Normal and Abnormal Tissue Classification in Positron Emission Tomography Oncological Studies

K. L. Newbold, M. Partridge, G. Cook, B. Sharma, P. Rhys-Evans, K. J. Harrington, and C. M. Nutting, “Evaluation of the role of 18fdg-pet/ct in radiotherapy target definition in patients with head and neck cancer,” Acta Oncol. 47, 1229–1236 (2008).

L. K. Shankar, J. M. Hoffman, S. Bacharach, M. M. Graham, J. Karp, A. A. Lammertsma, S. Larson, D. A. Mankoff, B. A. Siegel, A. Van den Abbeele, et al., “Consensus recommendations for the use of 18ffdg pet as an indicator of therapeutic response in patients in national cancer institute trials,” J. Nucl. Med. 47, 1059–1066 (2006).

L. Rundo, A. Stefano, C. Militello, G. Russo, M. G. Sabini, C. D’Arrigo, F. Marletta, M. Ippolito, G. Mauri, S. Vitabile, et al., “A fully automatic approach for multimodal pet and mr image segmentation in gamma knife treatment planning,” Comput. Methods Programs Biomed. 144, 77–96 (2017).

S. M. Larson, Y. Erdi, T. Akhurst, M. Mazumdar, H. A. Macapinlac, R. D. Finn, C. Casilla, M. Fazzari, N. Srivastava, H. W. D. Yeung, et al., “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 Imag. 2, 159–171 (1999).

A. Stefano, S. Vitabile, G. Russo, M. Ippolito, M. G. Sabini, D. Sardina, O. Gambino, R. Pirrone, E. Ardizzone, and M. C. Gilardi, “An enhanced random walk algorithm for delineation of head and neck cancers in pet studies,” Med. Biol. Eng. Comput. 55 (6), 897–908 (2016).

D. Hellwig, T. P. Graeter, D. Ukena, A. Groeschel, G. W. Sybrecht, H.-J. Schaefers, and C.-M. Kirsch, “18f-fdg pet for mediastinal staging of lung cancer: which suv threshold makes sense,” J. Nucl. Med. 48, 1761–1766 (2007).

R. L. Wahl, H. Jacene, Y. Kasamon, and M. A. Lodge, “From recist to percist: evolving considerations for pet response criteria in solid tumors,” J. Nucl. Med. 50, 122S–150S (2009).

N. C. Nguyen, A. Kaushik, M. K. Wolverson, and M. M. Osman, “Is there a common suv threshold in oncological fdg pet/ct, at least for some common indications a retrospective study,” Acta Oncol. 50, 670–677 (2011).

C. Ballangan, X. Wang, S. Eberl, M. Fulham, and D. Feng, “Automated lung tumor segmentation for whole body pet volume based on novel downhill region growing,” Proc. SPIE Med. Imag. 7623, 76233O–76233O (2010).

A. Stefano, S. Vitabile, G. Russo, M. Ippolito, F. Marletta, C. D’arrigo, D. D’urso, O. Gambino, R. Pirrone, E. Ardizzone, et al., “A fully automatic method for biological target volume segmentation of brain metastases,” Int. J. Imag. Syst. Technol. 26, 29–37 (2016).

B. Wu, P.-L. Khong, and T. Chan, “Automatic detection and classification of nasopharyngeal carcinoma on pet/ct with support vector machine,” Int. J. Comput. Assisted Radiol. Surgery 7, 635–646 (2012).

Hongkai Wang, Zongwei Zhou, Yingci Li, Zhonghua Chen, Peiou Lu, Wenzhi Wang, Wanyu Liu, and Lijuan Yu, “Comparison of machine learning methods for classifying mediastinal lymph node metastasis of nonsmall cell lung cancer from 18 f-fdg pet/ct images,” EJNMMI Res. 7, 11 (2017).

C. Lartizien, M. Rogez, E. Niaf, and F. Ricard, “Computer- aided staging of lymphoma patients with fdg pet/ct imaging based on textural information,” IEEE J. Biomed. Health Inf. 18, 946–955 (2014).

H. Yu, C. Caldwell, K. Mah, and D. Mozeg, “Coregistered fdg pet/ct-based textural characterization of head and neck cancer for radiation treatment planning,” IEEE Trans. Med. Imag. 28, 374–383 (2009).

Lei Bi, Jinman Kim, Lingfeng Wen, Dagan Feng, and M. Fulham, “Automated thresholded region classification using a robust feature selection method for pet-ct,” in Proc. 12th IEEE Int. Symp. on Biomedical Imaging (ISBI) (IEEE, 2015), pp. 1435–1438.

M. A. Nogueira, P. H. Abreu, P. Martins, P. Machado, H. Duarte, and J. Santos, “An artificial neural networks approach for assessment treatment response in oncological patients using pet/ct images,” BMC Med. Imag. 17, 13 (2017).

R. Boellaard, R. Delgado-Bolton, W. J. G. Oyen, F. Giammarile, K. Tatsch, W. Eschner, F. J. Verzijlbergen, S. F. Barrington, L. C. Pike, W. A. Weber, et al., “Fdg pet/ct: Eanm procedure guidelines for tumour imaging: version 2.0,” Europ. J. Nucl. Med. Mol. Imag. 42, 328–354 (2015).

S. Armand, E. Watelain, E. Roux, M. Mercier, and F.-X. Lepoutre, “Linking clinical measurements and kinematic gait patterns of toe-walking using fuzzy decision trees,” Gait Posture 25, 475–484 (2007).

L. Agnello, A. Comelli, E. Ardizzone, and S. Vitabile, “Unsupervised tissue classification of brain mr images for voxel-based morphometry analysis,” Int. J. Imag. Syst. Technol. 26, 136–150 (2016).

M. Soret, S. L. Bacharach, and I. Buvat, “Partial-volume effect in pet tumor imaging,” J. Nucl. Med. 48, 932–945 (2007).

F. Gallivanone, A. Stefano, E. Grosso, C. Canevari, L. Gianolli, C. Messa, M. C. Gilardi, and I. Castiglioni, “Pve correction in pet-ct wholebody oncological studies from pve-affected images,” IEEE Trans. Nucl. Sci. 58, 736–747 (2011).