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Prostatic Neoplasms

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Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
BACKGROUND: This study aims to evaluate the capabilities and limitations of large language models (LLMs) for providing patient education for men undergoing radiotherapy for localized prostate cancer, incorporating assessments from both clinicians and...

Ascertaining provider-level implicit bias in electronic health records with rules-based natural language processing: A pilot study in the case of prostate cancer.

PloS one
PURPOSE: Implicit, unconscious biases in medicine are personal attitudes about race, ethnicity, gender, and other characteristics that may lead to discriminatory patterns of care. However, there is no consensus on whether implicit bias represents a t...

Deep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer.

Radiology
Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasib...

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...

Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.

Radiology. Imaging cancer
Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI ...

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...

The role of endothelial cell-related gene COL1A1 in prostate cancer diagnosis and immunotherapy: insights from machine learning and single-cell analysis.

Biology direct
BACKGROUND: Endothelial cells are integral components of the tumor microenvironment and play a multifaceted role in tumor immunotherapy. Targeting endothelial cells and related signaling pathways can improve the effectiveness of immunotherapy by norm...

Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states.

Scientific reports
Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics ...

Predicting intermediate-risk prostate cancer using machine learning.

International urology and nephrology
PURPOSES: Intermediate-risk prostate cancer (IR PCa) is the most common risk group for localized prostate cancer. This study aimed to develop a machine learning (ML) model that utilizes biopsy predictors to estimate the probability of IR PCa and asse...