Purpose: To investigate the impact of preoperative contrast-enhanced CT-based radiomics model on PD-1 prediction in hepatocellular carcinoma (HCC) patients.
Methods: The study included 105 HCC patients (training cohort: 72; validation cohort: 33) who underwent preoperative contrast-enhanced CT and received systemic sorafenib treatment after surgery. Radiomics score was built for each patient and was integrated with independent clinic radiologic predictors into the radiomics model using multivariable logistic regression analysis.
Results: Seventeen radiomics features were finally selected to construct the radiomics score. In multivariate analysis, serum creatine and peritumoral enhancement were significant independent factors for PD-1 prediction. The radiomics model integrated radiomics signature with serum creatine and peritumoral enhancement showed good discriminative performance (AUC of 0.897 and 0.794 in the training and validation cohort). Overall survival (OS) was significantly different between the radiomics-predicted PD-1-positive and PD-1-negative groups (OS: 29.66 months, CI:16.03-44.40 vs. 31.04 months, CI: 17.10-44.07, P<0.001). Radiomics-predicted PD-1 was an independent predictor of OS of patients treated with sorafenib after surgery. (Hazard ratio [HR]: 1.61 [1.23-2.1], P<0.001).
Conclusion: The proposed model based on radiomic signature helps to evaluate PD-1 status of HCC patients and may be used for evaluating patients most likely to benefit from sorafenib as a potentially combination therapy regimen with immune checkpoint therapies.
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http://dx.doi.org/10.3389/fimmu.2025.1435668 | DOI Listing |
Front Oncol
February 2025
Department of Radiology, Grossman School of Medicine, New York University, New York, NY, United States.
Introduction: The intravoxel incoherent motion (IVIM) model of diffusion weighted imaging (DWI) provides imaging biomarkers for breast tumor characterization. It has been extensively applied for both diagnostic and prognostic goals in breast cancer, with increasing evidence supporting its clinical relevance. However, variable performance exists in literature owing to the heterogeneity in datasets and quantification methods.
View Article and Find Full Text PDFCancer Imaging
March 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China.
Background: To construct and assess a deep learning (DL) signature that employs computed tomography imaging to predict the expression status of programmed cell death ligand 1 in patients with bladder cancer (BCa).
Methods: This retrospective study included 190 patients from two hospitals who underwent surgical removal of BCa (training set/external validation set, 127/63). We used convolutional neural network and radiomics machine learning technology to generate prediction models.
Cancer Imaging
March 2025
Department of Radiology, Shaoxing People's Hosipital, Shaoxing, China.
Objectives: To evaluate the feasibility and value of dynamic contrast-enhanced MRI (DCE-MRI) quantitative analysis and MRI-based radiomics in predicting the efficacy of microwave ablation (MWA) in lung cancers (LCs).
Methods: Forty-three patients with LCs who underwent DCE-MRI within 24 h of receiving MWA were enrolled in the study and divided into two groups according to the modified response evaluation criteria in solid tumors (m-RECIST) criteria: the effective treatment (complete response + partial response + stable disease, n = 28) and the ineffective treatment (progressive disease, n = 15). DCE-MRI datasets were processed by Omni.
BMC Med Imaging
March 2025
Imaging Center, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, Xinjiang, China.
Objectives: To evaluate the performance of CT-based intralesional combined with different perilesional radiomics models in predicting the microvascular density (MVD) of hepatic alveolar echinococcosis (HAE).
Methods: This study retrospectively analyzed preoperative CT data from 303 patients with HAE confirmed by surgical pathology (MVD positive, n = 182; MVD negative, n = 121). The patients were randomly divided into the training cohort (n = 242) and test cohort (n = 61) at a ratio of 8:2.
NPJ Digit Med
March 2025
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
Malaria remains a major global health challenge. Although effective control relies on testing all suspected cases, asymptomatic infections in school-age children are frequently overlooked. Advances in retinal imaging and computer vision have enhanced malaria detection.
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