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Purpose: To evaluate the efficacy of radiomic analysis applied to pretreatment gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI (Gd-EOB-DTPA-MRI) for predicting the response to transcatheter arterial chemoembolization (TACE) for hepatocellular carcinoma.
Methods: Data and images from 40 consecutive patients (28 men, 12 women) who underwent pretreatment Gd-EOB-DTPA-MRI and a total of 52 TACE procedures for 75 non-treated hepatocellular carcinomas were retrospectively analyzed. Two radiologists manually outlined lesions on pretreatment arterial- and hepatobiliary-phase hepatic images to extract radiomic features. The radiomics data from one observer were randomly divided into a training dataset and a validation dataset in the ratio of 7:3. Radiomic features extracted using least absolute shrinkage and selection operator (LASSO) binomial regression applied to the training dataset and that showed intraclass correlation coefficients (ICC) >0.7 were used to construct a radiomic model. The predictive performance of the model was evaluated using receiver operating characteristics curves. Lesions classified as showing a complete or partial response according to the modified RECIST criteria were allocated to a response group.
Results: There was no significant difference in Child-Pugh score, tumor marker values, or TACE procedure between response and non-response groups. Six radiomic features were selected using the LASSO binomial regression and 5 of them showing an ICC >0.7 were used to establish the radiomic model. The area under the curve of the radiomic model was 0.89 for the training dataset, 0.83 for the validation dataset, and 0.83 for the other observer's data. The sensitivity and specificity for the prediction of tumor response to TACE were 78% and 92% for the training dataset; 71% and 50% for the validation dataset; and 75% and 79% for the other observer's data.
Conclusion: The pretreatment Gd-EOB-DTPA-MRI-based radiomic model is useful for predicting the response to TACE of hepatocellular carcinoma.
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http://dx.doi.org/10.2463/mrms.mp.2025-0055 | DOI Listing |
Proteomics Clin Appl
September 2025
AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.
Background: Endometrial carcinoma (EC) represents a significant clinical challenge due to its pronounced molecular heterogeneity, directly influencing prognosis and therapeutic responses. Accurate classification of molecular subtypes (CNV-high, CNV-low, MSI-H, POLE) and precise tumor mutational burden (TMB) assessment is crucial for guiding personalized therapeutic interventions. Integrating proteomics data with advanced machine learning (ML) techniques offers a promising strategy for achieving precise, clinically actionable classification and biomarker discovery in EC.
View Article and Find Full Text PDFJ Magn Reson Imaging
September 2025
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.
Background: Automated cardiac MR segmentation enables accurate and reproducible ventricular function assessment in Tetralogy of Fallot (ToF), whereas manual segmentation remains time-consuming and variable.
Purpose: To evaluate the deep learning (DL)-based models for automatic left ventricle (LV), right ventricle (RV), and LV myocardium segmentation in ToF, compared with manual reference standard annotations.
Study Type: Retrospective.
Respirology
September 2025
Radiology Department, Huadong Hospital, Fudan University, Shanghai, China.
Background And Objective: Diagnosing pulmonary ground-glass nodules (GGNs) on chest CT imaging remains challenging in clinical practice. Moreover, different stages of GGNs may require different clinical treatments. Hence, we sought to predict the progressive state of pulmonary GGNs (absorption or persistence) for accurate clinical treatment and decision-making.
View Article and Find Full Text PDFJ Microbiol Methods
September 2025
Department of Microbiology and Immunology, Faculty of Medicine, Fukuoka University, Japan.
The Microscopic Agglutination Test (MAT) is widely recognized as the gold standard for diagnosing zoonosis leptospirosis. However, the MAT relies on subjective evaluations by human experts, which can lead to inconsistencies and inter-observer variability. In this study, we aimed to emulate expert assessments using deep learning and convert them into reproducible numerical outputs to achieve greater objectivity.
View Article and Find Full Text PDFInt J Radiat Oncol Biol Phys
September 2025
Radiation Oncology, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143. Electronic address:
Purpose: Accelerating MR acquisition is essential for image guided therapeutic applications. Compressed sensing (CS) has been developed to minimize image artifacts in accelerated scans, but the required iterative reconstruction is computationally complex and difficult to generalize. Convolutional neural networks (CNNs)/Transformers-based deep learning (DL) methods emerged as a faster alternative but face challenges in modeling continuous k-space, a problem amplified with non-Cartesian sampling commonly used in accelerated acquisition.
View Article and Find Full Text PDF