Atypical Endometrial Hyperplasia and Concurrent Cancer: A Comprehensive Overview on a Challenging Clinical Condition.

The present review regarding atypical endometrial hyperplasia (AEH) focused on the main debated factors regarding this challenging clinical condition: (i) predictive variables of occult endometrial cancer (EC); (ii) the rate of EC underestimation according to different endometrial sampling methods; and (iii) the appropriateness of lymph node status assessment. When cancer is detected, approximately 90% of cases include low-risk EC, although intermediate/high-risk cases have been found in 10-13% of women with cancer. Older age, diabetes, high BMI, and increased endometrial thickness are the most recurrent factors in women with EC. However, the predictive power of these independent variables measured on internal validation sets showed disappointing results. Relative to endometrial sampling methods, hysteroscopic endometrial resection (Hys-res) provided the lowest EC underestimation, ranging between 6 and 11%. Further studies, including larger sample sizes of women undergoing Hys-res, are needed to confirm these findings. These data are urgently needed, especially for female candidates for conservative treatment. Finally, the evaluation of lymph node status measured on 660 of over 20,000 women showed a lymph node positivity of 2.3%. Although there has been an increase in the use of this procedure in AEH in recent years, the present data cannot recommend this option in AEH based on a cost/risk/benefit ratio.

HPV-Negative Adenocarcinomas of the Uterine Cervix: From Molecular Characterization to Clinical Implications.

Cervical cancer is the fourth most common cancer in women. It is the leading cause of female deaths in developing countries. Most of these cervical neoplasms are represented by squamous lesions. Cervical adenocarcinoma causes about a quarter of cervical cancers. In contrast to squamous lesions, cervical glandular disease is HPV-negative in about 15-20% of cases. HPV-negative cervical adenocarcinomas typically present in advanced stages at clinical evaluation, resulting in a poorer prognosis. The overall and disease-free survival of glandular lesions is lower than that of squamous lesions. Treatment options require definitive treatments, as fertility-sparing is not recommended. Moreover, the impact of HPV vaccination and primary HPV screening is likely to affect these lesions less; hence, the interest in this challenging topic for clinical practice. An updated review focusing on clinical and molecular characterization, prognostic factors, and therapeutic options may be helpful for properly managing such cervical lesions.

Development of a convolutional neural network for the identification and the measurement of the median nerve on ultrasound images acquired at carpal tunnel level.

BACKGROUND: Deep learning applied to ultrasound (US) can provide a feedback to the sonographer about the correct identification of scanned tissues and allows for faster and standardized measurements. The most frequently adopted parameter for US diagnosis of carpal tunnel syndrome is the increasing of the cross-sectional area (CSA) of the median nerve. Our aim was to develop a deep learning algorithm, relying on convolutional neural networks (CNNs), for the localization and segmentation of the median nerve and the automatic measurement of its CSA on US images acquired at the proximal inlet of the carpal tunnel. METHODS: Consecutive patients with rheumatic and musculoskeletal disorders were recruited. Transverse US images were acquired at the carpal tunnel inlet, and the CSA was manually measured. Anatomical variants were registered. The dataset consisted of 246 images (157 for training, 40 for validation, and 49 for testing) from 103 patients each associated with manual annotations of the nerve boundary. A Mask R-CNN, state-of-the-art CNN for image semantic segmentation, was trained on this dataset to accurately localize and segment the median nerve section. To evaluate the performances on the testing set, precision (Prec), recall (Rec), mean average precision (mAP), and Dice similarity coefficient (DSC) were computed. A sub-analysis excluding anatomical variants was performed. The CSA was automatically measured by the algorithm. RESULTS: The algorithm correctly identified the median nerve in 41/49 images (83.7%) and in 41/43 images (95.3%) excluding anatomical variants. The following metrics were obtained (with and without anatomical variants, respectively): Prec 0.86 ± 0.33 and 0.96 ± 0.18, Rec 0.88 ± 0.33 and 0.98 ± 0.15, mAP 0.88 ± 0.33 and 0.98 ± 0.15, and DSC 0.86 ± 0.19 and 0.88 ± 0.19. The agreement between the algorithm and the sonographer CSA measurements was excellent [ICC 0.97 (0.94-0.98)]. CONCLUSIONS: The developed algorithm has shown excellent performances, especially if excluding anatomical variants. Future research should aim at expanding the US image dataset including a wider spectrum of normal anatomy and pathology. This deep learning approach has shown very high potentiality for a fully automatic support for US assessment of carpal tunnel syndrome.

Learning-Based Median Nerve Segmentation From Ultrasound Images For Carpal Tunnel Syndrome Evaluation.

Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy. Ultrasound imaging (US) may help to diagnose and assess CTS, through the evaluation of median nerve morphology. To support sonographers, this paper proposes a fully-automatic deep-learning approach to median nerve segmentation from US images. The approach relies on Mask R-CNN, a convolutional neural network that is trained end-to-end. The segmentation head of Mask R-CNN is here evaluated with three different configurations, with the goal of studying the effect of the segmentation-head output resolution on the overall Mask R-CNN segmentation performance. For this study, we collected and annotated a dataset of 151 images acquired in the actual clinical practice from 53 subjects with CTS. To our knowledge, this is the largest dataset in the field in terms of subjects. We achieved a median Dice similarity coefficient equal to 0.931 (IQR = 0.027), demonstrating the potentiality of the proposed approach. These results are a promising step towards providing an effective tool for CTS assessment in the actual clinical practice.