Gestational Trophoblastic Disease with Coexisting Progressing Pregnancy: Personalised Treatment Modalities.

Purpose: Gestational trophoblastic disease (GTD) coexisting with a steadily progressing pregnancy is an extremely rare condition presented in the literature as a single case or case series of successful delivery. The purpose of this study was to describe five cases of GTD and present possible management strategies for such patients. Methods: Clinical data of five pregnancies with coexisting GTD were identified within the Almazov National Medical Research Centre from 2018 to 2021. Results: Three cases of multiple pregnancies with complete hydatidiform moles and two cases of singleton pregnancies with intraplacental choriocarcinoma and invasive hydatidiform moles were identified. Three pregnancies were prolonged and ended with preterm deliveries. Malignant transformation of the GTD accounted for 60% of the cases. The condition of newborns was based on the level of prematurity and functional immaturity, and in all cases, it was aggravated by anemia. Conclusion: GTD coexisting with progressing pregnancy is threatened by the risks of preterm delivery, miscarriage, hemorrhage, and disease progression and requires monitoring in a multidisciplinary clinic experienced in the management of patients with malignant tumors during pregnancy. In cases of prolonged pregnancy against the background of GTD, we suggest the following monitoring during pregnancy: pelvic, abdominal ultrasound/MRI (without contrast), prenatal invasive fetal karyotype testing in cases of singleton pregnancy, lung X-ray/CT with uterine shielding, weekly assessment of ß-hCG levels, and dynamic monitoring of the fetus. The following postnatal monitoring should be performed: morphological examination of the placenta, weekly assessment of ß-hCG levels up to normalization, then monthly assessment up to six months, and control of ß-hCG level of the newborn.

Simplified Convolutional Neural Network Application for Cervix Type Classification via Colposcopic Images.

The inner parts of the human body are usually inspected endoscopically using special equipment. For instance, each part of the female reproductive system can be examined endoscopically (laparoscopy, hysteroscopy, and colposcopy). The primary purpose of colposcopy is the early detection of malignant lesions of the cervix. Cervical cancer (CC) is one of the most common cancers in women worldwide, especially in middle- and low-income countries. Therefore, there is a growing demand for approaches that aim to detect precancerous lesions, ideally without quality loss. Despite its high efficiency, this method has some disadvantages, including subjectivity and pronounced dependence on the operator's experience. The objective of the current work is to propose an alternative to overcoming these limitations by utilizing the neural network approach. The classifier is trained to recognize and classify lesions. The classifier has a high recognition accuracy and a low computational complexity. The classification accuracies for the classes normal, LSIL, HSIL, and suspicious for invasion were 95.46%, 79.78%, 94.16%, and 97.09%, respectively. We argue that the proposed architecture is simpler than those discussed in other articles due to the use of the global averaging level of the pool. Therefore, the classifier can be implemented on low-power computing platforms at a reasonable cost.