Norhashimah Mohd Norsuddin

Background: Breast cancer continues to be a major contributor to illness among women in Jordan, particularly in underserved regions. Despite the establishment of mammography services in southern Jordan, no formal quality assurance audit has evaluated diagnostic performance or geographic equity of access. This study aimed to evaluate the diagnostic performance of mammography services using established quality assurance indicators and to investigate geographic variation in mammographic findings and diagnostic pathways among women attending a regional referral center in southern Jordan. Methods: This study employed a retrospective hospital-based clinical audit that was conducted using mammography records from Ma’an Hospital between 2016 and 2024. A total of 592 women aged 20 years and older who underwent mammography were included. Data extracted from electronic and paper-based medical records included demographic characteristics, mammography indication, BI-RADS^® classifications, follow-up procedures, reporting timelines, and histopathological outcomes. Histopathological confirmation served as the reference standard for diagnostic accuracy analyses. Diagnostic performance was evaluated using Cancer Detection Rate (CDR), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Geographic variation was evaluated according to place of residence, while multivariable logistic regression was used to determine whether associations remained independent of age and mammography indication. Results: Of the 592 mammography examinations, 354 (59.8%) were performed for screening purposes and 238 (40.2%) for diagnostic evaluation. BI-RADS^® 1 and BI-RADS^® 2 were the most frequently assigned categories, accounting for 38.3% and 28.7% of examinations, respectively, whereas BI-RADS^® 4 and 5 findings accounted for 6.3% and 1.0%. The overall CDR was 52.4 per 1000 examinations with sensitivity (93.9% (95% CI 80.4–98.3%)) and specificity (97.4% (95% CI 95.3–98.5%)), positive predictive value (PPV) (73.8% (95% CI 58.0–86.1%)), and negative predictive value (NPV) (99.5% (95% CI 98.2–99.9%)). Suspicious findings (BI-RADS^® 4–5) were significantly more frequent among urban than rural women (9.4% vs. 3.7%; adjusted OR 3.05, 95% CI 1.37–6.78), whereas reporting delay, ultrasound use, biopsy rate, and further follow-up did not differ significantly by residence. Conclusions: Mammography services at Ma’an Hospital exhibit high diagnostic accuracy and strong clinical performance in breast cancer detection over the nine-year study period. Women in rural areas were less likely to have suspicious mammographic findings detected than those in urban areas, regardless of age and mammography type. No significant differences were observed in reporting timelines or follow-up procedures by place of residence. These findings underscore the value of regional quality assurance audits in identifying service disparities and guiding improvements in breast cancer screening and diagnostic services, especially in underserved communities.

Background and Aims: Machine learning (ML) has shown promise in diagnosing tuberculosis (TB), but systematic evidence on its role in predicting and diagnosing drug‐resistant tuberculosis (DR‐TB) is lacking. This study integrates a systematic review and meta‐analysis to consolidate ML's performance in DR‐TB diagnosis and prediction to promote artificial intelligence in this field. Methods: Relevant studies were retrieved from PubMed, Cochrane, Embase, and Web of Science up to August 20, 2025, complemented by a manual search of Google Scholar. Risk of bias was evaluated with PROBAST. A bivariate mixed‐effects model pooled accuracy measures, with subgroup analyses stratified by ML tasks (diagnosis and prediction). Results: Twenty‐six studies, including 35,472 participants, were analysed. Diagnostic models outperformed prediction models, with a higher pooled AUC (0.94 vs. 0.87). Deep learning (DL)‐based diagnostic models consistently surpassed traditional ML across all key metrics, AUC (0.97 vs. 0.89). In the diagnostic model, internal validation showed superior performance to external validation AUC (0.95 vs. 0.85), and in the predictive model, the overall performance of the model in internal validation is slightly better than that in external validation AUC (0.88 vs. 0.85). Conclusion: ML models, particularly DL, demonstrate high diagnostic efficacy for DR‐TB, though performance declines in external data sets. Predictive models show moderate accuracy but remain useful for early risk stratification. Large multi‐center validations are needed to ensure robustness and clinical applicability.

Background/Objectives: This study investigated the lifetime attributable risk (LAR) of radiation-induced breast cancer from mammography screening in Dubai. It aimed to explore the relationship between breast thickness, patient age, and the associated radiation dose during mammographic examinations. Methods: A retrospective analysis was conducted on 2601 patients aged 40 to 69 across five screening facilities in Dubai’s healthcare system. Due to a low correlation between the mean glandular dose (MGD) and breast thickness, both glandular and non-glandular doses were included in calculations as the organ dose (OD). This comprehensive approach examined the impact of whole breast tissue on risk assessments. Key exposure parameters such as the kilovoltage peak (kVp), milliampere-seconds (mAs), and source-to-skin distance were extracted from the dose survey. Results: Our findings reveal that the organ dose increases with breast thickness, emphasizing the need for dose optimization in denser tissues. The LAR decreases with age across all thickness categories, but higher initial LAR values were seen in younger patients with thicker tissue. This study emphasizes the increased sensitivity of younger women, who usually have denser breast tissue, to radiation-induced cancer risks. Conclusions: Personalized screening protocols considering age and breast thickness are crucial for balancing early cancer detection benefits with radiation risks. Future research should improve mammography protocols, explore alternative methods, and consider generic testing for young high-risk patients to mitigate risks while maintaining diagnostic efficacy.

Background: This study compares system-reported organ doses (ODs) to manually calculated mean glandular doses (MGDs) in mammography across multiple centers and manufacturers in Dubai. Methods: A retrospective study of 2754 anonymized mammograms from six clinics in Dubai were randomly retrieved from a central dose survey database. Organ doses were documented along with other dosimetry information like kVp, mAs, filter, target, compression force, and breast thickness. Mean glandular doses, MGDs, were calculated manually for all the patients using the Dance formula and inferential statistical analyses were run to compare the two figures and verify the factors affecting each. Results: Our study’s analysis revealed that manually calculated mean glandular doses (MGDs) provide a more reliable indicator of radiation exposure than organ doses (ODs) reported by DICOM, particularly in multi-vendor scenarios. Manually calculated MGD values were consistently lower than system-reported ODs (MLO view: 0.96 ± 0.37 mGy vs. 1.38 ± 0.45 mGy; CC view: 0.81 ± 0.33 mGy vs. 1.22 ± 0.38 mGy). Significant differences in both system-reported ODs and manually calculated MGDs were observed across centers (p < 0.001). Strong correlations between system-reported ODs and manually calculated MGDs were found for Siemens equipment (r = 0.923, p < 0.001) but only moderate correlations for GE systems (r = 0.638, p < 0.001). Calculated MGD values were significantly higher for GE equipment compared to Siemens (1.49 ± 0.77 mGy vs. 0.93 ± 0.33 mGy, p < 0.001). Conclusions: This study addresses concerns regarding mammography dosimetry accuracy by demonstrating the superiority of mean glandular doses over DICOM-generated organ doses. These findings empower practitioners to optimize dose levels, ensuring safer and more effective breast cancer screening protocols.

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.

As the total volume of mammograms in Dubai is increasing consistently, it is crucial to focus on the process of dose optimization by determining dose reference levels for such sensitive radiographic examinations as mammography. This work aimed to determine local diagnostic reference levels (DRLs) for mammography procedures in Dubai at different ranges of breast thickness. A total of 2599 anonymized mammograms were randomly retrieved from a central dose survey database. Mammographic cases for screening women aged from 40 to 69 years were included, while cases of breast implants and breast thickness outside the range of 20–100 mm were excluded. Mean, median, and 75 percentiles were obtained for the mean glandular dose (MGD) distribution of each mammography projection for all compressed breast thickness (CBT) ranges. The local DRLs for mammography in Dubai were found to be between 0.80 mGy and 0.82 mGy for the craniocaudal (CC) projection and between 0.89 mGy and 0.971.8 mGy for the mediolateral oblique (MLO) projection. Local DRLs were proposed according to different breast thicknesses, starting from 20 to 100 mm. All groups of CBT showed a slight difference in MGD values, with higher values in MLO views rather than CC views. The local DRLs in this study were lower than some other Middle Eastern countries and lower than the standard reference levels reported by the International Atomic Energy Agency (IAEA) at 3 mGy/view.

A set of national diagnostic reference levels (DRLs) was established in Malaysia for a range of breast thicknesses in 2013, but no updates for full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT). Due to the increasing number of DBTs used and concern over radiation exposure, this study aimed to explore and establish local diagnostic reference levels for FFDM and DBT in Malaysia health facilities at different compressed breast thickness (CBT) ranges. The CBT, kilovoltage peak (kVp), Entrance surface dose (ESD), and average glandular dose (AGD) were retrospectively extracted from the mammography Digital Imaging and Communications in Medicine (DICOM) header. The 75th and 95th percentile values were obtained for the AGD distribution of each mammography projection for three sets of CBT range. The difference in AGD values between FFDM and DBT at three CBT ranges was determined. The DRLs for FFDM were 1.13 mGy, 1.52 mGy, and 2.87 mGy, while DBT were 1.18 mGy, 1.88 mGy, and 2.78 mGy at CBT ranges of 20–39 mm, 40–59 mm, and 60–99 mm, respectively. The AGD of DBT was significantly higher than FFDM for both mammographic views (p < 0.005). All three CBT groups showed a significant difference in AGD values for FFDM and DBT (p < 0.005). The local DRLs from this study were lower than the national DRLs, with the AGD of FFDM significantly lower than DBT.