Radiogenomics: a key component of precision cancer medicine

[1]  J. Suri,et al.  Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework , 2022, Cancers.

[2]  Cyrus Chargari,et al.  Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? , 2022, Journal for ImmunoTherapy of Cancer.

[3]  George Teodoro,et al.  Deep Learning-Based Pathology Image Analysis Enhances Magee Feature Correlation With Oncotype DX Breast Recurrence Score , 2022, Frontiers in Medicine.

[4]  Xingwei Wang,et al.  Differential Diagnosis of Type 1 and Type 2 Papillary Renal Cell Carcinoma Based on Enhanced CT Radiomics Nomogram , 2022, Frontiers in Oncology.

[5]  Pravda Jith Ray Prasad,et al.  Deep learning for image-based liver analysis - A comprehensive review focusing on malignant lesions , 2022, Artif. Intell. Medicine.

[6]  J. Suri,et al.  Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine , 2022, Cancers.

[7]  S. Bakas,et al.  Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma , 2022, Scientific Reports.

[8]  Jianjun Zheng,et al.  A Review of the Correlation Between Epidermal Growth Factor Receptor Mutation Status and 18F-FDG Metabolic Activity in Non-Small Cell Lung Cancer , 2022, Frontiers in Oncology.

[9]  Xiang Li,et al.  Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information , 2022, Frontiers in Pharmacology.

[10]  Shaoli Song,et al.  Heterogeneity derived from 18F‐FDG PET/CT predicts immunotherapy outcome for metastatic triple‐negative breast cancer patients , 2022, Cancer medicine.

[11]  Yan Ren,et al.  A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas , 2022, European Radiology.

[12]  S. Bakas,et al.  Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine , 2021, Cancers.

[13]  R. Boellaard,et al.  A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features , 2021, Physics and imaging in radiation oncology.

[14]  S. Park,et al.  Reproducible imaging-based prediction of molecular subtype and risk stratification of gliomas across different experience levels using a structured reporting system , 2021, European Radiology.

[15]  K. Dong,et al.  m6A Modification: A Double-Edged Sword in Tumor Development , 2021, Frontiers in Oncology.

[16]  E. Schaafsma,et al.  Impact of Oncotype DX testing on ER+ breast cancer treatment and survival in the first decade of use , 2021, Breast Cancer Research.

[17]  R. Gillies,et al.  Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images , 2021, Journal for ImmunoTherapy of Cancer.

[18]  K. Kao,et al.  Dynamic Contrast Enhanced MRI and Intravoxel Incoherent Motion to Identify Molecular Subtypes of Breast Cancer with Different Vascular Normalization Gene Expression , 2021, Korean journal of radiology.

[19]  Peng Liu,et al.  Prediction of microvascular invasion in solitary hepatocellular carcinoma ≤ 5 cm based on computed tomography radiomics , 2021, World journal of gastroenterology.

[20]  V. Ambrosini,et al.  Baseline total metabolic tumour volume on 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography-computed tomography as a promising biomarker in patients with advanced non-small cell lung cancer treated with first-line pembrolizumab. , 2021, European journal of cancer.

[21]  H. Groen,et al.  Simultaneous Identification of EGFR, KRAS, ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics , 2021, Cancers.

[22]  Yuling Luo,et al.  Integrative radiogenomics analysis for predicting molecular features and survival in clear cell renal cell carcinoma , 2021, Aging.

[23]  R. Salgia,et al.  Predicting Survival Duration With MRI Radiomics of Brain Metastases From Non-small Cell Lung Cancer , 2021, Frontiers in Oncology.

[24]  Gang Wang,et al.  A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma , 2021, Frontiers in Oncology.

[25]  P. Lambin,et al.  Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma , 2021, Cancers.

[26]  W. Vermi,et al.  CT texture analysis for prediction of EGFR mutational status and ALK rearrangement in patients with non-small cell lung cancer , 2021, La radiologia medica.

[27]  Sijie Jiang,et al.  Application of radiomics and machine learning in head and neck cancers , 2021, International journal of biological sciences.

[28]  Tao Jiang,et al.  Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning , 2020, European Radiology.

[29]  K. Yeom,et al.  Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant–Positive Non–Small Cell Lung Cancer , 2020, JAMA network open.

[30]  F. Jia,et al.  Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma , 2020, Frontiers in Oncology.

[31]  X. Kong,et al.  Oncotype DX Breast Recurrence Score Distribution and Chemotherapy Benefit Among Women of Different Age Groups With HR-Positive, HER2-Negative, Node-Negative Breast Cancer in the SEER Database , 2020, Frontiers in Oncology.

[32]  T. Cloughesy,et al.  MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting , 2020, International journal of molecular sciences.

[33]  R. Gillies,et al.  Non-invasive decision support for NSCLC treatment using PET/CT radiomics , 2020, Nature Communications.

[34]  Jingliang Cheng,et al.  Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma , 2020, Frontiers in Oncology.

[35]  M. Su,et al.  Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers , 2020, European Radiology.

[36]  Lihua Li,et al.  Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer , 2020, Nature Communications.

[37]  H. Cui,et al.  Aberrant NSUN2-mediated m5C modification of H19 lncRNA is associated with poor differentiation of hepatocellular carcinoma , 2020, Oncogene.

[38]  Seok-Gu Kang,et al.  Fully Automated Hybrid Approach to Predict the IDH Mutation Status of Gliomas via Deep Learning and Radiomics. , 2020, Neuro-oncology.

[39]  V. Lowe,et al.  Prediction of MGMT Status for Glioblastoma Patients Using Radiomics Feature Extraction from 18F-DOPA-PET Imaging. , 2020, International journal of radiation oncology, biology, physics.

[40]  F. Petrella,et al.  Association of a CT-Based Clinical and Radiomics Score of Non-Small Cell Lung Cancer (NSCLC) with Lymph Node Status and Overall Survival , 2020, Cancers.

[41]  Weijun Peng,et al.  Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis , 2020, European Radiology.

[42]  Bainan Xu,et al.  The Diagnostic Value of Conventional MRI and CT Features in the Identification of the IDH1-Mutant and 1p/19q Co-Deletion in WHO Grade II Gliomas. , 2020, Academic radiology.

[43]  Zhengyu Jin,et al.  Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients , 2020, Frontiers in Oncology.

[44]  Zhan Feng,et al.  Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings , 2020, Frontiers in Oncology.

[45]  A. Sabri,et al.  Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer , 2020, Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.

[46]  Huanhuan Liu,et al.  Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer , 2020, European Radiology.

[47]  M. Nittka,et al.  Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18F-FET PET-MRI and MR Fingerprinting , 2019, European Journal of Nuclear Medicine and Molecular Imaging.

[48]  Xinming Zhao,et al.  Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer , 2019, European Journal of Nuclear Medicine and Molecular Imaging.

[49]  Joseph A. Maldjian,et al.  A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas , 2019, bioRxiv.

[50]  L. Marcu,et al.  The Potential Role of Radiomics and Radiogenomics in Patient Stratification by Tumor Hypoxia Status. , 2019, Journal of the American College of Radiology : JACR.

[51]  Christian Desrosiers,et al.  Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma , 2019, Cancers.

[52]  Danny F. Martinez,et al.  Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes , 2019, Molecular Imaging and Biology.

[53]  Jing Zhang,et al.  Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. , 2019, Journal of hepatology.

[54]  Mateusz Buda,et al.  Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm , 2019, Comput. Biol. Medicine.

[55]  Hongbing Shen,et al.  The inherited variations of a p53-responsive enhancer in 13q12.12 confer lung cancer risk by attenuating TNFRSF19 expression , 2019, Genome Biology.

[56]  Derick R. Peterson,et al.  Radiogenomics Consortium Genome-Wide Association Study Meta-Analysis of Late Toxicity After Prostate Cancer Radiotherapy , 2019, Journal of the National Cancer Institute.

[57]  Richard Ha,et al.  Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm , 2019, Journal of Digital Imaging.

[58]  D. La Forgia,et al.  Breast MRI background parenchymal enhancement as an imaging bridge to molecular cancer sub-type. , 2019, European journal of radiology.

[59]  Anita Burgun,et al.  Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers , 2019, Front. Oncol..

[60]  Y. Huang,et al.  Renal cell carcinoma: predicting RUNX3 methylation level and its consequences on survival with CT features , 2019, European Radiology.

[61]  Alberto Traverso,et al.  Multicenter CT phantoms public dataset for radiomics reproducibility tests , 2019, Medical physics.

[62]  Eric P. Winer,et al.  Breast Cancer Treatment: A Review , 2019, JAMA.

[63]  Xiaoping Zhou,et al.  Relationship between the expression of PD-1/PD-L1 and 18F-FDG uptake in bladder cancer , 2019, European Journal of Nuclear Medicine and Molecular Imaging.

[64]  Guangtao Zhai,et al.  Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective , 2018, European Radiology.

[65]  Lihua Li,et al.  Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging , 2018, European Radiology.

[66]  Hongbing Lu,et al.  Noninvasive Prediction of IDH1 Mutation and ATRX Expression Loss in Low‐Grade Gliomas Using Multiparametric MR Radiomic Features , 2018, Journal of magnetic resonance imaging : JMRI.

[67]  Simukayi Mutasa,et al.  Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score , 2018, Journal of magnetic resonance imaging : JMRI.

[68]  D. Dong,et al.  A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication , 2018, European Radiology.

[69]  Jacob G. Scott,et al.  Machine Learning and Radiogenomics: Lessons Learned and Future Directions , 2018, Front. Oncol..

[70]  S. Kannan,et al.  Nomograms based on preoperative multiparametric magnetic resonance imaging for prediction of molecular subgrouping in medulloblastoma: results from a radiogenomics study of 111 patients , 2018, Neuro-oncology.

[71]  David T. W. Jones,et al.  Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma , 2018, Neuro-oncology.

[72]  J. Canales‐Vázquez,et al.  Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. , 2018, Radiology.

[73]  Chaofeng Liang,et al.  Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study , 2018, European Radiology.

[74]  J. Trojanowski,et al.  Dysregulation of the epigenetic landscape of normal aging in Alzheimer’s disease , 2018, Nature Neuroscience.

[75]  T. Jiang,et al.  Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature , 2018, European Radiology.

[76]  E. Morris,et al.  Apparent diffusion coefficient in estrogen receptor‐positive and lymph node‐negative invasive breast cancers at 3.0T DW‐MRI: A potential predictor for an oncotype Dx test recurrence score , 2018, Journal of magnetic resonance imaging : JMRI.

[77]  Di Dong,et al.  Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? , 2018, European Radiology.

[78]  D. Dong,et al.  Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer , 2017, Translational oncology.

[79]  X. Lan,et al.  Value of 18F–FDG PET/CT for predicting EGFR mutations and positive ALK expression in patients with non-small cell lung cancer: a retrospective analysis of 849 Chinese patients , 2017, European Journal of Nuclear Medicine and Molecular Imaging.

[80]  Tonje G. Lien,et al.  Prognostic value of PAM50 and risk of recurrence score in patients with early-stage breast cancer with long-term follow-up , 2017, Breast Cancer Research.

[81]  P. Lambin,et al.  Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.

[82]  Jun Zhang,et al.  5-Hydroxymethylcytosine signatures in circulating cell-free DNA as diagnostic biomarkers for human cancers , 2017, Cell Research.

[83]  P. Ferrari,et al.  Prognostic and predictive biomarkers in breast cancer: Past, present and future. , 2017, Seminars in cancer biology.

[84]  A. Matsui,et al.  Clinical predictors of pathological complete response to neoadjuvant chemotherapy in triple-negative breast cancer , 2017, Oncology letters.

[85]  Evangelia I. Zacharaki,et al.  Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma , 2017, Comput. Methods Programs Biomed..

[86]  Eun Sook Ko,et al.  Breast cancer heterogeneity: MR Imaging Texture Analysis and Survival Outcomes1 , 2016 .

[87]  J. Medema,et al.  Intra-tumor heterogeneity from a cancer stem cell perspective , 2017, Molecular Cancer.

[88]  Stefan Leger,et al.  Image biomarker standardisation initiative version 1 . 4 , 2016, 1612.07003.

[89]  Martin Sill,et al.  Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features. , 2016, Radiology.

[90]  Kyung Ah Kim,et al.  Prediction of KRAS Mutation in Rectal Cancer Using MRI. , 2016, Anticancer research.

[91]  Matthew A. Zapala,et al.  The radiogenomic risk score stratifies outcomes in a renal cell cancer phase 2 clinical trial , 2016, European Radiology.

[92]  G. Reifenberger,et al.  The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary , 2016, Acta Neuropathologica.

[93]  Erich P Huang,et al.  MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. , 2016, Radiology.

[94]  M. Kool,et al.  Risk stratification of childhood medulloblastoma in the molecular era: the current consensus , 2016, Acta Neuropathologica.

[95]  Alexander Brook,et al.  Prediction of Low versus High Recurrence Scores in Estrogen Receptor-Positive, Lymph Node-Negative Invasive Breast Cancer on the Basis of Radiologic-Pathologic Features: Comparison with Oncotype DX Test Recurrence Scores. , 2016, Radiology.

[96]  R. Handsaker,et al.  Recurring exon deletions in the haptoglobin ( HP ) gene associate with lower blood cholesterol levels , 2016 .

[97]  Joseph O. Deasy,et al.  Breast cancer subtype intertumor heterogeneity: MRI‐based features predict results of a genomic assay , 2015, Journal of magnetic resonance imaging : JMRI.

[98]  Matthew A. Zapala,et al.  The Radiogenomic Risk Score: Construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for Renal Cell Carcinoma. , 2015, Radiology.

[99]  J. Chang-Claude,et al.  XRCC1 Polymorphism Associated With Late Toxicity After Radiation Therapy in Breast Cancer Patients. , 2015, International journal of radiation oncology, biology, physics.

[100]  M. Mazurowski.  Radiogenomics: what it is and why it is important. , 2015, Journal of the American College of Radiology : JACR.

[101]  A. Rutman,et al.  A Computed Tomography Radiogenomic Biomarker Predicts Microvascular Invasion and Clinical Outcomes in Hepatocellular Carcinoma , 2015, Hepatology.

[102]  Alejandro Munoz del Rio,et al.  CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes , 2015, Abdominal Imaging.

[103]  Maciej A Mazurowski,et al.  Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. , 2014, Radiology.

[104]  M. Kattan,et al.  ClearCode34: A prognostic risk predictor for localized clear cell renal cell carcinoma. , 2014, European urology.

[105]  Ahmed Bilal Ashraf,et al.  Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. , 2014, Radiology.

[106]  Neema Jamshidi,et al.  Illuminating radiogenomic characteristics of glioblastoma multiforme through integration of MR imaging, messenger RNA expression, and DNA copy number variation. , 2013, Radiology.

[107]  Corbin E. Meacham,et al.  Tumour heterogeneity and cancer cell plasticity , 2013, Nature.

[108]  Molin Wang,et al.  Aspirin use and risk of colorectal cancer according to BRAF mutation status. , 2013, JAMA.

[109]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[110]  Kathleen R. Cho,et al.  Mutant KRAS promotes hyperplasia and alters differentiation in the colon epithelium but does not expand the presumptive stem cell pool. , 2011, Gastroenterology.

[111]  Scott L Pomeroy,et al.  Epigenetic antagonism between polycomb and SWI/SNF complexes during oncogenic transformation. , 2010, Cancer cell.

[112]  Hiroshi Ishiguro,et al.  Economic evaluation of 21-gene reverse transcriptase-polymerase chain reaction assay in lymph-node-negative, estrogen-receptor-positive, early-stage breast cancer in Japan , 2008, Breast Cancer Research and Treatment.

[113]  M. Kuo,et al.  Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. , 2007, Journal of vascular and interventional radiology : JVIR.

[114]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[115]  Kimberly M Ray,et al.  Qualitative Radiogenomics: Association between Oncotype DX Test Recurrence Score and BI-RADS Mammographic and Breast MR Imaging Features. , 2018, Radiology.

[116]  Raymond Y Huang,et al.  Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas , 2017, Neuro-oncology.

[117]  Dana C Crawford,et al.  Definition and clinical importance of haplotypes. , 2005, Annual review of medicine.