With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.

Breast cancer is the most commonly diagnosed cancer among women in the Kingdom of Saudi Arabia and other Middle East countries. This analytical cross-sectional study assessed knowledge, attitude towards breast cancer, and barriers to mammogram screening among 414 randomly selected female healthcare workers from multiple healthcare facilities in northern Saudi Arabia. Of the studied population, 48.6% had low knowledge, and 16.1% had a low attitude towards breast cancer risk factors and symptoms. The common barriers to mammogram screening were fear to discover cancer (57.2%) and apprehension regarding radiation exposure (57%). Logistic regression analysis found that lack of awareness regarding mammogram was significantly associated with age (p = 0.030) and healthcare workers category (ref: physicians: p = 0.016). In addition, we found a significant negative correlation between knowledge and barrier scores (Spearman’s rho: −0.315, p < 0.001). It is recommended to develop target-oriented educational programs for the healthcare workers, which would empower them to educate the community regarding the risk factors and the importance of mammogram screening. Furthermore, a prospective study is warranted in other regions of the Kingdom of Saudi Arabia to understand the region-specific training needs for the healthcare workers.

Introduction Primary osteosarcoma of the breast is extremely rare, and an osteosarcoma arising from an intraductal papilloma is exceptional. Case Presentation A 72-year-old Saudi Arabian woman presented with a solid, bone-containing breast mass that was diagnosed as primary osteosarcoma of the breast on biopsy. She had a history of untreated intraductal papilloma. Treatment was completed with a modified mastectomy after excluding extramammary metastases. However, she subsequently developed multiple recurrent lesions at the same site. Conclusion Primary osteogenic sarcomas of the breast are very rare. Although the main treatment is resection the optimal management remains uncertain and prognosis is poor.

BACKGROUNDCurrently, there are no data on the prevalence of breast arterial calcification (BAC) in Saudi women. Furthermore, there are no data on the relationship between BAC and coronary artery calcium score (CACS) as a coronary artery disease (CAD) risk factor in Saudi women who undergo mammography.OBJECTIVEExamine the role of BAC as a potential female-specific risk factor for CAD in Saudi women in order to investigate the relationship between BAC and CACS in women who undergo a screening mammography, and study the relationship between BAC and CAD risk factors, including age, diabetes mellitus, hypertension, chronic kidney disease (CKD), dyslipidemia, and family history of CAD.DESIGNRetrospective, medical records review.SETTINGSingle tertiary care center.PATIENTS AND METHODSThe study cohort included women who had mammograms and a CACS scan, and for whom data on CAD risk stratification and CAD risk factors had been collected within one year of each other from 2014 to 2017. Women with CAD were excluded from the study.MAIN OUTCOME MEASURESBreast arterial calcification as a marker for coronary artery disease.SAMPLE SIZE307 Saudi women.RESULTSBAC was found in 142 (46%) patients in the study population. BAC+ women were significantly older than the BAC− women (P=.001), and a strong association was found between BAC and CACS (P=.0001), diabetes (P=.0001), hypertension (P=.021), and CKD (P=.0031). However, no association was found between BAC and tobacco smoking, dyslipidemia, and family history of CAD. In addition, a strong correlation was found between CACS and the components of the BAC score (P<.001). Multivariate linear regression analysis revealed that age, CAC, and CKD are the only strong predictors of BAC.CONCLUSIONSThe proportion of BAC in Saudi women is 46%, and there may be a strong association between BAC and CAC, age, hypertension, and CKD. A large-scale prospective research study is necessary to validate the role of BAC on screening mammography as a CAD risk stratification tool and before routine reporting of BAC on a mammography report.LIMITATIONSBecause this was a retrospective study, patient selection bias cannot be excluded.

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