AIMC Topic: Prostatic Neoplasms

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Requirements for AI Development and Reporting for MRI Prostate Cancer Detection in Biopsy-Naive Men: PI-RADS Steering Committee, Version 1.0.

Radiology
This document defines the key considerations for developing and reporting an artificial intelligence (AI) interpretation model for the detection of clinically significant prostate cancer (PCa) at MRI in biopsy-naive men with a positive clinical scree...

Do explainable AI (XAI) methods improve the acceptance of AI in clinical practice? An evaluation of XAI methods on Gleason grading.

The journal of pathology. Clinical research
This work aimed to evaluate both the usefulness and user acceptance of five gradient-based explainable artificial intelligence (XAI) methods in the use case of a prostate carcinoma clinical decision support system environment. In addition, we aimed t...

MRI-based Deep Learning Algorithm for Assisting Clinically Significant Prostate Cancer Detection: A Bicenter Prospective Study.

Radiology
Background Although artificial intelligence is actively being developed for prostate MRI, few studies have prospectively validated these tools. Purpose To compare the diagnostic performance of a commercial deep learning algorithm (DLA) and radiologis...

Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI.

Radiology. Artificial intelligence
Purpose To evaluate the performance of Physics-Informed Autoencoder (PIA), a self-supervised deep learning model, in measuring tissue-based biomarkers for prostate cancer (PCa) using hybrid multidimensional MRI. Materials and Methods This retrospecti...

Prediction of Prostate Cancer Grades Using Radiomic Features.

Acta medica Okayama
We developed a machine learning model for predicting prostate cancer (PCa) grades using radiomic features of magnetic resonance imaging. 112 patients diagnosed with PCa based on prostate biopsy between January 2014 and December 2021 were evaluated. L...

Integrating multimodal imaging and peritumoral features for enhanced prostate cancer diagnosis: A machine learning approach.

PloS one
BACKGROUND: Prostate cancer is a common malignancy in men, and accurately distinguishing between benign and malignant nodules at an early stage is crucial for optimizing treatment. Multimodal imaging (such as ADC and T2) plays an important role in th...

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...

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 ...

Artificial intelligence-based personalized clinical decision-making for patients with localized prostate cancer: surgery versus radiotherapy.

The oncologist
BACKGROUND: Surgery and radiotherapy are primary nonconservative treatments for prostate cancer (PCa). However, personalizing treatment options between these treatment modalities is challenging due to unclear criteria. We developed an artificial inte...

An Integrated Approach to Develop a Potent Vaccine Candidate Construct Against Prostate Cancer by Utilizing Machine Learning and Bioinformatics.

Cancer reports (Hoboken, N.J.)
BACKGROUND: Prostate cancer is the most common malignancy among males. Prostaglandin G/H synthase (PGHS) is an essential enzyme in the synthesis of prostaglandins, and its activation has been linked to many malignancies, including colorectal cancer.