Abstract
We present a computerized image-based method to automatically identify aggressive tumors on combined positron emission tomography and magnetic resonance imaging (PET-MRI) using radiomics texture features from both PET and multi-parametric MRI (MP-MRI). The work aims at investigating the potential use of new composite textures from PET-MRI for the assessment of different biological properties present in cancer and non-cancer regions, and eventually for early detection of malignant tumors in real clinical practice. Towards this goal, a large number of radiomics features are extracted to characterize the intratumoural heterogeneity and microarchitectural morphologic differences within tumors. These image attributes are valuable for determining tumor aggressiveness. The radiomics model was evaluated on three types of cancers (pancreas, gallbladder, and liver). Compared to single image modality (PET or MRI), the fused PET and MP-MRI achieved the best classification performance in differentiating cancer and non-cancer regions with the area of under curve (AUC) of 0.87 for pancreas cancer, 0.89 for gallbladder cancer, and 0.82 for liver cancer. The results indicated that PET-MRI based imaging biomarkers could be useful in identifying aggressive tumors.
T. Wan and B. Cui are the co-first authors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bashir, U., Mallia, A., Stirling, J., Joemon, J., MacKewn, J., Charles-Edwards, G., Goh, V., Cook, G.: PET/MRI in oncological imaging: state of the art. Diagnostics 21(5), 333–357 (2015)
Chen, S., He, H., Garcia, E.: RAMOBoost: ranked minority oversampling in boosting. IEEE Trans. Neural Networks 21(10), 1624–1642 (2010)
Edwards, B., Brown, M., Wingo, P., Howe, H., Ward, E., Ries, L., Schrag, D., Jamison, P., Jemal, A., Wu, X., Friedman, C., Harlan, L., Warren, J., Anderson, R., Pickle, L.: Annual report to the nation on the status of cancer, 1975–2002, featuring population-based trends in cancer treatment. J. Natl. Cancer Inst. 97(19), 1407–1427 (2005)
Ginsburg, S., Lee, G., Ali, S., Madabhushi, A.: Feature importance in nonlinear embeddings (FINE): applications in digital pathology. IEEE Trans. Med. Imaging 35(1), 76–88 (2016)
Haralick, R.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R., Granton, P., Zegers, C., Gillies, R., Boellard, R., Dekker, A., Aerts, H.: Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48(4), 441–446 (2012)
Li, C., Xu, C., Gui, C., Fox, M.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19, 3243–3254 (2010)
Li, L., Rusu, M., Viswanath, S., Penzias, G., Pahwa, S., Gollamudi, J., Madabhushi, A.: Multi-modality registration via multi-scale textural and spectral embedding representations. In: Proceedings of SPIE, p. 978446-1 (2016)
Lian, C., Ruan, S., Denaux, T., Jardin, F., Vera, P.: Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. Med. Image Anal. 32, 257–267 (2016)
Prasanna, P., Tiwari, P., Madabhushi, A.: Co-occurrence of local anisotropic gradient orientations (CoLlAGe): a new radiomics descriptor. Sci. Rep. 22(6), 37241 (2016)
Qiao, X., Zhang, L.: Distance-weighted support vector machine. Stat. Interface 8, 331–345 (2015)
Riola-Parada, C., Garcia-Canamaque, L., Perez-Duenas, V., Garcerant-Tafur, M., Carreras-Delgado, J.: Simultaneous PET/MRI vs PET/CT in oncology. A systematic review. Rev. Esp. Med. Nucl. Imagen. Mol. 35(5), 306–312 (2016)
Siegel, R., Miller, K., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 66(1), 7–30 (2016)
Tiwari, P., Prasanna, P., Wolansky, L., Pinho, M., Cohen, M., Nayate, A., Gupta, A., Singh, G., Hatanpaa, K., Sloan, A., Rogers, L., Madabhushi, A.: Computer-extracted texture features to distinguish cerebral radionecrosis from recurrent brain tumors on multiparametric MRI: a feasibility study. AJNR Am. J. Neuroradiol. 37(12), 2231–2236 (2016)
Vallières, M., Freeman, C., Skamene, S., El Naqa, I.: A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys. Med. Biol. 60(14), 5471–5496 (2015)
Wan, T., Bloch, B., Plecha, D., Thompson, C., Gilmore, H., Jaffe, C., Harris, L., Madabhushi, A.: A radio-genomics approach for identifying high risk estrogen receptor-positive breast cancers on DCE-MRI: preliminary results in predicting Oncotypedx risk scores. Sci. Rep. 18(6), 21394 (2016)
Wan, T., Madabhushi, A., Phinikaridou, A., Hamilton, J.A., Hua, N., Pham, T., Danagoulian, J., Kleiman, R., Buckler, A.: Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model. Med. Phys. 41(4), 042303 (2014)
Zhao, B., Tan, Y., Tsai, W., Qi, J., Xie, C., Lu, L., Schwartz, L.: Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci. Rep. 24(6), 23428 (2016)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under award No. 61401012.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wan, T., Cui, B., Wang, Y., Qin, Z., Lu, J. (2017). A Radiomics Approach for Automated Identification of Aggressive Tumors on Combined PET and Multi-parametric MRI. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_77
Download citation
DOI: https://doi.org/10.1007/978-3-319-70136-3_77
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70135-6
Online ISBN: 978-3-319-70136-3
eBook Packages: Computer ScienceComputer Science (R0)