Male Breast: A Review of the Literature and Current State of the Art of Diagnostic Imaging Work-Up.

Pathological conditions affecting the male breast (MB) share some similarities with those found in women, while others are specific to men. The first part of this review provides an overview of MB disorders, exploring the most common types of MB diseases. The second part then emphasizes the state-of-the-art approaches proposed in the literature for screening and follow-up with MB cancer patients, which highlights the importance of tailored strategies for diagnosis, follow-up, and identifying high-risk populations. Considering the increasing attention in recent years on the topic, transgender individuals are also included in this review. Together with the MB, it is an understudied category thus far. This review aims to raise awareness among radiologists that MBs should be approached differently from female breasts, contributing to the advancement of medical knowledge, improving patient outcomes, and promoting early detection of MB disorders. The review also provides an update on breast cancer and screening in the transgender population.

Virtual monoenergetic dual-layer dual-energy CT images in colorectal cancer: CT diagnosis could be improved?

PURPOSE: To compare conventional CT images and virtual monoenergetic images (VMI) at dual-layer dual-energy CT (dlDECT) in patients with colorectal cancer (CRC) through quantitative analysis and to investigate the added value of VMI. MATERIAL AND METHODS: Sixty-six consecutive patients with histologically documented CRC and available VMI reconstructions were retrospectively investigated. Subsequently, forty-two patients, without any colonic disease at colonoscopy, were selected as control group. Conventional CT images and VMI reconstructions at energy levels ranging from 40 (VMI40) to 100 keV (VMI100) in 10 keV increments, were obtained from the late arterial phase. First, signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were obtained to select the best VMI reconstruction. Finally, the diagnostic accuracy of conventional CT and VMI40 in late arterial phase was evaluated. RESULTS: On quantitative analysis, SNR and CNR were higher for VMI40 (19.5 ± 7.7 and 11.8 ± 6.2, respectively) with statistically significant differences compared to conventional CT (P < 0.05) and all the other VMI reconstructions (P < 0.05), except for VMI50 (P > 0.05). The addition of VMI40 to conventional CT images significantly improved the area under the curve (AUC) for the diagnosis of CRC, increasing it from 0.875 to 0.943 for reader 1 (P < 0.05) and from 0.916 to 0.954 for reader 2 (P < 0.05). The improvement was greater in the less experienced radiologist (0.068) compared to the more experienced one (0.037). CONCLUSION: VMI40 has showed the highest quantitative image parameters. Furthermore, the use of VMI40 can lead to a significant improvement in the diagnostic performance for detecting CRC.

Male breast: An unusual case of metastasis of squamous cell carcinoma of the skin.

Breast metastasis from extra-mammary neoplasm is a rare condition, accounting for approximately 1.2%-2% of all breast malignancies. Melanoma, lung cancer, gynecological, and hematological cancers can metastasis to the breast. Male breast metastasis is extremely rare and, no evidence of metastasis from cutaneous squamous cell carcinoma in a male breast have been reported to our knowledge. We describe a case of an 81-year-old man who came to our attention for a palpable solid mass in the upper-outer aspect of the left breast with the final histological diagnosis of breast metastasis from non-keratoblastic cutaneous squamous cell carcinoma.

Artificial Intelligence in Breast Imaging: A Special Focus on Advances in Digital Mammography & Digital Breast Tomosynthesis.

Breast cancer accounts for 30% of female cancers and is the second leading cause of cancerrelated deaths in women. The rate is rising at 0.4% per year. Early detection is crucial to improve treatment efficacy and overall survival of women diagnosed with breast cancer. Digital Mammography and Digital Breast Tomosynthesis have widely demonstrated their role as a screening tool. However, screening mammography is limited by radiologist's experience, unnecessarily high recalls, overdiagnosis, overtreatment and, in the case of Digital Breast Tomosynthesis, long reporting time. This is compounded by an increasing shortage of manpower and resources issue, especially among breast imaging specialists. Recent advances in image analysis with the use of artificial intelligence (AI) in breast imaging have the potential to overcome some of these needs and address the clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. This article focuses on the most important clinical implication and future application of AI in the field of digital mammography and digital breast tomosynthesis, providing the readers with a comprehensive overview of AI impact in cancer detection, diagnosis, reduction of workload and breast cancer risk stratification.