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Thromboembolic events are one of the leading causes of maternal death during the postpartum period. Postpartum thrombolytic therapy with recombinant tissue plasminogen activator (rt-PA) is controversial because the treatment may lead to massive bleeding. Data centralization may be beneficial for analyzing the safety and effectiveness of systemic thrombolysis during the early postpartum period. We performed a computerized MEDLINE and EMBASE search. We collected data for 13 cases of systemic thrombolytic therapy during the early postpartum period, when limiting the early postpartum period to 48 hours after delivery. Blood transfusion was necessary in all cases except for one (12/13; 92%). In seven cases (7/13; 54%), a large amount of blood was required for transfusion. Subsequent laparotomy to control bleeding was required in five cases (5/13; 38%), including three cases of hysterectomy and two cases of hematoma removal, all of which involved cesarean delivery. In cases of transvaginal delivery, there was no report of laparotomy. The occurrence of severe bleeding was high in relation to cesarean section, compared with vaginal deliveries. Using rt-PA in relation to cesarean section might be worth avoiding. However, the paucity of data in the literature makes it difficult to assess the ultimate outcomes and safety of this treatment.

Background/Aim: This study aimed to use artificial intelligence (AI) to predict the pathological diagnosis of ovarian tumors using patient information and data from preoperative examinations. Patients and Methods: A total of 202 patients with ovarian tumors were enrolled, including 53 with ovarian cancer, 23 with borderline malignant tumors, and 126 with benign ovarian tumors. Using 5 machine learning classifiers, including support vector machine, random forest, naive Bayes, logistic regression, and XGBoost, we derived diagnostic results from 16 features, commonly available from blood tests, patient background, and imaging tests. We also analyzed the importance of 16 features on the prediction of disease. Results: The highest accuracy was 0.80 in the machine learning algorithm of XGBoost. The evaluation of importance of the features showed different results among the correlation coefficient of the features, the regression coefficient, and the features importance of random forest. Conclusion: AI could play a role in the prediction of pathological diagnosis of ovarian cancer from preoperative examinations.

ObjectivesMalignant transformation of mature cystic teratoma (MCT) is rare. Unlike squamous cell carcinoma (SCC) in MCT, the other types of neoplasm in MCT have not been discussed in publications. We analyzed the clinical characteristics and prognosis of the other types of neoplasm (non-SCC) compared with those of SCC.MethodsA systematic literature search of literature published from 2000 to 2017 was conducted in PubMed, Web of Science, and Scopus. We reviewed case series that included all pathological types of malignant transformation.ResultsA total of 155 cases from 15 case series, including our cases, were included. Of the cases, 90 (58%) were SCC and 65 (42%) were non-SCC, including adenocarcinoma, carcinoid tumor, thyroid carcinoma, sarcoma, adenosquamous carcinoma, melanoma, sebaceous carcinoma, oligodendroglioma, signet ring cell carcinoma, and transitional cell carcinoma, in descending order of frequency. The mean ages of patients with SCC and non-SCC were 50.5 and 48.9 years, respectively. The mean tumor sizes were 14.7 cm in SCC and 13.9 cm in non-SCC. Surgical approaches were similar. First-line chemotherapy for epithelial ovarian cancers was the most commonly used regimen in SCC and non-SCC. Overall survival did not differ significantly, showing better prognosis in stage I and poor prognosis in stages II, III, and IV. A difference in overall survival was observed among pathological types of non-SCC.ConclusionsClinical characteristics and outcomes did not differ significantly between SCC and non-SCC. However, chemotherapy regimens differed to some extent, and the possibility of difference in overall survival among pathological types of non-SCC was suggested.

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