Dataset Information

Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept.

ABSTRACT: BACKGROUND:Opportunistic prostate cancer (PCa) screening is a controversial topic. Magnetic resonance imaging (MRI) has proven to detect prostate cancer with a high sensitivity and specificity, leading to the idea to perform an image-guided prostate cancer (PCa) screening; Methods: We evaluated a prospectively enrolled cohort of 49 healthy men participating in a dedicated image-guided PCa screening trial employing a biparametric MRI (bpMRI) protocol consisting of T2-weighted (T2w) and diffusion weighted imaging (DWI) sequences. Datasets were analyzed both by human readers and by a fully automated artificial intelligence (AI) software using deep learning (DL). Agreement between the algorithm and the reports-serving as the ground truth-was compared on a per-case and per-lesion level using metrics of diagnostic accuracy and k statistics; Results: The DL method yielded an 87% sensitivity (33/38) and 50% specificity (5/10) with a k of 0.42. 12/28 (43%) Prostate Imaging Reporting and Data System (PI-RADS) 3, 16/22 (73%) PI-RADS 4, and 5/5 (100%) PI-RADS 5 lesions were detected compared to the ground truth. Targeted biopsy revealed PCa in six participants, all correctly diagnosed by both the human readers and AI. CONCLUSIONS:The results of our study show that in our AI-assisted, image-guided prostate cancer screening the software solution was able to identify highly suspicious lesions and has the potential to effectively guide the targeted-biopsy workflow.

SUBMITTER: Winkel DJ

PROVIDER: S-EPMC7697194 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept.

Winkel David J DJ Wetterauer Christian C Matthias Marc Oliver MO Lou Bin B Shi Bibo B Kamen Ali A Comaniciu Dorin D Seifert Hans-Helge HH Rentsch Cyrill A CA Boll Daniel T DT

Diagnostics (Basel, Switzerland) 20201114 11

<h4>Background</h4>Opportunistic prostate cancer (PCa) screening is a controversial topic. Magnetic resonance imaging (MRI) has proven to detect prostate cancer with a high sensitivity and specificity, leading to the idea to perform an image-guided prostate cancer (PCa) screening; Methods: We evaluated a prospectively enrolled cohort of 49 healthy men participating in a dedicated image-guided PCa screening trial employing a biparametric MRI (bpMRI) protocol consisting of T2-weighted (T2w) and di ...[more]

PMID: 33202680

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Project description:Despite the key-role of the Prostate Imaging and Reporting and Data System (PI-RADS) in the diagnosis and characterization of prostate cancer (PCa), this system remains to be affected by several limitations, primarily associated with the interpretation of equivocal PI-RADS 3 lesions and with the debated role of Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), which is only used to upgrade peripheral PI-RADS category 3 lesions to PI-RADS category 4 if enhancement is focal. We aimed at investigating the usefulness of radiomics for detection of PCa lesions (Gleason Score ≥ 6) in PI-RADS 3 lesions and in peripheral PI-RADS 3 upgraded to PI-RADS 4 lesions (upPI-RADS 4). Multiparametric MRI (mpMRI) data of patients who underwent prostatic mpMRI between April 2013 and September 2018 were retrospectively evaluated. Biopsy results were used as gold standard. PI-RADS 3 and PI-RADS 4 lesions were re-scored according to the PI-RADS v2.1 before and after DCE-MRI evaluation. Radiomic features were extracted from T2-weighted MRI (T2), Apparent diffusion Coefficient (ADC) map and DCE-MRI subtracted images using PyRadiomics. Feature selection was performed using Wilcoxon-ranksum test and Minimum Redundancy Maximum Relevance (mRMR). Predictive models were constructed for PCa detection in PI-RADS 3 and upPI-RADS 4 lesions using at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. 41 PI-RADS 3 and 32 upPI-RADS 4 lesions were analyzed. Among 293 radiomic features, the top selected features derived from T2 and ADC. For PI-RADS 3 stratification, second order model showed higher performances (Area Under the Receiver Operating Characteristic Curve-AUC- = 80%), while for upPI-RADS 4 stratification, first order model showed higher performances respect to superior order models (AUC = 89%). Our results support the significant role of T2 and ADC radiomic features for PCa detection in lesions scored as PI-RADS 3 and upPI-RADS 4. Radiomics models showed high diagnostic efficacy in classify PI-RADS 3 and upPI-RADS 4 lesions, outperforming PI-RADS v2.1 performance.

Project description:ObjectiveCurrent prevention and/or treatment options for respiratory syncytial virus (RSV) infections are limited as no vaccine is available. Prophylaxis with palivizumab is very expensive and requires multiple intramuscular injections over the RSV season. Here we present proof-of-concept data using nebulized palivizumab delivery as a promising new approach for the prevention or treatment of severe RSV infections, documenting both aerosol characteristics and pulmonary deposition patterns in the lungs of lambs.DesignProspective animal study.SettingBiosecurity Control Level 2-designated large animal research facility at the Murdoch Children's Research Institute, Melbourne, Australia.SubjectsFour weaned Border-Leicester/Suffolk lambs at 5 months of age.InterventionsFour lambs were administered aerosolized palivizumab conjugated to Tc-99m, under gaseous anesthesia, using either the commercially available AeroNeb Go® or the investigational HYDRA device, placed in-line with the inspiratory limb of a breathing circuit. Lambs were scanned in a single-photon emission computed tomography (SPECT/CT) scanner in the supine position during the administration procedure.Measurements and main resultsBoth the HYDRA and AeroNeb Go® produced palivizumab aerosols in the 1-5 µm range with similar median (geometric standard deviation and range) aerosol droplet diameters for the HYDRA device (1.84 ± 1.40 μm, range = 0.54-5.41μm) and the AeroNeb Go® (3.07 ± 1.56 μm, range = 0.86-10 μm). Aerosolized palivizumab was delivered to the lungs at 88.79-94.13% of the total aerosolized amount for all lambs, with a small proportion localized to either the trachea or stomach. No difference between devices were found. Pulmonary deposition ranged from 6.57 to 9.25% of the total dose of palivizumab loaded in the devices, mostly in the central right lung.ConclusionsAerosolized palivizumab deposition patterns were similar in all lambs, suggesting a promising approach in the control of severe RSV lung infections.

Dataset Information

Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept.

Publications

Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-Labeled Deep Learning and Biparametric Imaging: Proof of Concept.

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