Novel multiparametric magnetic resonance imaging-based deep learning and clinical parameter integration for the prediction of long-term biochemical recurrence-free …

HW Lee, E Kim, I Na, CK Kim, SI Seo, H Park - Cancers, 2023 - mdpi.com
Simple Summary Existing research on predicting biochemical recurrence after prostate
surgery has been insufficient. Here, we aimed to predict biochemical recurrence after radical …

[HTML][HTML] A Systematic Review of the Diagnostic Accuracy of Deep Learning Models for the Automatic Detection, Localization, and Characterization of Clinically …

S Molière, D Hamzaoui, G Ploussard, R Mathieu… - European Urology …, 2024 - Elsevier
Background and objective Magnetic resonance imaging (MRI) plays a critical role in prostate
cancer diagnosis, but is limited by variability in interpretation and diagnostic accuracy. This …

[HTML][HTML] Imaging in translational cancer research

FT Kurz, HP Schlemmer - Cancer Biology & Medicine, 2022 - ncbi.nlm.nih.gov
This review is aimed at presenting some of the recent developments in translational cancer
imaging research, with a focus on novel, recently established, or soon to be established …

Boost-up efficiency of defective solar panel detection with pre-trained attention recycling

YH Park, MJ Kim, U Gim, J Yi - IEEE Transactions on Industry …, 2023 - ieeexplore.ieee.org
Methods that enable the visual inspection of solar panels are currently in demand, as a huge
number of solar panels are now being deployed as a sustainable energy source. One of the …

Combined radiomics-clinical model to predict platinum-sensitivity in advanced high-grade serous ovarian carcinoma using multimodal MRI

I Na, JJ Noh, CK Kim, JW Lee, H Park - Frontiers in Oncology, 2024 - frontiersin.org
Introduction We aimed to predict platinum sensitivity using routine baseline multimodal
magnetic resonance imaging (MRI) and established clinical data in a radiomics framework …

Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer

W Krauss, J Frey, J Heydorn Lagerlöf… - Acta …, 2024 - journals.sagepub.com
Background Magnetic resonance imaging (MRI) is useful in the diagnosis of clinically
significant prostate cancer (csPCa). MRI-derived radiomics may support the diagnosis of …

[HTML][HTML] Accurate prediction of glioma grades from radiomics using a multi-filter and multi-objective-based method

J Niu, Q Tan, X Zou, S Jin - Mathematical Biosciences and …, 2023 - aimspress.com
Radiomics, providing quantitative data extracted from medical images, has emerged as a
critical role in diagnosis and classification of diseases such as glioma. One main challenge …

Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors

J Jeong, H Choi, M Kim, SS Kim, J Goh… - Frontiers in Veterinary …, 2024 - frontiersin.org
Radiomics models have been widely exploited in oncology for the investigation of tumor
classification, as well as for predicting tumor response to treatment and genomic sequence; …

Exploring Trends and Gaps in Osteoarthritis Biomarker Research (1999-2024): A Citation Analysis of Top 50 Cited Articles

W Hu, J Yang, L Liu, D Li, Y Zhao, A Wang - Cartilage, 2024 - journals.sagepub.com
Purpose This study aimed to comprehensively analyze the landscape of osteoarthritis (OA)
biomarker research through the citation analysis of top-cited articles, identifying trends and …

[HTML][HTML] Is it possible to detect cribriform adverse pathology in prostate cancer with magnetic resonance imaging machine learning-based radiomics?

H Bıçakçıoğlu, S Soyupek, O Ertunç… - Computing and …, 2024 - ojs.acad-pub.com
Rationale and objectives: Cribriform patterns are accepted as aggressive variants of
prostate cancer. These adverse pathologies are closely associated with early biochemical …