Machine learning-driven survival prediction in gestational trophoblastic neoplasms: a focus on PSTT and ETT prognosis.

Introduction: The clinicopathological characteristics and prognosis of placental site trophoblastic tumor (PSTT) and epithelioid trophoblastic tumor (ETT) have not been well summarized. Consequently, we conducted the largest to date series of samples of both types and employed machine learning (ML) to assess treatment effectiveness and develop accurate prognostic models for patients with GTN. Gestational choriocarcinoma (GCC) was used as the control group to show the clinical features of PTSS and ETT. Methods: The Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five ML algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of the ML models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. Results: The study population comprised 725 patients. Among them, 139 patients had ETT, 107 had PSTT, and 479 had GCC. There were no significant differences in survival between the different tumor groups. Multivariate Cox regression analysis revealed that metastasis was a significant prognostic factor for GCC, while older age and radiotherapy were significant prognostic factors for PTSS and ETT. ML models revealed that the Gradient Boosting classifier accurately predicted the outcomes, followed by the random forest classifier, K-Nearest Neighbors, Logistic Regression, and multilayer perceptron models. The most significant contributing factors were tumor size, year of diagnosis, age, and race. Discussion: Our study provides a method for treatment and prognostic assessment of patients with GTN. The ML we developed can be used as a convenient individualized tool to facilitate clinical decision making.

From flank pain to splenic abscess: a complex case of infective endocarditis with literature review.

BACKGROUND: Infective endocarditis (IE) is a severe condition characterized by inflammation of the heart endocardium and valves, commonly caused by Gram-positive bacteria. Complications such as embolic phenomena and organ abscesses can arise, necessitating timely diagnosis and intervention. CASE PRESENTATION: We report the case of a 20-year-old female with a history of cerebral and splenic infarctions due to IE. The patient presented with left-sided flank pain, urinary burning, and fever. Examination revealed mitral and aortic valve involvement, splenomegaly, and neurological deficits. Despite initial antibiotic therapy, the patient developed a splenic abscess and drug-induced neutropenia. She required aortic valve replacement and was successfully managed with a multidisciplinary approach. CONCLUSION: Multidisciplinary management, including timely surgical intervention and advanced imaging, is essential for favorable outcomes in IE patients. This case underscores the importance of early detection and tailored treatment strategies in managing severe complications associated with IE.

Personalized approach to malignant struma ovarii: Insights from a web-based machine learning tool.

OBJECTIVES: Malignant struma ovarii (MSO) is a rare ovarian tumor characterized by mature thyroid tissue. The diverse symptoms and uncommon nature of MSO can create difficulties in its diagnosis and treatment. This study aimed to analyze data and use machine learning methods to understand the prognostic factors and potential management strategies for MSO. METHODS: In this retrospective cohort, the Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five machine learning algorithms to predict the 5-year survival. A validation method incorporating the area under the curve of the receiver operating characteristic curve was used to validate the accuracy and reliability of the machine learning models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. RESULTS: The study population comprised 329 patients. Multivariate Cox regression analysis revealed that older age, unmarried status, chemotherapy, and the total number of tumors in patients were poor prognostic factors. Machine learning models revealed that the multilayer perceptron accurately predicted outcomes, followed by the random forest classifier, gradient boosting classifier, K-nearest neighbors, and logistic regression models. The factors that contributed the most were age, marital status, and the total number of tumors in the patients. CONCLUSION: The present study offers a comprehensive approach for the treatment and prognosis assessment of patients with MSO. The machine learning models we have developed serve as a practical, personalized tool to aid in clinical decision-making processes.