AIMC Topic: Carcinoma, Ovarian Epithelial
Clear Filters Showing 21 to 30 of 39 articles
Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 16, 2022
Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70 % of advanced patients are with recurrent cancer and decease. Surgical debulking of tumors following chemotherapy is the conventiona...
A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.
Archives of gynecology and obstetrics
May 9, 2022
In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric decision-making algorithms. Regarding ovarian cancer (OC), despite the standardization of features t...
Prognostic implications of body composition change during primary treatment in patients with ovarian cancer: A retrospective study using an artificial intelligence-based volumetric technique.
Gynecologic oncology
May 14, 2021
OBJECTIVE: To investigate the impact of changes in body composition during primary treatment on survival outcomes in patients with epithelial ovarian cancer (EOC).
Peri-operative blood management of Jehovah's Witnesses undergoing cytoreductive surgery for advanced ovarian cancer.
Blood transfusion = Trasfusione del sangue
Feb 25, 2021
BACKGROUND: The aim of this study was to evaluate the efficacy and feasibility of a peri-operative bloodless medicine and surgery (BMS) protocol in reducing severe post-operative anaemia (haemoglobin [Hb] <7 g/dL) in Jehovah's Witnesses undergoing cy...
Comparing Laparotomy with Robot-assisted Interval Debulking Surgery for Patients with Advanced Epithelial Ovarian Cancer Receiving Neoadjuvant Chemotherapy.
Journal of minimally invasive gynecology
Nov 26, 2020
STUDY OBJECTIVE: Compare survival of patients with advanced epithelial ovarian cancer (EOC) undergoing interval debulking surgery (IDS) with either robot-assisted (R-IDS) or open (O-IDS) approach. Second, we assessed the impact of adjuvant and neoadj...
A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models.
Journal of advanced research
Nov 11, 2020
INTRODUCTION: Ovarian cancer (OC) is one of the most frequent gynecologic cancers among women, and high-accuracy risk prediction techniques are essential to effectively select the best intervention strategies and clinical management for OC patients a...
Prediction of Postoperative Length of Hospital Stay Based on Differences in Nursing Narratives in Elderly Patients with Epithelial Ovarian Cancer.
Methods of information in medicine
Apr 29, 2020
OBJECTIVES:  The current study sought to evaluate whether nursing narratives can be used to predict postoperative length of hospital stay (LOS) following curative surgery for ovarian cancer.
Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers.
Clinical cancer research : an official journal of the American Association for Cancer Research
Apr 11, 2019
PURPOSE: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.
Neoplasm Grading
Carcinoma, Ovarian Epithelial
Preoperative Care
Support Vector Machine
Ovarian Neoplasms
Bayes Theorem
Neoplasm Staging
Aged
Aged, 80 and over
Young Adult
Adult
Survival Rate
Biomarkers, Tumor
Machine Learning
Middle Aged
Algorithms
ROC Curve
Humans
Prognosis
Neural Networks, Computer
Female
Artificial Intelligence
Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods.
Journal of gynecologic oncology
Mar 11, 2019
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.
MALDI-Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods.
Proteomics. Clinical applications
Dec 14, 2018
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...
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