Background: To evaluate the clinical utility of an Artificial Intelligence (AI) radiology solution, Quantib Prostate, for prostate cancer (PCa) lesions detection on multiparametric Magnetic Resonance Images (mpMRI). Methods: Prostate mpMRI exams of 108 patients were retrospectively studied. The diagnostic performance of an expert radiologist (>8 years of experience) and of an inexperienced radiologist aided by Quantib software were compared. Three groups of patients were assessed: patients with positive mpMRI, positive target biopsy, and/or at least one positive random biopsy (group A, 73 patients); patients with positive mpMRI and a negative biopsy (group B, 14 patients), and patients with negative mpMRI who did not undergo biopsy (group-C, 21 patients). Results: In group A, the AI-assisted radiologist found new lesions with positive biopsy correlation, increasing the diagnostic PCa performance when compared with the expert radiologist, reaching an SE of 92.3% and a PPV of 90.1% (vs. 71.7% and 84.4%). In group A, the expert radiologist found 96 lesions on 73 mpMRI exams (17.7% PIRADS3, 56.3% PIRADS4, and 26% PIRADS5). The AI-assisted radiologist found 121 lesions (0.8% PIRADS3, 53.7% PIRADS4, and 45.5% PIRADS5). At biopsy, 33.9% of the lesions were ISUP1, 31.4% were ISUP2, 22% were ISUP3, 10.2% were ISUP4, and 2.5% were ISUP5. In group B, where biopsies were negative, the AI-assisted radiologist excluded three lesions but confirmed all the others. In group-C, the AI-assisted radiologist found 37 new lesions, most of them PIRADS 3, with 32.4% localized in the peripherical zone and 67.6% in the transition zone. Conclusions: Quantib software is a very sensitive tool to use specifically in high-risk patients (high PIRADS and high Gleason score).
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415513 | PMC |
http://dx.doi.org/10.3390/tomography8040168 | DOI Listing |
Emerg Med J
January 2025
Department of Emergency Medicine, Medisch Centrum Leeuwarden, Leeuwarden, Fryslân, The Netherlands.
Background: Point-of-care ultrasound (POCUS) can potentially be used in the triage of patients with elbow injuries. However, the diagnostic accuracy of POCUS performed by non-radiologists for the exclusion of elbow fractures is yet unknown. This study aimed to investigate the diagnostic potential of POCUS of the posterior fatpad performed by non-radiologists in the workup of adult patients presenting with elbow injuries.
View Article and Find Full Text PDFPLOS Digit Health
January 2025
FIND, Geneva, Switzerland.
AI based software, including computer aided detection software for chest radiographs (CXR-CAD), was developed during the pandemic to improve COVID-19 case finding and triage. In high burden TB countries, the use of highly portable CXR and computer aided detection software has been adopted more broadly to improve the screening and triage of individuals for TB, but there is little evidence in these settings regarding COVID-19 CAD performance. We performed a multicenter, retrospective cross-over study evaluating CXRs from individuals at risk for COVID-19.
View Article and Find Full Text PDFCan Assoc Radiol J
January 2025
University of Alberta, Edmonton, AB, Canada.
The Canadian Association of Radiologists (CAR) Cancer Expert Panel is made up of physicians from the disciplines of radiology, medical oncology, surgical oncology, radiation oncology, family medicine/general practitioner oncology, a patient advisor, and an epidemiologist/guideline methodologist. The Expert Panel developed a list of 29 clinical/diagnostic scenarios, of which 16 pointed to other CAR guidelines. A rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of the remaining 13 scenarios.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Department of Medical Ultrasound, West China Hospital of Sichuan University, Chengdu, China.
Background: Ultrasound imaging is pivotal for point of care non-invasive diagnosis of musculoskeletal (MSK) injuries. Notably, MSK ultrasound demands a higher level of operator expertise compared to general ultrasound procedures, necessitating thorough checks on image quality and precise categorization of each image. This need for skilled assessment highlights the importance of developing supportive tools for quality control and categorization in clinical settings.
View Article and Find Full Text PDFCancer Imaging
January 2025
Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Background: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significant reproducibility issues. We compare the prediction of overall survival (OS) in recurrent high-grade glioma(HGG) patients undergoing immunotherapy, using deep learning (DL) classification networks along with radiomic signatures derived from manual and convolutional neural networks (CNN) automated segmentation.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!