AIMC Topic: Prostatic Neoplasms

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An innovative approach for predicting prostate cancer Gleason grading: machine learning-based fusion of multimodal ultrasound, clinical and laboratory indicators.

European journal of medical research
BACKGROUND: Prostate cancer is a common malignancy among elderly males with a growing incidence. While prostate biopsy remains the gold standard for diagnosis, this invasive procedure is poorly tolerated by some patients. The Gleason grade group (GGG...

[Ga]Ga-PSMA-11 PET Tumor Volume Predicts Overall Survival of Patients with Metastatic Prostate Cancer Undergoing Taxane-Based Chemotherapy.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Prostate-specific membrane antigen (PSMA) PET has the potential to monitor the response to taxane-based chemotherapy in patients with prostate cancer and shows promise for predicting outcomes and improving response evaluation. This retrospective stud...

Automated Multimodal Image Registration for Prostate Cancer Using Squeeze-and-Excitation ResNet with Thin Plate Spline Transformation: A Deep Learning Approach.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Accurate spatial correlation between preoperative prostate MRI and post-prostatectomy histopathology is critical for improving prostate cancer diagnosis, treatment planning, and MRI interpretation. Current manual registration methods are t...

A modified deep learning approach for seminal vesicle region localization in prostate MRI.

Scientific reports
The seminal vesicle region plays a crucial role in male reproductive health, and its accurate evaluation is essential for diagnosing infertility and carcinoma. Magnetic resonance imaging (MRI) is the primary modality for assessment; however, manual e...

Modelling of immune infiltration in prostate cancer treated with HDR-brachytherapy using Raman spectroscopy and machine learning.

Scientific reports
Prostate cancer is characterized by an immunosuppressive tumour environment. This work combines Raman spectroscopy with group-and-bases-restricted non-negative matrix factorization (GBR-NMF) and machine learning to assemble models of immune cell dens...

Multi-layer stratified oncology platform utilizing transcriptomics, prostate cancer organoids, and modeling of drug response.

Journal of experimental & clinical cancer research : CR
The high intra-patient heterogeneity in multifocal primary prostate cancer (PCa) has curtailed the efficacy of current treatment options. By employing twin biopsies from multiple lesions with matched patient-derived organoids (PDO) models, the PCa mo...

Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer.

Nature communications
The aggressiveness of prostate cancer is primarily assessed from histopathological data using the Gleason scoring system. Conventional artificial intelligence (AI) approaches can predict Gleason scores, but often lack explainability, which may limit ...

Automating prostate volume acquisition using abdominal ultrasound scans for prostate-specific antigen density calculations.

Scientific reports
Proposed methods for prostate cancer screening are currently prohibitively expensive (due to the high costs of imaging equipment such as magnetic resonance imaging and traditional ultrasound systems), inadequate in their detection rates, require high...

Attention-enhanced hybrid U-Net for prostate cancer grading and explainability.

Scientific reports
Prostate cancer remains a leading cause of mortality, necessitating precise histopathological segmentation for accurate Gleason Grade assessment. However, existing deep learning-based segmentation models lack contextual awareness and explainability, ...

Machine learning-based prediction of post-operative outcomes in robotic-assisted radical prostatectomy: a multi-variable analysis of 758 cases.

Journal of robotic surgery
Robotic-assisted radical prostatectomy (RARP) has become the gold standard treatment for localized prostate cancer. However, predicting post-operative outcomes remains challenging. This study aims to develop and validate predictive models for key out...