Download PDF
CC BY-NC-ND 4.0 · Laryngorhinootologie 2018; 97(S 01): S114-S141
DOI: 10.1055/s-0043-121964
Referat
Eigentümer und Copyright ©Georg Thieme Verlag KG 2018

Radiomics: Big Data statt Biopsie in der Zukunft?

Article in several languages: deutsch | English
Kathrin Scheckenbach
1   Klinik für Hals-Nasen-Ohrenheilkunde, Universitätsklinikum Düsseldorf
› Author Affiliations
Further Information

Publication History

Publication Date:
22 March 2018 (online)

Also available at
   Permissions and Reprints

Zusammenfassung

Die Präzisionsmedizin wird – auch mit Zunahme gezielter Therapieoptionen durch Biologicals – immer mehr vorangetrieben. Die individuelle Charakterisierung einer Erkrankung auf der Grundlage von Biomarkern im weitesten Sinne ist eine grundlegende Voraussetzung hierfür. Diese Biomarker können bisher klinisch, histologisch oder molekular bestimmt werden. Die Entwicklung breiter Screening Methoden und gleichzeitig die Möglichkeit, große Datenmengen („Omics“ ) mit geeigneter Software immer besser zu analysieren, führen dazu, dass man sich nicht nur auf einzelne Biomarker beschränkt, sondern Biomarker-Signaturen darstellen kann. Die „Radiomics” finden neben „Genomics“, „Proteomics“ oder „Metabolomics” in den letzten Jahren zunehmendes Interesse und erweitern das Biomarkerfeld. Basierend auf radiologischen Bildern werden eine Vielzahl von Merkmalen mithilfe spezifischer Algorithmen extrahiert. Diese Merkmale werden mit klinischen, (immun-) histopathologischen und genomischen Daten korreliert. Erfasste Strukturunterschiede sind Bestandteil der den Bildern zugrundeliegenden unbearbeiteten Metadaten. Diese sind für das bloße Auge oft nicht sichtbar und insofern ohne spezifische Software für den Untersucher nicht zu erfassen. Sie lassen sich aber numerisch abbilden und grafisch visualisieren. Ein besonderer Reiz der „Radiomics“ ist, dass bereits routinemäßig durchgeführte Bildgebungen einen Biomarker-Charakter zeigen. Dieser hat das Potenzial, Zusatzuntersuchungen oder Biopsien idealerweise durch eine „Radiomics“ Analyse zu ersetzen. Alternativ könnten „Radiomics“-Signaturen andere Biomarker suffizient ergänzen und hierdurch eine präzisere, multimodale Aussage ermöglichen. Bisher finden Radiomics vor allem in der Onkologie solider Tumore Anwendung. Auch bei Kopf-Hals-Karzinomen wurden bereits erste vielversprechende Untersuchungen veröffentlicht.

Schlüsselwörter

Radiomics - Biomarker - Kopf-Hals-Karzinome
 

  • Literatur

  • 1 Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Phys Med Biol 2016; 61: R150-R166
    CrossrefPubMedGoogle Scholar
  • 2 Giger ML, Chan H-P, Boone J. Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys 2008; 35: 5799-5820
    CrossrefPubMedGoogle Scholar
  • 3 Giger ML, Karssemeijer N, Schnabel JA. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng 2013; 15: 327-357
    CrossrefPubMedGoogle Scholar
  • 4 Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48: 441-446
    CrossrefPubMedGoogle Scholar
  • 5 Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology 2016; 278: 563-577
    CrossrefPubMedGoogle Scholar
  • 6 van den Burg EL, van Hoof M, Postma AA, Janssen AML, Stokroos RJ, Kingma H. et al. An exploratory study to detect ménière's disease in conventional MRI scans using radiomics. Front Neurol 2016; 7: 190
    PubMedGoogle Scholar
  • 7 Pota M, Scalco E, Sanguineti G, Farneti A, Cattaneo GM, Rizzo G. et al. Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification. Artif Intell Med 2017; DOI: 10.1016/j.artmed.2017.03.004.
    CrossrefPubMedGoogle Scholar
  • 8 Sutton RN, Hall EL. Texture measures for automatic classification of pulmonary disease. IEEE Trans Comput 1972; C-21: 667-676
    PubMedGoogle Scholar
  • 9 Al-Kadi OS, Watson D. Texture analysis of aggressive and nonaggressive lung tumor CE CT images. IEEE Trans Biomed Eng 2008; 55: 1822-1830
    CrossrefPubMedGoogle Scholar
  • 10 Ganeshan B, Abaleke S, Young RCD, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 2010; 10: 137-143
    CrossrefPubMedGoogle Scholar
  • 11 Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA. Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 2013; 266: 326-336
    CrossrefPubMedGoogle Scholar
  • 12 Miles KA, Ganeshan B, Hayball MP. CT texture analysis using the filtration-histogram method: what do the measurements mean?. Cancer Imaging 2013; 13: 400-406
    CrossrefPubMedGoogle Scholar
  • 13 Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications 2014; 5: 4006
    CrossrefPubMedGoogle Scholar
  • 14 van Timmeren JE, Leijenaar RTH, van Elmpt W, Reymen B, Oberije C, Monshouwer R. et al. Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. Radiother Oncol 2017; 123: 363-369
    CrossrefPubMedGoogle Scholar
  • 15 Coroller TP, Grossmann P, Hou Y, Rios-Velazquez E, Leijenaar RTH, Hermann G. et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 2015; 114: 345-350
    CrossrefPubMedGoogle Scholar
  • 16 Grove O, Berglund AE, Schabath MB, Aerts HJWL, Dekker A, Wang H. et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS ONE 2015; 10: e0118261
    CrossrefPubMedGoogle Scholar
  • 17 Yuan M, Zhang Y-D, Pu X-H, Zhong Y, Li H, Wu J-F. et al. Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol 2017; 6: 244
    PubMedGoogle Scholar
  • 18 Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F. Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer. Sci Rep 2017; 7: 46349
    CrossrefPubMedGoogle Scholar
  • 19 Huynh E, Coroller TP, Narayan V, Agrawal V, Romano J, Franco I. et al. Associations of radiomic data extracted from static and respiratory-gated CT Scans with disease recurrence in lung cancer patients treated with SBRT. PLoS ONE 2017; 12: e0169172
    CrossrefPubMedGoogle Scholar
  • 20 Rios-Velazquez E, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O. et al. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res 2017; 77: 3922-3930
    CrossrefPubMedGoogle Scholar
  • 21 Yuan M, Pu X-H, Xu X-Q, Zhang Y-D, Zhong Y, Li H. et al. Lung adenocarcinoma: Assessment of epidermal growth factor receptor mutation status based on extended models of diffusion-weighted image. J Magn Reson Imaging 2017; 46: 281-289
    CrossrefPubMedGoogle Scholar
  • 22 Vaidya M, Creach KM, Frye J, Dehdashti F, Bradley JD, Naqa El I. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol 2012; 102: 239-245
    CrossrefPubMedGoogle Scholar
  • 23 Cook GJR, Yip C, Siddique M, Goh V, Chicklore S, Roy A. et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy?. J Nucl Med 2013; 54: 19-26
    CrossrefPubMedGoogle Scholar
  • 24 Coroller TP, Agrawal V, Huynh E, Narayan V, Lee SW, Mak RH. et al. Radiomic-Based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol 2017; 12: 467-476
    CrossrefPubMedGoogle Scholar
  • 25 Magnin IE, Cluzeau F, Odet CL, Bremond A. Mammographic Texture Analysis: An evaluation of risk for developing breast cancer. Opt Eng 1986; 25: 156780-156780
    CrossrefPubMedGoogle Scholar
  • 26 Wei D, Chan H-P, Helvie MA, Sahiner B, Petrick N, Adler DD. et al. Classification of mass and normal breast tissue on digital mammograms: Multiresolution texture analysis. Med Phys 1995; 22: 1501-1513
    CrossrefPubMedGoogle Scholar
  • 27 Chan H-P, Sahiner B, Petrick N, Helvie MA, Lam KL, Adler DD. et al. Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network. Phys Med Biol 1997; 42: 549-567
    CrossrefPubMedGoogle Scholar
  • 28 Nandi RJ, Nandi AK, Rangayyan RM, Scutt D. Classification of breast masses in mammograms using genetic programming and feature selection. Med Bio Eng Comput 2006; 44: 683-694
    CrossrefPubMedGoogle Scholar
  • 29 Tourassi GD, Delong DM, Floyd Jr CE. A study on the computerized fractal analysis of architectural distortion in screening mammograms. Phys Med Biol 2006; 51: 1299-1312
    CrossrefPubMedGoogle Scholar
  • 30 Rangayyan RM, Nguyen TM. Fractal Analysis of Contours of Breast Masses in Mammograms. J Digit Imaging 2007; 20: 223-237
    CrossrefPubMedGoogle Scholar
  • 31 Huynh BQ, Li H, Giger ML. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging (Bellingham) 2016; 3: 034501-034501
    PubMedGoogle Scholar
  • 32 Garra B. Improving the distinction between benign and malignant breast Lesions: The value of sonographic texture analysis. Ultrasonic Imaging 1993; 15: 267-285
    CrossrefPubMedGoogle Scholar
  • 33 Sivaramakrishna R, Powell KA, Lieber ML, Chilcote WA, Shekhar R. Texture analysis of lesions in breast ultrasound images. Computerized Medical Imaging and Graphics 2002; 26: 303-307
    CrossrefPubMedGoogle Scholar
  • 34 Singh BK, Verma K, Thoke AS. Adaptive Gradient descent backpropagation for classification of breast tumors in ultrasound imaging. Procedia Computer Science 2015; 46: 1601-1609
    CrossrefPubMedGoogle Scholar
  • 35 Zhang Q, Xiao Y, Suo J, Shi J, Yu J, Guo Y. et al. Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography. Ultrasound in Medicine & Biology 2017; 43: 1058-1069
    CrossrefPubMedGoogle Scholar
  • 36 Sinha S, Lucas-Quesada FA, DeBruhl ND, Sayre J, Farria D, Gorczyca DP. et al. Multifeature analysis of Gd-enhanced MR images of breast lesions. J Magn Reson Imaging 1997; 7: 1016-1026
    CrossrefPubMedGoogle Scholar
  • 37 Gibbs P, Turnbull LW. Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 2003; 50: 92-98
    CrossrefPubMedGoogle Scholar
  • 38 Nie K, Chen J-H, Yu HJ, Chu Y, Nalcioglu O, Su M-Y. Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol 2008; 15: 1513-1525
    CrossrefPubMedGoogle Scholar
  • 39 Agner SC, Soman S, Libfeld E, McDonald M, Thomas K, Englander S. et al. Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging 2011; 24: 446-463
    CrossrefPubMedGoogle Scholar
  • 40 Cai H, Liu L, Peng Y, Wu Y, Li L. Diagnostic assessment by dynamic contrast-enhanced and diffusion-weighted magnetic resonance in differentiation of breast lesions under different imaging protocols. BMC Cancer 2014; 14: 366
    CrossrefPubMedGoogle Scholar
  • 41 Bickelhaupt S, Paech D, Kickingereder P, Steudle F, Lederer W, Daniel H. et al. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J Magn Reson Imaging 2017; 46: 604-616
    CrossrefPubMedGoogle Scholar
  • 42 Holli K, Lääperi A-L, Harrison L, Luukkaala T, Toivonen T, Ryymin P. et al. Characterization of breast cancer types by texture analysis of magnetic resonance images. Acad Radiol 2010; 17: 135-141
    CrossrefPubMedGoogle Scholar
  • 43 Wang J, Kato F, Oyama-Manabe N, Li R, Cui Y, Tha KK. et al. Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study. PLoS ONE 2015; 10: e0143308
    CrossrefPubMedGoogle Scholar
  • 44 Guo W, Li H, Zhu Y, Lan L, Yang S, Drukker K. et al. Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Med Imaging (Bellingham) 2015; 2: 041007-041007
    PubMedGoogle Scholar
  • 45 Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C. 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. Radiology 2016; 281: 382-391
    CrossrefPubMedGoogle Scholar
  • 46 Ahmed A, Gibbs P, Pickles M, Turnbull L. Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging 2013; 38: 89-101
    CrossrefPubMedGoogle Scholar
  • 47 Parikh J, Selmi M, Charles-Edwards G, Glendenning J, Ganeshan B, Verma H. et al. Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy. Radiology 2014; 272: 100-112
    CrossrefPubMedGoogle Scholar
  • 48 Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P. et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 2017; 19: 57
    CrossrefPubMedGoogle Scholar
  • 49 Reuzé S, Orlhac F, Chargari C, Nioche C, Limkin E, Riet F. et al. Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners. Oncotarget 2017; 8: 43169-43179
    PubMedGoogle Scholar
  • 50 Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X. et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 2007; 25: 675-680
    CrossrefPubMedGoogle Scholar
  • 51 Ganeshan B, Miles KA, Young RCD, Chatwin CR. In search of biologic correlates for liver texture on portal-phase CT. Acad Radiol 2007; 14: 1058-1068
    CrossrefPubMedGoogle Scholar
  • 52 Wang J, Wu C-J, Bao M-L, Zhang J, Wang X-N, Zhang Y-D. Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol 2017; 66: 7
    PubMedGoogle Scholar
  • 53 Lin Y-C, Lin G, Hong J-H, Lin Y-P, Chen F-H, Ng S-H. et al. Diffusion radiomics analysis of intratumoral heterogeneity in a murine prostate cancer model following radiotherapy: Pixelwise correlation with histology. J Magn Reson Imaging 2017; 46: 483-489
    CrossrefPubMedGoogle Scholar
  • 54 Cameron A, Khalvati F, Haider MA, Wong A. MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection. IEEE Trans Biomed Eng 2016; 63: 1145-1156
    CrossrefPubMedGoogle Scholar
  • 55 Khalvati F, Wong A, Haider MA. Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models. BMC Med Imaging 2015; 15: 27
    CrossrefPubMedGoogle Scholar
  • 56 Diehn M, Nardini C, Wang DS, McGovern S, Jayaraman M, Liang Y. et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci USA 2008; 105: 5213-5218
    CrossrefPubMedGoogle Scholar
  • 57 Yu J, Shi Z, Lian Y, Li Z, Liu T, Gao Y. et al. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 2016; 27: 3509-3522
    PubMedGoogle Scholar
  • 58 Li Z, Wang Y, Yu J, Guo Y, Cao W. Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci Rep 2017; 7: 5467
    CrossrefPubMedGoogle Scholar
  • 59 Ghosh P, Tamboli P, Vikram R, Rao A. Imaging-genomic pipeline for identifying gene mutations using three-dimensional intra-tumor heterogeneity features. J Med Imaging (Bellingham) 2015; 2: 041009-041009
    PubMedGoogle Scholar
  • 60 Karlo CA, Di Paolo PL, Chaim J, Hakimi AA, Ostrovnaya I, Russo P. et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology 2014; 270: 464-471
    CrossrefPubMedGoogle Scholar
  • 61 Yin Q, Hung S-C, Wang L, Lin W, Fielding JR, Rathmell WK. et al. Associations between Tumor Vascularity, Vascular Endothelial Growth Factor Expression and PET/MRI Radiomic Signatures in Primary Clear-Cell-Renal-Cell-Carcinoma: Proof-of-Concept Study. Sci Rep 2017; 7: 43356
    CrossrefPubMedGoogle Scholar
  • 62 Antunes J, Viswanath S, Rusu M, Valls L, Hoimes C, Avril N. et al. Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study. Transl Oncol 2016; 9: 155-162
    CrossrefPubMedGoogle Scholar
  • 63 Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges J-P. et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 2001; 52: 369-378
    PubMedGoogle Scholar
  • 64 Zhang B, He X, Ouyang F, Gu D, Dong Y, Zhang L. et al. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. Cancer Lett 2017; 403: 21-27
    CrossrefPubMedGoogle Scholar
  • 65 Zhang B, Tian J, Dong D, Gu D, Dong Y, Zhang L. et al. Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma. Clin Cancer Res 2017; 23: 4259-4269
    CrossrefPubMedGoogle Scholar
  • 66 Fakhry C, Westra WH, Li S, Cmelak A, Ridge JA, Pinto H. et al. Improved survival of patients with human papillomavirus-positive head and neck squamous cell carcinoma in a prospective clinical trial. J Natl Cancer Inst 2008; 100: 261-269
    CrossrefPubMedGoogle Scholar
  • 67 Ang KK, Harris J, Wheeler R, Weber R, Rosenthal DI, Nguyen-Tân PF. et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med 2010; 363: 24-35
    CrossrefPubMedGoogle Scholar
  • 68 Parmar C, Leijenaar RTH, Grossmann P, Rios-Velazquez E, Bussink J, Rietveld D. et al. Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer. Sci Rep 2015; 5: 11044
    CrossrefPubMedGoogle Scholar
  • 69 Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P. Aerts HJWL Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer. Front Oncol 2015; 5: 272
    PubMedGoogle Scholar
  • 70 MICCAI/M.D.. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group: Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges. Sci Data 2017; 4: 170077
    CrossrefPubMedGoogle Scholar
  • 71 Ou D, Blanchard P, Rosellini S, Levy A, Nguyen F, Leijenaar RTH. et al. Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to Human Papillomavirus status. Oral Oncol 2017; 71: 150-155
    CrossrefPubMedGoogle Scholar
  • 72 Folkert MR, Setton J, Apte AP, Grkovski M, Young RJ, Schöder H. et al. Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics. Phys Med Biol 2017; 62: 5327-5343
    CrossrefPubMedGoogle Scholar
  • 73 Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB. et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012; 30: 1234-1248
    CrossrefPubMedGoogle Scholar