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The effects of mis-centering on radiation dose during CT head examination: A phantom study

Isa INC, Rahmat SMS, Dom SM, Kayun Z, Karim MKA

J Xray Sci Technol, 2019;27(4):631-639.
PMID: 31205011 DOI: 10.3233/XST-190491

There are several factors that may contribute to the increase in radiation dose of CT including the use of unoptimized protocols and improper scanning technique. In this study, we aim to determine significant impact on radiation dose as a result of mis-centering during CT head examination. The scanning was performed by using Toshiba Aquilion 64 slices multi-detector CT (MDCT) scanner and dose were measured by using calibrated ionization chamber. Two scanning protocols of routine CT head; 120 kVp/ 180 mAs and 100 kVp/ 142 mAs were used represent standard and low dose, respectively. As reference measurement, the dose was first measured on standard cylindrical polymethyl methacrylate (PMMA) phantom that positioned at 104 cm from the floor (reference isocenter). The positions then were varied to simulate mis-centering by 5 cm from isocenter, superiorly and inferiorly at 109 cm, 114 cm, 119 cm, 124 cm and 99 cm, 94 cm, 89 cm, 84 cm, respectively. Scanning parameter and dose information from the console were recorded for the radiation effective dose (E) measurement. The highest mean CTDIvol value for MCS and MCI were 105.06 mGy (at +10 cm) and 105.51 mGy (at - 10 cm), respectively which differed significantly (p
Fulltext Hyperparameter Tuning and Pipeline Optimization via Grid Search Method and Tree-Based AutoML in Breast Cancer Prediction

Radzi SFM, Karim MKA, Saripan MI, Rahman MAA, Isa INC, Ibahim MJ

J Pers Med, 2021 Sep 29;11(10).
PMID: 34683118 DOI: 10.3390/jpm11100978

Automated machine learning (AutoML) has been recognized as a powerful tool to build a system that automates the design and optimizes the model selection machine learning (ML) pipelines. In this study, we present a tree-based pipeline optimization tool (TPOT) as a method for determining ML models with significant performance and less complex breast cancer diagnostic pipelines. Some features of pre-processors and ML models are defined as expression trees and optimal gene programming (GP) pipelines, a stochastic search system. Features of radiomics have been presented as a guide for the ML pipeline selection from the breast cancer data set based on TPOT. Breast cancer data were used in a comparative analysis of the TPOT-generated ML pipelines with the selected ML classifiers, optimized by a grid search approach. The principal component analysis (PCA) random forest (RF) classification was proven to be the most reliable pipeline with the lowest complexity. The TPOT model selection technique exceeded the performance of grid search (GS) optimization. The RF classifier showed an outstanding outcome amongst the models in combination with only two pre-processors, with a precision of 0.83. The grid search optimized for support vector machine (SVM) classifiers generated a difference of 12% in comparison, while the other two classifiers, naïve Bayes (NB) and artificial neural network-multilayer perceptron (ANN-MLP), generated a difference of almost 39%. The method's performance was based on sensitivity, specificity, accuracy, precision, and receiver operating curve (ROC) analysis.
Fulltext Evaluating Factors Affecting Mean Glandular Dose in Mammography: Insights from a Retrospective Study in Dubai

Noor KAM, Norsuddin NM, Karim MKA, Isa INC, Ulaganathan V

Diagnostics (Basel), 2024 Nov 15;14(22).
PMID: 39594234 DOI: 10.3390/diagnostics14222568

BACKGROUND/OBJECTIVE: This study evaluates the mean glandular dose (MGD) in mammography screening for women aged 40-69 in Dubai, based on a retrospective analysis of a dose survey involving 2599 participants.
METHODS: MGD was calculated using the Dance formula.
RESULTS: The average MGD was 0.96 ± 0.39 mGy for mediolateral oblique (MLO) views and 0.81 ± 0.33 mGy for craniocaudal (CC) views. Weak inverse correlations were found between age and organ dose (OD) for both views, while a direct relationship was observed between breast thickness and entrance skin dose (ESD). In adjusted models, ESD was strongly associated with MGD (β = 1.04, 95% CI: 0.97, 1.09), while OD showed a moderate association (β = 0.44, 95% CI: 0.40, 0.49). Significant variations in ESD, OD, and MGD were noted across age groups and breast thicknesses.
CONCLUSIONS: Lower MGD indicates reduced radiation exposure risk, while higher MGD in MLO views suggests improved imaging quality. Monitoring and optimizing MGD are essential for enhancing patient safety and screening efficacy.

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