'Radiogenomics' is the study of genetic variation associated with response to radiotherapy. Radiogenomics aims to uncover the genes and biologic pathways responsible for radiotherapy toxicity that could be targeted with radioprotective agents and; identify genetic markers that can be used in risk prediction models in the clinic. The long-term goal of the field is to develop single nucleotide polymorphism-based risk models that can be used to stratify patients to more precisely tailored radiotherapy protocols. The field has evolved over the last two decades in parallel with advances in genomics, moving from narrowly focused candidate gene studies to large, collaborative genome-wide association studies. Several confirmed genetic variants have been identified and the field is making progress toward clinical translation.
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Computed tomography (CT) derived radiomics to predict post-operative disease recurrence in gastric cancer; a systematic review and meta-analysis.
Curr Probl Diagn Radiol
September 2024
School of Medicine, Trinity College Dublin, Ireland; Department of Surgery, St. James's Hospital, Dublin, Ireland; Trinity St James Cancer Institute, Trinity College Dublin, Ireland.
Introduction: Radiomics offers the potential to predict oncological outcomes from pre-operative imaging in order to identify 'high risk' patients at increased risk of recurrence. The application of radiomics in predicting disease recurrence provides tailoring of therapeutic strategies. We aim to comprehensively assess the existing literature regarding the current role of radiomics as a predictor of disease recurrence in gastric cancer.
View Article and Find Full Text PDFThe current landscape of machine learning-based radiomics in arteriovenous malformations: a systematic review and radiomics quality score assessment.
Front Neurol
June 2024
Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
Article Synopsis
- Arteriovenous malformations (AVMs) are complex vascular defects lacking capillaries, and recent advancements in radiomics and machine learning (ML) are being explored to improve AVM management.
- A systematic review of 13 retrospective studies revealed that radiomics models are primarily focused on diagnosing AVMs, predicting outcomes, and assessing treatment responses, but none have been externally validated.
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Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review.
Abdom Radiol (NY)
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Department of Radiology, St. James's Hospital, Dublin, Ireland.
Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical imaging, which may help provide vital prognostic evaluation and help patient selection for radical treatment strategies.
View Article and Find Full Text PDFRadiogenomics and Texture Analysis to Detect von Hippel-Lindau (VHL) Mutation in Clear Cell Renal Cell Carcinoma.
Curr Issues Mol Biol
April 2024
Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy.
Radiogenomics, a burgeoning field in biomedical research, explores the correlation between imaging features and genomic data, aiming to link macroscopic manifestations with molecular characteristics. In this review, we examine existing radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the predominant renal cancer, and von Hippel-Lindau () gene mutation, the most frequent genetic mutation in ccRCC. A thorough examination of the literature was conducted through searches on the PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases.
View Article and Find Full Text PDFContrast Enhanced CT Radiogenomics in a Retrospective NSCLC Cohort: Models, Attempted Validation of a Published Model and the Relevance of the Clinical Context.
Acad Radiol
July 2024
Joint Department of Medical Imaging, Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, ON M5G 2C1, Canada.
Rationale And Objective: To develop a radiogenomic predictive model for non-small cell lung cancer (NSCLC) patients studied through contrast enhanced chest computed tomography (CE-CT) targeting the most frequent gene alterations. M&M: A retrospective study of patients with NSCLC imaged with CE-CT before treatment and had their tumor genomics sequenced at our institution was performed. Data was gathered from their imaging studies, their electronic medical records and a web-based database search (cBioPortal.
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