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2018 | OriginalPaper | Buchkapitel

Radiomics in Medical Imaging—Detection, Extraction and Segmentation

verfasst von : Jie Tian, Di Dong, Zhenyu Liu, Yali Zang, Jingwei Wei, Jiangdian Song, Wei Mu, Shuo Wang, Mu Zhou

Erschienen in: Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

Verlag: Springer International Publishing

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Abstract

Radiomics, as a newly emerging technology, converts medical images into high-dimensional data via high-throughput extraction of quantitative features, followed by subsequent data analysis for decision support. It identifies general diagnostic or prognostic phenotypes with target clinical need, providing an unprecedented opportunity to improve individualized treatment in cancer at low cost. In this chapter, we will introduce radiomics from its development to its clinical applications. We divide the clinical applications into three sections based on three most common medical modality, including computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET), to give a comprehensive introduction of how radiomics works with the example of a typical cancer type. The workflow and detailed technology skills are well described in each section.

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Vorheriges Kapitel Computer-Aided Prognosis: Accurate Prediction of Patients with Neurologic and Psychiatric Diseases via Multi-modal MRI Analysis

Nächstes Kapitel Markerless Tumor Gating and Tracking for Lung Cancer Radiotherapy based on Machine Learning Techniques

Aerts, H.J., Velazquez, E.R., Leijenaar, R.T., Parmar, C., Grossmann, P., Cavalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5 (2014)

Aerts, H.J., Velazquez, E.R., Leijenaar, R.T., Parmar, C., Grossmann, P., Cavalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M.M., Leemans, C.R., Dekker, A., Quackenbush, J., Gillies, R.J., Lambin, P.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014)

Agnihotri, S., Burrell, K.E., Wolf, A., Jalali, S., Hawkins, C., Rutka, J.T., Zadeh, G.: Glioblastoma, a brief review of history, molecular genetics, animal models and novel therapeutic strategies. Archivum Immunologiae Et Therapiae Experimentalis 61(1), 25–41 (2013)CrossRef

Al Gindi, A., Rashed, E., Sami, M.: Development and Evaluation of a computer-aided diagnostic algorithm for lung nodule characterization and classification in chest radiographs using multiscale wavelet transform. J. Am. Sci. 10(2) (2014)

Amadasun, M., King, R.: Textural features corresponding to textural properties. IEEE Trans. Syst. Man Cybern. 19(5), 1264–1274 (1989)CrossRef

Arbonès, D.R., Jensen, H.G., Jakobsen, A.L., af Rosenschöld, P.M., Hansen, A.E., Igel, C., Darkner, S.: Automatic FDG-PET-based tumor and metastatic lymph node segmentation in cervical cancer. In: SPIE Medical Imaging. International Society for Optics and Photonics (2014)

Armato III, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)CrossRef

Armato, S.G., Giger, M.L., MacMahon, H.: Automated detection of lung nodules in CT scans: preliminary results. Med. Phys. 28(8), 1552–1561 (2001)CrossRef

Armato, S.G., Sensakovic, W.F.: Automated lung segmentation for thoracic CT: impact on computer-aided diagnosis. Acad. Radiol. 11(9), 1011–1021 (2004)CrossRef

10.

Athelogou, M., Schmidt, G., Schäpe, A., Baatz, M., Binnig, G.: Cognition Network Technology—A Novel Multimodal Image Analysis Technique for Automatic Identification and Quantification of Biological Image Contents. Springer, Berlin (2006)

11.

Auffray, C., Sieweke, M.H., Geissmann, F.: Blood monocytes: development, heterogeneity, and relationship with dendritic cells. Annu. Rev. Immunol. 27, 669–692 (2009)CrossRef

12.

Bach Cuadra, M., De Craene, M., Duay, V., Macq, B., Pollo, C., Thiran, J.P.: Dense deformation field estimation for atlas-based segmentation of pathological MR brain images. Comput. Methods Programs Biomed. 84(2–3), 66–75 (2006)CrossRef

13.

Balagurunathan, Y., Gu, Y., Wang, H., Kumar, V., Grove, O., Hawkins, S., Kim, J., Goldgof, D.B., Hall, L.O., Gatenby, R.A., Gillies, R.J.: Reproducibility and prognosis of quantitative features extracted from CT images. Transl. Oncol. 7(1), 72–87 (2014)CrossRef

14.

Bauer, S., Nolte, L.-P., Reyes, M.: Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2011: 14th International Conference, Toronto, Canada, 18–22 Sept 2011, Proceedings, Part III, pp. 354–361. Springer, Berlin (2011)

15.

Belden, C.J., Valdes, P.A., Ran, C., Pastel, D.A., Harris, B.T., Fadul, C.E., Israel, M.A., Paulsen, K., Roberts, D.W.: Genetics of glioblastoma: a window into its imaging and histopathologic variability. Radiographics 31(6), 1717–1740 (2011)CrossRef

16.

Bendtsen, C., Kietzmann, M., Korn, R., Mozley, P.D., Schmidt, G., Binnig, G.: X-ray computed tomography: semiautomated volumetric analysis of late-stage lung tumors as a basis for response assessments. Int. J. Biomed. Imaging 2011, 361589 (2011)CrossRef

17.

Bian, Z., Tan, W., Yang, J., Liu, J., Zhao, D.: Accurate airway centerline extraction based on topological thinning using graph-theoretic analysis. Biomed. Mater. Eng. 24(6), 3239–3249 (2014)

18.

Bocchino, C., Carabellese, A., Caruso, T., Della Sala, G., Ricart, S., Spinella, A.: Use of gray value distribution of run lengths for texture analysis. Pattern Recogn. Lett. 11(6), 415–419 (1990)CrossRef

19.

Brett, M., Leff, A.P., Rorden, C., Ashburner, J.: Spatial normalization of brain images with focal lesions using cost function masking. NeuroImage 14(2), 486–500 (2001)CrossRef

20.

by Haralick, R.M., Dinstein, I., Shanmugam, K.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. (2012)

21.

Cameron, A., Khalvati, F., Haider, M., Wong, A.: MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection (2015)

22.

Campos, D.M., Simões, A., Ramos, I., Campilho, A.: Feature-Based Supervised Lung Nodule Segmentation 42, 23–26 (2014)

23.

Cancer, I. A. f. R. o.: World Cancer Report 2014. Lyon, International Agency for Research on Cancer Press (2014)

24.

Candemir, S., Jaeger, S., Palaniappan, K., Musco, J.P., Singh, R.K., Zhiyun, X., Karargyris, A., Antani, S., Thoma, G., McDonald, C.J.: Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans. Med. Imaging 33(2), 577–590 (2014)CrossRef

25.

Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)MATHCrossRef

26.

Cheng, N.-M., Fang, Y.-H.D., Chang, J.T.-C., Huang, C.-G., Tsan, D.-L., Ng, S.-H., Wang, H.-M., Lin, C.-Y., Liao, C.-T., Yen, T.-C.: Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma. J. Nucl. Med. 54(10), 1703–1709 (2013)CrossRef

27.

Chicklore, S., Goh, V., Siddique, M., Roy, A., Marsden, P.K., Cook, G.J.: Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur. J. Nucl. Med. Mol. Imaging 40(1), 133–140 (2013)CrossRef

28.

Choi, E.-S., Ha, S.-G., Kim, H.-S., Ha, J.H., Paeng, J.C., Han, I.: Total lesion glycolysis by 18F-FDG PET/CT is a reliable predictor of prognosis in soft-tissue sarcoma. Eur. J. Nucl. Med. Mol. Imaging 40(12), 1836–1842 (2013)CrossRef

29.

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M.: The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)CrossRef

30.

Clark, M.C., Hall, L.O., Goldgof, D.B., Velthuizen, R., Murtagh, F.R., Silbiger, M.S.: Automatic tumor segmentation using knowledge-based techniques. IEEE Trans. Med. Imaging 17(2), 187–201 (1998)CrossRef

31.

Cobzas, D., Schmidt, M.: Increased discrimination in level set methods with embedded conditional random fields. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009 (2009)

32.

Cook, G.J., Siddique, M., Taylor, B.P., Yip, C., Chicklore, S., Goh, V.: Radiomics in PET: principles and applications. Clin. Transl. Imaging 2(3), 269–276 (2014)CrossRef

33.

Cook, G.J., Yip, C., Siddique, M., Goh, V., Chicklore, S., Roy, A., Marsden, P., Ahmad, S., Landau, D.: Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J. Nucl. Med. 54(1), 19–26 (2013)CrossRef

34.

Coroller, T.P., Grossmann, P., Hou, Y., Velazquez, E.R., Leijenaar, R.T., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R.H., Aerts, H.J.: CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother. Oncol. 114(3), 345–350 (2015)CrossRef

35.

Corso, J.J., Sharon, E., Dube, S., El-Saden, S., Sinha, U., Yuille, A.: Efficient multilevel brain tumor segmentation with integrated Bayesian model classification. IEEE Trans. Med. Imaging 27(5), 629–640 (2008)CrossRef

36.

Dasarathy, B.V., Holder, E.B.: Image characterizations based on joint gray level—run length distributions. Pattern Recogn. Lett. 12(8), 497–502 (1991)CrossRef

37.

de Carvalho Filho, A.O., de Sampaio, W.B., Silva, A.C., de Paiva, A.C., Nunes, R.A., Gattass, M.: Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Artif. Intell. Med. 60(3), 165–177 (2014)CrossRef

38.

Diciotti, S., Lombardo, S., Falchini, M., Picozzi, G., Mascalchi, M.: Automated segmentation refinement of small lung nodules in CT scans by local shape analysis. IEEE Trans. Biomed. Eng. 58(12), 3418–3428 (2011)CrossRef

39.

Diciotti, S., Picozzi, G., Falchini, M., Mascalchi, M., Villari, N., Valli, G.: 3-D segmentation algorithm of small lung nodules in spiral CT images. IEEE Trans. Inf. Technol. Biomed. 12(1), 7–19 (2008)CrossRef

40.

Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4), 198–211 (2007)CrossRef

41.

Doi, K.: Current status and future potential of computer-aided diagnosis in medical imaging. Br. J. Radiol. (2014)

42.

Dong, X., Xing, L., Wu, P., Fu, Z., Wan, H., Li, D., Yin, Y., Sun, X., Yu, J.: Three-dimensional positron emission tomography image texture analysis of esophageal squamous cell carcinoma: relationship between tumor 18F-fluorodeoxyglucose uptake heterogeneity, maximum standardized uptake value, and tumor stage. Nucl. Med. Commun. 34(1), 40–46 (2013)CrossRef

43.

Dunn, G.P., Rinne, M.L., Jill, W., Giannicola, G., Quayle, S.N., Dunn, I.F., Agarwalla, P.K., Chheda, M.G., Benito, C., Alan, W.: Emerging insights into the molecular and cellular basis of glioblastoma. Genes Dev. 26(8), 756–784 (2012)CrossRef

44.

Eary, J.F., O’Sullivan, F., O’Sullivan, J., Conrad, E.U.: Spatial heterogeneity in sarcoma 18F-FDG uptake as a predictor of patient outcome. J. Nucl. Med. 49(12), 1973–1979 (2008)CrossRef

45.

El-Baz, A., Nitzken, M., Khalifa, F., Elnakib, A., Gimel’farb, G., Falk, R., El-Ghar, M.A.: 3D shape analysis for early diagnosis of malignant lung nodules. In: Information Processing in Medical Imaging. Springer (2011)

46.

El Naqa, I., Grigsby, P., Apte, A., Kidd, E., Donnelly, E., Khullar, D., Chaudhari, S., Yang, D., Schmitt, M., Laforest, R.: Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recogn. 42(6), 1162–1171 (2009)CrossRef

47.

Farag, A.A., Abd El Munim, H.E., Graham, J.H., Farag, A.A.: A novel approach for lung nodules segmentation in chest CT using level sets. IEEE Trans. Image Process. 22(12), 5202–5213 (2013)MathSciNetMATHCrossRef

48.

Görlitz, L., Menze, B.H., Weber, M.-A., Kelm, B.M., Hamprecht, F.A.: Semi-supervised tumor detection in magnetic resonance spectroscopic images using discriminative random fields. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) Pattern Recognition: 29th DAGM Symposium, Heidelberg, Germany, 12–14 Sept 2007. Proceedings, pp. 224–233. Springer, Berlin (2007)

49.

Galavis, P.E., Hollensen, C., Jallow, N., Paliwal, B., Jeraj, R.: Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol. 49(7), 1012–1016 (2010)CrossRef

50.

Galloway, M.M.: Texture analysis using gray level run lengths. Comput. Graph. Image Process. 4(2), 172–179 (1975)CrossRef

51.

Ganesan, K., Acharya, U., Chua, C.K., Min, L.C., Abraham, K.T., Ng, K.B.: Computer-aided breast cancer detection using mammograms: a review. IEEE Rev. Biomed. Eng. 6, 77–98 (2013)CrossRef

52.

Ganeshan, B., Goh, V., Mandeville, H.C., Ng, Q.S., Hoskin, P.J., Miles, K.A.: Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266(1), 326–336 (2013)CrossRef

53.

Ganeshan, B., Panayiotou, E., Burnand, K., Dizdarevic, S., Miles, K.: Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur. Radiol. 22(4), 796–802 (2012)CrossRef

54.

Ganeshan, B., Skogen, K., Pressney, I., Coutroubis, D., Miles, K.: Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin. Radiol. 67(2), 157–164 (2012)CrossRef

55.

Gatenby, R.A., Grove, O., Gillies, R.J.: Quantitative imaging in cancer evolution and ecology. Radiology 269(1), 8–14 (2013)CrossRef

56.

Gerlinger, M., Rowan, A.J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., Varela, I., Phillimore, B., Begum, S., McDonald, N.Q., Butler, A., Jones, D., Raine, K., Latimer, C., Santos, C.R., Nohadani, M., Eklund, A.C., Spencer-Dene, B., Clark, G., Pickering, L., Stamp, G., Gore, M., Szallasi, Z., Downward, J., Futreal, P.A., Swanton, C.: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366(10), 883–892 (2012)CrossRef

57.

Gevaert, O., Xu, J., Hoang, C.D., Leung, A.N., Xu, Y., Quon, A., Rubin, D.L., Napel, S., Plevritis, S.K.: Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data–methods and preliminary results. Radiology 264(2), 387–396 (2012)CrossRef

58.

Giger, M., MacMahon, H.: Image processing and computer-aided diagnosis. Radiol. Clin. North Am. 34(3), 565–596 (1996)

59.

Gillies, R.J., Kinahan, P.E., Hricak, H.: Radiomics: images are more than pictures, they are data. Radiology 151169 (2015)

60.

Glasser, O., Tucker, J.C., Boveri, M.: Wilhelm Conrad Röntgen and the Early History of the Roentgen Rays. C. C. Thomas, Springfield, IL (1934)

61.

Glover, G.H.: Abstract: MRI: basic principles and future potential. Comput. Aided Surg. 5(2), 132 (2000)

62.

Goh, V., Ganeshan, B., Nathan, P., Juttla, J.K., Vinayan, A., Miles, K.A.: Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology 261(1), 165–171 (2011)CrossRef

63.

Golosio, B., Masala, G.L., Piccioli, A., Oliva, P., Carpinelli, M., Cataldo, R., Cerello, P., De Carlo, F., Falaschi, F., Fantacci, M.E., Gargano, G., Kasae, P., Torsello, M.: A novel multithreshold method for nodule detection in lung CT. Med. Phys. 36(8), 3607–3618 (2009)CrossRef

64.

Gooya, A., Pohl, K.M., Bilello, M., Biros, G., Davatzikos, C.: Joint segmentation and deformable registration of brain scans guided by a tumor growth model. In: Fichtinger, G., Martel, A., Peters, T. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2011: 14th International Conference, Toronto, Canada, 18–22 Sept 2011, Proceedings, Part II, pp. 532–540. Springer, Berlin (2011)

65.

Greimel, E., Thiel, I., Peintinger, F., Cegnar, I., Pongratz, E.: Prospective assessment of quality of life of female cancer patients. Gynecol. Oncol. 85(1), 140–147 (2002)CrossRef

66.

Grove, O., Berglund, A.E., Schabath, M.B., Aerts, H.J., Dekker, A., Wang, H., Velazquez, E.R., Lambin, P., Gu, Y., Balagurunathan, Y.: Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PloS one 10(3) (2015)

67.

Gu, Y., Kumar, V., Hall, L.O., Goldgof, D.B., Li, C.-Y., Korn, R., Bendtsen, C., Velazquez, E.R., Dekker, A., Aerts, H.: Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. Pattern Recogn. 46(3), 692–702 (2013)CrossRef

68.

Gutman, D.A., Cooper, L.A.D., Hwang, S.N., Holder, C.A., Jingjing, G., Aurora, T.D., Dunn, W.D., Lisa, S., Tom, M., Rajan, J.: MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 267(2), 560–569 (2013)CrossRef

69.

Han, F., Wang, H., Zhang, G., Han, H., Song, B., Li, L., Moore, W., Lu, H., Zhao, H., Liang, Z.: Texture feature analysis for computer-aided diagnosis on pulmonary nodules. J. Digit. Imaging 28(1), 99–115 (2015)CrossRef

70.

Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural Features for Image Classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610–621 (1973)

71.

Hassner, M., Sklansky, J.: The use of Markov random fields as models of texture. Comput. Graph. Image Process. 12(4), 357–370 (1980)CrossRef

72.

Hegi, M.E.: MGMT gene silencing and benefit from temozolomide in glioblastoma. Dkgest World Latest Med. Inf. 352(10), 997–1003 (2005)

73.

Hu, S.Y., Hoffman, E.A., Reinhardt, J.M.: Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans. Med. Imaging 20(6), 490–498 (2001)CrossRef

74.

Hu, Z., Zou, J., Gui, J., Rong, J., Zhang, Q., Xia, D., Zheng, H.: geometric calibration based on identification of ellipse parameters of a micro-CT system for small-animal imaging. Sens. Lett. 9(5), 1938–1942 (2011)CrossRef

75.

Huang, R.Y., Neagu, M.R., Reardon, D.A., Wen, P.Y.: Pitfalls in the neuroimaging of glioblastoma in the era of antiangiogenic and immuno/targeted therapy—detecting illusive disease, defining response. Front. Neurol. 6, 33 (2015)CrossRef

76.

Hyun, S.H., Ahn, H.K., Kim, H., Ahn, M.-J., Park, K., Ahn, Y.C., Kim, J., Shim, Y.M., Choi, J.Y.: Volume-based assessment by 18F-FDG PET/CT predicts survival in patients with stage III non-small-cell lung cancer. Eur. J. Nucl. Med. Mol. Imaging 41(1), 50–58 (2014)CrossRef

77.

Itakura, H., Achrol, A.S., Mitchell, L.A., Loya, J.J., Liu, T., Westbroek, E.M., Feroze, A.H., Rodriguez, S., Echegaray, S., Azad, T.D.: Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci. Trans. Med. 7(303) (2015)

78.

Jain, R., Poisson, L., Narang, J., Gutman, D., Scarpace, L., Hwang, S.N., Holder, C., Wintermark, M., Colen, R.R., Kirby, J., Freymann, J., Brat, D.J., Jaffe, C., Mikkelsen, T.: Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology 267(1), 212–220 (2013)CrossRef

79.

Jalalian, A., Mashohor, S.B., Mahmud, H.R., Saripan, M.I.B., Ramli, A.R.B., Karasfi, B.: Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin. Imaging 37(3), 420–426 (2013)CrossRef

80.

Jiang, Y., Nishikawa, R.M., Schmidt, R.A., Metz, C.E., Giger, M.L., Doi, K.: Improving breast cancer diagnosis with computer-aided diagnosis. Acad. Radiol. 6(1), 22–33 (1999)

81.

Kakar, M., Olsen, D.R.: Automatic segmentation and recognition of lungs and lesion from CT scans of thorax. Comput. Med. Imaging Graph. 33(1), 72–82 (2009)CrossRef

82.

Kapur, T., Grimson, W.E.L., Wells Iii, W.M., Kikinis, R.: Segmentation of brain tissue from magnetic resonance images. Med. Image Anal. 1(2), 109–127 (1996)CrossRef

83.

Karlo, C.A., Pier Luigi, D.P., Joshua, C., Ari, H.A., Irina, O., Paul, R., Hedvig, H., Robert, M., Hsieh, J.J., Oguz, A.: Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology 270(2), 464–471 (2014)CrossRef

84.

Klabatsa, A., Chicklore, S., Barrington, S.F., Goh, V., Lang-Lazdunski, L., Cook, G.J.: The association of 18F-FDG PET/CT parameters with survival in malignant pleural mesothelioma. Eur. J. Nucl. Med. Mol. Imaging 41(2), 276–282 (2014)CrossRef

85.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. (2012)

86.

Kubota, T., Jerebko, A.K., Dewan, M., Salganicoff, M., Krishnan, A.: Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med. Image Anal. 15(1), 133–154 (2011)CrossRef

87.

Kumar, D., Shafiee, M.J., Chung, A.G., Khalvati, F., Haider, M.A., Wong, A.: Discovery Radiomics for Computed Tomography Cancer Detection. arXiv preprint arXiv:1509.00117 (2015)

88.

Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S.A., Schabath, M.B., Forster, K., Aerts, H.J., Dekker, A., Fenstermacher, D.: Radiomics: the process and the challenges. Magn. Reson. Imaging 30(9), 1234–1248 (2012)CrossRef

89.

Kuo, M.D., Neema, J.: Behind the numbers: decoding molecular phenotypes with radiogenomics–guiding principles and technical considerations. Radiology 270(2), 320–325 (2014)CrossRef

90.

Kuo, W.-J., Chang, R.-F., Chen, D.-R., Lee, C.C.: Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images. Breast Cancer Res. Treat. 66(1), 51–57 (2001)CrossRef

91.

Kyriacou, S.K., Davatzikos, C., Zinreich, S.J., Bryan, R.N.: Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model [MRI]. IEEE Trans. Med. Imaging 18(7), 580–592 (1999)CrossRef

92.

Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R.G., Granton, P., Zegers, C.M., Gillies, R., Boellard, R., Dekker, A.: Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48(4), 441–446 (2012)CrossRef

93.

Larson, S.M., Erdi, Y., Akhurst, T., Mazumdar, M., Macapinlac, H.A., Finn, R.D., Casilla, C., Fazzari, M., Srivastava, N., Yeung, H.W.: Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging: the visual response score and the change in total lesion glycolysis. Clin. Positron Imaging 2(3), 159–171 (1999)CrossRef

94.

Lassen, B., van Rikxoort, E.M., Schmidt, M., Kerkstra, S., van Ginneken, B., Kuhnigk, J.M.: Automatic segmentation of the pulmonary lobes from chest CT scans based on fissures, vessels, and bronchi. IEEE Trans. Med. Imaging 32(2), 210–222 (2013)CrossRef

95.

LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRef

96.

Lee, C.-H., Schmidt, M., Murtha, A., Bistritz, A., Sander, J., Greiner, R.: Segmenting brain tumors with conditional random fields and support vector machines. In: Liu, Y., Jiang, T., Zhang, C. (eds.) Computer Vision for Biomedical Image Applications: First International Workshop, CVBIA 2005, Beijing, China, 21 Oct 2005. Proceedings, pp. 469–478. Springer, Berlin (2005)

97.

Leijenaar, R.T., Nalbantov, G., Carvalho, S., van Elmpt, W.J., Troost, E.G., Boellaard, R., Aerts, H.J., Gillies, R.J., Lambin, P.: The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci. Rep. 5 (2015)

98.

Li, M., Zhou, Z.-H.: Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 37(6), 1088–1098 (2007)CrossRef

99.

Liang, W., Zhang, L., Jiang, G., Wang, Q., Liu, L., Liu, D., Wang, Z., Zhu, Z., Deng, Q., Xiong, X., Shao, W., Shi, X., He, J.: Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer. J. Clin. Oncol. 33(8), 861–869 (2015)CrossRef

100.

Liu, J., Udupa, J.K., Odhner, D., Hackney, D., Moonis, G.: A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Comput. Med. Imaging Graph. 29(1), 21–34 (2005)CrossRef

101.

Louis, D.N., Ohgaki, H., Wiestler, O.D., Cavenee, W.K., Burger, P.C., Jouvet, A., Scheithauer, B.W., Kleihues, P.: The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114(2), 97–109 (2007)CrossRef

102.

Macyszyn, L., Akbari, H., Pisapia, J.M., Da, X., Attiah, M., Pigrish, V., Bi, Y., Pal, S., Davuluri, R.V., Roccograndi, L.: 135 imaging patterns predict patient survival and molecular subtype in glioblastoma using machine learning techniques. Neurosurgery 62(Suppl 1) (2015). Clinical Neurosurgery

103.

Madabhushi, A., Metaxas, D.N.: Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans. Med. Imaging 22(2), 155–169 (2003)CrossRef

104.

Maffione, A.M., Ferretti, A., Grassetto, G., Bellan, E., Capirci, C., Chondrogiannis, S., Gava, M., Marzola, M.C., Rampin, L., Bondesan, C.: Fifteen different 18F-FDG PET/CT qualitative and quantitative parameters investigated as pathological response predictors of locally advanced rectal cancer treated by neoadjuvant chemoradiation therapy. Eur. J. Nucl. Med. Mol. Imaging 40(6), 853–864 (2013)CrossRef

105.

Mandeville, H.C., Ng, Q.S., Daley, F.M., Barber, P.R., Pierce, G., Finch, J., Burke, M., Bell, A., Townsend, E.R., Kozarski, R., Vojnovic, B., Hoskin, P.J., Goh, V.: Operable non-small cell lung cancer: correlation of volumetric helical dynamic contrast-enhanced CT parameters with immunohistochemical markers of tumor hypoxia. Radiology 264(2), 581–589 (2012)CrossRef

106.

Mansoor, A., Bagci, U., Xu, Z.Y., Foster, B., Olivier, K.N., Elinoff, J.M., Suffredini, A.F., Udupa, J.K., Mollura, D.J.: A generic approach to pathological lung segmentation (vol. 33, p. 2293, 2014). IEEE Trans. Med. Imaging 34(1), 354–354 (2015)

107.

Maximilian, D., Christine, N., Wang, D.S., Susan, M.G., Mahesh, J., Yu, L., Kenneth, A., Soonmee, C., Kuo, M.D.: Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc. Natl. Acad. Sci. U.S.A. 105(13), 5213–5218 (2008)CrossRef

108.

McNitt-Gray, M.F., Armato, S.G., Meyer, C.R., Reeves, A.P., McLennan, G., Pais, R.C., Freymann, J., Brown, M.S., Engelmann, R.M., Bland, P.H.: The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation. Acad. Radiol. 14(12), 1464–1474 (2007)CrossRef

109.

Menze, B.H., Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., Golland, P.: A generative model for brain tumor segmentation in multi-modal images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A.: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2010: 13th International Conference, Beijing, China, 20–24 Sept 2010, Proceedings, Part II, pp. 151–159. Springer, Berlin (2010)

110.

Messay, T., Hardie, R.C., Rogers, S.K.: A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med. Image Anal. 14(3), 390–406 (2010)CrossRef

111.

Miller, T.R., Grigsby, P.W.: Measurement of tumor volume by PET to evaluate prognosis in patients with advanced cervical cancer treated by radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 53(2), 353–359 (2002)CrossRef

112.

Miller, T.R., Pinkus, E., Dehdashti, F., Grigsby, P.W.: Improved prognostic value of 18F-FDG PET using a simple visual analysis of tumor characteristics in patients with cervical cancer. J. Nucl. Med. 44(2), 192–197 (2003)

113.

Mohamed, S., Youssef, A., El-Saadany, E., Salama, M.M.: Prostate tissue characterization using TRUS image spectral features, pp. 589–601. Springer, Image Anal. Recogn. (2006)

114.

Mu, W., Chen, Z., Liang, Y., Shen, W., Yang, F., Dai, R., Wu, N., Tian, J.: Staging of cervical cancer based on tumor heterogeneity characterized by texture features on 18F-FDG PET images. Phys. Med. Biol. 60(13), 5123 (2015)CrossRef

115.

Mu, W., Chen, Z., Shen, W., Yang, F., Liang, Y., Dai, R., Wu, N., Tian, J.: A Segmentation Algorithm for Quantitative Analysis of Heterogeneous Tumors of the Cervix with 18F-FDG PET/CT (2015)

116.

Murphy, K., van Ginneken, B., Schilham, A.M.R., de Hoop, B.J., Gietema, H.A., Prokop, M.: A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med. Image Anal. 13(5), 757–770 (2009)CrossRef

117.

Nair, V.S., Gevaert, O., Davidzon, G., Napel, S., Graves, E.E., Hoang, C.D., Shrager, J.B., Quon, A., Rubin, D.L., Plevritis, S.K.: Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer. Cancer Res. 72(15), 3725–3734 (2012)CrossRef

118.

Neema, J., Maximilian, D., Markus, B., Kuo, M.D.: Illuminating radiogenomic characteristics of glioblastoma multiforme through integration of MR imaging, messenger RNA expression, and DNA copy number variation. Radiology 270(1), 212–222 (2014)

119.

Nestle, U., Kremp, S., Grosu, A.-L.: Practical integration of [18F]-FDG-PET and PET-CT in the planning of radiotherapy for non-small cell lung cancer (NSCLC): the technical basis, ICRU-target volumes, problems, perspectives. Radiother. Oncol. 81(2), 209–225 (2006)CrossRef

120.

Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. Adv. Neural. Inf. Process. Syst. 28(3), 169–187 (2001)

121.

Ng, F., Ganeshan, B., Kozarski, R., Miles, K.A., Goh, V.: Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266(1), 177–184 (2013)CrossRef

122.

Nishizuka, Y.: The molecular heterogeneity of protein kinase C and its implications for cellular regulation. Nature 334(6184), 661–665 (1988)CrossRef

123.

O’Sullivan, F., Roy, S., O’Sullivan, J., Vernon, C., Eary, J.: Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. Biostatistics 6(2), 293–301 (2005)MATHCrossRef

124.

O’Sullivan, F., Wolsztynski, E., O’Sullivan, J., Richards, T., Conrad, E., Eary, J.: A statistical modeling approach to the analysis of spatial patterns of FDG-PET uptake in human sarcoma. IEEE Trans. Med. Imag. 30(12), 2059–2071 (2011)CrossRef

125.

Ohgaki, H., Kleihues, P.: Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas. J. Neuropathol. Exp. Neurol. 64(6), 479–489 (2005)CrossRef

126.

Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)MATHCrossRef

127.

Okazumi, S., Dimitrakopoulou-Strauss, A., Schwarzbach, M., Strauss, L.G.: Quantitative, dynamic 18F-FDG-PET for the evaluation of soft tissue sarcomas: relation to differential diagnosis, tumor grading and prediction of prognosis. Hellenic J. Nucl. Med. 12(3), 223–228 (2008)

128.

Olivier, G., Mitchell, L.A., Achrol, A.S., Jiajing, X., Sebastian, E., Steinberg, G.K., Cheshier, S.H., Sandy, N., Greg, Z., Plevritis, S.K.: Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 276(1), 168–174 (2015)

129.

Oncology, F. C. o. G.: FIGO staging for carcinoma of the vulva, cervix, and corpus uteri. Int. J. Gynecol. Obstet. 125(2), 97–98 (2014)

130.

Oransky, I.: Sir Godfrey N. Hounsfield. Lancet 364(9439), 1032 (2004)CrossRef

131.

Orban, G., Horvath, G.: Lung nodule detection on digital tomosynthesis images: a preliminary study. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). IEEE (2014)

132.

Ostrom, Q.T., Gittleman, H., Farah, P., Ondracek, A., Chen, Y.W., Wolinsky, Y., Stroup, N.E., Kruchko, C., Barnholtz-Sloan, J.S.: CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2006–2010. Neuro Oncol. 15, 1–56 (2013)CrossRef

133.

Ozkan, E., West, A., Dedelow, J.A., Chu, B.F., Zhao, W., Yildiz, V.O., Otterson, G.A., Shilo, K., Ghosh, S., King, M.: CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung. Am. J. Roentgenol. 205(5), 1016–1025 (2015)CrossRef

134.

Pannu, H.K., Corl, F.M., Fishman, E.K.: CT evaluation of cervical cancer: spectrum of disease 1. Radiographics 21(5), 1155–1168 (2001)CrossRef

135.

Parmar, C., Leijenaar, R.T., Grossmann, P., Velazquez, E.R., Bussink, J., Rietveld, D., Rietbergen, M.M., Haibe-Kains, B., Lambin, P., Aerts, H.J.: Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci. Rep. 5 (2015)

136.

Phillips, H.S., Kharbanda, S., Chen, R., Forrest, W.F., Soriano, R.H., Wu, T.D., Misra, A., Nigro, J.M., Colman, H., Soroceanu, L., Williams, P.M., Modrusan, Z., Feuerstein, B.G., Aldape, K.: Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9(3), 157–173 (2006)CrossRef

137.

Pignon, J.P., Tribodet, H., Scagliotti, G.V., Douillard, J.Y., Shepherd, F.A., Stephens, R.J., Dunant, A., Torri, V., Rosell, R., Seymour, L., Spiro, S.G., Rolland, E., Fossati, R., Aubert, D., Ding, K., Waller, D., Le Chevalier, T., L. C. Group: Lung adjuvant cisplatin evaluation: a pooled analysis by the LACE Collaborative Group. J. Clin. Oncol. 26(21), 3552–3559 (2008)

138.

Pohl, K.M., Fisher, J., Levitt, J.J., Shenton, M.E., Kikinis, R., Grimson, W.E.L., Wells, W.M.: A unifying approach to registration, segmentation, and intensity correction. In: Duncan, J.S., Gerig, G. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2005: 8th International Conference, Palm Springs, CA, USA, 26–29 Oct 2005, Proceedings, Part I, pp. 310–318. Springer, Berlin (2005)

139.

Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., Gerig, G.: Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad. Radiol. 10(12), 1341–1348 (2003)CrossRef

140.

Rahim, M.K., Kim, S.E., So, H., Kim, H.J., Cheon, G.J., Lee, E.S., Kang, K.W., Lee, D.S.: Recent trends in PET image interpretations using volumetric and texture-based quantification methods in nuclear oncology. Nucl. Med. Mol. Imaging 48(1), 1–15 (2014)CrossRef

141.

Roman-Jimenez, G., Leseur, J., Devillers, A., David, J.: Segmentation and characterization of tumors in 18F-FDG PET-CT for outcome prediction in cervical cancer radio-chemotherapy. In: Image-Guidance and Multimodal Dose Planning in Radiation Therapy: A MICCAI Workshop, vol. 17 (2012)

142.

Rutman, A.M., Kuo, M.D.: Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur. J. Radiol. 70(2), 232–241 (2009)CrossRef

143.

Ryu, I.S., Kim, J.S., Roh, J.-L., Cho, K.-J., Choi, S.-H., Nam, S.Y., Kim, S.Y.: Prognostic significance of preoperative metabolic tumour volume and total lesion glycolysis measured by 18F-FDG PET/CT in squamous cell carcinoma of the oral cavity. Eur. J. Nucl. Med. Mol. Imaging 41(3), 452–461 (2014)CrossRef

144.

Schmidt, M., Levner, I., Greiner, R., Murtha, A., Bistritz, A.: Segmenting brain tumors using alignment-based features. In: Fourth International Conference on Machine Learning and Applications, 2005. Proceedings (2005)

145.

Schmidt, M.C., Antweiler, S., Urban, N., Mueller, W., Kuklik, A., Meyer-Puttlitz, B., Wiestler, O.D., Louis, D.N., Fimmers, R., von Deimling, A.: Impact of genotype and morphology on the prognosis of glioblastoma. J. Neuropathol. Exp. Neurol. 61(4), 321–328 (2002)CrossRef

146.

Schover, L.R.: Quality counts: the value of women’s perceived quality of life after cervical cancer. Gynecol. Oncol. 76(1), 3–4 (2000)CrossRef

147.

Segal, E., Sirlin, C.B., Ooi, C., Adler, A.S., Gollub, J., Chen, X., Chan, B.K., Matcuk, G.R., Barry, C.T., Chang, H.Y., Kuo, M.D.: Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat. Biotechnol. 25(6), 675–680 (2007)CrossRef

148.

Shen, W., Zhou, M., Yang, F., Yang, C., Tian, J.: Multi-scale convolutional neural networks for lung nodule classification. In: Information Processing in Medical Imaging. Springer (2015)

149.

Shota, Y., Maki, D.D., Korn, R.L., Kuo, M.D.: Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. Am. J. Roentgenol. 199(3), 654–663 (2012)CrossRef

150.

Siegel, R., Naishadham, D., Jemal, A.: Cancer statistics, 2013. CA Cancer J. Clin. 63(1), 11–30 (2013)CrossRef

151.

Simon, R.: Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Personalized Med. 7(1), 33–47 (2010)CrossRef

152.

Slattery, M.L., Robison, L.M., Schuman, K.L., French, T.K., Abbott, T.M., Overall, J.C., Gardner, J.W.: Cigarette smoking and exposure to passive smoke are risk factors for cervical cancer. JAMA 261(11), 1593–1598 (1989)CrossRef

153.

Song, J., Liu, Z., Zhong, W., Huang, Y., Ma, Z., Dong, D., Liang, C., Tian, J.: Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis. Sci. Rep. 6 (2016)

154.

Song, J.D., Yang, C.Y., Fan, L., Wang, K., Yang, F., Liu, S.Y., Tian, J.: Lung lesion extraction using a toboggan based growing automatic segmentation approach. IEEE Trans. Med. Imaging 35(1), 337–353 (2016)CrossRef

155.

Stupp, R., Mason, W.P., van den Bent, M.J., Weller, M., Fisher, B., Taphoorn, M.J., Belanger, K., Brandes, A.A., Marosi, C., Bogdahn, U., Curschmann, J., Janzer, R.C., Ludwin, S.K., Gorlia, T., Allgeier, A., Lacombe, D., Cairncross, J.G., Eisenhauer, E., Mirimanoff, R.O.: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352(10), 987–996 (2005)CrossRef

156.

Sun, S.S., Guo, Y.: Juxta-vascular nodule segmentation based on the flowing entropy and geodesic distance feature. J. Invest. Med. 61(4), S8–S8 (2013)

157.

Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)MATHCrossRef

158.

Tan, M., Deklerck, R., Jansen, B., Bister, M., Cornelis, J.: A novel computer-aided lung nodule detection system for CT images. Med. Phys. 38(10), 5630–5645 (2011)CrossRef

159.

Tan, S., Kligerman, S., Chen, W., Lu, M., Kim, G., Feigenberg, S., D’Souza, W.D., Suntharalingam, M., Lu, W.: Spatial-temporal [18 F] FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. Int. J. Radiat. Oncol.* Biol.* Phys. 85(5), 1375–1382 (2013)CrossRef

160.

Thibault, G., Fertil, B., Navarro, C., Pereira, S., Cau, P., Levy, N., Sequeira, J., Mari, J.: Texture Indexes and Gray Level Size Zone Matrix Application to Cell Nuclei Classification (2009)

161.

Thibault, G., Fertil, B., Navarro, C., Pereira, S., Levy, N., Sequeira, J., Mari, J.L.: Texture indexes and gray level size zone matrix application to cell nuclei classification. In: Pattern Recognition and Information Processing (PRIP) (2009)

162.

Tixier, F., Le Rest, C.C., Hatt, M., Albarghach, N., Pradier, O., Metges, J.-P., Corcos, L., Visvikis, D.: Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J. Nucl. Med. 52(3), 369–378 (2011)CrossRef

163.

Tomasi, G., Turkheimer, F., Aboagye, E.: Importance of quantification for the analysis of PET data in oncology: review of current methods and trends for the future. Mol. Imag. Biol. 14(2), 131–146 (2012)CrossRef

164.

Vaidya, M., Creach, K.M., Frye, J., Dehdashti, F., Bradley, J.D., El Naqa, I.: Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother. Oncol. 102(2), 239–245 (2012)CrossRef

165.

van Ginneken, B., Setio, A.A., Jacobs, C., Ciompi, F.: Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). IEEE (2015)

166.

van Velden, F.H., Cheebsumon, P., Yaqub, M., Smit, E.F., Hoekstra, O.S., Lammertsma, A.A., Boellaard, R.: Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur. J. Nucl. Med. Mol. Imaging 38(9), 1636–1647 (2011)CrossRef

167.

Verhaak, R.G.W., Hoadley, K.A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M.D., Miller, C.R., Ding, L., Golub, T., Mesirov, J.P., Alexe, G., Lawrence, M., O’Kelly, M., Tamayo, P., Weir, B.A., Gabriel, S., Winckler, W., Gupta, S., Jakkula, L., Feiler, H.S., Hodgson, J.G., James, C.D., Sarkaria, J.N., Brennan, C., Kahn, A., Spellman, P.T., Wilson, R.K., Speed, T.P., Gray, J.W., Meyerson, M., Getz, G., Perou, C.M., Hayes, D.N.: Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17(1), 98–110 (2010)CrossRef

168.

Verma, R., Zacharaki, E.I., Ou, Y., Cai, H., Chawla, S., Lee, S.-K., Melhem, E.R., Wolf, R., Davatzikos, C.: Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR images. Acad. Radiol. 15(8), 966–977 (2008)CrossRef

169.

Waggoner, S.E.: Cervical cancer. Lancet 361(9376), 2217–2225 (2003)CrossRef

170.

Wahl, R.L., Wagner, H.N., Beanlands, R.S.: Principles and Practice of PET and PET/CT. Lippincott Williams & Wilkins Philadelphia, PA (2009)

171.

Wang, Q., Song, E.M., Jin, R.C., Han, P., Wang, X.T., Zhou, Y.Y., Zeng, J.C.: Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques. Acad. Radiol. 16(6), 678–688 (2009)CrossRef

172.

Watabe, T., Tatsumi, M., Watabe, H., Isohashi, K., Kato, H., Yanagawa, M., Shimosegawa, E., Hatazawa, J.: Intratumoral heterogeneity of F-18 FDG uptake differentiates between gastrointestinal stromal tumors and abdominal malignant lymphomas on PET/CT. Ann. Nucl. Med. 26(3), 222–227 (2012)CrossRef

173.

Wels, M., Carneiro, G., Aplas, A., Huber, M., Hornegger, J., Comaniciu, D.: A discriminative model-constrained graph cuts approach to fully automated pediatric brain tumor segmentation in 3-D MRI. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2008: 11th International Conference, New York, NY, USA, 6–10 Sept 2008, Proceedings, Part I, pp. 67–75. Springer, Berlin (2008)

174.

Wong, K.-P., Zhang, X., Huang, S.-C.: Improved derivation of input function in dynamic mouse [18F] FDG PET using bladder radioactivity kinetics. Mol. Imag. Biol. 15(4), 486–496 (2013)CrossRef

175.

Wright, A.A., Howitt, B.E., Myers, A.P., Dahlberg, S.E., Palescandolo, E., Hummelen, P., MacConaill, L.E., Shoni, M., Wagle, N., Jones, R.T.: Oncogenic mutations in cervical cancer. Cancer 119(21), 3776–3783 (2013)CrossRef

176.

Wu, D.J., Lu, L., Bi, J.B., Shinagawa, Y., Boyer, K., Krishnan, A., Salganicoff, M.: Stratified Learning of Local Anatomical Context for Lung Nodules in CT Images. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (Cvpr), pp. 2791–2798 (2010)

177.

Xiaoou, T.: Texture information in run-length matrices. IEEE Trans. Image Process. 7(11), 1602–1609 (1998)CrossRef

178.

Yan, J., Chu-Shern, J.L., Loi, H.Y., Khor, L.K., Sinha, A.K., Quek, S.T., Tham, I.W., Townsend, D.: Impact of image reconstruction settings on texture features in 18F-FDG PET. J. Nucl. Med. 56(11), 1667–1673 (2015)CrossRef

179.

Yan, W., Li, R., Liu, Y., Yang, P., Wang, Z., Zhang, C., Bao, Z., Zhang, W., You, Y., Jiang, T.: MicroRNA expression patterns in the malignant progression of gliomas and a 5-microRNA signature for prognosis. Oncotarget 5(24), 12908–12915 (2014)CrossRef

180.

Yang, F., Thomas, M.A., Dehdashti, F., Grigsby, P.W.: Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer. Eur. J. Nucl. Med. Mol. Imaging 40(5), 716–727 (2013)CrossRef

181.

Yankelevitz, D.F., Reeves, A.P., Kostis, W.J., Zhao, B., Henschke, C.I.: Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 217(1), 251–256 (2000)CrossRef

182.

Zacharaki, E.I., Shen, D., Lee, S.K., Davatzikos, C.: ORBIT: a multiresolution framework for deformable registration of brain tumor images. IEEE Trans. Med. Imaging 27(8), 1003–1017 (2008)CrossRef

183.

Zhang, J., Ma, K.-K., Er, M.-H., Chong, V.: Tumor Segmentation from Magnetic Resonance Imaging by Learning via one-class support vector machine. In: International Workshop on Advanced Image Technology (IWAIT ‘04), Singapore (2004)

184.

Zhao, B., James, L.P., Moskowitz, C.S., Guo, P., Ginsberg, M.S., Lefkowitz, R.A., Qin, Y., Riely, G.J., Kris, M.G., Schwartz, L.H.: Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. Radiology 252(1), 263–272 (2009)CrossRef

185.

Zhao, B., Schwartz, L.H., Moskowitz, C.S., Ginsberg, M.S., Rizvi, N.A., Kris, M.G.: Lung cancer: computerized quantification of tumor response–initial results. Radiology 241(3), 892–898 (2006)CrossRef

186.

Zhou, M., Hall, L.O., Goldgof, D.B., Gillies, R.J., Gatenby, R.A.: Survival time prediction of patients with glioblastoma multiforme tumors using spatial distance measurement. In: Medical Imaging 2013: Computer-Aided Diagnosis 8670 (2013)

187.

Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O.M., Das, T., Jena, R., Price, S.J.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012: 15th International Conference, Nice, France, October 1–5, 2012, Proceedings, Part III, pp. 369–376. Springer, Berlin (2012)

188.

Zikic, D., Glocker, B., Shotton, J., Criminisi, A., Thomas, O.M., Das, T., Konukoglu, E., Ye, D.H., Demiralp, C., Jena, R.: Context-sensitive Classification Forests for Segmentation of Brain Tumor Tissues. Miccai (2012)

189.

Zinn, P.O., Bhanu, M., Bhanu, M., Pratheesh, S., Singh, S.K., Sadhan, M., Jolesz, F.A., Colen, R.R.: Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. Plos One 6(10), e25451 (2011)

Titel: Radiomics in Medical Imaging—Detection, Extraction and Segmentation
verfasst von: Jie Tian
Di Dong
Zhenyu Liu
Yali Zang
Jingwei Wei
Jiangdian Song
Wei Mu
Shuo Wang
Mu Zhou
Verlag: Springer International Publishing
Buch: Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging
Print ISBN: 978-3-319-68842-8

Electronic ISBN: 978-3-319-68843-5

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-68843-5_11

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