Computer-aided prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data

Computerized Medical Imaging and Graphics

Volumn 35, Issue 7-8, 2011, Pages 506-514

  Madabhushi, Anant ^a   Agner, Shannon ^a   Basavanhally, Ajay ^a   Doyle, Scott ^a   Lee, George ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

Breast cancer; Computer aided prognosis (CAP); Data fusion; Digital pathology; Gleason grade; Mass spectrometry; Multi modal; Outcome; Personalized medicine; Prostate cancer; Protein expression

Indexed keywords

BREAST CANCER; COMPUTER-AIDED PROGNOSIS (CAP); DIGITAL PATHOLOGY; GLEASON GRADE; MULTI-MODAL; OUTCOME; PERSONALIZED MEDICINES; PROSTATE CANCERS; PROTEIN EXPRESSION;

BIOINFORMATICS; BIOPSY; CLUSTERING ALGORITHMS; COMPUTER AIDED ANALYSIS; DATA FUSION; DISEASES; FORECASTING; GENES; HOSPITAL DATA PROCESSING; IMAGE ANALYSIS; INFORMATION FUSION; MAGNETIC RESONANCE IMAGING; MASS SPECTROMETRY; MEDICAL COMPUTING; MODAL ANALYSIS; PATHOLOGY; PATIENT TREATMENT; PROTEINS; TISSUE; UROLOGY;

COMPUTER AIDED DIAGNOSIS;

ESTROGEN RECEPTOR; KI 67 ANTIGEN; PROSTATE SPECIFIC ANTIGEN; PROTEIN P53; UVOMORULIN; VASCULOTROPIN;

ANGIOGENESIS; ARTICLE; BIOINFORMATICS; BREAST CANCER; CANCER RISK; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; DIGITAL IMAGING; FLUORESCENCE IN SITU HYBRIDIZATION; GLEASON SCORE; GOLD STANDARD; HIGH RISK PATIENT; HISTOPATHOLOGY; IMAGE ANALYSIS; IMMUNOHISTOCHEMISTRY; INDEPENDENT COMPONENT ANALYSIS; LYMPHOCYTIC INFILTRATION; MASS SPECTROMETRY; MICROSATELLITE INSTABILITY; NEEDLE BIOPSY; NUCLEAR MAGNETIC RESONANCE IMAGING; OUTCOME ASSESSMENT; PREDICTOR VARIABLE; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROGNOSIS; PROSTATE CANCER; PROSTATECTOMY; PROTEIN EXPRESSION; RISK ASSESSMENT; TUMOR BIOPSY; TUMOR SUPPRESSOR GENE; TUMOR VASCULARIZATION;

ALGORITHMS; BREAST NEOPLASMS; DIAGNOSTIC IMAGING; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; NEOPLASM GRADING; OUTCOME ASSESSMENT (HEALTH CARE); PATHOLOGY, CLINICAL; PROGNOSIS; PROSTATIC NEOPLASMS;

EID: 79957990380     PISSN: 08956111     EISSN: 18790771     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2011.01.008     Document Type: Article

References (112)

1. Madabhushi A., Basavanhally A., Doyle S., Agner S., Lee G. Computer-aided prognosis: predicting patient and disease outcome via multi-modal image analysis. IEEE Int Symp Biomed Imaging (ISBI) 2010, 1415-1418.
2. Janowczyk A., Chandran S., Singh R., Sasaroli D., Coukos G., Feldman M.D., et al. Hierarchical normalized cuts: unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays. Med Image Comput Comput Assist Interv 2009, 12:230-238.
3. Madabhushi A., Doyle S., Lee G., Basavanhally A., Monaco J., Masters S., et al. Integrated diagnostics: a conceptual framework with examples, Clinical Chemistry and Laboratory Medicine. Clin Chem Lab Med 2010, 48:989-998.
4. Madabhushi A. Digital pathology image analysis: opportunities and challenges. Imaging Med 2009, 1:7-10.
5. Agner S., Madabhushi A., Rosen M., Schnall M., Nosher J., Somans S., et al. A comprehensive multi-attribute manifold learning scheme-based computer aided diagnostic system for breast MRI. SPIE medical imaging 2008.
6. Doyle S., Tomaszewski J., Feldman M., Madabhushi A. A boosted Bayesian multi-resolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans Biomed Eng 2010.
7. Madabhushi A., Shi J., Rosen M., Tomaszeweski J., Feldman M. Graph embedding to improve supervised classification: detecting prostate cancer. Palm Springs 2005, vol. 3749:729-738. Springer Verlag, CA.
8. Lenkinski R.E., Bloch B.N., Liu F., Frangioni J.V., Perner S., Rubin M.A., et al. An illustration of the potential for mapping MRI/MRS parameters with genetic over-expression profiles in human prostate cancer. Magma 2008, 21(November):411-421.
9. Monaco J., Tomaszweski J., Feldman M., Moradi M., Mousavi P., Boag A., et al. Pairwise probabilistic models for markov random fields: detecting prostate cancer from digitized whole-mount histopathology. Med Image Anal 2010, 14:617-629.
10. Juan D., Alexe G., Antes T., Liu H., Madabhushi A., Delisi C., et al. Identification of a MicroRNA panel for clear-cell kidney cancer. Urology 2009, 24(Decmber).
11. Lexe G., Monaco J., Doyle S., Basavanhally A., Reddy A., Seiler M., et al. Towards improved cancer diagnosis and prognosis using analysis of gene expression data and computer aided imaging. Exp Biol Med (Maywood) 2009, 234(August):860-879.
12. Pene F., Courtine E., Cariou A., Mira J.P. Toward theragnostics. Crit Care Med 2009, 37(January):S50-S58.
13. Lippi G. Wisdom of theragnostics, other changes. MLO Med Lab Obs 2008, 40(January):6.
14. Ozdemir V., Williams-Jones B., Cooper D.M., Someya T., Godard B. Mapping translational research in personalized therapeutics: from molecular markers to health policy. Pharmacogenomics 2007, 8(February):177-185.
15. Mirza A.R. An architectural selection framework for data fusion in sensor platforms. Systems design and management program 2006, 102. Massachusetts Institute of Technology, MS Boston.
16. Hall D.L. Perspectives on the fusion of image and non-image data. AIPR 2003.
17. Breiman L. Bagging predictors. Mach Learn 1996, 24:123-140.
18. Burges C.A. Tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998, 2:121-167.
19. Freund R.S.Y. Experiments with a new boosting algorithm in proceedings of national conference. Mach Learn 1996, 148-156.
20. Madabhushi A., Feldman M., Metaxas D., Chute D., Tomaszeweski J. Optimally combining 3D texture features for automated segmentation of prostatic adenocarcinoma from high resolution MR images. IEEE international conference of engineering in medicine and biology society Cancun 2003, 614-617.
21. Madabhushi A., Feldman M., Metaxas D., Tomaszeweski J., Chute D. Automated detection of prostatic adenocarcinoma from high resolution ex vivo MRI. IEEE Trans Med Imaging 2005, 24:1611-1625.
22. Madabhushi A., Shi J., Rosen M., Tomaszeweski J., Feldman M. Comparing classification performance of feature ensembles: detecting prostate cancer from high resolution MRI. Computer vision methods in medical image analysis (in conjunction with ECCV) 2006, vol. 4241:25-36. Springer Verlag.
23. Twellmann T., Saalbach A., Gerstung O., Leach M.O., Nattkemper T.W. Image fusion for dynamic contrast enhanced magnetic resonance imaging. Biomed Eng Online 2004, 3:35.
24. Jesneck J.L., Nolte L.W., Baker J.A., Floyd C.E., Lo J.Y. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis. Med Phys 2006, 33(August):2945-2954.
25. Duda P.E.H.R.O. Pattern classification and scene analysis 1973, Wiley, New York.
26. Smeulders A.W., van Ginneken A.M. An analysis of pathology knowledge and decision making for the development of artificial intelligence-based consulting systems. Anal Quant Cytol Histol 1989, 11(June):154-165.
27. Cizek J., Herholz K., Vollmar S., Schrader R., Klein J., Heiss W.D. Fast and robust registration of PET and MR images of human brain. Neuroimage 2004, 22(May):434-442.
28. Dube S., El-Saden S., Cloughesy T.F., Sinha U. Content based image retrieval for MR image studies of brain tumors. Conf Proc IEEE Eng Med Biol Soc 2006, 1:3337-3340.
29. Heckemann R.A., Hajnal J.V., Aljabar P., Rueckert D., Hammers A. Multiclassifier fusion in human brain MR segmentation: modelling convergence. Med Image Comput Comput Assist Interv Int Conf 2006, 9:815-822.
30. Hunsche S., Sauner D., Maarouf M., Lackner K., Sturm V., Treuer H. Combined X-ray and magnetic resonance imaging facility: application to image-guided stereotactic and functional neurosurgery. Neurosurgery 2007, 60(April):352-360. [discussion 360-1].
31. Liu T., Li H., Wong K., Tarokh A., Guo L., Wong S.T. Brain tissue segmentation based on DTI data. Neuroimage 2007, 38(October):114-123.
32. Mascott C.R., Summers L.E. Image fusion of fluid-attenuated inversion recovery magnetic resonance imaging sequences for surgical image guidance. Surg Neurol 2007, 67(June):589-603. [discussion 603].
33. Rohlfing T., Pfefferbaum A., Sullivan E.V., Maurer C.R. Information fusion in biomedical image analysis: combination of data vs. combination of interpretations. Inf Process Med Imaging 2005, 19:150-161.
34. Wong T.Z., Turkington T.G., Hawk T.C., Coleman R.E. PET and brain tumor image fusion. Cancer J 2004, 10(July-August):234-242.
35. Bloch I., Geraud T., Maitre H. Representation and fusion of heterogeneous fuzzy information in the 3D space for model-based structural recognition - application to 3D brain imaging. Artif Intell 2003, 148:141-175.
36. Bruno E., Moenne-Loccoz N., Marchand-Maillet S. Design of multimodal dissimilarity spaces for retrieval of video documents. IEEE Trans Pattern Anal Mach Intell 2008, 30(September):1520-1533.
37. Lanckriet G.R., Deng M., Cristianini N., Jordan M.I., Noble W.S. Kernel-based data fusion and its application to protein function prediction in yeast. Pac Symp Biocomput 2004, 300-311.
38. Lewis D.P., Jebara T., Noble W.S. Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure. Struct Bioinform 2006, 22:2753-2760.
39. Mandic D.P., Goh S.L. Sequential data fusion via vector spaces: fusion of heterogeneous data in the complex domain. J VLSI Signal Process 2007, 48:99-108.
40. Coppock S., Mazlack L.J. Multi-modal data fusion: a description. Lecture notes in computer science 2004, vol. 3214:1136-1142. Springer, Berlin/Heidelberg.
41. Friston K.J., Frith C.D., Fletcher P., Liddle P.F., Frackowiak R.S. Functional topography: multidimensional scaling and functional connectivity in the brain. Cereb Cortex 1996, 6(March-April):156-164.
42. Ye J., Chen K., Wu T., Li J., Zhao Z., Patel R. Heterogeneous data fusion for alzheimer's disease study. Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining 2008.
43. Rao S., Rodriguez A., Benson G. Evaluating distance functions for clustering tandem repeats. Genome Inform 2005, 16:3-12.
44. Simonetti A.W., Melssen W.J., Szabo de Edelenyi F., van Asten J.J., Heerschap A., Buydens L.M. Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification. NMR Biomed 2005, 18(February):34-43.
45. Devos A., Simonetti A.W., van der Graaf M., Lukas L., Suykens J.A., Vanhamme L., et al. The use of multivariate MR imaging intensities versus metabolic data from MR spectroscopic imaging for brain tumour classification. J Magn Reson 2005, 173(April):218-228.
46. Simonetti A.W., Melssen W.J., van der Graaf M., Postma G.J., Heerschap A., Buydens L.M. A chemometric approach for brain tumor classification using magnetic resonance imaging and spectroscopy. Anal Chem 2003, 75(October (15)):5352-5361.
47. Provencher S.W. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 1993, 30(December):672-679.
48. Yu K., Tresp V. Heterogenous data fusion via a probabilistic latent-variable model. ARCS 2004, 20-30.
49. Tenenbaum V.D.S.A.J.C.L.J.B. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290:2319-2323.
50. Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290:2323-2326.
51. Jolliffe I.T. Principal component analysis 1986, Springer-Verlag, New York.
52. Duda R., Hart P., Stork D. Pattern classification 2001, John Wiley & Sons, Inc.
53. Lee G., Rodrigues C., Madabhushi A. An empirical comparison of dimensionality reduction methods for classifying gene protein expression datasets. International symposium on bioinformatics research applications 2007, vol. 4463:170-181. LNBI, Atlanta GA.
54. Lee G., Rodrigues C., Madabhushi A. Investigating the efficacy of nonlinear dimensionality reduction schemes in classifying gene- and protein-expression studies. IEEE/ACM Trans Comput Biol Bioinform 2008, 5:1-17.
55. Samko O., Marshall A.D., Rosin P.L. Selection of the optimal parameter value for the Isomap algorithm. Pattern Recogn Lett 2006, 27:968-979.
56. Wang J., Zhang Z., Zha H. Adaptive manifold learning. NIPS 2004.
57. Jia W., Hong P., Yi-Shen L., Zhi-Mao H., Jia-Bing W. Adaptive neighborhood selection for manifold learning. Int Conf Mach Learn Cybern 2008, 380-384.
58. Mekuz N., Tsotsos J. Parameterless Isomap with adaptive neighborhood selection. Pattern Recogn 2006, 364-373.
59. Wen G., Jiang L., Wen J. Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding. Pattern Recogn 2008, 41:2226-2236.
60. Rajpoot D.K., Gomez A., Tsang W., Shanberg A. Ureteric and urethral stenosis: a complication of BK virus infection in a pediatric renal transplant patient. Pediatr Transpl 2007, 11(June):433-435.
61. Polzlbauer G., Lidy T., Rauber A. Decision manifolds - a supervised learning algorithm based on self-organization. IEEE Trans Neural Netw 2008, 19:1518-1530.
62. Angot F., Clouard R., Elmoataz A., Revenu M. Neighborhood graphs and image processing. SPIE 1996, vol. 2785:12-23.
63. Amod Jog A.J., Chandran S., Madabhushi A. Classifying ayurvedic pulse diagnosis via concensus locally linear embedding. Biosignals 2009, 388-395.
64. Tiwari P., Rosen M., Madabhushi A. Consensus-locally linear embedding (C-LLE): application to prostate cancer detection on magnetic resonance spectroscopy. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 2008, 11:330-338.
65. Viswanath S., Madabhushi A., Rosen M. A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery. SPIE medical imaging 2008.
66. Lee G., Monaco J., Doyle S., Master S., Feldman M., Tomaszewski J., et al. A knowledge representation framework for integrating multi-modal, multi-scale imaging and non-imaging data: predicting prostate cancer recurrence by fusing mass spectrometry and histology. IEEE Int Symp Biomed Imaging (ISBI) 2009, 77-80.
67. Tiwari P., Rosen M., Galen P., Kurhanewicz J., Madabhushi A. Spectral Embedding based Probabilistic boosting Tree (ScEPTre): classifying high dimensional heterogeneous biomedical data. Medical image computing and computer assisted intervention 2009, 844-851.
68. Tiwari P., Rosen M., Madabhushi A. Consensus-locally linear embedding (C-LLE): application to prostate cancer detection on magnetic resonance spectroscopy. Med Image Comput Comput Assist Interv 2008, 11:330-338.
69. Viswanath S., Tiwari P., Madabhushi A., Rosen M. A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy magnetic resonance imaging. SPIE medical imaging 2008.
70. Bloch B.N., Furman-Haran E., Helbich T.H., Lenkinski R.E., Degani H., Kratzik C., et al. Prostate cancer: accurate determination of extracapsular extension with high-spatial-resolution dynamic contrast-enhanced and T2-weighted MR imaging-initial results. Radiology 2007, 245(October):176-185.
71. Tiwari P, Madabhushi A, Rosen M. A hierarchical unsupervised spectral clustering scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS). In: Medical image computing and computer assisted intervention, vol. 4792. Brisbane Australia: 2007. p. 278-286.
72. Viswanath S, Chappelow J, Toth R, Rofsky N, Lenkinski R, Genega E, et al. An integrated segmentation, registration, and cancer detection scheme on 3 Tesla in vivo prostate DCE MRI. In: Medical image computing and computer assisted intervention 2008; accepted for publication.
73. Viswanath S., Bloch B.N., Rosen M., Chappelow J., Rofsky N., Lenkinski R., et al. Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI. SPIE medical imaging 2009.
74. Bostwick D.G. Grading prostate cancer. Am J Clin Pathol 1994, 102(October):S38-S56.
75. Djavan B., Kadesky K., Klopukh B., Marberger M., Roehrborn C.G. Gleason scores from prostate biopsies obtained with 18-gauge biopsy needles poorly predict Gleason scores of radical prostatectomy specimens. Eur Urol 1998, 33:261-270.
76. Ito K., Nakashima J., Mukai M., Asakura H., Ohigashi T., Saito S., et al. Prognostic implication of microvascular invasion in biochemical failure in patients treated with radical prostatectomy. Urol Int 2003, 70:297-302.
77. Gao X., Porter A., Grignon D., Pontes J., Honn K. Diagnostic and prognostic markers for human prostate cancer. Prostate 1997, 31:264-281.
78. Khatami A. Early prostate cancer: on prognostic markers and predictors of treatment outcome and radical prostaectomy 2007, Intellecta DocuSys AB, Gothenburg.
79. Klotz L. Active surveillance for prostate cancer: for whom?. J Clin Oncol 2005, 23.
80. Jonas Hugosson G.A.H.L.P.L.C.-G.P. Results of a randomized, population-based study of biennial screening using serum prostate-specific antigen measurement to detect prostate carcinoma 2004, 100:1397-1405.
81. Gao X., Porter A.T., Grignon D.J., Pontes J.E., Honn K.V. Diagnostic and prognostic markers for human prostate cancer. Prostate 1997, 31(June):264-281.
82. Graefen M, Augustin H., Karakiewicz P.I., Hammerer P.G., Haese A., Palisaar J., et al. Can predictive models for prostate cancer patients derived in the United States of America be utilized in European patients? A validation study of the partin tables. Eur Urol 2003, 43:6.
83. Stephenson A.J., Kattan M.W., Eastham J.A., Dotan Z.A., Bianco F.J., Lilja H., et al. Defining biochemical recurrence of prostate cancer after radical prostatectomy: a proposal for a standardized definition. J Clin Oncol 2006, 24(August):3973-3978.
84. Stephenson A.J., Scardino P.T., Eastham J.A., Bianco F.J., Dotan Z.A., DiBlasio C.J., et al. Postoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Clin Oncol 2005, 23(October):7005-7012.
85. Stephenson A.J., Scardino P.T., Eastham J.A., Bianco F.J., Dotan Z.A., Fearn P.A., et al. Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Natl Cancer Inst 2006, 98(May):715-717.
86. Marcelpoil R. Normalization of the minimum spanning tree. Anal Cell Pathol 1993, 5(May):177-186.
87. Albert R., Schindewolf T., Baumann I., Harms H. Three-dimensional image processing for morphometric analysis of epithelium sections. Cytometry 1992, 13:759-765.
88. Basavanhally A., Agner A., Alexe G., Ganesan S., Bhanot G., Madabhushi A. Manifold learning with graph-based features for identifying extent of lymphocytic infiltration from high grade breast cancer histology. MMBIA workshop in conjunction with MICCAI 2008 2008.
89. Basavanhally A., Xu J., Ganesan S., Madabhushi A. Computer-aided prognosis (CAP) of ER+ breast cancer histopathology and correlating survival outcome with oncotype Dx assay. IEEE international symposium on biomedical imaging (ISBI) 2009, 851-854.
90. Doyle S., Hwang M., Shah K., Madabhushi A., Tomaszeweski J., Feldman M. Automated grading of prostate cancer using architectural and textural image features. International symposium biomedical imaging 2007, 1284-1287.
91. Doyle S., Hwang M., Naik S., Feldman M., Tomaszeweski J. Using manifold learning for content-based image retrieval of prostate histopathology in MICCAI 2007. Workshop on content-based image retrieval for biomedical image archives 2007, 53-62.
92. Doyle S., Madabhushi A., Feldman M., Tomaszeweski J. A computer-aided diagnosis system for automated Gleason grading of prostatic adenocarcinoma from digitized histology advancing practice. Instruction and innovation through informatics (APIII) 2007.
93. Doyle S., Hwang M., Naik S., Madabhushi A., Feldman M., Tomaszeweski J. Quantitative investigation of graph-based features for automated grading of prostate cancer. Retreat Cancer Res 2007, 51.
94. Doyle S., Agner S., Madabhushi A., Feldman M., Tomaszeweski J. Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. International symposium on biomedical imaging 2008, 496-499. IEEE, Paris France.
95. Zhang L., Conejo-Garcia J.R., Katsaros D., Gimotty P.A., Massobrio M., Regnani G., et al. Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. N Engl J Med 2003, 348(January):203-213.
96. Sudbo J., Bankfalvi A., Bryne M., Marcelpoil R., Boysen M., Piffko J., et al. Prognostic value of graph theory-based tissue architecture analysis in carcinomas of the tongue. Lab Invest 2000, 80(December):1881-1889.
97. Sudbo J., Marcelpoil R., Reith A. New algorithms based on the Voronoi Diagram applied in a pilot study on normal mucosa and carcinomas. Anal Cell Pathol 2000, 21:71-86.
98. Sudbo J., Marcelpoil R., Reith A. Caveats: numerical requirements in graph theory based quantitation of tissue architecture. Anal Cell Pathol 2000, 21:59-69.
99. Lee G., Doyle S., Monaco J., Madabhushi A., Feldman M.D., Master S.R., et al. A knowledge representation framework for integration, classification of multi-scale imaging and non-imaging data: preliminary results in predicting prostate cancer recurrence by fusing mass spectrometry and histology. ISBI 2009, 77-80.
100. Agner S., Soman S., Libfeld E., McDonald M., Thomas K., Englander S., et al. Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging 2010.
101. Basavanhally A., Xu J., Madabhushi A., Ganesan S. Computer-aided prognosis of ER+ breast cancer histopathology and correlating survival outcome with oncotype DX assay. ISBI 2009, 851-854.
102. Madabhushi A., Shi J., Rosen M., Tomaszeweski J., Feldman M.D. Graph embedding to improve supervised classification and novel class detection: application to prostate cancer. MICCAI 2005, 8:729-737.
103. Basavanhally A.N., Ganesan S., Agner S., Monaco J.P., Feldman M.D., Tomaszewski J.E., et al. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE Trans Biomed Eng 2010, 57(March):642-653.
104. A. Basavanhally, S. Ganesan, S. Agner, J. Monaco, M. Feldman, J. Tomaszewski, et al. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE Trans Biomed Eng 2009; accepted for publication.
105. Alexe G., Dalgin G.S., Scanfeld D., Tamayo P., Mesirov J.P., DeLisi C., et al. High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates. Cancer Res 2007, 67(November):10669-10676.
106. Gleason D.F. Histologic grading of prostate cancer: a perspective. Hum Pathol 1992, 23(March):273-279.
107. Bostwick D.G., Graham S.D., Napalkov P., Abrahamsson P.A., di Sant'agnese P.A., Algaba F., et al. Staging of early prostate cancer: a proposed tumor volume-based prognostic index. Urology 1993, 41(May):403-411.
108. Doyle S., Hwang M., Shah K., Madabhushi A., Feldman M., Tomaszeweski J. Automated grading of prostate cancer using architectural and textural image features. ISBI 2007, 1284-1287.
109. Stephenson A.J., Slawin K.M. The value of radiotherapy in treating recurrent prostate cancer after radical prostatectomy. Nat Clin Pract Urol 2004, 1(December):90-96.
110. Allsbrook W.C., Mangold K.A., Johnson M.H., Lane R.B., Lane C.G., Amin M.B., et al. Interobserver reproducibility of Gleason grading of prostatic carcinoma: urologic pathologists. Hum Pathol 2001, 32(January):74-80.
111. Lexander H., Palmberg C., Auer G., Hellstrom M., Franzen B., Jornvall H., et al. Proteomic analysis of protein expression in prostate cancer. Anal Quant Cytol Histol 2005, 27(October):263-272.
112. Cooper L., Sertel O., Kong J., Lozanski G., Huang K., Gurcan M. Feature-based registration of histopathology images with different stains: an application for computerized follicular lymphoma prognosis. Comput Methods Programs Biomed 2009, 96:182-193.