AIMC Topic: Endometrial Neoplasms

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Molecular landscape of endometrioid Cancer: Integrating multiomics and deep learning for personalized survival prediction.

Computers in biology and medicine
BACKGROUND: The endometrioid subtype of endometrial cancer is a significant health concern for women, making it crucial to study the factors influencing patient outcomes.

Immunohistochemistry and machine learning study of DNA replication-associated proteins in uterine epithelial tumors and precursor lesions.

Acta histochemica
Endometrioid adenocarcinoma (EA) has been on the increase in recent years in developed countries. Early detection of endometrioid adenocarcinoma in the endometrial corpus is crucial for patient prognosis and early treatment, although their distinctio...

MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images.

Cell reports. Medicine
Combining molecular classification with clinicopathologic methods improves risk assessment and chooses therapies for endometrial cancer (EC). Detecting mismatch repair (MMR) deficiencies in EC is crucial for screening Lynch syndrome and identifying i...

Enhanced recovery programme in robotic hysterectomy.

British journal of nursing (Mark Allen Publishing)
The standard care for endometrial cancer includes total hysterectomy, bilateral salpingo-oophorectomy, peritoneal washings with or without bilateral pelvic and/or paraaortic lymph node dissection/sampling with or without omental biopsy or omentectomy...

Endometrial tumorigenesis involves epigenetic plasticity demarcating non-coding somatic mutations and 3D-genome alterations.

Genome biology
BACKGROUND: The incidence and mortality of endometrial cancer (EC) is on the rise. Eighty-five percent of ECs depend on estrogen receptor alpha (ERα) for proliferation, but little is known about its transcriptional regulation in these tumors.

Critical view of safety assessment in sentinel node dissection for endometrial and cervical cancer: artificial intelligence to enhance surgical safety and lymph node detection (LYSE study).

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society
OBJECTIVE: This study aims to evaluate the feasibility of video-based assessment rate of Critical Views of Safety criteria for sentinel lymph node dissection in endometrial and cervical cancer. Goal of these Critical Views of Safety is to help standa...

A Machine Learning Approach to Build and Evaluate a Molecular Prognostic Model for Endometrial Cancer Based on Tumour Microenvironment.

Journal of cellular and molecular medicine
Endometrial cancer (EC) incidence and the associated tumour burden have increased globally. To build a molecular expression prognostic model based on the tumour microenvironment to guide personalised treatment using a machine learning approach. Two d...

Construction of a prognostic model for endometrial cancer related to programmed cell death using WGCNA and machine learning algorithms.

Frontiers in immunology
BACKGROUND: Programmed cell death (PCD) refers to a regulated and active process of cellular demise, initiated by specific biological signals. PCD plays a crucial role in the development, progression, and drug resistance of uterine corpus endometrial...

A prediction model based on deep learning and radiomics features of DWI for the assessment of microsatellite instability in endometrial cancer.

Cancer medicine
BACKGROUND: To explore the efficacy of a prediction model based on diffusion-weighted imaging (DWI) features extracted from deep learning (DL) and radiomics combined with clinical parameters and apparent diffusion coefficient (ADC) values to identify...

Unraveling Endometrial Cancer Survival Predictors Through Advanced Machine Learning Techniques.

Studies in health technology and informatics
This study explores endometrial cancer (EC) within the broader context of oncogynecology, focusing on 3,845 EC patients at the Almazov National Research Center. The research analyzes clinical data, employing machine learning techniques like random fo...