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Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082656 | PMC |
http://dx.doi.org/10.3348/kjr.2019.0752 | DOI Listing |
Radiother Oncol
September 2025
Department of Radiotherapy Center, 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; School of Basic Medical Science, Wenzhou Medical University, Wenzhou 325000, China. Electronic address:
Background: Accurate delineation of regions of interest (ROIs) is critical for feature extraction and selection in radiomics-based prediction models.
Purpose: To develop a combined dosiomics and deep learning (DL) model for predicting grade ≥ 2 radiation esophagitis (RE) in lung cancer patients undergoing radiotherapy, we propose a multi-task auxiliary learning approach to define accurate and objective ROIs based on radiation dose distribution (RDD) images.
Materials And Methods: Lung cancer patients who underwent radiotherapy were gathered retrospectively from hospital 1 (January 2020 and December 2022) for model development.
J Ultrasound Med
September 2025
Department of Ultrasound, Donghai Hospital Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China.
Objective: The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics (DLRN) features derived from ultrasound and multi-sequence magnetic resonance imaging (MRI) for predicting survival, recurrence, and metastasis in patients diagnosed with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC).
Methods: This retrospective, multicenter study included 103 patients with histopathologically confirmed TNBC across four institutions. The training group comprised 72 cases from the First People's Hospital of Lianyungang, while the validation group included 31 cases from three external centers.
Front Oncol
August 2025
Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Objective: To develop a deep learning radiomics(DLR)model integrating PET/CT radiomics, deep learning features, and clinical parameters for early prediction of bone oligometastases (≤5 lesions) in breast cancer.
Methods: We retrospectively analyzed 207 breast cancer patients with 312 bone lesions, comprising 107 benign and 205 malignant lesions, including 89 lesions with confirmed bone metastases. Radiomic features were extracted from computed tomography (CT), positron emission tomography (PET), and fused PET/CT images using PyRadiomics embedded in the uAI Research Portal.
Int J Chron Obstruct Pulmon Dis
September 2025
Department of Cardiovascular Center, The First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, People's Republic of China.
Objective: This study aimed to develop and validate a deep learning radiomics (DLR) nomogram for individualized CHD risk assessment in the COPD population.
Methods: This retrospective study included 543 COPD patients from two different centers. Comprehensive clinical and imaging data were collected for all participants.
Oncol Lett
November 2025
Department of Radiology, Zibo Central Hospital, Zibo, Shandong 255020, P.R. China.
Clear cell renal cell carcinoma (ccRCC) is a malignant tumor, originating from the renal epithelium, and accounts for ~85% of RCC cases. The present study aimed to validate the efficacy of an MRI deep learning (DL) model to preoperatively predict the pathological grading of ccRCC. Therefore, a DL algorithm was constructed and trained using diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) sequence images.
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