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http://dx.doi.org/10.2214/ajr.175.5.1751466 | DOI Listing |
Radiol Artif Intell
January 2025
https://www.procancer-i.eu/.
Purpose To assess the impact of scanner manufacturer and scan protocol on the performance of deep learning models to classify prostate cancer (PCa) aggressiveness on biparametric MRI (bpMRI). Materials and Methods In this retrospective study, 5,478 cases from ProstateNet, a PCa bpMRI dataset with examinations from 13 centers, were used to develop five deep learning (DL) models to predict PCa aggressiveness with minimal lesion information and test how using data from different subgroups-scanner manufacturers and endorectal coil (ERC) use (Siemens, Philips, GE with and without ERC and the full dataset)-impacts model performance. Performance was assessed using the area under the receiver operating characteristic curve (AUC).
View Article and Find Full Text PDFAcad Radiol
January 2025
Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD (O.T.E., E.C.Y., B.D.S., S.A.H., D.G.G., Y.L., M.J.B., P.L.C., B.T.). Electronic address:
Rationale And Objectives: Accurate preoperative mpMRI-based detection of extraprostatic extension (EPE) in prostate cancer (PCa) is critical for surgical planning and patient outcomes. This study aims to evaluate the impact of endorectal coil (ERC) use on the diagnostic performance of mpMRI in detecting EPE.
Materials And Methods: This retrospective study with prospectively collected data included participants who underwent mpMRI and subsequent radical prostatectomy for PCa between 2007 and 2024.
Cancers (Basel)
January 2025
Department of Economic and Medical Informatics, University of Lodz, 90-214 Lodz, Poland.
: The certification of hospitals as colorectal cancer centers aims to improve treatment quality, but evidence supporting its effectiveness remains limited. This study evaluated the impact of certification on treatment outcomes for rectal cancer patients in Germany. : We conducted a retrospective analysis of 14,905 patients with primary rectal cancer (UICC Stages I-III) treated at 271 hospitals.
View Article and Find Full Text PDFAnn Surg Oncol
December 2024
Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China.
Main Objectives: We aimed at comparing intratumoral and peritumoral deep learning, radiomics, and fusion models in predicting KRAS mutations in rectal cancer using endorectal ultrasound imaging.
Methods: This study included 304 patients with rectal cancer from Fujian Medical University Union Hospital. The patients were randomly divided into a training group (213 patients) and a test group (91 patients) at a 7:3 ratio.
PLoS One
December 2024
Department of Medical Ultrasonics, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Background: Forecasting the patient's response to neoadjuvant chemoradiotherapy (nCRT) is crucial for managing locally advanced rectal cancer (LARC). This study investigates whether a predictive model using image-text features extracted from endorectal ultrasound (ERUS) via Contrastive Language-Image Pretraining (CLIP) can predict tumor regression grade (TRG) before nCRT.
Methods: A retrospective analysis of 577 LARC patients who received nCRT followed by surgery was conducted from January 2018 to December 2023.
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