Occupational dose measurement in interventional cardiology practice

Ensuring the safety of healthcare workers in interventional cardiology necessitates effective monitoring of occupational radiation exposure. This study aims to assess the accuracy of the over-apron single dosimetric approach compared with double dosimetric methods and explore the relationship between under-apron and over-apron doses. This investigation showed that the prescribed annual dose constraint of 20 mSv year-1 was not exceeded by the maximum annual occupational doses determined by dosimetric algorithms, which were 0.13 ± 0.02, 0.15 ± 0.02 and 0.27 ± 0.04 mSv, respectively. The study demonstrated excellent statistically significant correlations among single and double dosimetric algorithms and between direct under-apron and over-apron doses. Consequently, single dosimetric algorithms could effectively estimate doses for double dosimetric algorithms, highlighting the limited added value of under-apron measurements. These findings significantly impact the practice of interventional cardiology in Sri Lanka, playing a crucial role in enhancing radiation protection measures.

Assessment of dosimetric approaches in evaluating radiation exposure for interventional cardiologists in Sri Lanka

Interventional cardiologists face significant radiation exposure during interventional cardiology procedures. Therefore, this study focuses on assessing radiation exposure among interventional cardiologists during their procedures. Specifically, it aims to determine the effectiveness of both single and double dosimeter methods in estimating annual occupational radiation doses. This research holds pioneering significance as it represents the very first study undertaken in Sri Lanka. Thirteen interventional cardiologists performed 486 interventional cardiology procedures over three months in three different healthcare institutes. Active Hp(10) dosimeters were placed to measure radiation exposure. Effective doses were calculated using single and double dosimetric algorithms. Annual occupational doses were assessed on an operator basis. Statistical analyses were conducted to assess algorithmic differences and dose variations using the Kruskal-Wallis test and linear regression. The highest annual occupational dose for each dosimetric algorithm received as 2.00 ± 0.24 mSv, 2.29 ± 0.48 mSv, 3.35 ± 0.71 mSv, and 2.64 ± 0.42 mSv, respectively, and remained below the recommended safety limit of 20 mSv/year. The Kruskal-Wallis test revealed no significant differences in the effective doses among double dosimetric algorithms, as well as between single and double dosimetric algorithms (p > 0.05). Linear regression showed strong correlations among various algorithms, demonstrating consistency. The findings of this study hold significant effects on interventional cardiology practice in Sri Lanka, enhancing radiation safety and monitoring.

Identification of Radiomic Features as an Imaging Marker to Differentiate Benign and Malignant Breast Masses Based on Magnetic Resonance Imaging

Background - Breast cancer is one of the most common cancers among women globally and early identification is known to increase patient outcomes. Therefore, the main aim of this study is to identify the essential radiomic features as an image marker and compare the diagnostic feasibility of feature parameters derived from radiomics analysis and conventional Magnetic Resonance Imaging (MRI) to differentiate benign and malignant breast masses.

Methods and Material - T1-weighted Dynamic Contrast-Enhanced (DCE) breast MR axial images of 151 (benign (79) and malignant (72)) patients were chosen. Regions of interest were selected using both manual and semi-automatic segmentation from each lesion. 382 radiomic features computed on the selected regions. A random forest model was employed to detect the most important features that differentiate benign and malignant breast masses. The ten most important radiomics features were obtained from manual and semi-automatic segmentation based on the Gini index to train a support vector machine. MATLAB and IBM SPSS Statistics Subscription software used for statistical analysis.

Results - The accuracy (sensitivity) of the models built from the ten most significant features obtained from manual and semi-automatic segmentation were 0.815 (0.84), 0.821 (0.87), respectively. The top 10 features obtained from manual delineation and semi-automatic segmentation showed a significant difference (p<0.05) between benign and malignant breast lesions.

Conclusion - This radiomics analysis based on DCE-BMRI revealed distinct radiomic features to differentiate benign and malignant breast masses. Therefore, the radiomics analysis can be used as a supporting tool in detecting breast MRI lesions.