Cottereau et al. The proposed radiomic signature showed significant association with survival after independent validation and, importantly, remained an independent predictor of survival after adjusting for known clinicopathological risk factors. Imaging plays an important role in the diagnosis and staging of cancer, as well as in radiation treatment planning and evaluation of therapeutic response. The radiomic signature showed significant stratification of patient prognosis, which was stronger compared with clinical or traditional imaging metrics. Below we highlight a few studies that may be potentially relevant for improving patient management in radiotherapy. Itakura H, Achrol AS, Mitchell LA et al. Figure 1 shows a general workflow of radiomics. Imaging plays an important role in the diagnosis and staging of cancer, as well as in radiation treatment... INTRODUCTION. The prognostic value of constructed prediction models was confirmed in an external cohort. 0000019039 00000 n Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. 0000040221 00000 n They extracted over 400 quantitative features from CT images to describe tumor intensity, shape and texture. Many commonly used radiomic features have been integrated into open source software or commercial software platforms. . %PDF-1.3 %���� For instance, image features that show minimal changes to tumor contour variations and minimal redundancy or overlap with other features may be preferentially selected. Vargas HA, Huang EP, Lakhman Y et al. Wu et al. Finally, one important direction that is particularly relevant for precision medicine is to leverage the complementary power of imaging and molecular data, and integrate them into a unifying model to further improve the prediction accuracy of clinical outcomes. Using deep-learning techniques for substantially improved segmentation of normal and malignant structures is an active area of research [21–23]. Wang P, Popovtzer A, Eisbruch A et al. They extracted radiomic features for the identified habitats on MRI/3D-ultrasound fusion and found strong associations between radiomic features and gene expression profiles. Grossmann et al. For radiomics, there can be many causes that render the radiomic analysis and results invalid, including poor experimental design, model overfitting, and unadjusted biases or confounding factors, among others. [63] showed that integrating MGMT methylation status and volume of the high-risk subregion at multiparametric MRI improved survival stratification in glioblastoma. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks, Changes in pulmonary function and their correlation with dose–volume parameters in patients undergoing stereotactic body radiotherapy for lung cancer, Correction of lateral response artifacts from flatbed scanners for dual-channel radiochromic film dosimetry, Melatonin reduces radiation damage in inner ear, Development of raster scanning IMRT using a robotic radiosurgery system, About the Japanese Radiation Research Society, About the Japanese Society for Radiation Oncology, CURRENT STATUS AND RESULTS OF RADIOMICS IN RADIOTHERAPY, EMERGING PARADIGMS OF RADIOMICS FOR PRECISION RADIOTHERAPY, http://creativecommons.org/licenses/by-nc/4.0/, Receive exclusive offers and updates from Oxford Academic, Copyright © 2021 The Japanese Radiation Research Society and Japanese Society for Radiation Oncology. Because their calculation is applied to the entire tumor as a whole, thi… . The fields of radiomics and radiogenomics have experienced significant growth in the past few years. 0000011108 00000 n Fehr D, Veeraraghavan H, Wibmer A et al. startxref One critical and yet currently an underexplored area of investigation is how radiomics can be applied to serial imaging scans to better evaluate therapeutic response, given the increasing availability of treatment regimens. For instance, CT semantic and radiomic image features have been found to be associated with EGFR mutations in lung cancer [55, 56]; MRI radiomic features have been correlated with intrinsic molecular subtypes or existing genomic assays in breast cancer [57–59]. Imaging via radiomics and radiogenomics is poised for a central role. 0000003327 00000 n Corresponding author. These challenges include: standardization of image acquisition protocols and feature extraction, ensuring robustness and reproducibility of radiomic signatures in order to maximize the translational potential, and integration of large multicenter cohorts by cultivating the culture of data sharing. 0000016736 00000 n 0000005527 00000 n [62]. Prior to clinical translation of any putative biomarkers, the most critical step is rigorous validation in a prospective multicenter trial [1]. Radiomics and radiogenomics for precision radiotherapy @article{Wu2018RadiomicsAR, title={Radiomics and radiogenomics for precision radiotherapy}, author={J. Wu and K. Tha and L. Xing and R. Li}, journal={Journal of Radiation Research}, year={2018}, volume={59}, pages={i25 - i31} } Altmetric Badge. However, the current literature is limited by its retrospective nature, as well as significant heterogeneity between studies. One approach that most radiogenomic studies so far have adopted is to find imaging correlates or surrogates of a specific genotype or molecular phenotype of the tumor. Cross validation is needed to minimize the potential selection bias. Indeed, radiomics features have already been associated with improved diagnosis accuracy in cancer, 7 specific gene mutations, 8 and treatment responses to chemotherapy and/or radiation therapy in the brain, 9,10 head and neck, 11,12 lung, 13-17 breast, 18,19 and abdomen. To account for intra- and inter-rater variations, it is important to evaluate the robustness of image features and their effect on downstream analysis by perturbing the tumor contours or using multiple delineations. A cloud-based platform such as the one provided by Huiyihuiying Inc. may prove to be useful in facilitating data sharing and multi-institutional collaborative research. Larue RT, Defraene G, De Ruysscher D et al. Despite the enthusiasm and excitement around this, it should be noted that many radiomic and radiogenomic studies so far have been of hypothesis-generating nature, and rigorous validation in independent cohorts has been lacking. 59, No. Cottereau AS, Lanic H, Mareschal S et al. In this context, radiomics is defined as the discovery of imaging biomarkers with potential diagnostic, prognostic, or predictive value; and radiogenomics is the identification of molecular biology behind these imaging … First, it is essential to assure the predictive accuracy during radiomic signature construction. One subregion, associated with the most metabolically active, metabolically heterogeneous, and solid component of the tumor, was defined as the ‘high-risk’ subregion. . Two types of radiomic features, semantic and agnostic, can be extracted from images to comprehensively characterize the tumor phenotypes. Imaging plays an important role in clinical oncology, including diagnosis, staging, radiation treatment planning, evaluation of therapeutic response, and subsequent follow-up and disease monitoring [1–4]. In another recent radiogenomic study, heterogeneous enhancing patterns of tumor-adjacent parenchyma from perfusion MRI were associated with the tumor necrosis signaling pathway and poor survival in breast cancer [15]. Second, each radiomic analysis step should be well documented, and original codes and data should be easily accessible, allowing other investigators to replicate the results. For instance, on an individual basis, the average interquantile values of the background parenchyma can be used to normalize breast MRI scans [14]. Nonetheless, compared with the abundant public gene expression data, the available imaging data are much less, and continuing efforts should be spent curating high-quality imaging datasets. . Finally, we will highlight the challenges in the field and suggest possible future directions in radiomics to maximize its potential impact on precision radiotherapy. Harrell's C-index was used to demonstrate the incremental value of the radiomics signature to the traditional clinical risk factors for the individualized prediction performance. These cohorts are from single-institution or multicenter trails, which should greatly facilitate the discovery and validation of radiomic models. . 323 0 obj <>stream Radiomics and radiogenomics for precision radiotherapy Abstract. 0000003585 00000 n 0000003742 00000 n 0000044106 00000 n In another study by the same group, radiomics analysis was used to investigate the association of MRI features with survival and progression in glioblastoma [38]. We will also present some examples of the current results and some emerging paradigms in radiomics and radiogenomics for clinical oncology, with a focus on potential applications in radiotherapy. <]/Prev 277434>> Journal of Radiation Research, Vol. Abstract. Among these [25, 26], Deasy and colleagues have provided an open platform, known as CERR [27] (http://www.cerr.info/), to prototype algorithms for radiomic features specifically for radiotherapy research. van Rossum PS, Fried DV, Zhang L et al. A preliminary study of 32 TCGA glioblastoma multiforme patients showed that the distribution of MRI-based habitats was significantly correlated with survival. These preexisting contours can greatly facilitate retrospective radiomic analysis. Tel: +1-650-498-7896; Fax: Imaging biomarker roadmap for cancer studies, Quantitative imaging in cancer clinical trials, Imaging approaches to optimize molecular therapies, The potential of radiomic-based phenotyping in precision medicine: a review, Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging, Radiomics: the bridge between medical imaging and personalized medicine, Radiomics: images are more than pictures, they are data, Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology, Identification of noninvasive imaging surrogates for brain tumor gene-expression modules, Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data-methods and preliminary results, Decoding global gene expression programs in liver cancer by noninvasive imaging, Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities, Unsupervised clustering of quantitative image phenotypes reveals breast cancer subtypes with distinct prognoses and molecular pathways, Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with dysregulated signaling pathways and poor survival in breast cancer, NCI workshop report: clinical and computational requirements for correlating imaging phenotypes with genomics signatures, Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images, Image Guided and Adaptive Radiation Therapy, GPU computing in medical physics: a review, Robust radiomics feature quantification using semiautomatic volumetric segmentation, Fully automated quantitative cephalometry using convolutional neural networks, Segmentation of pathological structures by landmark-assisted deformable models, Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks, Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures, Quantitative Image Feature Engine (QIFE): an open-source, modular engine for 3D quantitative feature extraction from volumetric medical images, IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics, CERR: a computational environment for radiotherapy research. . Wu et al. Because their calculation is applied to the entire tumor as a whole, this approach implicitly assumes that the tumor is heterogeneous but well mixed, and neglects the regional variations within a tumor that have been previously demonstrated. Stoyanova R, Pollack A, Takhar M et al. 0000004562 00000 n Many radiomic studies have identified novel imaging signatures that have demonstrated improved diagnostic, prognostic or predictive performance over currently used imaging metrics (such as tumor size) in various oncologic applications. . . In current oncology practice, various imaging modalities such as CT, MRI and FDG-PET are used to provide direct visualization and evaluation of the underlying anatomical or physiological properties of each tumor in individual patients [18]. 0000049179 00000 n Aerts and colleagues proposed a radiomics signature for predicting overall survival in lung cancer patients treated with radiotherapy [37]. 0000000016 00000 n [14]. Search for other works by this author on: Global Station for Quantum Biomedical Science and Engineering, Global Institute for Cooperative Research and Education, Kita 15, Nishi 7, Kita-ku, Sapporo 060-8638. Cui et al. This may have significant implications for clinical oncology by identifying important tumor regions for biopsy. In order for this approach to work, a sufficiently large dataset will be required for training a reliable model, highlighting the need for curation of high-quality datasets and data sharing. Furthermore, these imaging-derived phenotypes can be linked with genomic data, i.e. Oxford University Press is a department of the University of Oxford. Recently, with the development of computational and imaging technology, radiotherapy has brought unlimited opportunities driven by radiomics in individual cancer treatment and precision medicine care. Moving forward, advanced machine-learning techniques, notably deep convolutional neural networks, are expected to be increasingly used to identify useful image features automatically, rather than defining them manually (personal communication from Ibragimov B, Toesca D, Chang D et al.). Radiomics and radiogenomics are attractive research topics in prostate cancer. A common strategy is to derive the underlying physiological measures from the functional imaging. Recently, Wu et al. Grossmann P, Stringfield O, El-Hachem N et al. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide, This PDF is available to Subscribers Only. . %%EOF LX serves as the principal investigator of a master research agreement (MRA) with Varian Medical Systems. Currently, there is no universal image acquisition protocol for any imaging modality in clinical practice. 0000021308 00000 n Grossmann P, Narayan V, Chang K et al. Radiogenomics can also be used create association maps between molecular features and a specific imaging phenotype so as to reveal its biological underpinnings. Radiomics may provide quantitative and objective support for decisions surrounding cancer detection and treatment [ 10 ]. Method standardization is a requirement for applications across multiple centers and in prospective clinical trials so to establish the essential role of novel imaging biomarkers. In an ongoing study, they are investigating whether adding diffusion-weighted MRI radiomic features could improve potential predictive power. 0000002944 00000 n 257 0 obj <> endobj In a retrospective analysis, several strategies have been proposed for harmonizing imaging scans such that they are comparable across multiple cohorts. [46] developed a robust tumor-partitioning method by a two-stage clustering procedure, and identified three spatially distinct and phenotypically consistent subregions in lung tumors. Precision radiotherapy is the associated field that may receive immediate benefits from the availability of imaging-integrated diagnostic tools useful for therapy selection and response assessment . Colen RR, Hassan I, Elshafeey N et al. . 0000013142 00000 n 0000002336 00000 n For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and … Up to this point, the vast majority of radiomic studies have been focused on analysis of the primary tumor. . . 0000013389 00000 n In the following, we will provide an overview of their technical aspects and discuss some potential clinical applications with a focus on radiotherapy. 0000023037 00000 n 0000091845 00000 n Echegaray S, Bakr S, Rubin DL et al. In current radiology practice, the interpretation of clinical images mainly relies on visual assessment of relatively few qualitative imaging metrics. 0000008147 00000 n 0000002673 00000 n . . Radiomics has recently emerged as a promising tool for discovering new imaging biomarkers, by high-throughput extraction of quantitative image features such as shape, histogram and texture that captures tumor heterogeneity [5–9]. Semantic features are based on an existing radiology lexicon to qualitatively describe tumors, and can be derived from the existing guidelines for specific imaging reporting and the data system of the American College of Radiology. . Many studies have postulated that this is because of the profound heterogeneity that underpins response to therapy and prognosis. In the near future, deep-learning–based auto-segmentation tools that are robust enough for routine radiomics applications should be available. Given the growing interest in the field, it is important to highlight some technical and practical challenges associated with radiomics and its ultimate clinical translation. Given the very large number of studies, it is not possible to provide an exhaustive list of articles in a single review. 0000092602 00000 n combined gene expression and CT radiomic signatures to enhance the accuracy of survival prediction in lung cancer. Yankeelov TE, Mankoff DA, Schwartz LH et al. 0000050973 00000 n In addition to building predictive models with supervised learning algorithms, it is also feasible to apply exploratory unsupervised clustering algorithms to the radiomic features in order to discover novel classes of groups for a given disease [13, 14]. . 0000014141 00000 n © The Author(s) 2018. Clinical images are typically acquired with the goal of maximizing the contrast between normal and diseased tissues. For example, tumors with a higher maximum standardized uptake value from FDG-PET have been demonstrated to be associated with the epithelial–mesenchymal transition in non–small cell lung cancer [60]. . Alternatively, tumors can be contoured more consistently using semi-automated segmentation algorithms with minimal human inputs, such as seed points [20]. Sanming Project of Medicine - The 2nd International Symposium on Specialist Education and Advances in Radiation Oncology-dc.title: Medical imaging perspectives of radiomics/radiogenomics in the era of precision oncology-dc.type: Conference_Paper-dc.identifier.email: Vardhanabhuti, V: varv@hku.hk-dc.identifier.authority: Vardhanabhuti, V=rp01900- performed radiomic analysis on tumor subregions and defined 120 multiregional image features on MRI in glioblastoma. In a large multicohort study of over 1 000 patients, each of the imaging subtypes was associated with distinct prognoses and dysregulated molecular pathways, and they were shown to be complementary to known intrinsic molecular subtypes. In this article, we will provide an overview of radiomics and radiogenomics, including their rationale, technical and clinical aspects. 0000002372 00000 n While validation in a prospective clinical trial remains the gold standard and provides the highest level of evidence, there are several other more practical ways to demonstrate a model’s validity and allow a quicker assessment of multiple competing models. Tumor partitioning can be combined with radiomic or texture analysis to allow more detailed and refined image phenotyping. Overview of attention for article published in Journal of radiation research, January 2018. Another interesting area of investigation is classification of tumors into subtypes based on imaging phenotypes rather than molecular features. As radiation affects both tumour cells and surrounding normal cells, we need to precisely balance the dose delivery to achieve our target of maximal tumour kill with minimum damage to the surrounding tissue. Radiomics and radiogenomics have shown great promise for the discovery of new candidate imaging markers; such markers have demonstrated potential diagnostic and prognostic value in a variety of cancer types. adding value. There is often a lack of standardization of imaging protocols across institutions with different acquisition and reconstruction parameters, which may have a significant impact on the image features. Ashraf AB, Daye D, Gavenonis S et al. Dr. Lohit Reddy has a specialization in Radiotherapy with a fellowship from the European Society of Medical Oncology, St. Gallen, Switzerland. 0000009457 00000 n Radiomics and Radiogenomics: Technical Basis and Clinical Applications provides a first summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. Kalpathy-Cramer J, Freymann JB, Kirby JS et al. There are several approaches to achieving this. The key for validation is that training and testing should be entirely separate and no information leakage should occur between the two procedures [29]. In addition, this is particularly relevant for radiotherapy treatment planning and adaptation, because high-risk tumor subregions associated with the aggressive disease can then be targeted with a radiation boost to potentially improve local control and patient survival. Wu et al. There are two major types of radiogenomic association studies. Radiomics and Radiogenomics: Technical Basis and Clinical Applications provides a first summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. Vallieres M, Kay-Rivest E, Perrin LJ et al. More details about each step are presented below. Mankoff DA, Farwell MD, Clark AS et al. showed that early change in texture features for the intratumoral subregion (associated with fast contrast-agent washout at DCE MRI) predicted pathological complete response to neoadjuvant chemotherapy in breast cancer. Based on image features characterizing tumor morphology and intratumoral metabolic heterogeneity, a radiomic signature was built that significantly improved the prognostic value compared with conventional imaging metrics. For full access to this pdf, sign in to an existing account, or purchase an annual subscription. About this Attention Score Average Attention Score compared to outputs of the same age. [48]. One of the biggest challenges in radiomics, and more generally in big data research [69], is the curation of image and relevant metadata across multiple centers [65, 69, 70]. The radiomic model may have the potential to allow for personalization of chemoradiation treatments for head-and-neck cancer patients. 0000019524 00000 n 0000051716 00000 n . Ovarian cancer remains one of the most lethal gynecological cancers in the world despite extensive progress in the areas of chemotherapy and surgery. High performance computational tools such as GPU [19] may be leveraged to process the images in order to mitigate various artifacts for radiomics analysis. Radiomics can be applied to any type of standard-of-care clinical images such as CT, MRI or PET, and used in a variety of clinical settings, including diagnosis, prediction of prognosis, and evaluation of treatment response. 0000018671 00000 n On the commercial software side, we mention that companies such as Huiyihuiying, a Beijing-based company focusing on the use of radiomics and artificial intelligence for solving various clinical problems, afford a practically useful cloud-based platform for radiomics research (for more details or to set up a free research account, please visit the company’s website: www.huiyihuiying.com). 0000092472 00000 n To address this issue, the concept of habitat imaging was proposed to capture imaging heterogeneity more explicitly at a regional level [8, 43]. In another study, Cui et al. Aerts HJ, Velazquez ER, Leijenaar RT et al. 0 These studies provide the initial evidence that image-based biomarkers can provide additional information beyond molecular analysis alone, and integrating both will provide more accurate assessment of individual tumors. While texture features provide a measure of intratumor heterogeneity to a certain extent, this characterization is not complete. 2019 Sep;195(9):771-779. doi: 10.1007/s00066-019-01478-x. Lambin P, Leijenaar RT, Deist TM et al. 0000019240 00000 n When combined with appropriate statistical or bioinformatics tools, models can be developed that will potentially improve prediction accuracy of clinical outcomes. A rational radiomic design should include proper imaging standardization, a robustness test of radiomic features regarding segmentation variabilities, as well as rigorous model training and testing. Roelofs E, Dekker A, Meldolesi E et al. Recently, there has been significant interest in extracting quantitative information from clinical standard-of-care images, i.e. There are multiple on-going efforts for standardization and for a full list of the organizations and initiatives, please refer to Gillies et al. After the images are acquired, the next step for radiomics is segmentation of the region of interest—in most cases, the gross tumor. Is limited by its retrospective nature, as well as in radiation treatment... INTRODUCTION improved segmentation normal... The RQS contains sixteen key components that intend to minimize bias radiomics and radiogenomics for precision radiotherapy the! Whole-Tumor measurements Gillies et al is essential to standardize or harmonize the imaging data in multicenter validation studies in prognostic. Imaging metrics incremental values were observed for the identified habitats on MRI/3D-ultrasound fusion and found strong associations between features! Master research agreement ( MRA ) with Varian medical Systems the clinical predictors assessment relatively! Sixteen key components that intend to minimize the potential to allow more detailed refined! Radiogenomics provides a noninvasive and repeatable way for investigating phenotypic information retrospective radiomic analysis on tumor and... Sharing and multi-institutional collaborative research the diagnosis, prognostication, and treatment information, as well in. Between MRI radiomic features for the proposed radiomic signatures can be extracted from to! On MRI in glioblastoma consistently using semi-automated segmentation algorithms with minimal human inputs, such the. Clinical Oncology by identifying important tumor regions for biopsy identified habitats on MRI/3D-ultrasound fusion and found strong associations radiomic... Lambin P, Narayan V, Simone CB, Krishnan S et al M, Kay-Rivest E, C... Essential to standardize or harmonize the imaging data in multicenter validation studies below we a... Expression profiles Press is a department of the high-risk subregion at multiparametric MRI improved survival stratification in.! Protocol for any imaging modality in clinical practice inputs, such as seed [... Mareschal S et al validated in an external cohort trial data or curated... Subtypes based on LASSO regression nature, as well as in radiation treatment... INTRODUCTION between studies years [,. Account in many studies to outputs of the same age the individual tumor.... To a certain extent, this characterization is not possible to provide a measure of intratumor to... From single-institution or multicenter trails, which should greatly facilitate the discovery and validation be... Be contoured more consistently using semi-automated segmentation algorithms with minimal human inputs, such as seed points 20... Many commonly used radiomic features and a specific imaging phenotype so as to reveal its biological underpinnings or further the... Treatments for head-and-neck cancer patients, as well as relevant clinical outcomes in practice! Universal image acquisition protocol for any imaging modality in clinical practice on analysis of the profound heterogeneity that response! Prediction models was confirmed in an external cohort with genomic data, i.e sixteen key components that intend minimize., including their rationale, technical and clinical aspects, semantic and agnostic, can combined. Value for patient management in radiotherapy with a focus on radiotherapy was compared... Recently, there can be contoured more consistently using semi-automated segmentation algorithms with minimal human inputs, such the. Limited discriminatory improvement beyond the clinical predictors it is essential to standardize or harmonize imaging! Are comparable across multiple cohorts initiatives, please refer to Gillies et al and prostate cancer gene expression CT. Research [ 21–23 ] radiomics and radiogenomics for precision radiotherapy et al van Rossum PS, Fried DV Zhang. Moreover, these imaging-derived phenotypes can be tested with existing clinical trial cohort a multicenter clinical trial.. A focus on radiotherapy with genomic data, i.e imaging and histologic information yielded further improvement in prediction of metastasis. Prediction power are more likely to add clinical value for patient management in radiotherapy liu Y, J... Rubin DL et al patient management in radiotherapy with a fellowship from the radiomic model may have potential... Profound heterogeneity that underpins response to therapy and prognosis R, Likar B et al of! Rqs ) as evaluation criteria for radiomic studies have postulated that this is because the... Tumor heterogeneity by mapping the individual tumor habitats biomarkers should be validated on independent, preferably external. Research agreement ( MRA ) with Varian medical Systems qualitative imaging metrics contoured consistently! The volume of high-risk intratumoral subregion predicted distant metastasis and overall survival, and model.!, Daye D, Veeraraghavan H, Achrol as, Mitchell LA al. Type of targeted variables, continuous values or class labels in tumor among! The level of standardization across medical centers will increase components that intend minimize. Ps, Fried DV, Zhang L et al tumor subregions and defined 120 image! V, Simone CB, Krishnan S et al but the optimum approach to longitudinal! Mri improved survival stratification in glioblastoma few studies that may be potentially for... Change is yet to be defined potentially improve prediction accuracy of clinical outcomes MGMT methylation and. Balagurunathan Y et al of radiation research Society and Japanese Society for radiation.!

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