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Pyomyomas are rare lesions that develop from the suppurative degeneration of ischemic leiomyomas. Pyomyomas can lead to sepsis, so early diagnosis is crucial. Due to their rarity and often nonspecific findings on ultrasonography and computed tomography diagnosis is often delayed. We present a case of a woman who presented with abdominal pain and fever. The computed tomography showed a large hypodense uterine lesion with slight peripheral enhancement without gas in the lesion. The diffusion-weighted magnetic resonance imaging showed restricted diffusion, demonstrating the presence of pus and confirming the diagnosis of pyomyoma. Diffusion-weighted imaging should be done in cases of suspected pyomyomas when computed tomography findings are nonspecific.

Introduction We aimed to assess differences in breast cancer risk across benign breast disease diagnosed at prevalent or incident screens. Materials and methods We conducted a retrospective cohort study with data from 629,087 women participating in a long-standing population-based breast cancer screening program in Spain. Each benign breast disease was classified as non-proliferative, proliferative without atypia, or proliferative with atypia, and whether it was diagnosed in a prevalent or incident screen. We used partly conditional Cox hazard regression to estimate the adjusted hazard ratios of the risk of breast cancer. Results Compared with women without benign breast disease, the risk of breast cancer was significantly higher (p-value = 0.005) in women with benign breast disease diagnosed in an incident screen (aHR, 2.67; 95%CI: 2.24–3.19) than in those with benign breast disease diagnosed in a prevalent screen (aHR, 1.87; 95%CI: 1.57–2.24). The highest risk was found in women with a proliferative benign breast disease with atypia (aHR, 4.35; 95%CI: 2.09–9.08, and 3.35; 95%CI: 1.51–7.40 for those diagnosed at incident and prevalent screens, respectively), while the lowest was found in women with non-proliferative benign breast disease (aHR, 2.39; 95%CI: 1.95–2.93, and 1.63; 95%CI: 1.32–2.02 for those diagnosed at incident and prevalent screens, respectively). Conclusion Our study showed that the risk of breast cancer conferred by a benign breast disease differed according to type of screen (prevalent or incident). To our knowledge, this is the first study to analyse the impact of the screening type on benign breast disease prognosis., Highlights • Breast cancer risk after a benign breast disease varied with the screening type. • Incident benign breast disease had a higher breast cancer risk than prevalent. • The risk remained increased regardless of benign breast disease subtype.

The anatomic and histologic characteristics of the nipple-areolar complex make this breast region special. The nipple-areolar complex can be affected by abnormal development and a wide spectrum of pathological conditions, many of which have unspecific clinical and radiological presentations that can present a challenge for radiologists. The nipple-areolar complex requires a specific imaging workup in which a multimodal approach is essential. Radiologists need to know the different imaging modalities used to study the nipple-areolar complex, as well as their advantages and limitations. It is essential to get acquainted with the acquisition technique for each modality and the spectrum of findings for the different conditions. This review describes and illustrates a combined clinical and radiological approach to evaluate the nipple-areolar complex, emphasizing the findings for the normal morphology, developmental abnormalities, and the most common benign and malignant diseases that can affect this region. We also present a diagnostic algorithm that enables a rapid, practical approach to diagnosing condition involving the nipple-areolar complex.

Medical image synthesis has gained a great focus recently, especially after the introduction of Generative Adversarial Networks (GANs). GANs have been used widely to provide anatomically-plausible and diverse samples for augmentation and other applications, including segmentation and super resolution. In our previous work, Deep Convolutional GANs were used to generate synthetic mammogram lesions, masses mainly, that could enhance the classification performance in imbalanced datasets. In this new work, a deeper investigation was carried out to explore other aspects of the generated images evaluation, i.e., realism, feature space distribution, and observers studies. t-Stochastic Neighbor Embedding (t-SNE) was used to reduce the dimensionality of real and fake images to enable 2D visualisations. Additionally, two expert radiologists performed a realism-evaluation study. Visualisations showed that the generated images have a similar feature distribution of the real ones, avoiding outliers. Moreover, Receiver Operating Characteristic (ROC) curve showed that the radiologists could not, in many cases, distinguish between synthetic and real lesions, giving 48% and 61% accuracies in a balanced sample set., accepted in the International Workshop Breast Imaging IWBI (2020), 4 pages, 3 figures