AIMC Topic: Cystadenocarcinoma, Serous

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Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets.

Briefings in bioinformatics May 20, 2021
The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its molecular pathology rather than an organ. The molecular similarities among multi-omics data found in...
Neoplasms Gene Regulatory Networks Female Breast Neoplasms Humans Databases, Genetic Machine Learning Ovarian Neoplasms Genomics Cystadenocarcinoma, Serous Computational Biology
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Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning.

Cancer control : journal of the Moffitt Cancer Center Jan 1, 2021
INTRODUCTION: Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning can serve this purpose by making predictions based upon generalizable clinical patterns embedded within le...
Machine Learning Prospective Studies Humans Aged Aged, 80 and over Adult Prognosis Female Ovarian Neoplasms Patient Acuity Cystadenocarcinoma, Serous Support Vector Machine Age Factors Middle Aged Logistic Models
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Intraoperative Diagnosis Support Tool for Serous Ovarian Tumors Based on Microarray Data Using Multicategory Machine Learning.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society Jan 1, 2016
OBJECTIVES: Serous borderline ovarian tumors (SBOTs) are a subtype of serous ovarian carcinoma with atypical proliferation. Frozen-section diagnosis has been used as an intraoperative diagnosis tool in supporting the fertility-sparing surgery by diag...
Cystadenocarcinoma, Serous Blotting, Western Neoplasm Staging Immunoenzyme Techniques Neoplasms, Glandular and Epithelial Carcinoma, Ovarian Epithelial Ovarian Neoplasms Predictive Value of Tests Support Vector Machine Databases, Genetic Machine Learning Prognosis Humans Monitoring, Intraoperative Real-Time Polymerase Chain Reaction RNA, Messenger Gene Expression Regulation, Neoplastic Reverse Transcriptase Polymerase Chain Reaction Biomarkers, Tumor Gene Expression Profiling Female Survival Rate
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Comparison of lymphedema incidence between 2 lymphadenectomy techniques in patients with uterine cancer undergoing robotic staging.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society Jan 1, 2015
OBJECTIVE: This study aimed to compare the incidence of lower extremity lymphedema in patients with uterine cancer after robotic staging using the following 2 methods: standard and selective pelvic lymphadenectomy.
Cystadenocarcinoma, Serous Para-Aortic Bodies Lymphedema Adenocarcinoma, Clear Cell Pelvic Neoplasms Aged, 80 and over Connecticut Female Endometrial Neoplasms Adult Lymph Node Excision Carcinoma, Endometrioid Aged Carcinosarcoma Incidence Neoplasm Staging Robotics Humans Lymph Nodes Follow-Up Studies Uterine Neoplasms Prognosis Middle Aged Lymphatic Metastasis
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