Prostate Cancer

Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator.

To create a diagnostic tool before biopsy for patients with prostate-specific antigen (PSA) levels < 20 ng/ml to minimize prostate biopsy-related discomfort and risks. Data from 655 patients who underwent transperineal prostate biopsy at the First Affiliated Hospital of Wannan Medical College from July 2021 to January 2023 were collected and analyzed. After applying the Synthetic Minority Over-sam...

Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.

Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. This paper proposes a novel Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network (MS-PSA-SOC) for ERP Detection. ...

An Automatic Deep-Radiomics Framework for Prostate Cancer Diagnosis and Stratification in Patients with Serum Prostate-Specific Antigen of 4.0-10.0 ng/mL: A Multicenter Retrospective Study.

RATIONALE AND OBJECTIVES: To develop an automatic deep-radiomics framework that diagnoses and stratifies prostate cancer in patients with prostate-spe...

Multi-branch CNNFormer: a novel framework for predicting prostate cancer response to hormonal therapy.

PURPOSE: This study aims to accurately predict the effects of hormonal therapy on prostate cancer (PC) lesions by integrating multi-modality magnetic ...

Towards U-Net-based intraoperative 2D dose prediction in high dose rate prostate brachytherapy.

BACKGROUND: Poor needle placement in prostate high-dose-rate brachytherapy (HDR-BT) results in sub-optimal dosimetry and mentally predicting these eff...

Automatic plan selection using deep network-A prostate study.

BACKGROUND: Recently, high-dose-rate (HDR) brachytherapy treatment plans generation was improved with the development of multicriteria optimization (M...

Psoriatic arthritis in psoriasis: optimizing the current screening system for psoriatic arthritis based on serum data from U.S. and Chinese populations.

BACKGROUND: Psoriatic arthritis (PSA) is an inflammatory joint disease associated with psoriasis (PSO) that can be easily missed. Existing PSA screeni...

Ultra-low dose rate brachytherapy (uLDR-BT) in treatment of patients with unfavorable intermediate-risk group prostate cancer - retrospective analysis.

BACKGROUND: Treatment with sole ultra-low dose rate brachytherapy (uLDR-BT) for unfavorable intermediate risk factor (IUR) group prostate cancer patie...

Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine.

Prostate-specific antigen (PSA) is the most commonly used marker of prostate cancer. However, nearly 25% of men with elevated PSA levels do not have c...

Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach.

BACKGROUND: Prostate cancer is the second leading cause of death among American men. If detected and treated at an early stage, prostate cancer is oft...

Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators.

BACKGROUND: Formulating a clinically acceptable plan within the time-constrained clinical setting of brachytherapy poses challenges to clinicians. Dee...

Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction.

Prostate cancer (PCa) is a clinically heterogeneous disease. Predicting clinically significant PCa with low-intermediate prostate-specific antigen (PS...

A Novel Machine Learning-based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator.

OBJECTIVE: To create a machine-learning predictive model combining prostate imaging-reporting and data system (PI-RADS) score, PSA density, and clinic...

The shared biomarkers and immune landscape in psoriatic arthritis and rheumatoid arthritis: Findings based on bioinformatics, machine learning and single-cell analysis.

OBJECTIVE: Psoriatic arthritis (PsA) and rheumatoid arthritis (RA) are the most common types of inflammatory musculoskeletal disorders that share over...

A Multimodal Deep Learning Nomogram for the Identification of Clinically Significant Prostate Cancer in Patients with Gray-Zone PSA Levels: Comparison with Clinical and Radiomics Models.

RATIONALE AND OBJECTIVES: To establish a multimodal deep learning nomogram for predicting clinically significant prostate cancer in patients with gray...

Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet.

To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined with 3D nnUNet for rapid and consistent autosegmentat...

Robotic MR-guided high dose rate brachytherapy needle implantation in the prostate (ROBiNSon)-a proof-of-concept study.

A robotic needle implant device for MR-guided high-dose-rate (HDR) prostate brachytherapy was developed. This study aimed to assess the feasibility an...

Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy.

Urinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been a...

Self-normalization for a 1 mmresolution clinical PET system using deep learning.

This work proposes, for the first time, an image-based end-to-end self-normalization framework for positron emission tomography (PET) using conditiona...

Automatic segmentation of high-risk clinical target volume and organs at risk in brachytherapy of cervical cancer with a convolutional neural network.

PURPOSE: This study aimed to design an autodelineation model based on convolutional neural networks for generating high-risk clinical target volumes a...