AI Article Synopsis

  • This study compares Monte Carlo (MC) and Collapsed Cone (CC) dose algorithms in RayStation 12A for 6 MV and 6 MV flattening filter-free (FFF) photon beams used in radiotherapy plans involving small and highly modulated fields.
  • It assesses how these algorithms perform in output dose measurements, examining their accuracy across various configurations such as rectangular and complex clinical plans.
  • Results indicate that while both algorithms show dependency on collimator angles in narrow fields, MC may perform better in highly modulated scenarios, but is less reliable with inhomogeneous ArcCHECK images, suggesting the need for homogeneous phantom setups for verification.

Article Abstract

Purpose: Many studies have demonstrated superior performance of Monte Carlo (MC) over type B algorithms in heterogeneous structures. However, even in homogeneous media, MC dose simulations should outperform type B algorithms in situations of electronic disequilibrium, such as small and highly modulated fields. Our study compares MC and Collapsed Cone (CC) dose algorithms in RayStation 12A. Under consideration are 6 MV and 6 MV flattening filter-free (FFF) photon beams, relevant for VMAT plans such as head-and-neck and stereotactic lung treatments with heterogeneities, as well as plans for multiple brain metastases in one isocenter, involving highly modulated small fields. We aim to investigate collimator angle dependence of small fields and performance differences between different combinations of ArcCHECK configuration and dose algorithm.

Methods: Several verification tests were performed, ranging from simple rectangular fields to highly modulated clinical plans. To evaluate and compare the performance of the models, the agreements between calculation and measurement are compared between MC and CC. Measurements include water tank measurements for test fields, ArcCHECK measurements for test fields and VMAT plans, and film dosimetry for small fields.

Results And Conclusions: In very small or narrow fields, our measurements reveal a strong dependency of dose output to collimator angle for VersaHD with Agility MLC, reproduced by both dose algorithms. ArcCHECK results highlight a suboptimal agreement between measurements and MC calculations for simple rectangular fields when using inhomogeneous ArcCHECK images. Therefore, we advocate for the use of homogeneous phantom images, particularly for static fields, in ArcCHECK verification with MC. MC might offer performance benefits for more modulated treatment fields. In ArcCHECK results for clinical plans, MC performed comparable to CC for 6 MV. For 6 MV FFF and the preferred homogeneous phantom image, MC resulted in consistently better results (13%-64% lower mean gamma index) compared to CC.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11633799PMC
http://dx.doi.org/10.1002/acm2.14522DOI Listing

Publication Analysis

Top Keywords

dose algorithms
12
small fields
12
highly modulated
12
fields arccheck
12
fields
11
monte carlo
8
collapsed cone
8
cone dose
8
type algorithms
8
vmat plans
8

Similar Publications

Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.

Support Care Cancer

January 2025

Oral Diagnosis Department, Faculdade de Odontolodia de Piracicaba, Universidade de Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.

Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.

View Article and Find Full Text PDF

Introduction/objective: Several nutraceuticals, food, and cosmetic products can be developed using royal jelly. It is known for its potential health benefits, including its ability to boost the immune system and reduce inflammation. It is rich in vitamins, minerals, and antioxidants, which can improve general health.

View Article and Find Full Text PDF

Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy.

View Article and Find Full Text PDF

Purpose: High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often employed during the procedure, minimizing overall planning time is crucial. In this study, we explore the clinical feasibility and time-saving potential of artificial intelligence (AI)-driven auto-reconstruction of transperineal needles in the context of US-guided prostate BT planning.

View Article and Find Full Text PDF

The kinetically-derived maximal dose (KMD) is defined as the maximum external dose at which kinetics are unchanged relative to lower doses, e.g., doses at which kinetic processes are not saturated.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!