ABSTRACT
Purpose: The aim of the current study is to commission compensator-based total body irradiation (TBI) and to compare surface dose using percentage depth dose (PDD) while varying the distance between beam spoiler and phantom surface. Materials and Methods: TBI commissioning was performed on Elekta Synergy® Platform linear accelerator for bilateral extended source to surface distance treatment technique. The PDD was measured by varying the distance (10 cm, 20 cm, 30 cm, and 40 cm) between the beam spoiler and the phantom surface. Beam profile and half-value layer (HVL) measurement were carried out using the FC65 ion-chamber. Quality assurance (QA) was performed using an in-house rice-flour phantom (RFP). In-vivo diodes (IVD) were placed on the RFP at various regions to measure the delivered dose, and it was compared to the calculated dose. Results: An increase in Dmax and surface dose was observed when beam spoiler was moved away from the phantom surface. The flatness and symmetry of the beam profile were calculated. The HVL of Perspex and aluminum is 17 cm and 8 cm, respectively. The calculated dose of each region was compared to the measured dose on the RFP with IVD, and the findings showed that the variation was <4.7% for both Perspex and Aluminum compensators. Conclusion: The commissioning of the compensator-based TBI technique was performed and its QA measurements were carried out. The Mayneord factor corrected PDD and measured PDD values were compared. The results are well within the clinical tolerance limit. This study concludes that 10 cm -20 cm is the optimal distance from the beam spoiler to phantom surface to achieve prescribed dose to the skin.
ABSTRACT
CONTEXT: Cancer Radiomics is an emerging field in medical imaging and refers to the process of converting routine radiological images that are typically qualitatively interpreted to quantifiable descriptions of the tumor phenotypes and when combined with statistical analytics can improve the accuracy of clinical outcome prediction models. However, to understand the radiomic features and their correlation to molecular changes in the tumor, first, there is a need for the development of robust image analysis methods, software tools and statistical prediction models which is often limited in low- and middle-income countries (LMIC). AIMS: The aim is to build a framework for machine learning of radiomic features of planning computed tomography (CT) and positron emission tomography (PET) using open source radiomics and data analytics platforms to make it widely accessible to clinical groups. The framework is tested in a small cohort to predict local disease failure following radiation treatment for head-and-neck cancer (HNC). The predictors were also compared with the existing Aerts HNC radiomics signature. SETTINGS AND DESIGN: Retrospective analysis of patients with locally advanced HNC between 2017 and 2018 and 31 patients with both pre- and post-radiation CT and evaluation PET were selected. SUBJECTS AND METHODS: Tumor volumes were delineated on baseline PET using the semi-automatic adaptive-threshold algorithm and propagated to CT; PyRadiomics features (total of 110 under shape/intensity/texture classes) were extracted. Two feature-selection methods were tested for model stability. Models were built based on least absolute shrinkage and selection operator-logistic and Ridge regression of the top pretreatment radiomic features and compared to Aerts' HNC-signature. Average model performance across all internal validation test folds was summarized by the area under the receiver operator curve (ROC). RESULTS: Both feature selection methods selected CT features MCC (GLCM), SumEntropy (GLCM) and Sphericity (Shape) that could predict the binary failure status in the cross-validated group and achieved an AUC >0.7. However, models using Aerts' signature features (Energy, Compactness, GLRLM-GrayLevelNonUniformity and GrayLevelNonUniformity-HLH wavelet) could not achieve a clear separation between outcomes (AUC = 0.51-0.54). CONCLUSIONS: Radiomics pipeline included open-source workflows which makes it adoptable in LMIC countries. Additional independent validation of data is crucial for the implementation of radiomic models for clinical risk stratification.
