analysis.

Purpose: Patient-specific quality assurance (PSQA) for vertebra stereotactic body radiation therapy (SBRT) presents challenges due to highly modulated small fields with high-dose gradients between the target and spinal cord. This study aims to explore the use of the SRS MapCHECK® (SRSMC) for vertebra SBRT PSQA.

Methods: Twenty vertebra SBRT treatment plans including prescriptions 20 Gy/1 fraction and 24 Gy/2 fractions were selected for each of Millennium (M)-Multileaf Collimator (MLC), and high-definition (HD)-MLC.

Active small bowel sparing in intracavitary brachytherapy for cervical cancer.

Objective: To propose and evaluate an active method for sparing the small bowel in the treatment field of cervical cancer brachytherapy by prone position procedure.

Methods: The prone position procedure consists of five steps: making bladder empty, prone-positioning a patient on belly board, making the small bowel move to abdomen, filling the bladder with Foley catheter and finally turning the patient into the supine position. The proposed method was applied for the treatment of seven cervical cancer patients.

A customizable anthropomorphic phantom for dosimetric verification of 3D-printed lung, tissue, and bone density materials.

Purpose: To design and manufacture a customized thoracic phantom slab utilizing the 3D printing process, also known as additive manufacturing, consisting of different tissue density materials. Here, we demonstrate the 3D-printed phantom's clinical feasibility for imaging and dosimetric verification of volumetric modulated arc radiotherapy (VMAT) plans for lung and spine stereotactic ablative body radiotherapy (SABR) through end-to-end dosimetric verification.

Methods: A customizable anthropomorphic phantom slab was designed using the CT dataset of a commercial phantom (adult female ATOM dosimetry phantom, CIRS Inc.

Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.

Background: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subject to inter-observer variability. This study aims to a) investigate whether customized, deep-learning-based auto-segmentation could overcome the limitations of manual contouring and b) compare its performance against a typical, atlas-based auto-segmentation method organ structures in liver cancer.