Cell-based quantification of molecular biomarkers in histopathology specimens

Histopathology

Volumn 59, Issue 1, 2011, Pages 40-54

  Al Kofahi, Yousef ^a   Lassoued, Wiem ^b,c   Grama, Kedar ^d   Nath, Sumit K ^a   Zhu, Jianliang ^b   Oueslati, Ridha ^c   Feldman, Michael ^b   Lee, William M F ^b   Roysam, Badrinath ^a,d  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c UNIVERSITY OF CARTHAGE   (Tunisia)

^d UNIVERSITY OF HOUSTON   (United States)

Author keywords

Automated image analysis; Cell typing; Digital histopathology; Molecular biomarkers; Multi spectral imaging; Multiplexed immunolabelling

Indexed keywords

BIOLOGICAL MARKER; EPIDERMAL GROWTH FACTOR RECEPTOR 2; ESTROGEN RECEPTOR; KI 67 ANTIGEN; MITOGEN ACTIVATED PROTEIN KINASE; MOLECULAR MARKER; PROGESTERONE RECEPTOR; S6 KINASE;

ARTICLE; BREAST CARCINOMA; CELL COMPARTMENTALIZATION; CELL CULTURE; CELL DIFFERENTIATION; CELL NUCLEUS; CELL STRUCTURE; CELL SUBPOPULATION; CELL TYPE; CELLULAR DISTRIBUTION; COMPUTER AIDED DESIGN; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; IMAGE PROCESSING; IMMUNOHISTOCHEMISTRY; MICROSCOPY; MULTISPECTRAL MICROSCOPY; PHENOTYPE; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; PROTEIN EXPRESSION;

AUTOMATION, LABORATORY; BIOLOGICAL MARKERS; BREAST NEOPLASMS; CELL MEMBRANE; CELL NUCLEUS; CYTOPLASM; DIAGNOSIS, COMPUTER-ASSISTED; EXTRACELLULAR SIGNAL-REGULATED MAP KINASES; FEMALE; HISTOLOGICAL TECHNIQUES; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; IMMUNOHISTOCHEMISTRY; KERATINS; KI-67 ANTIGEN; RECEPTORS, ESTROGEN; RECEPTORS, PROGESTERONE; TUMOR MARKERS, BIOLOGICAL;

EID: 79960440027     PISSN: 03090167     EISSN: 13652559     Source Type: Journal    
DOI: 10.1111/j.1365-2559.2011.03878.x     Document Type: Article

References (49)

1. Cheng J, Qiu S, Raju U, Wolman SR, Worsham MJ. Benign breast disease heterogeneity: association with histopathology, age, and ethnicity. Breast Cancer Res. Treat. 2008; 111; 289-296.
2. Hammerschmied CG, Walter B, Hartmann A. Renal cell carcinoma 2008: histopathology, molecular genetics and new therapeutic options. Pathologe 2008; 29; 354-363.
3. Crocker J, Murray P. Molecular biology in cellular pathology. Chichester: Wiley, 2003.
4. Research Signpost (Trivandrum India). Recent research developments in histopathology. Trivandrum, India: Research Signpost, 2002; v.
5. Menard S, Balsari A, Tagliabue E et al. Biology, prognosis and response to therapy of breast carcinomas according to HER2 score. Ann. Oncol. 2008; 19; 1706-1712.
6. Chivukula M, Bhargava R, Brufsky A, Surti U, Dabbs DJ. Clinical importance of HER2 immunohistologic heterogeneous expression in core-needle biopsies vs resection specimens for equivocal (immunohistochemical score 2+) cases. Mod. Pathol. 2008; 21; 363-368.
7. Montironi R, Mazzucchelli R, Barbisan F et al. HER2 expression and gene amplification in pT2a Gleason score 6 prostate cancer incidentally detected in cystoprostatectomies: comparison with clinically detected androgen-dependent and androgen-independent cancer. Hum. Pathol. 2006; 37; 1137-1144.
8. Bowles DW, Rabinovitch R, Borges V et al. A young woman with a small ER-positive breast cancer, a micrometastatic axillary lymph node, and an intermediate oncotype DX recurrence score. Oncology (Williston Park, N.Y.) 2007; 21; 1212-1217.
9. Remmele W, Stegner HE. Recommendation for uniform definition of an immunoreactive score (IRS) for immunohistochemical estrogen receptor detection (ER-ICA) in breast cancer tissue. Pathologe 1987; 8; 138-140.
10. Neudert M, Fischer C, Krempien B, Seibel MJ, Bauss F. A rapid histological score for the semiquantitative assessment of bone metastases in experimental models of breast cancer. Onkologie 2008; 31; 521-527.
11. Egyed Z, Jaray B, Kulka J, Pentek Z. Triple test score for the evaluation of invasive ductal and lobular breast cancer. Pathol. Oncol. Res. 2009; 15; 159-166.
12. Kurosumi M. Immunohistochemical assessment of hormone receptor status using a new scoring system (J-Score) in breast cancer. Breast Cancer 2007; 14; 189-193.
13. Remmele W, Schicketanz KH. Immunohistochemical determination of estrogen and progesterone receptor content in human breast cancer. Computer-assisted image analysis (QIC score) vs. subjective grading (IRS). Pathol. Res. Pract. 1993; 189; 862-866.
14. Camp RL, Chung GG, Rimm DL. Automated subcellular localization and quantification of protein expression in tissue microarrays. Nat. Med. 2002; 8; 1323-1327.
15. Mulrane L, Rexhepaj E, Penney S, Callanan JJ, Gallagher WM. Automated image analysis in histopathology: a valuable tool in medical diagnostics. Expert Rev. Mol. Diagn. 2008; 8; 707-725.
16. Taylor CR, Levenson RM. Quantification of immunohistochemistry - issues concerning methods, utility and semiquantitative assessment II. Histopathology 2006; 49; 411-424.
17. Tamai S. Expert systems and automatic diagnostic systems in histopathology - a review. Rinsho Byori 1999; 47; 126-131.
18. Peterson RA, Krull DL, Butler L. Applications of laser scanning cytometry in immunohistochemistry and routine histopathology. Toxicol. Pathol. 2008; 36; 117-132.
19. Davis DW, Takamori R, Raut CP et al. Pharmacodynamic analysis of target inhibition and endothelial cell death in tumors treated with the vascular endothelial growth factor receptor antagonists SU5416 or SU6668. Clin. Cancer Res. 2005; 11(2 Pt 3); 678-689.
20. Al-Kofahi Y, Lassoued W, Lee W, Roysam B. Improved automatic detection & segmentation of cell nuclei in histopathology images. IEEE Trans. Biomed. Eng. 2010; 57; 841-852.
21. Chang H, Yang Q, Parvin B. Segmentation of heterogeneous blob objects through voting and level set formulation. Pattern Recognit. Lett. 2007; 28; 1781-1787.
22. Parvin B, Yang Q, Han J et al. Iterative voting for inference of structural saliency and characterization of subcellular events. IEEE Trans. Image Process. 2007; 16; 615-623.
23. Wahlby C, Sintorn IM, Erlandsson F, Borgefors G, Bengtsson E. Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. J. Microsc. 2004; 215; 67-76.
24. Ortiz de Solorzano C, Garcia Rodriguez E, Jones A et al. Segmentation of confocal microscope images of cell nuclei in thick tissue sections. J. Microsc. 1999; 193(Pt 3); 212-226.
25. Lin G, Chawla MK, Olson K et al. A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images. Cytometry A 2007; 71; 724-736.
26. Lin G, Chawla MK, Olson K et al. Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei. Cytometry A 2005; 63; 20-33.
27. Mackin RW Jr, Newton LM, Turner JN, Holmes TJ, Roysam B. Accuracy of nuclear classification in cervical smear images. Quantitative impact of computational deconvolution and 3-D feature computation. Anal. Quant. Cytol. Histol. 1998; 20; 77-91.
28. Mackin RW Jr, Newton LM, Turner JN, Roysam B. Advances in high-speed, three-dimensional imaging and automated segmentation algorithms for thick and overlapped clusters in cytologic preparations. Application to cervical smears. Anal. Quant. Cytol. Histol. 1998; 20; 105-121.
29. Ancin H, Roysam B, Dufresne TE et al. Advances in automated 3-D image analyses of cell populations imaged by confocal microscopy. Cytometry 1996; 25; 221-234.
30. Byun JY, Verardo MR, Sumengen B et al. Automated tool for the detection of cell nuclei in digital microscopic images: application to retinal images. Mol. Vis. 2006; 12; 949-960.
31. Chawla MK, Lin G, Olson K et al. 3D-catFISH: a system for automated quantitative three-dimensional compartmental analysis of temporal gene transcription activity imaged by fluorescence in situ hybridization. J. Neurosci. Methods 2004; 139; 13-24.
32. De Solorzano CO, Malladi R, Lelievre SA, Lockett SJ. Segmentation of nuclei and cells using membrane related protein markers. J. Microsc. 2001; 201; 404-415.
33. Gudla PR, Nandy K, Collins J et al. A high-throughput system for segmenting nuclei using multiscale techniques. Cytometry A 2008; 73; 451-466.
34. Li G, Liu TM, Nie JX et al. Detection of blob objects in microscopic zebrafish images based on gradient vector diffusion. Cytometry A 2007; 71A; 835-845.
35. Lin G, Adiga U, Olson K et al. A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry A 2003; 56; 23-36.
36. Kittler J, Illingworth J. Minimum error thresholding. Pattern Recogn. 1986; 19; 41-47.
37. Pal NR, Pal SK. Image model, Poisson distribution and object extraction. Int. J. Pattern Recognit. Artif. Intell. 1991; 5; 25.
38. Wu XW, Chen YD, Brooks BR, Su YA. The local maximum clustering method and its application in microarray gene expression data analysis. EURASIP J. Appl. Signal Processing 2004; 2004; 53-63.
39. Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 2001; 23; 1222-1239.
40. Nath SK, Bunyak F, Palaniappan K. Robust tracking of migrating cells using four-color level set segmentation. Lect. Notes Comput. Sci. 2006; 4179; 920-932.
41. Vincent L, Soille P. Watersheds in digital spaces - an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 1991; 13; 583-598.
42. Bertin E, Parazza F, Chassery JM. Segmentation and measurement based on 3D Voronoi diagram: application to confocal microscopy. Comput. Med. Imaging Graph. 1993; 17; 175-182.
43. Sanchez-Marin FJ. A simple procedure for simulating samples of tissue using Voronoi diagrams. Anal. Quant. Cytol. Histol. 2005; 27; 225-231.
44. Nath SK, Palaniappan K, Bunyak F. Accurate spatial neighborhood relationships for arbitrarily-shaped objects using Hamilton-Jacobi GVD. Lect. Notes Comput. Sci. 2007; 4522; 421-431.
45. Bjornsson CS, Lin G, Al-Kofahi Y et al. Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue. J. Neurosci. Methods 2008; 170; 165-178.
46. Cantrell D. T cell antigen receptor signal transduction pathways. Annu. Rev. Immunol. 1996; 14; 259-274.
47. Kerbel RS. Tumor angiogenesis. N. Engl. J. Med. 2008; 358; 2039-2049.
48. Orimo A, Gupta PB, Sgroi DC et al. Stromal fibroblasts present in invasive human breast carcinomas promote tumor growth and angiogenesis through elevated SDF-1/CXCL12 secretion. Cell 2005; 121; 335-348.
49. Baeuerle PA, Baltimore D. I kappa B: a specific inhibitor of the NF-kappa B transcription factor. Science 1988; 242; 540-546.