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Rationale And Objectives: Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality. Natural Killer (NK) cells play a crucial role in immune defense against HCC, but their activity is often impaired by the tumor microenvironment (TME). This study aims to integrate radiomics and transcriptomics to develop a prognostic model linking NK cell characteristics to clinical outcomes in HCC.
Methods: Transcriptomic data from five cohorts (734 HCC patients) from the Gene Expression Omnibus and The Cancer Genome Atlas databases were analyzed using the Microenvironment Cell Populations-counter algorithm. NK cell-related prognostic biomarkers were identified via weighted gene co-expression network analysis and LASSO-Cox regression. Radiomics models were established using CT imaging features from 239 patients in three datasets from The Cancer Imaging Archive and Shanghai East Hospital. HCC radiogenomic subtypes were proposed by integrating genetic biomarkers and radiomics models.
Results: CD2 expression was identified as an independent NK cell-related prognostic biomarker, with a positive impact on prognosis and a strong correlation with NK cell-associated biological processes in HCC. A robust radiomics model was constructed, and the integration of CD2 expression with radioscore identified potential radiogenomic subtypes of HCC.
Conclusion: Radiomics has potential to link TME immune phenotypes with HCC prognosis. CD2 is a key biomarker connecting NK cells with radiomic features, offering a new classification of HCC into radiogenomic subtypes. This approach supports the use of radiogenomics in personalized HCC treatment.
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http://dx.doi.org/10.1016/j.acra.2024.10.043 | DOI Listing |
Sci Rep
September 2025
Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, 518035, P.R. China.
Glioblastoma multiforme (GBM) is a lethal brain tumor with limited therapies. NUF2, a kinetochore protein involved in cell cycle regulation, shows oncogenic potential in various cancers; however, its role in GBM pathogenesis remains unclear. In this study, we investigated NUF2's function and mechanisms in GBM and developed an MRI-based machine learning model to predict its expression non-invasively, and evaluated its potential as a therapeutic target and prognostic biomarker.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
August 2025
Department of Radiology, Technical University Munich, Ismaninger Str. 22, München, 81675, Germany.
Purpose: The isocitrate dehydrogenase (IDH) genotype is crucial for diagnosing and managing adult-type diffuse glioma. We investigated spatial tumour characteristics in treatment-naïve glioma using an U-Net-based CNN and evaluated associations with metabolic dysfunction and IDH genotype.
Methods: Between 2015 and 2024 patients with confirmed contrast-enhancing glioma were pre-operatively investigated using MRI or [18 F]FET PET/MRI.
Elife
August 2025
Molecular and Experimental Surgery, Clinic for General-, Visceral -, Vascular- and Transplantation Surgery, Medical Faculty and University Hospital Magdeburg, Otto-von-Guericke University, Magdeburg, Germany.
Pancreatic cancer (PC) is a highly aggressive malignancy in humans, where early diagnosis significantly improves patient outcomes. However, effective methods for accurate and early detection remain limited. In this multiethnic study involving human subjects, we developed a liquid biopsy signature based on extracellular vesicle (EV)-derived microRNAs (miRNAs) linked to radiomics features extracted from patients' tumor imaging.
View Article and Find Full Text PDFCureus
June 2025
Department of Artificial Intelligence in Diagnostic Radiology, The University of Osaka Graduate School of Medicine, Suita, JPN.
Objectives: Given the variable pathological complete response (pCR) rate of neoadjuvant chemotherapy (NAC) in patients with breast cancer, identifying predictive markers is crucial. This study evaluated the predictive accuracy of three machine learning-based models: (1) radiomics using MRI features; (2) genomics based on DNA microarray data; and (3) radiogenomics integrating both MRI and microarray data to predict pCR after NAC across all breast cancer subtypes. This study aimed to determine which model provides the most precise non-invasive prediction by utilizing a consistent dataset and analytical pipeline.
View Article and Find Full Text PDFRadiol Med
July 2025
Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
The fifth edition of the World Health Organization classification of central nervous system tumors represents a significant advancement in the molecular-genetic classification of pediatric-type diffuse gliomas. This article comprehensively summarizes the clinical, molecular, and radiological imaging features in pediatric-type low-grade gliomas (pLGGs), including MYB- or MYBL1-altered tumors, polymorphous low-grade neuroepithelial tumor of the young (PLNTY), and diffuse low-grade glioma, MAPK pathway-altered. Most pLGGs harbor alterations in the RAS/MAPK pathway, functioning as "one pathway disease".
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