AIMC Topic: Prostatic Neoplasms

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Improving Clinically Significant Prostate Cancer Detection with a Multimodal Machine Learning Approach: A Large-Scale Multicenter Study.

Radiology. Imaging cancer
Purpose To develop and prospectively validate a clinical and radiologic model to predict clinically significant prostate cancer (csPCa) using biparametric MRI (bpMRI). Materials and Methods Retrospective data (acquired before March 31, 2022) from 12 ...

Deep sensorless tracking of ultrasound probe orientation during freehand transperineal biopsy with spatial context for symmetry disambiguation.

Computers in biology and medicine
BACKGROUND: Diagnosis of prostate cancer requires histopathology of tissue samples. Following an MRI to identify suspicious areas, a biopsy is performed under ultrasound (US) guidance. In existing assistance systems, 3D US information is generally av...

Impact of three-dimensional prostate models during robot-assisted radical prostatectomy on surgical margins and functional outcomes.

BJU international
BACKGROUND: Robot-assisted radical prostatectomy (RARP) is the standard surgical procedure for the treatment of prostate cancer. RARP requires a trade-off between performing a wider resection in order to reduce the risk of positive surgical margins (...

A Deep Learning Model for Comprehensive Automated Bone Lesion Detection and Classification on Staging Computed Tomography Scans.

Academic radiology
RATIONALE AND OBJECTIVES: A common site of metastases for a variety of cancers is the bone, which is challenging and time consuming to review and important for cancer staging. Here, we developed a deep learning approach for detection and classificati...

MRI Radiomics and Automated Habitat Analysis Enhance Machine Learning Prediction of Bone Metastasis and High-Grade Gleason Scores in Prostate Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To explore the value of machine learning models based on MRI radiomics and automated habitat analysis in predicting bone metastasis and high-grade pathological Gleason scores in prostate cancer.

Study of AI algorithms on mpMRI and PHI for the diagnosis of clinically significant prostate cancer.

Urologic oncology
OBJECTIVE: To study the feasibility of multiple factors in improving the diagnostic accuracy of clinically significant prostate cancer (csPCa).

Accelerating prostate rs-EPI DWI with deep learning: Halving scan time, enhancing image quality, and validating in vivo.

Magnetic resonance imaging
OBJECTIVES: This study aims to evaluate the feasibility and effectiveness of deep learning-based super-resolution techniques to reduce scan time while preserving image quality in high-resolution prostate diffusion-weighted imaging (DWI) with readout-...

Creatine kinase in prostate cancer: A biosensor-driven diagnostic paradigm.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Prostate cancer (PC) remains a leading cause of cancer-related morbidity in men worldwide. Emerging evidence suggests that the brain-type creatine kinase isoenzyme (CK-BB) is overexpressed in PC tissue and correlates with tumor progressio...

Daily proton dose re-calculation on deep-learning corrected cone-beam computed tomography scans.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Synthetic CT (sCT) generation from cone-beam CT (CBCT) must maintain stable performance and allow for accurate dose calculation across all treatment fractions to effectively support adaptive proton therapy. This study evaluate...