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Radiotherapy Physics
Published in Debbie Peet, Emma Chung, Practical Medical Physics, 2021
Andrea Wynn-Jones, Caroline Reddy, John Gittins, Philip Baker, Anna Mason, Greg Jolliffe
Current radiotherapy paradigms typically personalise treatment plans based mainly on anatomical and morphological information and increasingly using functional imaging to define target volumes. The doses and fractionation schemes, however, are typically based on population data with little account made of individual responses to radiation. Every patient and every tumour is different. Some patients will be more sensitive to radiation, potentially increasing side effects and reducing quality of life, whereas some tumours may require higher radiation doses to effect control. Increasing the level of personalisation of treatment could involve the inclusion of phenotypic and genotypic differences that potentially exist between patients and their tumours. Kerns et al. (2014) described how normal-tissue adverse effects could not be completely accounted for by dosimetric, treatment or demographic factors. They argued that radiogenomics has two main goals:To develop assays for predicting which people may suffer increased toxicity.To identify molecular pathways for radiation-induced normal tissue toxicities.
Radiotherapy outcomes modeling in the big data era
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
Joseph O. Deasy, Aditya P. Apte, Maria Thor, Jeho Jeong, Aditi Iyer, Jung Hun Oh, Andrew Jackson
Radiogenomics. Across medicine, there have now been many studies that seek to better understand intersubject variations of health states based on common variations in the genetic code. Such studies are commonly referred to as genomewide association studies (GWAS). In radiotherapy, this strategy applied to normal tissue effects is called “radiogenomics.” GWAS studies to date have seen limited success (Wijmenga and Zhernakova 2018). In our view, this is likely due to modeling approaches that are too simple, often assuming that genetic markers (single nucleotide polymorphisms, or SNPs) individually contribute large and independent risks. In contrast, Oh et al. (2017) used nonlinear machine learning and statistical modeling methods that naturally accounted for complicated multi-SNP dependencies (Lee et al. 2018). In our view, this approach is likely to continue to be successful. The approach is general: It can be applied to any normal tissue reaction endpoint as well as a general range of medical endpoints. Unlike for in vitro lymphocyte assays, GWAS measurements are easy to make, via mouth swabs or blood samples, and analysis costs are now quite low, often less than $100 per subject.
Radiogenomics
Published in Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin, Radiomics and Radiogenomics, 2019
It is important to disambiguate radiogenomics from a more narrow definition in the area of radiation therapy response prediction [8]. In the context of radiation therapy, radiogenomics has been used to refer to the study of genetic variation associated with response to radiation, aka radiation genomics. In this chapter, we will discuss radiogenomics in the broader definition as defined above.
Circular RNA differential expression profiles and bioinformatics analysis of hsa_circRNA_079422 in human endometrial carcinoma
Published in Journal of Obstetrics and Gynaecology, 2023
Yuwei Li, Ziling Yang, Mengyao Zeng, Ying Wang, Xin Chen, Shibo Li, Xiaotong Zhao, Yuhui Sun
Based on the type of mutations and somatic copy-number variations, genome and exome sequencing, and microsatellite instability (MSI) assay, the Cancer Gene Atlas (TCGA) Research Network divides EC into four prognostically relevant groups: polymerase epsilon (POLE) ultramutated, MSI hypermutated, copy-number (CN) low, and CN high (Kandoth et al., 2013). This classification takes into account the histopathologic and clinical features of EC. It goes beyond the limitations of traditional binary classification. However, due to the high cost and complexity of TCGA research in clinical applications, scientists need to develop cheaper, more practical, and more accurate methods (Cuccu et al., 2023). Then, the proposal of the ProMisE (Proactive Molecular Risk Classifier for Endometrial Cancer) model increases the possibility of establishing targeted therapeutic methods based on tumour molecular biology (Kommoss et al., 2018). Radiogenomics, which is more advantageous, can achieve precision medicine by combining molecular genetics and radiology data (Lo Gullo et al., 2020). In EC patients, targeted operative or postoperative treatment may be tailored by combining radiomic and molecular biological features of ultrasound images (Bogani et al., 2022).
Applications of artificial intelligence in clinical management, research, and health administration: imaging perspectives with a focus on hemophilia
Published in Expert Review of Hematology, 2023
The term radiogenomics relates to a science that combines quantitative data extracted from medical images with individual genomic phenotypes and constructs prediction models. This science uses deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes of patients which enables correlation of imaging findings and gene expression of patients. The final goal of radiogenomics is to provide us with a better understanding of disease processes [26]. Clusters are created by merging genes and proteins that determine an outcome (e.g. synovitis, degeneration of cartilage) with corresponding clinical-imaging characteristics both at the structural (conventional imaging) and functional (e.g. functional MRI) levels. By evaluating the pattern of joint degeneration (outcome) of a patient with hemophilia one could look back at the clusters of patients who had a similar outcome and understand the mechanism of disease based on affected tissues. This better understanding of inflammation, damage and repair can lead to drug development and to personalized clinical management of patients.
Establishing mechanisms affecting the individual response to ionizing radiation
Published in International Journal of Radiation Biology, 2020
Dietrich Averbeck, Serge Candéias, Sudhir Chandna, Nicolas Foray, Anna A. Friedl, Siamak Haghdoost, Penelope A. Jeggo, Katalin Lumniczky, Francois Paris, Roel Quintens, Laure Sabatier
Worldwide, a subset of patients (usually around 10–15%) receiving RT display pronounced reactions assessed via a grading system of post-IR responses (Figure 2). Such patients have been termed RS. Although factors associated with the delivery of RT and the precise response assessment differs between hospitals and countries, the relative uniformity in identifying such individuals is striking. Only a minor fraction (<10% of RS patients) can be attributed to mutations in known DDR genes (Figure 2). Such patients, however, tend to be those with dramatic RS (grade 4–5) reactions and, despite being a minor subset, are important to identify. For these patients, RS can be attributed to genetic factors. For the large majority of less marked RS individuals (those with grade 2–4 reactions), it is currently unclear if the cause is predominantly genetic or a consequence of non-genetic factors. Significantly, several candidate gene studies and genome wide association studies (GWAS) have suggested evidence for genetic signatures of RS. However, the number of investigated patients was usually quite small resulting in insufficient statistical power (Andreassen, Schack, et al. 2016). Thus, a more profound exploration of the emerging field of radiogenomics is important to take advantage of the full potential of genomics technologies and their application to personalized RT medicine.