1 |
苏会芳,周国锋,谢传淼,等.放射组学的兴起和研究进展[J].中华医学杂志,2015,95(7):553-556.
|
2 |
Parmar C, Leijenaar RT, Grossmann P, et al.Radiomic feature clusters and Prognostic Signatures specific forLung and Head & Neck cancer[J]. Sci Rep, 2015, 5: 11044.
|
3 |
Lambin P, Rios-Velazquez E, Leijenaar R, et al.Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446.
|
4 |
Chung CH, Levy S, Chaurand P, et al.Genomics and proteomics: emerging technologies in clinical cancer research[J]. Crit Rev Oncol Hemat, 2007, 61(1): 1-25.
|
5 |
Aens HJ, Velazquez ER, Leijenaar RT, et al.Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J]. Nat Commun, 2014, 5: 4006.
|
6 |
Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential[J]. Comput Med Imaging Graph, 2007, 31(4/5): 198-211.
|
7 |
Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data[J]. Radiology, 2016, 278(2): 563-577.
|
8 |
Rutman AM, Kuo MD.Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging[J]. Eur J Radiol, 2009, 70(2): 232-241.
|
9 |
Kuo MD, Jamshidi N. Behind the Numbers: Decoding Molecular Phenotypes with Radiogenomics-Guiding Principles and Technical Considerations[J]. Radiology, 2014, 270(2): 320-325.
|
10 |
Campbell PJ Yachida S, Mudie LJ, et al.The patterns and dynamics of genomic instability in metastatic pancreatic cancer[J]. Nature, 2010, 467(7319): 1109-1113.
|
11 |
Sequist LV, Waltman BA, Dias-Santagata D, et al.Genotypic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors[J]. Sci Transl Med, 2011, 3(75): 75ra26.
|
12 |
Lambin P, Rios-Velazquez E, Leijenaar R, et al.Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446.
|
13 |
Yachida S, Jones S, Bozic I, et al.Distant metastasis occurs late during the genetic evolution of pancreatic cancer[J]. Nature, 2010, 467(7319): 1114-1117.
|
14 |
Gerlinger M, Rowan AJ, Horswell S, et al.Intratumor heterogeneity and branched evolution revealed by multiregion sequencing[J]. N Engl J Med, 2012, 366(10): 883-892.
|
15 |
Sottoriva A, Spiteri I, Piccirillo SG, et al.Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics[J]. Proc Natl Acad Sci U S A, 2013, 110(10): 4009-4014.
|
16 |
Jackson A, O′Connor JPB, Parker GJM, et al.Imaging tumor vascular heterogeneity and angiogenesis using dynamic contrast-enhanced magnetic resonance imaging[J]. Clin Cancer Res, 2007, 13(12): 3449-3459.
|
17 |
Diehn M, Nardini C, Wang DS, et al.Identification of noninvasive imaging surrogates for brain tumor gene-expression modules[J]. Proc Natl Acad Sci U S A, 2008, 105(13): 5213-5218.
|
18 |
Segal E, Sirlin CB, Ooi C, et al.Decoding global gene expression programs in liver cancer by noninvasive imaging[J]. Nat Biotechnol, 2007, 25(6): 675-680.
|
19 |
Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification[J]. IEEE Trans Syst Man Cybern, 1973, 3(6): 610-621.
|
20 |
Davnall F, Yip CS, Ljungqvist G, et al.Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? [J]. Insights Imaging, 2012, 3(6): 573-589.
|
21 |
O′Connor JP, Rose CJ, Waterton JC, et al.Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome[J]. Clin Cancer Res, 2015, 21(2): 249-257.
|
22 |
Rose CJ, Mills SJ, O′Connor JP, et al.Quantifying spatial heterogeneity in dynamic contrast-enhanced MRI parameter maps[J]. Magn Reson Med, 2009, 62(2): 488-499.
|
23 |
Larkin TJ, Canuto HC, Kettunen MI, et al.Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment[J]. Magn Reson Med, 2014, 71(1): 402-410.
|
24 |
Grossmann P, Grove O, El-Hachem N, et al.TU-CD-BRB-02: Best in physics(joint imaging-therapy): Identification of Molecular Phenotypes by Integrating Radiomics and Genomics[J]. Med Phys, 2015, 42(6Part32): 3602-3602.
|
25 |
Wibmer A, Hricak H, Gondo T, et al.Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores[J]. Eur Radiol, 2015, 25(10): 2840-2850.
|
26 |
Fehr D, Veeraraghavan H, Wibmer A, et al.Automatic classification of prostate cancer Gleason scores from multi-parametric magnetic resonance images[J]. Proc Natl Acad Sci USA, 2015, 112(46): E6265-6273.
|
27 |
Song B, Zhang G, Lu H, et al.Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography[J]. Int J Comput Assist Radiol Surg, 2014, 9(6): 1021-1031.
|
28 |
Ganeshan B, Skogen K, Pressney I, et al.Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival[J]. Clin Radiol, 2012, 67(2): 157-164.
|
29 |
Liang C, Huang Y, He L et al.The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer[J]. Oncotarget, 2016, 7(21): 31401-31412.
|
30 |
Hayano K, Yoshida H, Zhu AX, et al.Fractal analysis of contrast-enhanced CT images to predict survival of patients with hepatocellular carcinoma treated with sunitinib[J]. Dig Dis Sci, 2014, 59(8): 1996-2003.
|
31 |
Huang YQ, Liang CH, He L, et al.Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer[J]. J Clin Oncol, 2016, 34(18): 2157-2164.
|
32 |
Giganti F, Antunes S, Salerno A, et al.Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker[J]. Eur Radiol, 2017, 27(5): 1831-1839.
|
33 |
Simpson AL, Doussot A, Creasy JM, et al.Computed Tomography Image Texture: A Noninvasive Prognostic Marker of Hepatic Recurrence After Hepatectomy for Metastatic Colorectal Cancer[J]. Ann Surg Oncol, 2017 May 30.
|
34 |
Kuo MD, Gollub J, Sirlin CB, et al.Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma[J]. J Vasc Interv Radiol, 2007, 18(7): 821-831.
|
35 |
Yip, CS, Davnall F, Kozarski R, et al.CT Tumoral Heterogeneity as a Prognostic Marker in Primary Esophageal Cancer Following Neoadjuvant Chemotherapy[J]. Pract Radiat Oncol, 2013, 3(2 Suppl 1): S3.
|
36 |
Nougaret S, Vargas HA, Lakhman Y, et al.Intravoxel Incoherent Motion-derived Histogram Metrics for Assessment of Response after Combined Chemotherapy and Radiation Therapy in Rectal Cancer: Initial Experience and Comparison between Single-Section and Volumetric Analyses[J]. Radiology, 2016, 280(2): 446-454.
|
37 |
Beukinga RJ, Hulshoff JB, van Dijk LV, et al.Predicting response to neoadjuvant chemoradiotherapy in esophageal cancer with textural features derived from pretreatment 18F-FDG PET/CT imaging[J]. J Nucl Med, 2017, 58(5): 723-729.
|