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Magnetic Resonance Imaging-Based Radiogenomic Analysis Reveals Genomic Determinants for Nanoparticle Delivery into Tumors.
ACS Nano
December 2024
State Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering & Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University, Nanjing 211102, P. R. China.
Article Synopsis
- The EPR effect helps nanodrugs target solid tumors, but variability in this effect has hindered clinical success, making its mechanisms important to study.
- This study aimed to develop a method that combines radiogenomics and MRI to better understand how nanoparticles (NPs) penetrate tumors using a contrast agent made from PLGA-coated FeO NPs.
- Through gene enrichment analyses, key genes influencing vascular permeability were identified, which can enhance the understanding of how NPs behave in tumor environments, ultimately supporting personalized treatment strategies.
Natural Killer Cell-Associated Radiogenomics Model for Hepatocellular Carcinoma: Integrating CD2 and Enhanced CT-Derived Radiomics Signatures.
Acad Radiol
November 2024
Department of Radiation Oncology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China (Y.Z.C., Z.L.X.); Department of Radiation Oncology, Shanghai East Hospital Ji'an Hospital, Ji'an, China (Z.L.X.). Electronic address:
Article Synopsis
- Hepatocellular carcinoma (HCC) is a major cancer threat, and this study focuses on the role of Natural Killer (NK) cells in combatting it, particularly how the tumor microenvironment hinders their effectiveness.
- The researchers analyzed transcriptomic data from 734 HCC patients and created radiomics models based on CT imaging from 239 patients to identify key NK cell-related biomarkers and their relationship to patient outcomes.
- The study found that CD2 is a significant prognostic biomarker associated with NK cell activity, and the integration of genetic information and imaging data can help classify HCC into subtypes, which may enhance personalized treatment strategies.
The prognostic value of radiogenomics using CT in patients with lung cancer: a systematic review.
Insights Imaging
October 2024
Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
This systematic review aimed to evaluate the effectiveness of combining radiomic and genomic models in predicting the long-term prognosis of patients with lung cancer and to contribute to the further exploration of radiomics. This study retrieved comprehensive literature from multiple databases, including radiomics and genomics, to study the prognosis of lung cancer. The model construction consisted of the radiomic and genomic methods.
View Article and Find Full Text PDFIdentification of a rank-based radiomic signature with individualized prognostic value for lung adenocarcinoma in a multi-cohort study.
Eur J Radiol
December 2024
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China. Electronic address:
Objectives: Radiomics provides an opportunity to evaluate cancer prognosis noninvasively. However, the susceptibility of the radiomic quantitative features to multicenter effects, leads to the clinical dilemma of the radiomic signatures. This study aimed to develop a radiomic signature to circumvent multicenter effects, achieving the individualized prognostic assessment of lung adenocarcinoma (LUAD).
View Article and Find Full Text PDFThe Radiogenomic Landscape of Clear Cell Renal Cell Carcinoma: Insights into Lipid Metabolism through Evaluation of ADFP Expression.
Diagnostics (Basel)
August 2024
Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy.
This study aims to explore the relationship between radiological imaging and genomic characteristics in clear cell renal cell carcinoma (ccRCC), focusing on the expression of adipose differentiation-related protein (ADFP) detected through computed tomography (CT). The goal is to establish a radiogenomic lipid profile and understand its association with tumor characteristics. Data from The Cancer Genome Atlas (TCGA) and the Cancer Imaging Archive (TCIA) were utilized to correlate imaging features with adipose differentiation-related protein (ADFP) expression in ccRCC.
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