Integrative radiomics and transcriptomics analyses reveal subtype characterization of non-small cell lung cancer

[1]  Takuya Yagi,et al.  Role of intratumoral and peritumoral CT radiomics for the prediction of EGFR gene mutation in primary lung cancer , 2022, The British journal of radiology.

[2]  Hongyi Liu,et al.  Radiogenomics to characterize the immune-related prognostic signature associated with biological functions in glioblastoma , 2022, European Radiology.

[3]  A. McPherson,et al.  Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer , 2022, Nature Cancer.

[4]  Rosalyn W. Sayaman,et al.  Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies. , 2022, Cancer cell.

[5]  L. Pusztai,et al.  Biomarkers for Adjuvant Endocrine and Chemotherapy in Early-Stage Breast Cancer: ASCO Guideline Update , 2022, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[6]  Wei-min Li,et al.  Non-Invasive Measurement Using Deep Learning Algorithm Based on Multi-Source Features Fusion to Predict PD-L1 Expression and Survival in NSCLC , 2022, Frontiers in Immunology.

[7]  Yang Shen,et al.  Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer , 2022, Frontiers in Immunology.

[8]  David R. Jones,et al.  CT-based Radiogenomic Analysis of Clinical Stage I Lung Adenocarcinoma with Histopathologic Features and Oncologic Outcomes. , 2022, Radiology.

[9]  C. Sung,et al.  PET-Based Radiogenomics Supports mTOR Pathway Targeting for Hepatocellular Carcinoma. , 2022, Clinical cancer research : an official journal of the American Association for Cancer Research.

[10]  C. Sung,et al.  PET-Based Radiogenomics Supports mTOR Pathway Targeting for Hepatocellular Carcinoma. , 2022, Clinical cancer research : an official journal of the American Association for Cancer Research.

[11]  A. Rahmim,et al.  Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images , 2022, Comput. Biol. Medicine.

[12]  M. Hung,et al.  Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. , 2022, The Lancet. Digital health.

[13]  A. Madabhushi,et al.  Predicting cancer outcomes with radiomics and artificial intelligence in radiology , 2021, Nature Reviews Clinical Oncology.

[14]  V. Adamo,et al.  Radiomic Detection of EGFR Mutations in NSCLC , 2020, Cancer Research.

[15]  C. Wong,et al.  Radiogenomics of lung cancer , 2020, Journal of thoracic disease.

[16]  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.

[17]  F. Zhu,et al.  Radiomic Features at Contrast-enhanced CT Predict Recurrence in Early Stage Hepatocellular Carcinoma: A Multi-Institutional Study. , 2020, Radiology.

[18]  Raymond Y Huang,et al.  Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging , 2020, Clinical Cancer Research.

[19]  Qiong Li,et al.  Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology. , 2019, Lung Cancer.

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

[21]  Chuong D. Hoang,et al.  A radiogenomic dataset of non-small cell lung cancer , 2018, Scientific Data.

[22]  N. Paragios,et al.  A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. , 2018, The Lancet. Oncology.

[23]  Andriy Fedorov,et al.  Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.

[24]  Jiuyong Li,et al.  CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization , 2017, Bioinform..

[25]  Pornpimol Charoentong,et al.  Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade , 2016, bioRxiv.

[26]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[27]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[28]  Olivier Gevaert,et al.  Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.

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

[30]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[31]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[32]  H. Hansen,et al.  Lung cancer. , 1990, Cancer chemotherapy and biological response modifiers.