Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics.

Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively.

Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study.

Purpose: To develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment.

Material And Methods: A total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists.

Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study.

Purpose: This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with that of the Prostate Imaging and Reporting and Data System (PI-RADS) assessment by expert radiologists based on multiparametric MRI (mpMRI).

Methods: We included 1861 consecutive male patients who underwent radical prostatectomy or biopsy at seven hospitals with mpMRI. These patients were divided into the training (1216 patients in three hospitals) and external validation cohorts (645 patients in four hospitals).

Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI.

Background: To evaluate the potential of clinical-based model, a biparametric MRI-based radiomics model and a clinical-radiomics combined model for predicting clinically significant prostate cancer (PCa).

Methods: In total, 381 patients with clinically suspicious PCa were included in this retrospective study; of those, 199 patients did not have PCa upon biopsy, while 182 patients had PCa. All patients underwent 3.

Differentiating Transition Zone Cancers From Benign Prostatic Hyperplasia by Histogram Analysis of Apparent Diffusion Coefficient Maps With Standard and Ultrahigh b-value Diffusion-weighted MR Imaging.

Objective: To compare the diagnostic performance of standard and ultrahigh b-value Diffusion-weighted Imaging (DWI) using volumetric histogram analysis in differentiating transition zone (TZ) cancer from benign prostatic hyperplasia (BPH).

Methods: 57 TZ cancer and 61 BPH patients received standard (1000 s/mm) and ultrahigh b-value (2000 s/mm) DWI. The diagnostic ability of ADC histogram parameters derived from two DWI for differentiating TZ cancer from BPH was determined by receiver operating characteristic curve.

[Associations of apparent diffusion coefficient value from MR diffusion-weighted imaging with Ki-67 expression and differentiation grade in gastric cancer].

Objective: To examine the associations of apparent diffusion coefficient (ADC) value from MR diffusion-weighted imaging (DWI) with Ki-67 expression and differentiation grade in gastric cancer.

Methods: Images and pathologic data of 68 gastric cancer patients between September 2013 and February 2015 in Affiliated Changshu Hospital of Soochow University were analyzed retrospectively. The expression of Ki-67 antigen in cancer tissue sample was determined by immunohistochemistry.