Assessment of tumour viability in human lung cancer xenografts with texture-based image analysis

Journal of Clinical Pathology

Volumn 68, Issue 8, 2015, Pages 614-621

  Turkki, Riku ^a   Linder, Nina ^a   Holopainen, Tanja ^a   Wang, Yinhai ^a   Grote, Anne ^a   Lundin, Mikael ^a   Alitalo, Kari ^a   Lundin, Johan ^a,b  

^a UNIVERSITY OF HELSINKI   (Finland)

^b KAROLINSKA INSTITUTE   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANIMAL TISSUE; ARTICLE; AUTOMATION; CONTROLLED STUDY; HUMAN; HUMAN CELL; HUMAN TISSUE; IMAGE ANALYSIS; MOUSE; NON SMALL CELL LUNG CANCER; NONHUMAN; ONCOLOGICAL PARAMETERS; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; TUMOR VIABILITY; TUMOR XENOGRAFT; ADENOCARCINOMA; ANIMAL; AREA UNDER THE CURVE; ARTIFICIAL INTELLIGENCE; BAGG ALBINO MOUSE; CANCER TRANSPLANTATION; CELL SURVIVAL; COMPUTER ASSISTED DIAGNOSIS; EVALUATION STUDY; LABORATORY AUTOMATION; LUNG TUMOR; NUDE MOUSE; PATHOLOGY; PREDICTIVE VALUE; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; REPRODUCIBILITY; STAINING; TUMOR CELL LINE; TUMOR MICROENVIRONMENT; XENOGRAFT;

ADENOCARCINOMA; ALGORITHMS; ANIMALS; AREA UNDER CURVE; ARTIFICIAL INTELLIGENCE; AUTOMATION, LABORATORY; CARCINOMA, NON-SMALL-CELL LUNG; CELL LINE, TUMOR; CELL SURVIVAL; HETEROGRAFTS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LUNG NEOPLASMS; MICE; MICE, INBRED BALB C; MICE, NUDE; NEOPLASM TRANSPLANTATION; PREDICTIVE VALUE OF TESTS; REPRODUCIBILITY OF RESULTS; ROC CURVE; STAINING AND LABELING; TUMOR MICROENVIRONMENT;

EID: 84937848542     PISSN: 00219746     EISSN: 14724146     Source Type: Journal    
DOI: 10.1136/jclinpath-2015-202888     Document Type: Article

References (30)

1. Muley TR, Herth FJ, Schnabel PA, et al. From tissue to molecular phenotyping: Pre-Analytical requirements. Transl Lung Cancer Res 2012;1: 111-21.
2. Cregger M, Berger A, Rimm D. Immunohistochemistry and quantitative analysis of protein expression. Arch Pathol Lab Med 2006;130: 1026-30.
3. Richmond A, Su Y. Mouse xenograft models vs GEM models for human cancer therapeutics. Dis Models Mech 2008;1: 78-82.
4. Buchsbaum DJ, Bonner JA, Grizzle WE, et al. Treatment of pancreatic cancer xenografts with Erbitux (IMC-C225) anti-EGFR antibody, gemcitabine, and radiation. Int J Radiat Oncol Biol Phys 2002;54: 1180-93.
5. Mirmehdi M, Xie X, Suri J. Handbook of texture analysis. Imperial College Press, 2009.
6. Linder N, Konsti J, Turkki R, et al. Identification of tumor epithelium and stroma in tissue microarrays using texture analysis. Diagn Pathol 2012;7: 22.
7. Wang Y, Crookes D, Eldin OS, et al. Assisted diagnosis of cervical intraepithelial neoplasia (CIN). IEEE J Sel Top Signal Process 2009;3: 112-21.
8. Hamilton PW, Bartels PH, Thompson D, et al. Automated location of dysplastic fields in colorectal histology using image texture analysis. J Pathol 1997;182: 68-75.
9. Homeyer A, Schenk A, Arlt J, et al. Practical quantification of necrosis in histological whole-slide images. Comput Med Imaging Graph 2013;37: 313-22.
10. Sonal K, Phan JH, Osunkoya AO, et al. Biological interpretation of morphological patterns in histopathological whole-slide images. Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM. 2012.
11. Nandita N, Chang H, Borowsky A, et al. Classification of tumor histopathology via sparse feature learning. Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on. IEEE. 2013.
12. Kozaki K-i, Miyaishi O, Tsukamoto T, et al. Establishment and characterization of a human lung cancer cell line NCI-H460-LNM35 with consistent lymphogenous metastasis via both subcutaneous and orthotopic propagation. Cancer Res 2000;60: 2535-40.
13. Ojala T, Pietikäinen M, Mäenpää T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 2002;24: 971-87.
14. Pietikäinen M, Hadid A, Zhao G, et al. Computer vision using local binary patterns. Springer, 2011.
15. Fan R-E, Chang K-W, Hsieh C-J, et al. LIBLINEAR: A library for large linear classification. J Mach Learn Res 2008;9: 1871-4.
16. Vedaldi A, Zisserman A. Sparse kernel approximations for efficient classification and detection. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.
17. Vedaldi A, Fulkerson B. VLFeat: An open and portable library of computer vision algorithms. Proceedings of the international conference on Multimedia. ACM. 2010.
18. Pham DL, Xu C, Prince JL. Current methods in medical image segmentation 1. Annu Rev Biomed Eng 2000;2: 315-37.
19. Ahonen T, Hadid A, Pietikäinen M. Face description with local binary patterns: Application to face recognition. IEEE Trans Pattern Anal Mach Intell 2006;28: 2037-41.
20. Mäenpää T. An iterative algorithm for fast iris detection. Advances in Biometric Person Authentication, IWBRS 2005 Proceedings, Lecture Notes in Computer Science. 2005;3781: 127-34.
21. Nanni L, Lumini A. Local binary patterns for a hybrid fingerprint matcher. Pattern Recognit 2008;41: 3461-6.
22. Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 1996;29: 51-9.
23. Sertel O, Kong J, Shimada H, et al. Computer-Aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development. Pattern Recognit 2009;42: 1093-103.
24. Diamond J, Anderson NH, Bartels PH, et al. The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia. Hum Pathol 2004;35: 1121-31.
25. Ojansivu V, Heikkilä J. Blur insensitive texture classification using local phase quantization. Image and Signal Processing. Springer, 2008: 236-43.
26. Beck A, Sangoi A, Leung S, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med 2011;3: 108-13.
27. Pu RT, Schott AF, Sturtz DE, et al. Pathologic features of breast cancer associated with complete response to neoadjuvant chemotherapy: Importance of tumor necrosis. Am J Surg Pathol 2005;29: 354-8.
28. Pollheimer MJ, Kornprat P, Lindtner RA, et al. Tumor necrosis is a new promising prognostic factor in colorectal cancer. Hum Pathol 2010;41: 1749-57.
29. Swinson DE, Jones JL, Richardson D, et al. Tumour necrosis is an independent prognostic marker in non-small cell lung cancer: Correlation with biological variables. Lung Cancer 2002;37: 235-40.
30. Pichler M, Hutterer GC, Chromecki TF, et al. Histologic tumor necrosis is an independent prognostic indicator for clear cell and papillary renal cell carcinoma. Am J Clin Pathol 2012;137: 283-9.