AIMC Topic: Male

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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...

MRI-Based Quantification of Intratumoral Heterogeneity for Predicting Progression-Free Survival in Patients with Lung Cancer Brain Metastasis Receiving Radiotherapy.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Our aim was to investigate the potential of using MRI-based habitat features for predicting progression-free survival (PFS) in patients with lung cancer brain metastasis (LCBM) receiving radiotherapy.

Prediction of clinical outcomes of ST-elevated myocardial infarction patients using atmospheric solids analysis probe mass spectrometry and machine learning.

The Analyst
: Analysis of small molecule metabolites found in blood plasma of patients undergoing treatment for STEMI has the potential to be used as a clinical diagnostic and prognostic tool, capable of predicting disease progression, risk of negative outcomes,...

Prognostic Value of AI-Assisted Lesion Tracking on End-of-Treatment PSMA PET in mCRPC Patients Treated with Lu-PSMA: A Retrospective, Single-Center Study.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
This study aimed to explore the prognostic value of the artificial intelligence-assisted lesion tracking applied to prostate-specific membrane antigen (PSMA) PET in patients with metastatic castration-resistant prostate cancer (mCRPC) treated with PS...

Deep Learning for Automated Measures of SUV and Molecular Tumor Volume in [Ga]PSMA-11 or [F]DCFPyL, [F]FDG, and [Lu]Lu-PSMA-617 Imaging with Global Threshold Regional Consensus Network.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Metastatic castration-resistant prostate cancer has a high rate of mortality with a limited number of effective treatments after hormone therapy. Radiopharmaceutical therapy with [Lu]Lu-prostate-specific membrane antigen-617 (LuPSMA) is one treatment...

Fully Automated Image-Based Multiplexing of Serial PET/CT Imaging for Facilitating Comprehensive Disease Phenotyping.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Combined PET/CT imaging provides critical insights into both anatomic and molecular processes, yet traditional single-tracer approaches limit multidimensional disease phenotyping; to address this, we developed the PET Unified Multitracer Alignment (P...

[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...

Artificial Intelligence-Powered Quantification of Flortaucipir PET for Detecting Tau Pathology.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
We developed and evaluated an artificial intelligence (AI)-powered approach for easier quantification of tau PET uptake without requiring structural MR to aid earlier tracking of Alzheimer disease (AD). We implemented a deep neural network model tha...

A neural network approach to sarcopenia prediction based on bioelectrical impedance in community-dwelling older adults.

PloS one
This study aimed to apply a neural network to raw bioelectrical impedance analysis data and to test whether sarcopenia could be predicted with high accuracy. The study population comprised 727 community-dwelling older adults aged 65-85 years who part...

Machine learning models for predicting renal injury in patients with gout.

Renal failure
BACKGROUND: Renal injury is a severe complication among individuals diagnosed with gout. This research constructed a machine learning predictive model to assess renal injury risk in gout patients.