Browsing by Author "Saju Bhasi"

Now showing 1 - 2 of 2

A Monte Carlo simulation-based decision support system for radiation oncologists in the treatment of glioblastoma multiforme
(Springer Science and Business Media Deutschland GmbH, 2024) C. Praveen Kumar; Lalit M. Aggarwal; Saju Bhasi; Neeraj Sharma
In the present research, we have developed a model-based crisp logic function statistical classifier decision support system supplemented with treatment planning systems for radiation oncologists in the treatment of glioblastoma multiforme (GBM). This system is based on Monte Carlo radiation transport simulation and it recreates visualization of treatment environments on mathematical anthropomorphic brain (MAB) phantoms. Energy deposition within tumour tissue and normal tissues are graded by quality audit factors which ensure planned dose delivery to tumour site thereby minimising damages to healthy tissues. The proposed novel methodology predicts tumour growth response to radiation therapy from a patient-specific medicine quality audit perspective. Validation of the study was achieved by recreating thirty-eight patient-specific mathematical anthropomorphic brain phantoms of treatment environments by taking into consideration density variation and composition of brain tissues. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost-effective, nor patient-specific customized and is often less accurate. The above-highlighted drawbacks can be overcome by using open-source Electron Gamma Shower (EGSnrc) software and clinical case reports for MAB phantom synthesis which would result in accurate dosimetry with due consideration to the time factors. Considerable dose deviations occur at the tumour site for environments with intraventricular glioblastoma, haematoma, abscess, trapped air and cranial flaps leading to quality factors with a lower logic value of 0. Logic value of 1 depicts higher dose deposition within healthy tissues and also leptomeninges for majority of the environments which results in radiation-induced laceration. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Unified Dosimetry Quality Audit Index: an integrated Monte Carlo model-based quality assurance ranking for radiotherapy treatment of glioblastoma multiforme
(Taylor and Francis Ltd., 2023) Praveen Kumar C; Lalit M. Aggarwal; Saju Bhasi; Neeraj Sharma
In the present research, we have developed a Unified Dosimetry Quality Audit Index (UDQAI) decision support system supplemented with treatment planning systems for radiation oncologists. This will aid the radiotherapy treatment of Glioblastoma Multiforme and is based on the Integrated Monte Carlo Model (IMC). IMC model is a quality assurance strategy for the computation of total dose, scatter dose deposition at the GBM site and healthy tissues/layers within the brain. It is a combination of Proliferation-Hypoxia-Invasion-Necrosis-Angiogenesis-Radiotherapy-Quality Assurance model, Radiation-induced damage-Quality Assurance model, Boltzmann radiation transport, Talaraich Tournoux image coordinate system for positioning of the tumour from the CT image and GBM treatment environments/patient case reports. The IMC model was validated by recreating GBM patient treatment environments on a novel computational heterogeneous phantom, the Mathematical Anthropomorphic Brain (MAB) phantom. Dose computations accomplished through water phantom, tissue-equivalent head phantoms are neither cost effective; nor patient-specific and are non-customised and less accurate. Thirty-Eight patient-specific GBM treatment environments were recreated on the MAB phantom. MAB phantom synthesis requires mimicking real human brain tissues/layers. Open-source protein databases such as UniProt, Swiss-model and Peptide were atlas employed to compute the elemental composition of different brain layers/tissues. Brain layers and tissues were synthesised as slabs using the Constructive Solid Geometry technique within the MAB phantom on the Electron Gamma Shower radiation transport platform. Phantom slab dimensions were computed by superimposing CT scan images of the brain with GBM and associated comorbidities on Talairach-Tournoux coordinate system. Slab surfaces of the phantom were defined by constructive solid geometry approach using quadratic equations. Energy deposition inside different slabs of phantom is calculated by Analog Monte Carlo game. Computed total dose and scatter dose deposition within the tumour and normal tissues/brain layers were graded by UDQAI which ensures planned dose delivery to the tumour site for radiation-induced cancer cell death minimising healthy tissue damages. The results of the present experimentation show that the proposed framework is promising and outperforms other recent deep learning-based decision systems. Deep learning-based decision systems are a hidden process which is unaware of the physical transport process of charged particles. UDQAI classification of treatment environments predicts that 76.32% of the total environs deposited a substantial amount of dose to the GBM locus. During treatment, healthy tissues and brain layers receive a part of transported energy. This fact is reinforced by the average deviation at the GBM site −8.2% on the contrary, healthy tissues encircling GBM reported −3.909%. These encouraging results would pave the way for the development of a biomathematical tumour growth model and Monte Carlo radiation transport-linked decision assist algorithm for radiation oncologists in the near future. © 2022 Informa UK Limited, trading as Taylor & Francis Group.