Tehran University Medical Journal

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Background: The use of serial quantitative beta-human chorionic gonadotropin (β-HCG) with transvaginal ultrasound to enhance early diagnosis of ectopic pregnancy (EP) improves options for conservative treatment with methotrexate (MTX). The aim of this study was to evaluate the outcome of unruptured EP treated with a single dose of intramuscular MTX injection.

Methods: This clinical trial included 41 EP patients with specific inclusion criteria for medical treatment. For each patient, MTX (50 mg/ml) was administered intramuscularly and a repeat dose was given if the weekly decrease in the level of β-HCG was less than 15%. The therapy was considered successful if the level of β-HCG fell below 10 mIU/cc without surgical intervention.

Results: Overall, 78% of the patients were successfully treated, among whom 18.7% received second doses of MTX. Of the patients who were successfully treated, 60% presented with vaginal bleeding without pelvic pain however, of those patients in whom the treatment failed, 88% presented with pelvic pain together with vaginal bleeding. Furthermore, the presence of free peritoneal fluid on vaginal ultrasound was a significant predictor of treatment failure (p<0.005). There was no relation between the women's age, gravidity or parity, the size of the conceptus, gestational age, pretreatment serum β-HCG titer, endometrial thickness on vaginal ultrasound and the efficacy of treatment.

Conclusions: With a reasonably high success rate, we found systemic single-dose MTX treatment to be a safe, conservative therapy for EP. However, when free peritoneal fluid is noted upon transvaginal ultrasound or when the patient presents with pain, the threshold for surgical intervention may be lower.

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Breast cancer is one of the most common types of cancer in women. Screening mammography is a low‑dose X‑ray examination of breasts, which is conducted to detect breast cancer at early stages when the cancerous tumor is too small to be felt as a lump. Screening mammography is conducted for women with no symptoms of breast cancer, for early detection of cancer when the cancer is most treatable and consequently greatly reduce the death rate from the breast cancer. Screening mammography should be performed every year for women age 45-54, and every two years for women age 55 and older who are in good health. A mammogram is read by a radiologist to diagnose cancer.
To assist radiologists in reading mammograms, computer-aided detection (CAD) systems have been developed which can identify suspicious lesions on mammograms. CADs can improve the accuracy and confidence level of radiologists in decision making and have been approved by FDA for clinical use. Traditional CAD systems work based on conventional machine learning (ML) and image processing algorithms. With recent advances in software and hardware resources, a great breakthrough in deep learning (DL) algorithms was followed, which revolutionized various engineering areas including medical technologies. Recently, DL models have been applied in CAD systems in mammograms and achieved outstanding performance. In contrast to conventional ML, DL algorithms eliminate the need for the tedious task of human-designed feature engineering, as they are capable of learning useful features automatically from the raw data (mammogram). One of the most common DL frameworks is the convolutional neural network (CNN). To localize lesions in a mammogram, a CNN should be applied in region‑based algorithms such as R‑CNN, Fast R‑CNN, Faster R‑CNN, and YOLO.

Proper training of a DL‑based CAD requires a large amount of annotated mammogram data, where cancerous lesions have been marked by an experienced radiologist. This highlights the importance of establishing a large, annotated mammogram dataset for the development of a reliable CAD system. This article provides a brief review of the state‑of‑the‑art techniques for DL‑based CAD in mammography.