Assessment of Local Tumor Progression After Image-Guided Thermal Ablation for Renal Cell Carcinoma.

Korean J Radiol

Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Published: January 2024

Focal enhancement typically suggests local tumor progression (LTP) after renal cell carcinoma is percutaneously ablated. However, evaluating findings that are false positive or negative of LTP is less familiar to radiologists who have little experience with renal ablation. Various imaging features are encountered during and after thermal ablation. Ablation procedures and previous follow-up imaging should be reviewed before determining if there is LTP. Previous studies have focused on detecting the presence or absence of focal enhancement within the ablation zone. Therefore, various diagnostic pitfalls can be experienced using computed tomography or magnetic resonance imaging examinations. This review aimed to assess how to read images during or after ablation procedures, recognize imaging features of LTP and determine factors that influence LTP.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10788605PMC
http://dx.doi.org/10.3348/kjr.2023.0676DOI Listing

Publication Analysis

Top Keywords

local tumor
8
tumor progression
8
thermal ablation
8
renal cell
8
cell carcinoma
8
focal enhancement
8
imaging features
8
ablation procedures
8
ablation
6
ltp
5

Similar Publications

Introduction: The core objective of this study was to precisely locate metastatic lymph nodes, identify potential areas in nasopharyngeal carcinoma patients that may not require radiotherapy, and propose a hypothesis for reduced target volume radiotherapy on the basis of these findings. Ultimately, we reassessed the differences in dosimetry of organs at risk (OARs) between reduced target volume (reduced CTV2) radiotherapy and standard radiotherapy.

Methods And Materials: A total of 209 patients participated in the study.

View Article and Find Full Text PDF

An automatic cervical cell classification model based on improved DenseNet121.

Sci Rep

January 2025

Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.

The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.

View Article and Find Full Text PDF

The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.

View Article and Find Full Text PDF

MUC1 and glycan probing of CA19-9 captured biomarkers from cyst fluids and serum provides enhanced recognition of ovarian cancer.

Sci Rep

January 2025

Department of Life Technologies, Division of Biotechnology, University of Turku, Medisiina D, 5th floor, Kiinamyllynkatu 10, 20520, Turku, Finland.

Glycosylation changes of circulating proteins carrying the CA19-9 antigen may offer new targets for detection methods to be explored for the diagnosis of epithelial ovarian cancer (EOC). Search for assay designs for targets initially captured by a CA19-9 antigen reactive antibody from human body fluids by probing with fluorescent nanoparticles coated with lectins or antibodies to known EOC associated proteins. CA19-9 antigens were immobilized from ascites fluids, ovarian cyst fluids or serum samples using monoclonal antibody C192 followed by probing of carrier proteins using anti-MUC16, anti-MUC1 and, anti STn antibodies and seven lectins, all separately coated on nanoparticles.

View Article and Find Full Text PDF

Want 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!