METHODS: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results.
RESULTS: Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (κ =0.606, p < 0.001) and the BI-RADS category with and without AI (κ =0.74, p < 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively.
CONCLUSION: AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients.
KEY MESSAGES: • The use of artificial intelligence (AI) in mammography for population-based breast cancer screening has been validated in high-income nations, with reported improved diagnostic performance. Our study evaluated the usage of an AI tool in an opportunistic screening setting in a multi-ethnic and middle-income nation. • The application of AI in mammography enhances diagnostic accuracy, potentially leading to reduced unnecessary biopsies. • AI integration into the workflow did not disrupt the performance of trained breast radiologists, as there is a substantial inter-reader agreement for BI-RADS category assessment and breast density.
METHODS: This retrospective cohort study was carried out at the Jordan University Hospital (JUH), a tertiary facility located in Amman, Jordan. Non-cystic Fibrosis Bronchiectasis (NCFB) was defined as an HRCT scan typical for bronchiectasis along with a negative sweat chloride test to rule out cystic fibrosis. Patients' data were collected by the use of Electronic Medical Records (EMR) at our institution. Frequent exacerbation was defined as more than 2 exacerbations in 1 year of the onset of the diagnosis.
RESULTS: A total of 79 patients were included, and 54.4% of them were female. The mean and standard deviation of the patient's age was 48.61 ± 19.62. The etiologies of bronchiectasis were evident in 79.7% of the sample. Asthma, Chronic Obstructive Pulmonary Diseases (COPD), and Kartagener syndrome were the most prevalent etiologies, accounting for related illnesses in 21.8%, 21.5%, and 13.9% of the patients, respectively. The most frequent bacteria cultured in our cohort were Pseudomonas and Candida Species. Moreover, 43 patients of the study cohort were frequent exacerbators, and 5 patients died.
CONCLUSION: Our study supports the need to identify several bronchiectasis phenotypes linked to various causes. These findings provide information to clinicians for the early detection and treatment of bronchiectasis in Jordan.
OBJECTIVE: This study aims to evaluate the diagnostic efficacy of abbreviated MRI protocol in detecting breast cancer in screening and diagnostic populations, using histopathology as the reference standard.
MATERIALS AND METHODS: This is a single-centre retrospective cross-sectional study of 134 patients with 198 histologically proven breast lesions who underwent full diagnostic protocol contrast-enhanced breast MRI (FDP-MR) at the University Malaya Medical Centre (UMMC) from 1st January 2018 to 31st December 2019. AB-MR was pre-determined and evaluated with regard to the potential to detect and exclude malignancy from 3 readers of varying radiological experiences. The sensitivity of both AB-MR and FDP-MR were compared using the McNemar test, where both protocols' diagnostic performances were assessed via the receiver operating characteristic (ROC) curve. Inter-observer agreement was analysed using Fleiss Kappa.
RESULT: There were 134 patients with 198 lesions. The average age was 50.9 years old (range 27 - 80). A total of 121 (90%) MRIs were performed for diagnostic purposes. Screening accounted for 9.4% of the cases, 55.6% (n=110) lesions were benign, and 44.4% (n=88) were malignant. The commonest benign and malignant lesions were fibrocystic change (27.3%) and invasive ductal carcinoma (78.4%). The mean sensitivity, specificity, positive predictive value, and negative predictive value for AB-MR were 0.96, 0.57, 0.68 and 0.94, respectively. Both AB-MR and FDP-MR showed excellent diagnostic performance with AUC of 0.88 and 0.96, respectively. The general inter-observer agreement of all three readers for AB-MR was substantial (k=0.69), with fair agreement demonstrated between AB-MR and FDP-MR (k=0.36).
CONCLUSION: The study shows no evidence that the diagnostic efficacy of AB-MR is inferior to FDP-MR. AB-MR, with high sensitivity, has proven its capability in cancer detection and exclusion, especially for biologically aggressive cancers.