AIMC Topic: Prostatic Neoplasms

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Automated radiotherapy treatment planning guided by GPT-4Vision.

Physics in medicine and biology
. Radiotherapy treatment planning is a time-consuming and potentially subjective process that requires iterative adjustment of model parameters to balance multiple conflicting objectives. Recent advancements in frontier artificial intelligence (AI) m...

Using machine learning to discover DNA metabolism biomarkers that direct prostate cancer treatment.

Scientific reports
DNA metabolism genes play pivotal roles in the regulation of cellular processes that contribute to cancer progression, immune modulation, and therapeutic response in prostate cancer (PC). Understanding the mechanisms by which these genes influence th...

Urine-based Raman markers for prostate cancer diagnosis: A machine learning approach using fingerprint and lipid spectral region.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study investigates the potential of Raman spectroscopy in distinguishing between healthy individuals and prostate cancer patients using urine samples. The Boruta algorithm was applied to Raman spectral data in two distinct wavenumber regions: 80...

oDigital pathology biomarkers for guiding radiotherapy-based treatment concepts in prostate cancer - a systematic review and expert consensus.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Current risk-stratification systems for prostate cancer (PCa) do not sufficiently reflect the disease heterogeneity, and digital pathology (DP) combined with artificial intelligence (AI) tools (DP-AI) may offer a solution to this challenge. The aim o...

Artificial intelligence in prostate cancer.

Chinese medical journal
Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial...

The correlation of liquid biopsy genomic data to radiomics in colon, pancreatic, lung and prostatic cancer patients.

European journal of cancer (Oxford, England : 1990)
INTRODUCTION: With the advances in artificial intelligence (AI) and precision medicine, radiomics has emerged as a promising tool in the field of oncology. Radiogenomics integrates radiomics with genomic data, potentially offering a non-invasive meth...

Development and retrospective validation of an artificial intelligence system for diagnostic assessment of prostate biopsies: study protocol.

BMJ open
INTRODUCTION: Histopathological evaluation of prostate biopsies using the Gleason scoring system is critical for prostate cancer diagnosis and treatment selection. However, grading variability among pathologists can lead to inconsistent assessments, ...

Performance of GPT-4 for automated prostate biopsy decision-making based on mpMRI: a multi-center evidence study.

Military Medical Research
BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) has significantly advanced prostate cancer (PCa) detection, yet decisions on invasive biopsy with moderate prostate imaging reporting and data system (PI-RADS) scores remain ambiguous.

Interpretable machine learning models for survival prediction in prostate cancer bone metastases.

Scientific reports
Prostate cancer bone metastasis (PCBM) is a highly lethal condition with limited survival. Accurate survival prediction is essential for managing these typically incurable patients. However, existing clinical models lack precision. This study seeks t...

Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer.

BMC medical imaging
OBJECTIVE: To develop and evaluate a intralesional and perilesional radiomics strategy based on different machine learning model to differentiate International Society of Urological Pathology (ISUP) grade > 2 group and ISUP ≤ 2 prostate cancers (PCa)...