OBJECTIVES: This study aimed to investigate the influence of baseline sarcopenia and changes in body composition on survival during cervical cancer treatment.
OBJECTIVE: To develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovarian cancer patients at risk of early mortality more accurately than the Charlson Comorbidity Index (CCI) for use in health services research.
PURPOSE: We present a large real-world multicentric dataset of ovarian, uterine and cervical oligometastatic lesions treated with SBRT exploring efficacy and clinical outcomes. In addition, an exploratory machine learning analysis was performed.
OBJECTIVE: To compare long-term oncologic outcomes in patients with clinically uterine-confined endometrioid endometrial cancer who underwent surgical staging with robot-assisted (RA) versus conventional laparoscopy.
OBJECTIVE: Machine learning, deep learning, and artificial intelligence (AI) are terms that have made their way into nearly all areas of medicine. In the case of medical imaging, these methods have become the state of the art in nearly all areas from...
OBJECTIVE: Recent reports in both cervical and endometrial cancer suggest that minimally invasive surgery (MIS) had an unanticipated negative impact on long-term clinical outcomes, including recurrence and death. Given increasing use of robotic surge...
BACKGROUND: Number of involved lymph nodes (LNs) is a crucial stratification factor in staging of numerous disease sites, but has not been incorporated for endometrial cancer. We evaluated whether number of involved LNs provide improved prognostic va...
OBJECTIVE: To investigate the impact of changes in body composition during primary treatment on survival outcomes in patients with epithelial ovarian cancer (EOC).
OBJECTIVE: To develop and evaluate the performance of a radiomics and machine learning model applied to ultrasound (US) images in predicting the risk of malignancy of a uterine mesenchymal lesion.