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Uterine Cervical Neoplasms

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Objective and Subjective Assessment of Bladder Function after Robot-assisted Laparoscopic Radical Hysterectomy for Early-stage Cervical Cancer.

Journal of minimally invasive gynecology
STUDY OBJECTIVE: To examine whether objective bladder function after robot-assisted radical hysterectomy (RRH) for early-stage cervical cancer is correlated with subjective patient-reported outcomes and quality of life during the first year after RRH...

Deep learning-based multimodal image analysis for cervical cancer detection.

Methods (San Diego, Calif.)
Cervical cancer is the fourth most common cancer in women, and its precise detection plays a critical role in disease treatment and prognosis prediction. Fluorodeoxyglucose positron emission tomography and computed tomography, i.e., FDG-PET/CT and PE...

RGB Channel Superposition Algorithm with Acetowhite Mask Images in a Cervical Cancer Classification Deep Learning Model.

Sensors (Basel, Switzerland)
Cervical cancer is one of the main causes of death from cancer in women. However, it can be treated successfully at an early stage. This study aims to propose an image processing algorithm based on acetowhite, which is an important criterion for diag...

Hybrid Loss-Constrained Lightweight Convolutional Neural Networks for Cervical Cell Classification.

Sensors (Basel, Switzerland)
Artificial intelligence (AI) technologies have resulted in remarkable achievements and conferred massive benefits to computer-aided systems in medical imaging. However, the worldwide usage of AI-based automation-assisted cervical cancer screening sys...

Diagnosis of Early Cervical Cancer with a Multimodal Magnetic Resonance Image under the Artificial Intelligence Algorithm.

Contrast media & molecular imaging
This research was conducted to explore the value of multimodal magnetic resonance imaging (MRI) based on the alternating direction algorithm in the diagnosis of early cervical cancer. 64 patients diagnosed with early cervical cancer clinicopathologic...

Scrutinizing high-risk patients from ASC-US cytology via a deep learning model.

Cancer cytopathology
BACKGROUND: Atypical squamous cells of undetermined significance (ASC-US) is the most frequent but ambiguous abnormal Papanicolaou (Pap) interpretation and is generally triaged by high-risk human papillomavirus (hrHPV) testing before colposcopy. This...

The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer.

Journal of applied clinical medical physics
PURPOSE: To develop a 3D-Unet dose prediction model to predict the three-dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explo...

Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy.

Medical physics
PURPOSE: This study aims to develop a deep learning method that skips the time-consuming inverse optimization process for automatic generation of machine-deliverable intensity-modulated radiation therapy (IMRT) plans.

Automatic segmentation of magnetic resonance images for high-dose-rate cervical cancer brachytherapy using deep learning.

Medical physics
PURPOSE: Magnetic resonance (MR) imaging is the gold standard in image-guided brachytherapy (IGBT) due to its superior soft-tissue contrast for target and organs-at-risk (OARs) delineation. Accurate and fast segmentation of MR images are very importa...