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Endometrial Neoplasms

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A deep learning framework for predicting endometrial cancer from cytopathologic images with different staining styles.

PloS one
Endometrial cancer screening is crucial for clinical treatment. Currently, cytopathologists analyze cytopathology images is considered a popular screening method, but manual diagnosis is time-consuming and laborious. Deep learning can provide objecti...

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...

Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer.

Scientific reports
It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC). Based on the deep learning convolutional neural network (CNN) architecture residua...

A machine learning-based immune response signature to facilitate prognosis prediction in patients with endometrial cancer.

Scientific reports
Endometrial cancer is the most prevalent form of gynecologic malignancy, with a significant surge in incidence among youngsters. Although the advent of the immunotherapy era has profoundly improved patient outcomes, not all patients benefit from immu...

Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features.

BMC cancer
OBJECTIVES: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).

Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer.

Medical image analysis
In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit...

Clinical target volume (CTV) automatic delineation using deep learning network for cervical cancer radiotherapy: A study with external validation.

Journal of applied clinical medical physics
PURPOSE: To explore the accuracy and feasibility of a proposed deep learning (DL) algorithm for clinical target volume (CTV) delineation in cervical cancer radiotherapy and evaluate whether it can perform well in external cervical cancer and endometr...

Multi-Class Segmentation Network Based on Tumor Tissue in Endometrial Cancer Pathology Images: ECMTrans-net.

The American journal of pathology
Endometrial cancer has the second highest incidence of malignant tumors in the female reproductive system. Accurate and efficient analysis of endometrial cancer pathology images is one of the important research components of computer-aided diagnosis....

Enhancing endometrial cancer detection: Blood serum intrinsic fluorescence data processing and machine learning application.

Talanta
Endometrial cancer (EC) is the most prevalent cancer within the female reproductive system in developed countries. Despite its high incidence, there is currently no established laboratory screening test for EC, making early detection challenging. Thi...