AIMC Topic: Prostate

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Redefining prostate cancer care: innovations and future directions in active surveillance.

Current opinion in urology
PURPOSE OF REVIEW: This review provides a critical analysis of recent advancements in active surveillance (AS), emphasizing updates from major international guidelines and their implications for clinical practice.

Rethinking Copy-Paste for Consistency Learning in Medical Image Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Semi-supervised learning based on consistency learning offers significant promise for enhancing medical image segmentation. Current approaches use copy-paste as an effective data perturbation technique to facilitate weak-to-strong consistency learnin...

Use of deep learning-accelerated T2 TSE for prostate MRI: Comparison with and without hyoscine butylbromide admission.

Magnetic resonance imaging
OBJECTIVE: To investigate the use of deep learning (DL) T2-weighted turbo spin echo (TSE) imaging sequence with deep learning acceleration (T2DL) in prostate MRI regarding the necessity of hyoscine butylbromide (HBB) administration for high image qua...

Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques.

Scientific reports
In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant cancer (CsPCa). The prostate imaging-reporting and data system (PI-RA...

ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies.

European urology oncology
BACKGROUND AND OBJECTIVE: To assess whether conventional brightness-mode (B-mode) transrectal ultrasound images of the prostate reveal clinically significant cancers with the help of artificial intelligence methods.

An Artificial Intelligence Model Using Diffusion Basis Spectrum Imaging Metrics Accurately Predicts Clinically Significant Prostate Cancer.

The Journal of urology
PURPOSE: Conventional prostate magnetic resonance imaging has limited accuracy for clinically significant prostate cancer (csPCa). We performed diffusion basis spectrum imaging (DBSI) before biopsy and applied artificial intelligence models to these ...

Deep Learning for Predicting Difficulty in Radical Prostatectomy: A Novel Evaluation Scheme.

Urology
OBJECTIVE: To explore new metrics for assessing radical prostatectomy difficulty through a two-stage deep learning method from preoperative magnetic resonance imaging.

Clinical Application of Deep Learning-Assisted Needles Reconstruction in Prostate Ultrasound Brachytherapy.

International journal of radiation oncology, biology, physics
PURPOSE: High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often employed during the procedure, minimizing overall planning time is crucial. In this study, we explore t...

BMA-Net: A 3D bidirectional multi-scale feature aggregation network for prostate region segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Accurate segmentation of the prostate region in magnetic resonance imaging (MRI) is crucial for prostate-related diagnoses. Recent studies have incorporated Transformers into prostate region segmentation to better capture lo...

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

Scientific reports
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 Af...