Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/jgh.15414 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features.
BMC Cancer
January 2025
Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, Henan, China.
Objectives: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).
Methods: DWI and clinical data from 155 EC patients were included in this study, consisting of 80 in the training set, 35 in the test set, and 40 in the external validation set. Radiomics features, convolutional neural network-based DL features, and clinical variables were analyzed.
Deep learning-integrated MRI brain tumor analysis: feature extraction, segmentation, and Survival Prediction using Replicator and volumetric networks.
Sci Rep
January 2025
Department of Computer Engineering, Inha University, Incheon, Republic of Korea.
The most prevalent form of malignant tumors that originate in the brain are known as gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and resilient segmentation of the tumors, along with an estimation of the patients' overall survival rate. Therefore, we have introduced a deep learning approach that employs a combination of MRI scans to accurately segment brain tumors and predict survival in patients with gliomas.
View Article and Find Full Text PDFApplication of MRI-based tumor heterogeneity analysis for identification and pathologic staging of breast phyllodes tumors.
Magn Reson Imaging
January 2025
Background: Huai-he Hospital of Henan University, Kaifeng, China. Electronic address:
Objective: To explore the application value of MRI-based imaging histology and deep learning model in the identification and classification of breast phyllodes tumors.
Methods: Seventy-seven patients diagnosed as breast phyllodes tumors and fibroadenomas by pathological examination were retrospectively analyzed, and traditional radiomics features, subregion radiomics features, and deep learning features were extracted from MRI images, respectively. The features were screened and modeled using variance selection method, statistical test, random forest importance ranking method, Spearman correlation analysis, least absolute shrinkage and selection operator (LASSO).
Multiparametric MRI for Assessment of the Biological Invasiveness and Prognosis of Pancreatic Ductal Adenocarcinoma in the Era of Artificial Intelligence.
J Magn Reson Imaging
January 2025
Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
Pancreatic ductal adenocarcinoma (PDAC) is the deadliest malignant tumor, with a grim 5-year overall survival rate of about 12%. As its incidence and mortality rates rise, it is likely to become the second-leading cause of cancer-related death. The radiological assessment determined the stage and management of PDAC.
View Article and Find Full Text PDFCooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping.
Med Image Anal
December 2024
University Lyon, INSA Lyon, CNRS, Inserm, CREATIS UMR5220, U1206, Lyon 69621, France.
Deep learning methods have been widely used for various glioma predictions. However, they are usually task-specific, segmentation-dependent and lack of interpretable biomarkers. How to accurately predict the glioma histological grade and molecular subtypes at the same time and provide reliable imaging biomarkers is still challenging.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!