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Journal articles on the topic 'Radiogenomic'

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Author: Grafiati

Published: 10 December 2022

Last updated: 22 February 2023

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1

Corr, Felix, Dustin Grimm, Benjamin Saß, Mirza Pojskić, Jörg W. Bartsch, Barbara Carl, Christopher Nimsky, and Miriam H. A. Bopp. "Radiogenomic Predictors of Recurrence in Glioblastoma—A Systematic Review." Journal of Personalized Medicine 12, no. 3 (March 4, 2022): 402. http://dx.doi.org/10.3390/jpm12030402.

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Glioblastoma, as the most aggressive brain tumor, is associated with a poor prognosis and outcome. To optimize prognosis and clinical therapy decisions, there is an urgent need to stratify patients with increased risk for recurrent tumors and low therapeutic success to optimize individual treatment. Radiogenomics establishes a link between radiological and pathological information. This review provides a state-of-the-art picture illustrating the latest developments in the use of radiogenomic markers regarding prognosis and their potential for monitoring recurrence. Databases PubMed, Google Scholar, and Cochrane Library were searched. Inclusion criteria were defined as diagnosis of glioblastoma with histopathological and radiological follow-up. Out of 321 reviewed articles, 43 articles met these inclusion criteria. Included studies were analyzed for the frequency of radiological and molecular tumor markers whereby radiogenomic associations were analyzed. Six main associations were described: radiogenomic prognosis, MGMT status, IDH, EGFR status, molecular subgroups, and tumor location. Prospective studies analyzing prognostic features of glioblastoma together with radiological features are lacking. By reviewing the progress in the development of radiogenomic markers, we provide insights into the potential efficacy of such an approach for clinical routine use eventually enabling early identification of glioblastoma recurrence and therefore supporting a further personalized monitoring and treatment strategy.

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Morris, Bethan, Lee Curtin, Andrea Hawkins-Daarud, Bernard Bendok, Maciej Mrugala, Jing Li, Nhan Tran, et al. "TMOD-15. IDENTIFYING THE SPATIAL AND TEMPORAL DYNAMICS OF GLIOBLASTOMA SUBPOPULATIONS WITHIN INDIVIDUAL PATIENTS." Neuro-Oncology 21, Supplement_6 (November 2019): vi265—vi266. http://dx.doi.org/10.1093/neuonc/noz175.1114.

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Abstract Glioblastomas (GBMs) are known to be complex tumors comprising multiple subpopulations of genetically-distinct cancer cells; it is thought that this genetic variation is a major factor in the lack of observed survival benefit of treatment regimes that target one of these subpopulations. The field of radiogenomics seeks to study correlations between MRI patterns and genetic features of GBM tumors. Spatial radiogenomic maps produced using machine-learning (ML) methods that are trained against information from image-localized patient biopsies identify regions where particular cancer sub-populations are predicted to occur within a GBM, thus non-invasively characterizing the regional genetic variability of these tumors. These tumor subpopulations may also interact with one another, in ways which may be of a competitive or cooperative nature to varying degrees. It is important to ascertain the nature of these interactions, as they may have implications for treatment response to targeted therapies, and characterization of the spatio-temporal dynamics of these co-evolving sub-populations will shed light on why some therapies fail. Here we combine mathematical modeling techniques and spatially-resolved radiogenomic maps to study the nature of these interactions between molecularly-distinct GBM subpopulations. We model the interactions between cell populations using a partial differential equation based formalism. The model is parameterized using radiogenomic ML maps from which we infer the nature of interactions between subpopulations. Furthermore, using maps as inputs, the model turns static maps into dynamic information, thus providing insight into how these subpopulations composing the tumor change over time and the effect this has on observed treatment response for individual patients.

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Slovak, Ryan, Meaghan Dendy Case, and Hyun S. Kim. "Genomics and Interventional Oncology in Primary Liver Cancer." Digestive Disease Interventions 04, no. 01 (March 2020): 053–59. http://dx.doi.org/10.1055/s-0040-1708533.

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AbstractPersonalized medicine is revolutionizing oncologic care. Molecular and imaging “fingerprinting” of cancer through genomics, radiomics, and radiogenomics has allowed for the meticulous characterization of many forms of malignancy, including primary liver cancers. With this data, treatments are being developed that precisely target and exploit key variations in individual tumors. As these methods continue to evolve, interventional oncologists are well positioned to capitalize on the advances being made. This article will provide a concise overview of the genomic, radiomic, and radiogenomic research on hepatocellular carcinoma and intrahepatic cholangiocarcinoma, in addition to discussions on how precision medicine would relate to interventional oncology.

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Zanfardino, Pane, Mirabelli, Salvatore, and Franzese. "TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review." International Journal of Molecular Sciences 20, no. 23 (November 29, 2019): 6033. http://dx.doi.org/10.3390/ijms20236033.

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In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular ‘omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular ‘omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular ‘omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.

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Khaleel, Sari, Andrew Katims, Shivaram Cumarasamy, Shoshana Rosenzweig, Kyrollis Attalla, A. Ari Hakimi, and Reza Mehrazin. "Radiogenomics in Clear Cell Renal Cell Carcinoma: A Review of the Current Status and Future Directions." Cancers 14, no. 9 (April 22, 2022): 2085. http://dx.doi.org/10.3390/cancers14092085.

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Radiogenomics is a field of translational radiology that aims to associate a disease’s radiologic phenotype with its underlying genotype, thus offering a novel class of non-invasive biomarkers with diagnostic, prognostic, and therapeutic potential. We herein review current radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the most common renal malignancy. A literature review was performed by querying PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases, identifying all relevant articles using the following search terms: “radiogenomics”, “renal cell carcinoma”, and “clear cell renal cell carcinoma”. Articles included were limited to the English language and published between 2009–2021. Of 141 retrieved articles, 16 fit our inclusion criteria. Most studies used computed tomography (CT) images from open-source and institutional databases to extract radiomic features that were then modeled against common genomic mutations in ccRCC using a variety of machine learning algorithms. In more recent studies, we noted a shift towards the prediction of transcriptomic and/or epigenetic disease profiles, as well as downstream clinical outcomes. Radiogenomics offers a platform for the development of non-invasive biomarkers for ccRCC, with promising results in small-scale retrospective studies. However, more research is needed to identify and validate robust radiogenomic biomarkers before integration into clinical practice.

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Sukhadia, Shrey S., Aayush Tyagi, Vivek Venkatraman, Pritam Mukherjee, Prathosh A.P., Mayur Divate, Olivier Gevaert, and Shivashankar H. Nagaraj. "Abstract 6341: ImaGene: A robust AI-based software platform for tumor radiogenomic evaluation and reporting." Cancer Research 82, no. 12_Supplement (June 15, 2022): 6341. http://dx.doi.org/10.1158/1538-7445.am2022-6341.

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Abstract The field of radiomics has undergone several advancements in approaches to uncovering hidden quantitative features from tumor imaging data for use in guiding clinical decision-making for cancer patients. Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest (ROIs), while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. These imaging and omics feature data can then be correlated and modeled using artificial intelligence (AI) techniques to highlight notable associations between tumor genotype and phenotype. Currently, however, the radiogenomics field lacks a unified and robust software platform capable of algorithmically analyzing imaging and omics features using modifiable parameters, detecting significant relationships among these features, and subjecting them to AI-based analysis. To address this gap, we developed ImaGene, a robust AI-based platform that uses omics and imaging features as inputs for different tumor types, performs statistical analyses of the correlations between these data types, and constructs AI models based upon significantly correlated features. It has several modifiable configuration parameters that provide users with complete control over their experiments. For each run, ImaGene produces comprehensive reports that can contribute to the construction of a novel radiogenomic knowledge base, in addition to enabling the deployment and sharing of AI models. To demonstrate the utility of ImaGene, we acquired imaging and omics datasets pertaining to Invasive Breast Cancer (IBC) and Head and Neck Squamous Cell Carcinoma (HNSCC) from public databases and analyzed them with this platform using specific parameters. In both cases, we uncovered significant associations between several imaging features and 11 genes: CRABP1, VRTN, SMTNL2, FABP1, HAND2, HAS1, C4BPA, FAM163A, DSG1, SMTNL2 and KCNJ16 for IBC, and 10 genes: CEACAM6, IGLL1, SERPINA1, NANOG, OCA2, PRLR, ACSM2B, CYP11B1, and VPREB1 for HNSCC. Overall, our software platform is capable of identifying, analyzing, and correlating important features from tumor scans, thereby providing researchers with a reliable and accurate tool for their radiogenomics experiments. We anticipate that ImaGene will become the gold standard for tumor analyses in the field of radiogenomics owing to its ease of use, flexibility, and reproducibility. Citation Format: Shrey S. Sukhadia, Aayush Tyagi, Vivek Venkatraman, Pritam Mukherjee, Prathosh A.P., Mayur Divate, Olivier Gevaert, Shivashankar H. Nagaraj. ImaGene: A robust AI-based software platform for tumor radiogenomic evaluation and reporting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6341.

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Gallivanone, Francesca, Gloria Bertoli, and Danilo Porro. "Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions." Methods and Protocols 5, no. 5 (October 3, 2022): 78. http://dx.doi.org/10.3390/mps5050078.

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Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with –omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).

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Abazeed, M., D. Adams, P. Tamayo, B. Yard, J. Loeffler, J. Suh, M. Meyerson, P. Hammerman, and S. Schreiber. "The Radiogenomic Landscape of Cancer." International Journal of Radiation Oncology*Biology*Physics 90, no. 1 (September 2014): S34. http://dx.doi.org/10.1016/j.ijrobp.2014.05.145.

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9

Trout, Andrew T., Matthew R. Batie, Anita Gupta, Rachel M. Sheridan, Gregory M. Tiao, and Alexander J. Towbin. "3D printed pathological sectioning boxes to facilitate radiological–pathological correlation in hepatectomy cases." Journal of Clinical Pathology 70, no. 11 (June 8, 2017): 984–87. http://dx.doi.org/10.1136/jclinpath-2016-204293.

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Radiogenomics promises to identify tumour imaging features indicative of genomic or proteomic aberrations that can be therapeutically targeted allowing precision personalised therapy. An accurate radiological–pathological correlation is critical to the process of radiogenomic characterisation of tumours. An accurate correlation, however, is difficult to achieve with current pathological sectioning techniques which result in sectioning in non-standard planes. The purpose of this work is to present a technique to standardise hepatic sectioning to facilitateradiological–pathological correlation. We describe a process in which three-dimensional (3D)-printed specimen boxes based on preoperative cross-sectional imaging (CT and MRI) can be used to facilitate pathological sectioning in standard planes immediately on hepatic resection enabling improved tumour mapping. We have applied this process in 13 patients undergoing hepatectomy and have observed close correlation between imaging and gross pathology in patients with both unifocal and multifocal tumours.

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10

Kim, Byung-Hoon, Hyeonhoon Lee, Kyu Sung Choi, Ju Gang Nam, Chul-Kee Park, Sung-Hye Park, Jin Wook Chung, and Seung Hong Choi. "Validation of MRI-Based Models to Predict MGMT Promoter Methylation in Gliomas: BraTS 2021 Radiogenomics Challenge." Cancers 14, no. 19 (October 3, 2022): 4827. http://dx.doi.org/10.3390/cancers14194827.

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O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the external validation of MGMT prediction models using large-scale multicenter datasets. We tested recent CNN architectures via extensive experiments to investigate whether MGMT methylation in gliomas can be predicted using MR images. Specifically, prediction models were developed and validated with different training datasets: (1) the merged (SNUH + BraTS) (n = 985); (2) SNUH (n = 400); and (3) BraTS datasets (n = 585). A total of 420 training and validation experiments were performed on combinations of datasets, convolutional neural network (CNN) architectures, MRI sequences, and random seed numbers. The first-place solution of the RSNA-MICCAI radiogenomic challenge was also validated using the external test set (SNUH). For model evaluation, the area under the receiver operating characteristic curve (AUROC), accuracy, precision, and recall were obtained. With unexpected negative results, 80.2% (337/420) and 60.0% (252/420) of the 420 developed models showed no significant difference with a chance level of 50% in terms of test accuracy and test AUROC, respectively. The test AUROC and accuracy of the first-place solution of the BraTS 2021 challenge were 56.2% and 54.8%, respectively, as validated on the SNUH dataset. In conclusion, MGMT methylation status of gliomas may not be predictable with preoperative MR images even using deep learning.

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Beig, Niha, Kaustav Bera, and Pallavi Tiwari. "Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges." Neuro-Oncology Advances 2, Supplement_4 (December 1, 2020): iv3—iv14. http://dx.doi.org/10.1093/noajnl/vdaa148.

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Abstract Neuro-oncology largely consists of malignancies of the brain and central nervous system including both primary as well as metastatic tumors. Currently, a significant clinical challenge in neuro-oncology is to tailor therapies for patients based on a priori knowledge of their survival outcome or treatment response to conventional or experimental therapies. Radiomics or the quantitative extraction of subvisual data from conventional radiographic imaging has recently emerged as a powerful data-driven approach to offer insights into clinically relevant questions related to diagnosis, prediction, prognosis, as well as assessing treatment response. Furthermore, radiogenomic approaches provide a mechanism to establish statistical correlations of radiomic features with point mutations and next-generation sequencing data to further leverage the potential of routine MRI scans to serve as “virtual biopsy” maps. In this review, we provide an introduction to radiomic and radiogenomic approaches in neuro-oncology, including a brief description of the workflow involving preprocessing, tumor segmentation, and extraction of “hand-crafted” features from the segmented region of interest, as well as identifying radiogenomic associations that could ultimately lead to the development of reliable prognostic and predictive models in neuro-oncology applications. Lastly, we discuss the promise of radiomics and radiogenomic approaches in personalizing treatment decisions in neuro-oncology, as well as the challenges with clinical adoption, which will rely heavily on their demonstrated resilience to nonstandardization in imaging protocols across sites and scanners, as well as in their ability to demonstrate reproducibility across large multi-institutional cohorts.

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Chen, Yu-Hung, Kun-Han Lue, Chih-Bin Lin, Kuang-Chi Chen, Sheng-Chieh Chan, Sung-Chao Chu, Bee-Song Chang, and Yen-Chang Chen. "Genomic and Glycolytic Entropy Are Reliable Radiogenomic Heterogeneity Biomarkers for Non-Small Cell Lung Cancer." International Journal of Molecular Sciences 24, no. 4 (February 16, 2023): 3988. http://dx.doi.org/10.3390/ijms24043988.

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Radiogenomic heterogeneity features in 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) have become popular in non-small cell lung cancer (NSCLC) research. However, the reliabilities of genomic heterogeneity features and of PET-based glycolytic features in different image matrix sizes have yet to be thoroughly tested. We conducted a prospective study with 46 NSCLC patients to assess the intra-class correlation coefficient (ICC) of different genomic heterogeneity features. We also tested the ICC of PET-based heterogeneity features from different image matrix sizes. The association of radiogenomic features with clinical data was also examined. The entropy-based genomic heterogeneity feature (ICC = 0.736) is more reliable than the median-based feature (ICC = −0.416). The PET-based glycolytic entropy was insensitive to image matrix size change (ICC = 0.958) and remained reliable in tumors with a metabolic volume of <10 mL (ICC = 0.894). The glycolytic entropy is also significantly associated with advanced cancer stages (p = 0.011). We conclude that the entropy-based radiogenomic features are reliable and may serve as ideal biomarkers for research and further clinical use for NSCLC.

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Kazerooni, Anahita Fathi, Hamed Akbari, Erik Toorens, Dimitris Grigoriadis, Xiaoju Hu, Chiharu Sako, Elizabeth Mamourian, et al. "NIMG-82. RADIOGENOMIC SIGNATURES OF KEY DRIVER GENES IN GBM REVEAL MOLECULAR HETEROGENEITY OF THE TUMOR MICROENVIRONMENT LINKED TO SPATIAL DISTRIBUTION: IMPACT ON THE TRAJECTORY OF GLIOMA EVOLUTION." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii184. http://dx.doi.org/10.1093/neuonc/noac209.700.

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Abstract Somatic genomic alterations acquired during GBM growth enhance adaptation of tumor cells to their microenvironment and give rise to molecular heterogeneity. Radiogenomics could facilitate exploration of the underlying pathobiology of tumor growth in specific microenvironments and thereby, promote precision medicine for the patients. We derived radiogenomic signatures of key driver genes and evaluated molecular compositions of tumor groups with predisposition to specific brain regions. Pre-operative multiparametric conventional MRI scans of 357 IDH-wildtype GBM patients with available targeted NGS data were jointly segmented and registered into a common template. We constructed spatial distribution atlases for tumors harboring mutations in driver genes and identified four distinct groups of tumor locations with predilection to the left frontal cingulate region (Group1), right temporal (Group2), right parietal (Group3), and occipital pole (Group4). Evaluation of the differences in molecular features of the tumor groups included: (1) exploring similarities of genomic profiles across all four groups by evaluating cosine similarity metric (CSM) between mutational signatures; (2) quantification of molecular heterogeneity based on Mutant Allele Tumor Heterogeneity (MATH) scores; and (3) inference of the evolutionary trajectories. Groups 1 and 4 were the most different, and Groups 2 and 3 were the most similar tumors, molecularly. The mutational signatures between Groups 1 and 4 revealed a CSM of 0.35. Group1 showed significantly lower MATH score (less heterogeneity) compared to Group4 (p< 0.05). Evaluation of evolutionary patterns suggested NF1 mutation as an early event in Group1, without subsequent gain of function or mutation in EGFR. In contrast, in Group4, EGFR mutations were early events triggering PTEN mutations later in the evolutionary trajectory. Radiogenomic signatures revealed distinct molecular underpinnings for the tumors with predilection towards specific brain regions that may suggest existence of different tumor microenvironments in different brain regions that cause intra- and inter-patient heterogeneity in the molecular tumor composition.

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Singh, Gagandeep, Sunil Manjila, Nicole Sakla, Alan True, Amr H. Wardeh, Niha Beig, Anatoliy Vaysberg, John Matthews, Prateek Prasanna, and Vadim Spektor. "Radiomics and radiogenomics in gliomas: a contemporary update." British Journal of Cancer 125, no. 5 (May 6, 2021): 641–57. http://dx.doi.org/10.1038/s41416-021-01387-w.

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AbstractThe natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.

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Smedley, Nova F., Suzie El-Saden, and William Hsu. "Discovering and interpreting transcriptomic drivers of imaging traits using neural networks." Bioinformatics 36, no. 11 (February 26, 2020): 3537–48. http://dx.doi.org/10.1093/bioinformatics/btaa126.

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Abstract Motivation Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and cellular-level information from genomics are needed. However, these ‘radiogenomic’ studies often use linear or shallow models, depend on feature selection, or consider one gene at a time to map images to genes. Moreover, no study has systematically attempted to understand the molecular basis of imaging traits based on the interpretation of what the neural network has learned. These studies are thus limited in their ability to understand the transcriptomic drivers of imaging traits, which could provide additional context for determining clinical outcomes. Results We present a neural network-based approach that takes high-dimensional gene expression data as input and performs non-linear mapping to an imaging trait. To interpret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks. In glioblastoma patients, our models outperformed comparable classifiers (>0.10 AUC) and our interpretation methods were validated using a similar model to identify known relationships between genes and molecular subtypes. We found that tumor imaging traits had specific transcription patterns, e.g. edema and genes related to cellular invasion, and 10 radiogenomic traits were significantly predictive of survival. We demonstrate that neural networks can model transcriptomic heterogeneity to reflect differences in imaging and can be used to derive radiogenomic traits with clinical value. Availability and implementation https://github.com/novasmedley/deepRadiogenomics. Contact whsu@mednet.ucla.edu Supplementary information Supplementary data are available at Bioinformatics online.

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Salami, Simpa Samuel, Jeremy B. Kaplan, Srinivas Nallandhighal, Mandeep Takhar, Jeffrey J. Tosoian, Matthew Lee, Junhee Yoon, et al. "Radiogenomic characterization of multifocal prostate cancer." Journal of Clinical Oncology 37, no. 7_suppl (March 1, 2019): 126. http://dx.doi.org/10.1200/jco.2019.37.7_suppl.126.

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126 Background: Up to 20% of patients with negative multiparametric magnetic resonance imaging (MRI) harbor Gleason score ≥7 prostate cancer (PCa). We sought to elucidate the molecular basis of and determine the prognostic significance of PCa visibility on MRI. Methods: We identified a retrospective cohort of patients who underwent MRI prior to prostatectomy with both MRI visible (PIRADS 3 – 5) and invisible PCa. MRI for each patient was re-reviewed and co-registered with whole-mount histopathology. DNA and RNA were co-isolated from all tumor foci pre-identified on FFPE specimens. High depth, targeted DNA and RNA next generation sequencing was performed to characterize the molecular profile of each tumor focus using the Oncomine Comprehensive Panel (DNA) and a custom targeted RNAseq panel assessing PCa relevant alterations. A multigene RNAseq model was developed and validated in two independent cohorts to predict MRI visible PCa and to determine the prognostic significance of MRI visibility. Results: A total of 26 primary tumor foci from 10 patients were analyzed. Of the 14 (54%) invisible lesions on MRI, 5 (36%) were Gleason 3+4 = 7 and the remainder were Gleason 6. We detected high-confidence prioritized PCa relevant mutations in 54% (14/26) of tumor foci, 43% (6/14) of which were in MRI invisible lesions. Notable point mutations were in APC, AR, ARID1B, ATM, ATRX, BRCA2, FAT1, MAP3K1, NF1, SPEN, SPOP, and TP53. A 9-gene RNA signature, the majority of which were under-expressed cellular organization and structure genes, was developed to predict MRI visibility with an AUC of 0.89. Validation of this signature in an independent data set (n = 16) yielded an AUC of 0.88 with a specificity of 100% for predicting MRI visible tumors. Using this signature in a cohort of 375 patients with clinical follow up, we found that predicted MRI visibility status was not an independent predictor of biochemical recurrence, metastasis-free survival, or PCa specific mortality (all p > 0.05). Conclusions: We observed under-expression of cellular organization and structural genes in MRI visible tumors compared to MRI invisible cancer foci. Using our validated signature to predict MRI visibility status, we found that MRI visibility is not a significant predictor of oncological outcomes.

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Salami, S., J. Kaplan, S. Nallandhighal, M. Takhar, J. Tosoian, M. Lee, J. Yoon, et al. "Radiogenomic characterization of multifocal prostate cancer." European Urology Supplements 18, no. 1 (March 2019): e726-e727. http://dx.doi.org/10.1016/s1569-9056(19)30533-0.

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De Ruysscher, Dirk, Gilles Defraene, Bram L. T. Ramaekers, Philippe Lambin, Erik Briers, Hilary Stobart, Tim Ward, et al. "Optimal design and patient selection for interventional trials using radiogenomic biomarkers: A REQUITE and Radiogenomics consortium statement." Radiotherapy and Oncology 121, no. 3 (December 2016): 440–46. http://dx.doi.org/10.1016/j.radonc.2016.11.003.

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Kazerooni, Anahita Fathi, Hamed Akbari, Spyridon Bakas, Erik Toorens, Chiharu Sako, Elizabeth Mamourian, Vikas Bommineni, et al. "NIMG-52. RADIOGENOMICS SIGNATURES IN KEY DRIVER GENES IN GLIOBLASTOMA EVALUATED WITH AND WITHOUT THE PRESENCE OF CO-OCCURRING MUTATIONS." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi141. http://dx.doi.org/10.1093/neuonc/noab196.550.

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Abstract PURPOSE Glioblastomas display significant heterogeneity on the molecular level, typically harboring several co-occurring mutations, which likely contributes to failure of molecularly targeted therapeutic approaches. Radiogenomics has emerged as a promising tool for in vivo characterization of this heterogeneity. We derive radiogenomic signatures of four mutations via machine learning (ML) analysis of multiparametric MRI (mpMRI) and evaluate them in the presence and absence of other co-occurring mutations. METHODS We identified a retrospective cohort of 359 IDH-wildtype glioblastoma patients, with available pre-operative mpMRI (T1, T1Gd, T2, T2-FLAIR) scans and targeted next generation sequencing (NGS) data. Radiomic features, including morphologic, histogram, texture, and Gabor wavelet descriptors, were extracted from the mpMRI. Multivariate predictive models were trained using cross-validated SVM with LASSO feature selection to predict mutation status in key driver genes, EGFR, PTEN, TP53, and NF1. ML models and spatial population atlases of genetic mutations were generated for stratification of the tumors (1) with co-occurring mutations versus wildtypes, (2) with exclusive mutations in each driver gene versus the tumors without any mutations in the pathways associated with these genes. RESULTS ML models yielded AUCs of 0.75 (95%CI:0.62-0.88) / 0.87 (95%CI:0.70-1) for co-occurring / exclusive EGFR mutations, 0.69 (95%CI:0.58-0.80) / 0.80 (95%CI:0.61-0.99) for co-occurring / exclusive PTEN mutations, and 0.77 (95%CI:0.65-0.88) / 0.86 (95%CI:0.69-1) for co-occurring / exclusive TP53 cases. Spatial atlases revealed a predisposition of left temporal lobe for NF1 and right frontotemporal region for TP53 in mutually exclusive tumors, which was not observed in the co-occurring mutation atlases. CONCLUSION Our results suggest the presence of distinct radiogenomic signatures of several glioblastoma mutations, which become even more pronounced when respective mutations do not co-occur with other mutations. These in vivo signatures can contribute to pre-operative stratification of patients for molecular targeted therapies, and potentially longitudinal monitoring of mutational changes during treatment.

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Idris, Tagwa, Maggie Barghash, Aikaterini Kotrotsou, Helen J. Huang, Vivek Subbiah, Ahmed Omar Kaseb, Sarina Anne Piha-Paul, et al. "CT-based radiogenomic signature to identify isocitrate dehydrogenase (IDH)1/2 mutations in advanced intrahepatic cholangiocarcinoma." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 4081. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.4081.

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4081 Background: IDH1/2 mutations have a high prevalence (20%) in intrahepatic cholangiocarcinoma (iCCA) and can be associated with therapeutic benefit from IDH inhibitors. Radiomics, a developing field within imaging, has shown its ability to discriminate between tumors of distinct genomic profiles and mutational status. Methods: We developed a radiogenomic signature to robustly predict IDH1/2 mutation status (mutated versus wild-type [WT]) in 22 patients with iCCA using the pretreatment CT scans. The triphasic hepatic CT scan was used to segment the lesion. After semiautomatic segmentation of the tumor, the extracted volume of interest (VOI) was imported into our in-house radiomic pipeline and 610 radiomic features were extracted. The least absolute shrinkage and selection operator regression (LASSO) and minimum redundancy and maximum relevance (mRMR) were used for feature selection. Selected features were used to build a classification model for prediction of IDH1/2 mutation status (XGboost). The performance of the radiomics model was assessed using leave-one-out cross-validation (LOOCV). Results: Of 22 patients, 16 patients (male, 6; female, 10; average age, 55.5 years) had IDH1 (N = 14) or IDH2 (N = 2) mutations and 6 patients (male, 4; female, 2; average age = 55.5 years) had IDH1/2 WT.The CT-derived radiomic signature robustly predicted presence of IDH1/2 mutations versus WT with an area under the curve (AUC), sensitivity and specificity of 98.4%, 83.3% and 93.8%, respectively ( P = 0.037) and in a subgroup analysis presence of IDH1 mutation versus WT with an AUC, sensitivity and specificity of 98.2%, 83.3% and 92.8%, respectively ( P = 0.035). Conclusions: To our knowledge, this is the first study investigating the ability of radiogenomics as a potential method to predict the IDH1/2 mutation status in iCCA patients. Our data suggest that radiogenomic signature may correlate with IDH1/2 mutations and represent a promising non-invasive tool to stratify the patients based on molecular alterations.

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Yu, Fanyang, Anahita Fathi Kazerooni, Erik Toorens, Hamed Akbari, Chiharu Sako, Elizabeth Mamourian, Stephen Bagley, et al. "NIMG-22. AN AI-BASED COORDINATE SYSTEM ELUCIDATES RADIOGENOMIC HETEROGENEITY OF GLIOBLASTOMA VIA DEEP LEARNING AND MANIFOLD EMBEDDINGS." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii166. http://dx.doi.org/10.1093/neuonc/noac209.640.

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Abstract PURPOSE There is evidence that molecular heterogeneity of glioblastoma is associated with heterogeneity of MR imaging signatures. Modern machine learning models, such as deep neural networks, provide a tool for capturing such complex relationships in high-dimensional datasets. This study leverages recent advances in visualizing neural networks to construct a radiogenomics coordinate system whose axes reflect the expression of imaging signatures of genetic mutations commonly found in glioblastoma. METHODS Multi-parametric MRI (mpMRI) scans (T1, T1-Gd, T2, T2-FLAIR, DSC, DTI) of 254 subjects with glioblastoma were retrospectively collected. Radiomics features, including histograms, morphologic and textural descriptors, were derived. Genetic markers were obtained through next generation sequencing (NGS) panel. A multi-label classification deep neural network was trained for predicting mutation status in key driver genes, EGFR, PTEN, NF1, TP53 and RB1. We utilized a nonlinear manifold learning method called Intensive Principal Component Analysis (InPCA), to visualize the output probability distributions from the trained model. The first three principal components (PCs) were selected for constructing the coordinate system. RESULTS The axes derived from InPCA analysis were associated with molecular pathways known to be implicated in glioblastoma: (1) Increasing values of PC1 were associated with primary involvement of P53 then RB1 then MAPK then RTK/PI3K; (2) Increasing values of PC2 were associated with primary involvement of RTK then RB1/P53/MAPK then PI3K; (3) Increasing values of PC3 were associated with primary involvement of MAPK then RB1/P53/RTK/PI3K. Imaging features significantly associated with each of three PCs (p< 0.05) were identified by Pearson correlation analysis. CONCLUSION Deep learning followed by nonlinear manifold embedding identifies a radiogenomics coordinate system spanned by three components which were associated with different molecular pathways of glioblastoma. The heterogeneity of radiogenomic signatures captured by this coordinate system offers in vivo biomarkers of the molecular heterogeneity of glioblastoma.

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Li, Yanmeng, Xiao Li, Hao Li, Yifan Zhao, Ziyang Liu, Kunkun Sun, Xiang Zhu, et al. "Genomic characterisation of pulmonary subsolid nodules: mutational landscape and radiological features." European Respiratory Journal 55, no. 2 (November 7, 2019): 1901409. http://dx.doi.org/10.1183/13993003.01409-2019.

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BackgroundLung adenocarcinomas (LUADs) that display radiologically as subsolid nodules (SSNs) exhibit more indolent biological behaviour than solid LUADs. SSNs, commonly encompassing pre-invasive and invasive yet early-stage adenocarcinomas, can be categorised as pure ground-glass nodules and part-solid nodules. The genomic characteristics of SSNs remain poorly understood.MethodsWe subjected 154 SSN samples from 120 treatment-naïve Chinese patients to whole-exome sequencing. Clinical parameters and radiological features of these SSNs were collected. The genomic landscape of SSNs and differences from that of advanced-stage LUADs were defined. In addition, we investigated the intratumour heterogeneity and clonal relationship of multifocal SSNs and conducted radiogenomic analysis to link imaging and molecular characteristics of SSNs. Fisher's exact and Wilcoxon rank sum tests were used in the statistical analysis.ResultsThe median somatic mutation rate across the SSN cohort was 1.12 mutations per Mb. Mutations in EGFR were the most prominent and significant variation, followed by those in RBM10, TP53, STK11 and KRAS. The differences between SSNs and advanced-stage LUADs at a genomic level were unravelled. Branched evolution and remarkable genomic heterogeneity were demonstrated in SSNs. Although multicentric origin was predominant, we also detected early metastatic events among multifocal SSNs. Using radiogenomic analysis, we found that higher ratios of solid components in SSNs were accompanied by significantly higher mutation frequencies in EGFR, TP53, RBM10 and ARID1B, suggesting that these genes play roles in the progression of LUADs.ConclusionsOur study provides the first comprehensive description of the mutational landscape and radiogenomic mapping of SSNs.

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Springer, Elisabeth, Pedro Lima Cardoso, Bernhard Strasser, Wolfgang Bogner, Matthias Preusser, Georg Widhalm, Mathias Nittka, et al. "MR Fingerprinting—A Radiogenomic Marker for Diffuse Gliomas." Cancers 14, no. 3 (January 30, 2022): 723. http://dx.doi.org/10.3390/cancers14030723.

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(1) Background: Advanced MR imaging (MRI) of brain tumors is mainly based on qualitative contrast images. MR Fingerprinting (MRF) offers a novel approach. The purpose of this study was to use MRF-derived T1 and T2 relaxation maps to differentiate diffuse gliomas according to isocitrate dehydrogenase (IDH) mutation. (2) Methods: Twenty-four patients with histologically verified diffuse gliomas (14 IDH-mutant, four 1p/19q-codeleted, 10 IDH-wildtype) were enrolled. MRF T1 and T2 relaxation times were compared to apparent diffusion coefficient (ADC), relative cerebral blood volume (rCBV) within solid tumor, peritumoral edema, and normal-appearing white matter (NAWM), using contrast-enhanced MRI, diffusion-, perfusion-, and susceptibility-weighted imaging. For perfusion imaging, a T2* weighted perfusion sequence with leakage correction was used. Correlations of MRF T1 and T2 times with two established conventional sequences for T1 and T2 mapping were assessed (a fast double inversion recovery-based MR sequence (‘MP2RAGE’) for T1 quantification and a multi-contrast spin echo-based sequence for T2 quantification). (3) Results: MRF T1 and T2 relaxation times were significantly higher in the IDH-mutant than in IDH-wildtype gliomas within the solid part of the tumor (p = 0.024 for MRF T1, p = 0.041 for MRF T2). MRF T1 and T2 relaxation times were significantly higher in the IDH-wildtype than in IDH-mutant gliomas within peritumoral edema less than or equal to 1cm adjacent to the tumor (p = 0.038 for MRF T1 mean, p = 0.010 for MRF T2 mean). In the solid part of the tumor, there was a high correlation between MRF and conventionally measured T1 and T2 values (r = 0.913, p < 0.001 for T1, r = 0.775, p < 0.001 for T2), as well as between MRF and ADC values (r = 0.813, p < 0.001 for T2, r = 0.697, p < 0.001 for T1). The correlation was weak between the MRF and rCBV values (r = −0.374, p = 0.005 for T2, r = −0.181, p = 0.181 for T1). (4) Conclusions: MRF enables fast, single-sequence based, multi-parametric, quantitative tissue characterization of diffuse gliomas and may have the potential to differentiate IDH-mutant from IDH-wildtype gliomas.

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Incoronato, Mariarosaria, Marco Aiello, Teresa Infante, Carlo Cavaliere, Anna Grimaldi, Peppino Mirabelli, Serena Monti, and Marco Salvatore. "Radiogenomic Analysis of Oncological Data: A Technical Survey." International Journal of Molecular Sciences 18, no. 4 (April 12, 2017): 805. http://dx.doi.org/10.3390/ijms18040805.

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Kong, Doo-Sik, Jinkuk Kim, In-Hee Lee, Sung Tae Kim, Ho Jun Seol, Jung-Il Lee, Woong-Yang Park, et al. "Integrative radiogenomic analysis for multicentric radiophenotype in glioblastoma." Oncotarget 7, no. 10 (February 1, 2016): 11526–38. http://dx.doi.org/10.18632/oncotarget.7115.

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Goyen, Mathias. "Radiogenomic imaging-linking diagnostic imaging and molecular diagnostics." World Journal of Radiology 6, no. 8 (2014): 519. http://dx.doi.org/10.4329/wjr.v6.i8.519.

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Abazeed, Mohamed E., Drew J. Adams, Kristen E. Hurov, Pablo Tamayo, Chad J. Creighton, Dmitriy Sonkin, Andrew O. Giacomelli, et al. "Integrative Radiogenomic Profiling of Squamous Cell Lung Cancer." Cancer Research 73, no. 20 (August 26, 2013): 6289–98. http://dx.doi.org/10.1158/0008-5472.can-13-1616.

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Abazeed, M., D. Adams, K. Hurov, P. Tamayo, C. Creighton, D. Sonkin, A. Giacomelli, S. Schreiber, P. Hammerman, and M. Meyerson. "Integrative Radiogenomic Profiling of Squamous Cell Lung Cancer." International Journal of Radiation Oncology*Biology*Physics 87, no. 2 (October 2013): S138—S139. http://dx.doi.org/10.1016/j.ijrobp.2013.06.356.

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Kirienko, Margarita, Martina Sollini, Marinella Corbetta, Emanuele Voulaz, Noemi Gozzi, Matteo Interlenghi, Francesca Gallivanone, et al. "Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer." European Journal of Nuclear Medicine and Molecular Imaging 48, no. 11 (May 7, 2021): 3643–55. http://dx.doi.org/10.1007/s00259-021-05371-7.

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Abstract Objective The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC). Methods In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence. Results Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87. Conclusions Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.

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Coates, J., A. Jeyaseelan, N. Ybarra, J. Tao, M. David, S. Faria, L. Souhami, F. Cury, M. Duclos, and I. E. L. Naqa. "SP-0011 Unified Radiogenomic Prediction of Late Radiotherapy Toxicities." Radiotherapy and Oncology 133 (April 2019): S4—S5. http://dx.doi.org/10.1016/s0167-8140(19)30431-1.

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Fachal, L., A. Gómez-caamaño, M. Sánchez-garcía, L. León, A. Carballo, P. Peleteiro, P. Calvo, et al. "Radiotoxicity in prostate cancer: The first radiogenomic Spanish GWAS." Reports of Practical Oncology & Radiotherapy 18 (June 2013): S100. http://dx.doi.org/10.1016/j.rpor.2013.03.819.

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Morton, Lindsay M., Luisel Ricks-Santi, Catharine M. L. West, and Barry S. Rosenstein. "Radiogenomic Predictors of Adverse Effects following Charged Particle Therapy." International Journal of Particle Therapy 5, no. 1 (August 2018): 103–13. http://dx.doi.org/10.14338/ijpt-18-00009.1.

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Zhu, Zhe, Ehab Albadawy, Ashirbani Saha, Jun Zhang, Michael R. Harowicz, and Maciej A. Mazurowski. "Deep learning for identifying radiogenomic associations in breast cancer." Computers in Biology and Medicine 109 (June 2019): 85–90. http://dx.doi.org/10.1016/j.compbiomed.2019.04.018.

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Jiang, Lin, Chao You, Yi Xiao, He Wang, Guan-Hua Su, Bing-Qing Xia, Ren-Cheng Zheng, et al. "Radiogenomic analysis reveals tumor heterogeneity of triple-negative breast cancer." Cell Reports Medicine 3, no. 7 (July 2022): 100694. http://dx.doi.org/10.1016/j.xcrm.2022.100694.

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Gopal, P., B. Yard, A. Petty, J. Castrillon, and M. Abazeed. "P60.06 Systematic Variant Profiling Delineates the Radiogenomic Landscape of Cancer." Journal of Thoracic Oncology 16, no. 10 (October 2021): S1167. http://dx.doi.org/10.1016/j.jtho.2021.08.627.

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Qu, Ruyi, and Zhifeng Xiao. "An Attentive Multi-Modal CNN for Brain Tumor Radiogenomic Classification." Information 13, no. 3 (March 2, 2022): 124. http://dx.doi.org/10.3390/info13030124.

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Medical images of brain tumors are critical for characterizing the pathology of tumors and early diagnosis. There are multiple modalities for medical images of brain tumors. Fusing the unique features of each modality of the magnetic resonance imaging (MRI) scans can accurately determine the nature of brain tumors. The current genetic analysis approach is time-consuming and requires surgical extraction of brain tissue samples. Accurate classification of multi-modal brain tumor images can speed up the detection process and alleviate patient suffering. Medical image fusion refers to effectively merging the significant information of multiple source images of the same tissue into one image, which will carry abundant information for diagnosis. This paper proposes a novel attentive deep-learning-based classification model that integrates multi-modal feature aggregation, lite attention mechanism, separable embedding, and modal-wise shortcuts for performance improvement. We evaluate our model on the RSNA-MICCAI dataset, a scenario-specific medical image dataset, and demonstrate that the proposed method outperforms the state-of-the-art (SOTA) by around 3%.

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Gevaert, Olivier, Lex A. Mitchell, Achal S. Achrol, Jiajing Xu, Sebastian Echegaray, Gary K. Steinberg, Samuel H. Cheshier, Sandy Napel, Greg Zaharchuk, and Sylvia K. Plevritis. "Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features." Radiology 273, no. 1 (October 2014): 168–74. http://dx.doi.org/10.1148/radiol.14131731.

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Gevaert, Olivier, Lex A. Mitchell, Achal S. Achrol, Jiajing Xu, Sebastian Echegaray, Gary K. Steinberg, Samuel H. Cheshier, Sandy Napel, Greg Zaharchuk, and Sylvia K. Plevritis. "Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features." Radiology 276, no. 1 (July 2015): 313. http://dx.doi.org/10.1148/radiol.2015154019.

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Trivizakis, Eleftherios, John Souglakos, Apostolos Karantanas, and Kostas Marias. "Deep Radiotranscriptomics of Non-Small Cell Lung Carcinoma for Assessing Molecular and Histology Subtypes with a Data-Driven Analysis." Diagnostics 11, no. 12 (December 17, 2021): 2383. http://dx.doi.org/10.3390/diagnostics11122383.

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Radiogenomic and radiotranscriptomic studies have the potential to pave the way for a holistic decision support system built on genomics, transcriptomics, radiomics, deep features and clinical parameters to assess treatment evaluation and care planning. The integration of invasive and routine imaging data into a common feature space has the potential to yield robust models for inferring the drivers of underlying biological mechanisms. In this non-small cell lung carcinoma study, a multi-omics representation comprised deep features and transcriptomics was evaluated to further explore the synergetic and complementary properties of these diverse multi-view data sources by utilizing data-driven machine learning models. The proposed deep radiotranscriptomic analysis is a feature-based fusion that significantly enhances sensitivity by up to 0.174 and AUC by up to 0.22, compared to the baseline single source models, across all experiments on the unseen testing set. Additionally, a radiomics-based fusion was also explored as an alternative methodology yielding radiomic signatures that are comparable to several previous publications in the field of radiogenomics. Furthermore, the machine learning multi-omics analysis based on deep features and transcriptomics achieved an AUC performance of up to 0.831 ± 0.09/0.925 ± 0.04 for the examined molecular and histology subtypes analysis, respectively. The clinical impact of such high-performing models can add prognostic value and lead to optimal treatment assessment by targeting specific oncogenes, namely the response of tyrosine kinase inhibitors of EGFR mutated or predicting the chemotherapy resistance of KRAS mutated tumors.

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McAnena, Peter, Yvonne Fahy, Brian Moloney, Talha Iqbal, Declan Sheppard, Conal Dennedy, Michael Kerin, Denis Quill, and Aoife Lowery. "AB051. SOH21AS114. A radiogenomic model to classify response to neoadjuvant chemotherapy." Mesentery and Peritoneum 5 (April 2021): AB051. http://dx.doi.org/10.21037/map-21-ab051.

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Kawaguchi, Risa K., Masamichi Takahashi, Mototaka Miyake, Manabu Kinoshita, Satoshi Takahashi, Koichi Ichimura, Ryuji Hamamoto, Yoshitaka Narita, and Jun Sese. "Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals." Cancers 13, no. 14 (July 19, 2021): 3611. http://dx.doi.org/10.3390/cancers13143611.

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Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.

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Zinn, Pascal O., Bhanu Majadan, Pratheesh Sathyan, Sanjay K. Singh, Sadhan Majumder, Ferenc A. Jolesz, and Rivka R. Colen. "Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme." PLoS ONE 6, no. 10 (October 5, 2011): e25451. http://dx.doi.org/10.1371/journal.pone.0025451.

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You, Hye Jin, Ho-Young Park, Jinkuk Kim, In-Hee Lee, Ho Jun Seol, Jung-Il Lee, Sung Tae Kim, Doo-Sik Kong, and Do-Hyun Nam. "Integrative radiogenomic analysis for genomic signatures in glioblastomas presenting leptomeningeal dissemination." Medicine 95, no. 27 (July 2016): e4109. http://dx.doi.org/10.1097/md.0000000000004109.

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Yamamoto, Shota, Ronald L. Korn, Rahmi Oklu, Christopher Migdal, Michael B. Gotway, Glen J. Weiss, A. John Iafrate, Dong-Wan Kim, and Michael D. Kuo. "ALKMolecular Phenotype in Non–Small Cell Lung Cancer: CT Radiogenomic Characterization." Radiology 272, no. 2 (August 2014): 568–76. http://dx.doi.org/10.1148/radiol.14140789.

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Yahya, Noorazrul, Xin-Jane Chua, Hanani A. Manan, and Fuad Ismail. "Inclusion of dosimetric data as covariates in toxicity-related radiogenomic studies." Strahlentherapie und Onkologie 194, no. 8 (May 17, 2018): 780–86. http://dx.doi.org/10.1007/s00066-018-1303-5.

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Coates, J., K. Jeyaseelan, N. Ybarra, M. David, S. Faria, L. Souhami, F. Cury, and I. El Naqa. "Data Driven Radiogenomic Modeling of Radiation Induced Toxicities in Prostate Cancer." International Journal of Radiation Oncology*Biology*Physics 93, no. 3 (November 2015): S51. http://dx.doi.org/10.1016/j.ijrobp.2015.07.124.

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Islam, Mobarakol, Navodini Wijethilake, and Hongliang Ren. "Glioblastoma multiforme prognosis: MRI missing modality generation, segmentation and radiogenomic survival prediction." Computerized Medical Imaging and Graphics 91 (July 2021): 101906. http://dx.doi.org/10.1016/j.compmedimag.2021.101906.

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Fukunaga, Hisanori, Akinari Yokoya, Yasuyuki Taki, Karl T. Butterworth, and Kevin M. Prise. "Precision Radiotherapy and Radiation Risk Assessment: How Do We Overcome Radiogenomic Diversity?" Tohoku Journal of Experimental Medicine 247, no. 4 (2019): 223–35. http://dx.doi.org/10.1620/tjem.247.223.

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Gopal, P., B. Yard, A. Petty, J. Castrillon, J. D. Patel, and M. Abazeed. "Genome-Scale and Systematic Variant Profiling Delineates the Radiogenomic Landscape of Cancer." International Journal of Radiation Oncology*Biology*Physics 111, no. 3 (November 2021): S12. http://dx.doi.org/10.1016/j.ijrobp.2021.07.059.

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Manem, Venkata SK, Meghan Lambie, Ian Smith, Petr Smirnov, Victor Kofia, Mark Freeman, Marianne Koritzinsky, Mohamed E. Abazeed, Benjamin Haibe-Kains, and Scott V. Bratman. "Modeling Cellular Response in Large-Scale Radiogenomic Databases to Advance Precision Radiotherapy." Cancer Research 79, no. 24 (September 26, 2019): 6227–37. http://dx.doi.org/10.1158/0008-5472.can-19-0179.

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