OBJECTIVES: The aim of this study was to develop a new prognostic classification for epithelial ovarian cancer (EOC) patients using gradient boosting (GB) and to compare the accuracy of the prognostic model with the conventional statistical method.
PURPOSE: Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix-a...
BACKGROUND: The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, convent...
BACKGROUND: The study aimed at developing and validating a deep learning (DL) model based on the ultrasound imaging for predicting the platinum resistance of patients with epithelial ovarian cancer (EOC).
PURPOSE: Patients with epithelial ovarian cancer (EOC) have an elevated risk for venous thromboembolism (VTE). To assess the risk of VTE, models were developed by statistical or machine learning algorithms. However, few models have accommodated deep ...
Cancer control : journal of the Moffitt Cancer Center
Jan 1, 2023
INTRODUCTION: In patients affected by epithelial ovarian cancer (EOC) complete cytoreduction (CC) has been associated with higher survival outcomes. Artificial intelligence (AI) systems have proved clinical benefice in different areas of healthcare.
PURPOSE OF REVIEW: Robot-assisted laparoscopic staging (RALS) is increasingly used for staging epithelial ovarian cancer (EOC). Evidence of its safety is limited. The aim of this review is to compare the efficacy and safety of RALS in clinical early-...
Long-term outcome of high-grade serous epithelial ovarian carcinoma (HGSOC) remains poor as a result of recurrence and the emergence of drug resistance. Almost all the patients were given the same platinum-based chemotherapy after debulking surgery e...
Journal of biological regulators and homeostatic agents
Jan 1, 2016
The Risk of Malignancy Algorithm (ROMA) combines the diagnostic power of the CA125 and HE4 markers with menopausal status to predict the risk for developing epithelial ovarian cancer (EOC). The aim of this study was to evaluate the association betw...
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