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Diagnosis

Official Journal of the Society to Improve Diagnosis in Medicine (SIDM)

Editor-in-Chief: Graber, Mark L. / Plebani, Mario

Ed. by Argy, Nicolas / Epner, Paul L. / Lippi, Giuseppe / McDonald, Kathryn / Singh, Hardeep

Editorial Board: Basso , Daniela / Crock, Carmel / Croskerry, Pat / Dhaliwal, Gurpreet / Ely, John / Giannitsis, Evangelos / Katus, Hugo A. / Laposata, Michael / Lyratzopoulos, Yoryos / Maude, Jason / Newman-Toker, David / Singhal, Geeta / Sittig, Dean F. / Sonntag, Oswald / Zwaan, Laura

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2194-802X
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Histo-genomics: digital pathology at the forefront of precision medicine

Ivraym Barsoum
  • Department of Pathology and Molecular Medicine, Faculty of Health Sciences, Queen’s University, Kingston, Ontario, Canada
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/ Eriny Tawedrous
  • Department of Laboratory Medicine, and the Keenan Research Centre for Biomedical Science at the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada
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/ Hala Faragalla / George M. Yousef
  • Corresponding author
  • Department of Laboratory Medicine, and the Keenan Research Centre for Biomedical Science at the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada
  • Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
  • Department of Pediatric Laboratory Medicine, The Hospital for Sick Children, 555 University Avenue, Toronto, ON M5G 1X8, Canada
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Published Online: 2018-12-20 | DOI: https://doi.org/10.1515/dx-2018-0064

Abstract

The toughest challenge OMICs face is that they provide extremely high molecular resolution but poor spatial information. Understanding the cellular/histological context of the overwhelming genetic data is critical for a full understanding of the clinical behavior of a malignant tumor. Digital pathology can add an extra layer of information to help visualize in a spatial and microenvironmental context the molecular information of cancer. Thus, histo-genomics provide a unique chance for data integration. In the era of a precision medicine, a four-dimensional (4D) (temporal/spatial) analysis of cancer aided by digital pathology can be a critical step to understand the evolution/progression of different cancers and consequently tailor individual treatment plans. For instance, the integration of molecular biomarkers expression into a three-dimensional (3D) image of a digitally scanned tumor can offer a better understanding of its subtype, behavior, host immune response and prognosis. Using advanced digital image analysis, a larger spectrum of parameters can be analyzed as potential predictors of clinical behavior. Correlation between morphological features and host immune response can be also performed with therapeutic implications. Radio-histomics, or the interface of radiological images and histology is another emerging exciting field which encompasses the integration of radiological imaging with digital pathological images, genomics, and clinical data to portray a more holistic approach to understating and treating disease. These advances in digital slide scanning are not without technical challenges, which will be addressed carefully in this review with quick peek at its future.

Keywords: digital pathology; image analysis; omics; pathology informatics; precision medicine; virtual slides

References

  • 1.

    Nowell PC. The clonal evolution of tumor cell populations. Science 1976;194:23–8.CrossrefPubMedGoogle Scholar

  • 2.

    Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144:646–74.PubMedCrossrefGoogle Scholar

  • 3.

    Casas-Selves M, Degregori J. How cancer shapes evolution, and how evolution shapes cancer. Evolution (N Y) 2011;4:624–34.PubMedGoogle Scholar

  • 4.

    Barsoum IB, Hamilton TK, Li X, Cotechini T, Miles EA, Siemens DR, et al. Hypoxia induces escape from innate immunity in cancer cells via increased expression of ADAM10: role of nitric oxide. Cancer Res 2011;71:7433–41.PubMedCrossrefGoogle Scholar

  • 5.

    Barsoum IB, Koti M, Siemens DR, Graham CH. Mechanisms of hypoxia-mediated immune escape in cancer. Cancer Res 2014;74:7185–90.CrossrefPubMedGoogle Scholar

  • 6.

    Barsoum IB, Smallwood CA, Siemens DR, Graham CH. A mechanism of hypoxia-mediated escape from adaptive immunity in cancer cells. Cancer Res 2014;74:665–74.PubMedCrossrefGoogle Scholar

  • 7.

    Black M, Barsoum IB, Truesdell P, Cotechini T, Macdonald-Goodfellow SK, Petroff M, et al. Activation of the PD-1/PD-L1 immune checkpoint confers tumor cell chemoresistance associated with increased metastasis. Oncotarget 2016;7:10557–67.PubMedGoogle Scholar

  • 8.

    Graham C, Barsoum I, Kim J, Black M, Siemens RD. Mechanisms of hypoxia-induced immune escape in cancer and their regulation by nitric oxide. Redox Biol 2015;5:417.PubMedCrossrefGoogle Scholar

  • 9.

    Calderwood SK. Tumor heterogeneity, clonal evolution, and therapy resistance: an opportunity for multitargeting therapy. Discov Med 2013;15:188–94.PubMedGoogle Scholar

  • 10.

    Lichner Z, Saleh C, Subramaniam V, Seivwright A, Prud’homme GJ, Yousef GM. miR-17 inhibition enhances the formation of kidney cancer spheres with stem cell/tumor initiating cell properties. Oncotarget 2015;6:5567–81.PubMedGoogle Scholar

  • 11.

    Gillies RJ, Verduzco D, Gatenby RA. Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nat Rev Cancer 2012;12:487–93.CrossrefPubMedGoogle Scholar

  • 12.

    Chiche JD, Cariou A, Mira JP. Bench-to-bedside review: fulfilling promises of the Human Genome Project. Crit Care 2002;6:212–5.PubMedGoogle Scholar

  • 13.

    Arsanious A, Bjarnason GA, Yousef GM. From bench to bedside: current and future applications of molecular profiling in renal cell carcinoma. Mol Cancer 2009;8:20.CrossrefPubMedGoogle Scholar

  • 14.

    Gabril MY, Yousef GM. Informatics for practicing anatomical pathologists: marking a new era in pathology practice. Mod Pathol 2010;23:349–58.PubMedCrossrefGoogle Scholar

  • 15.

    Pasic MD, Samaan S, Yousef GM. Genomic medicine: new frontiers and new challenges. Clin Chem 2013;59:158–67.CrossrefPubMedGoogle Scholar

  • 16.

    Ibrahim R, Pasic M, Yousef GM. Omics for personalized medicine: defining the current we swim in. Expert Rev Mol Diagn 2016;16:719–22.PubMedCrossrefGoogle Scholar

  • 17.

    Yousef GM. Personalized medicine in kidney cancer: learning how to walk before we run. Eur Urol 2015;68:1021–2.CrossrefPubMedGoogle Scholar

  • 18.

    Butz H, Szabo PM, Nofech-Mozes R, Rotondo F, Kovacs K, Mirham L, et al. Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma. Clin Chem 2014;60:1314–26.CrossrefPubMedGoogle Scholar

  • 19.

    Girgis AH, Iakovlev VV, Beheshti B, Bayani J, Squire JA, Bui A, et al. Multilevel whole-genome analysis reveals candidate biomarkers in clear cell renal cell carcinoma. Cancer Res 2012;72:5273–84.CrossrefPubMedGoogle Scholar

  • 20.

    Kerr KM, Hirsch FR. Programmed death ligand-1 immunohistochemistry: friend or foe? Arch Pathol Lab Med 2016;140:326–31.CrossrefPubMedGoogle Scholar

  • 21.

    Lee KS, Lee K, Yun S, Moon S, Park Y, Han JH, et al. Prognostic relevance of programmed cell death ligand 1 expression in glioblastoma. J Neurooncol 2018;136:453–61.CrossrefPubMedGoogle Scholar

  • 22.

    Marginean EC, Melosky B. Is there a role for programmed death ligand-1 testing and immunotherapy in colorectal cancer with microsatellite instability? Part II – the challenge of programmed death ligand-1 testing and its role in microsatellite instability-high colorectal cancer. Arch Pathol Lab Med 2018;142:26–34.CrossrefGoogle Scholar

  • 23.

    Heindl A, Nawaz S, Yuan Y. Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology. Lab Invest 2015;95:377–84.CrossrefPubMedGoogle Scholar

  • 24.

    van den Bent MJ. Interobserver variation of the histopathological diagnosis in clinical trials on glioma: a clinician’s perspective. Acta Neuropathol 2010;120:297–304.PubMedCrossrefGoogle Scholar

  • 25.

    Cooper LA, Kong J, Gutman DA, Dunn WD, Nalisnik M, Brat DJ. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images. Lab Invest 2015;95:366–76.PubMedCrossrefGoogle Scholar

  • 26.

    Kothari S, Phan JH, Stokes TH. Pathology imaging informatics for quantitative analysis of whole-slide images. J Am Med Inform Assoc 2013;20:1099–108.CrossrefPubMedGoogle Scholar

  • 27.

    Cooper LA, Carter AB, Farris AB, Wang F, Kong J, Gutman DA, et al. Digital pathology: data-intensive frontier in medical imaging: health-information sharing, specifically of digital pathology, is the subject of this paper which discusses how sharing the rich images in pathology can stretch the capabilities of all otherwise well-practiced disciplines. Proc IEEE Inst Electr Electron Eng 2012;100:991–1003.PubMedGoogle Scholar

  • 28.

    Gurcan MN, Boucheron LE, Can A. Histopathological image analysis: a review. IEEE Rev Biomed Eng 2009;2:147–71.PubMedCrossrefGoogle Scholar

  • 29.

    Chang H, Han J, Borowsky A. Invariant delineation of nuclear architecture in glioblastoma multiforme for clinical and molecular association. IEEE Trans Med Imaging 2013;32:670–82.CrossrefPubMedGoogle Scholar

  • 30.

    Chang H, Nayak N, Spellman PT. Characterization of tissue histopathology via predictive sparse decomposition and spatial pyramid matching. Med Image Comput Comput Assist Interv 2013;16:91–8.PubMedGoogle Scholar

  • 31.

    Hipp J, Smith SC, Cheng J, Tomlins SA. Optimization of complex cancer morphology detection using the SIVQ pattern recognition algorithm. Anal Cell Pathol (Amst) 2012;35:41–50.PubMedCrossrefGoogle Scholar

  • 32.

    Hsu W, Markey MK, Wang MD. Biomedical imaging informatics in the era of precision medicine: progress, challenges, and opportunities. J Am Med Inform Assoc 2013;20:1010–3.PubMedCrossrefGoogle Scholar

  • 33.

    Janowczyk A, Chandran S, Singh R. High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts. IEEE Trans Biomed Eng 2012;59:1240–52.PubMedCrossrefGoogle Scholar

  • 34.

    Song Y, Treanor D, Bulpitt AJ. Unsupervised content classification based nonrigid registration of differently stained histology images. IEEE Trans Biomed Eng 2014;61:96–108.PubMedCrossrefGoogle Scholar

  • 35.

    Bautista PA, Yagi Y. Improving the visualization and detection of tissue folds in whole slide images through color enhancement. J Pathol Inform 2010;1:25.CrossrefPubMedGoogle Scholar

  • 36.

    Mosaliganti K, Janoos F, Irfanoglu O. Tensor classification of N-point correlation function features for histology tissue segmentation. Med Image Anal 2009;13:156–66.CrossrefPubMedGoogle Scholar

  • 37.

    Qi X, Xing F, Foran DJ. Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. IEEE Trans Biomed Eng 2012;59:754–65.CrossrefPubMedGoogle Scholar

  • 38.

    Samsi S, Krishnamurthy AK, Gurcan MN. An efficient computational framework for the analysis of whole slide images: application to follicular lymphoma immunohistochemistry. J Comput Sci 2012;3:269–79.CrossrefPubMedGoogle Scholar

  • 39.

    Beck AH, Sangoi AR, Leung S. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med 2011;3:108ra113.PubMedGoogle Scholar

  • 40.

    Mosaliganti K, Pan T, Ridgway R. An imaging workflow for characterizing phenotypical change in large histological mouse model datasets. J Biomed Inform 2008;41:863–73.CrossrefPubMedGoogle Scholar

  • 41.

    Kong J, Cooper LA, Wang F. Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates. PLoS One 2013;8:e81049.CrossrefPubMedGoogle Scholar

  • 42.

    Kolin DL, Sy K, Rotondo F, Bassily MN, Kovacs K, Brezden-Masley C, et al. Prognostic significance of human tissue kallikrein-related peptidases 6 and 10 in gastric cancer. Biol Chem 2014;395:1087–93.PubMedGoogle Scholar

  • 43.

    Scopa CD, Melachrinou M, Saradopoulou C, Merino MJ. The significance of the grooved nucleus in thyroid lesions. Mod Pathol 1993;6:691–4.PubMedGoogle Scholar

  • 44.

    Samaratunga H, Gianduzzo T, Delahunt B. The ISUP system of staging, grading and classification of renal cell neoplasia. J Kidney Cancer VHL 2014;1:26–39.CrossrefPubMedGoogle Scholar

  • 45.

    Appin CL, Gao J, Chisolm C. Glioblastoma with oligodendroglioma component (GBM-O): molecular genetic and clinical characteristics. Brain Pathol 2013;23:454–61.PubMedCrossrefGoogle Scholar

  • 46.

    Cooper LA, Kong J, Gutman DA. Integrated morphologic analysis for the identification and characterization of disease subtypes. J Am Med Inform Assoc 2012;19:317–23.PubMedCrossrefGoogle Scholar

  • 47.

    Gupta M, Djalilvand A, Brat DJ. Clarifying the diffuse gliomas: an update on the morphologic features and markers that discriminate oligodendroglioma from astrocytoma. Am J Clin Pathol 2005;124:755–68.CrossrefPubMedGoogle Scholar

  • 48.

    Hegi ME, Janzer RC, Lambiv WL. Presence of an oligodendroglioma-like component in newly diagnosed glioblastoma identifies a pathogenetically heterogeneous subgroup and lacks prognostic value: central pathology review of the EORTC_26981/NCIC_CE.3 trial. Acta Neuropathol 2012;123:841–52.CrossrefPubMedGoogle Scholar

  • 49.

    Peraud A, Watanabe K, Schwechheimer K. Genetic profile of the giant cell glioblastoma. Lab Invest 1999;79:123–9.PubMedGoogle Scholar

  • 50.

    Perry A, Aldape KD, George DH. Small cell astrocytoma: an aggressive variant that is clinicopathologically and genetically distinct from anaplastic oligodendroglioma. Cancer 2004;101:2318–26.CrossrefPubMedGoogle Scholar

  • 51.

    Wang C, Pecot T, Zynger DL. Identifying survival associated morphological features of triple negative breast cancer using multiple datasets. J Am Med Inform Assoc 2013;20:680–7.CrossrefPubMedGoogle Scholar

  • 52.

    Doyle S, Feldman M, Tomaszewski J. A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans Biomed Eng 2012;59:1205–18.CrossrefPubMedGoogle Scholar

  • 53.

    Tabesh A, Teverovskiy M, Pang HY. Multifeature prostate cancer diagnosis and Gleason grading of histological images. IEEE Trans Med Imaging 2007;26:1366–78.PubMedCrossrefGoogle Scholar

  • 54.

    Khan AM, Eldaly H, Rajpoot NM. A gamma-Gaussian mixture model for detection of mitotic cells in breast cancer histopathology images. J Pathol Inform 2013;4:11.PubMedCrossrefGoogle Scholar

  • 55.

    Roux L, Racoceanu D, Lomenie N. Mitosis detection in breast cancer histological images An ICPR 2012 contest. J Pathol Inform 2013;4:8.CrossrefGoogle Scholar

  • 56.

    Lewis JS Jr., Ali S, Luo J. A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma. Am J Surg Pathol 2014;38:128–37.PubMedCrossrefGoogle Scholar

  • 57.

    Kolin DL, Sy K, Rotondo F, Bassily MN, Kovacs K, Brezden-Masley C, et al. Prognostic significance of human tissue kallikrein-related peptidases 11 and 15 in gastric cancer. Tumour Biol 2016;37:437–46.CrossrefPubMedGoogle Scholar

  • 58.

    Yuan Y. Modelling the spatial and molecular heterogeneity of lymphocytic infiltration in triple-negative breast cancer. J R Soc Interface 2014;12:20141153.CrossrefGoogle Scholar

  • 59.

    Feichtenbeiner A, Haas M, Buttner M. Critical role of spatial interaction between CD8(+) and Foxp3(+) cells in human gastric cancer: the distance matters. Cancer Immunol Immunother 2014;63:111–9.CrossrefPubMedGoogle Scholar

  • 60.

    Galon J, Costes A, Sanchez-Cabo F. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 2006;313:1960–4.CrossrefPubMedGoogle Scholar

  • 61.

    Chang H, Fontenay GV, Han J. Morphometic analysis of TCGA glioblastoma multiforme. BMC Bioinformatics 2011;12:484.CrossrefPubMedGoogle Scholar

  • 62.

    Yuan Y, Failmezger H, Rueda OM. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci Transl Med 2012;4.PubMedGoogle Scholar

  • 63.

    Clarke GM, Murray M, Holloway CM, Liu K, Zubovits JT, Yaffe MJ. 3D pathology volumetric technique: a method for calculating breast tumour volume from whole-mount serial section images. Int J Breast Cancer 2012;2012:9.Google Scholar

  • 64.

    Langer DL, van der Kwast TH, Evans AJ, Plotkin A, Trachtenberg J, Wilson BC, et al. Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K(trans), v(e), and corresponding histologic features. Radiology 2010;255:485–94.CrossrefGoogle Scholar

  • 65.

    Hanahan D, Coussens LM. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 2012;21:309–22.CrossrefPubMedGoogle Scholar

  • 66.

    Junttila MR, de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 2013;501:346–54.PubMedCrossrefGoogle Scholar

  • 67.

    Paolino M, Choidas A, Wallner S. The E3 ligase Cbl-b and TAM receptors regulate cancer metastasis via natural killer cells. Nature 2014;507:508–12.CrossrefPubMedGoogle Scholar

  • 68.

    Tan KL, Scott DW, Hong F. Tumor-associated macrophages predict inferior outcomes in classic Hodgkin lymphoma: a correlative study from the E2496 Intergroup trial. Blood 2012;120:3280–7.PubMedCrossrefGoogle Scholar

  • 69.

    Elkabets M, Gifford AM, Scheel C. Human tumors instigate granulin-expressing hematopoietic cells that promote malignancy by activating stromal fibroblasts in mice. J Clin Invest 2011;121:784–99.PubMedCrossrefGoogle Scholar

  • 70.

    Wille C, Kohler C, Armacki M. Protein kinase D2 induces invasion of pancreatic cancer cells by regulating matrix metalloproteinases. Mol Biol Cell 2014;25:324–36.CrossrefPubMedGoogle Scholar

  • 71.

    Goddard JC, Sutton CD, Furness PN. A computer image analysis system for microvessel density measurement in solid tumours. Angiogenesis 2002;5:15–20.PubMedCrossrefGoogle Scholar

  • 72.

    Kim NT, Elie N, Plancoulaine B. An original approach for quantification of blood vessels on the whole tumour section. Anal Cell Pathol 2003;25:63–75.CrossrefPubMedGoogle Scholar

  • 73.

    Mikalsen LT, Dhakal HP, Bruland OS. Quantification of angiogenesis in breast cancer by automated vessel identification in CD34 immunohistochemical sections. Anticancer Res 2011;31:4053–60.PubMedGoogle Scholar

  • 74.

    Fridman WH, Pages F, Sautes-Fridman C. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer 2012;12:298–306.PubMedCrossrefGoogle Scholar

  • 75.

    Galon J, Mlecnik B, Bindea G. Towards the introduction of the ‘Immunoscore’ in the classification of malignant tumours. J Pathol 2014;232:199–209.CrossrefPubMedGoogle Scholar

  • 76.

    Ascierto ML, Kmieciak M, Idowu MO. A signature of immune function genes associated with recurrence-free survival in breast cancer patients. Breast Cancer Res Treat 2012;131:871–80.CrossrefPubMedGoogle Scholar

  • 77.

    Calabro A, Beissbarth T, Kuner R. Effects of infiltrating lymphocytes and estrogen receptor on gene expression and prognosis in breast cancer. Breast Cancer Res Treat 2009;116:69–77.PubMedCrossrefGoogle Scholar

  • 78.

    Gu-Trantien C, Loi S, Garaud S. CD4(+) follicular helper T cell infiltration predicts breast cancer survival. J Clin Invest 2013;123:2873–92.PubMedCrossrefGoogle Scholar

  • 79.

    Rody A, Holtrich U, Pusztai L. T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers. Breast Cancer Res 2009;11:R15.PubMedCrossrefGoogle Scholar

  • 80.

    Andre F, Dieci MV, Dubsky P. Molecular pathways: involvement of immune pathways in the therapeutic response and outcome in breast cancer. Clin Cancer Res 2013;19:28–33.CrossrefPubMedGoogle Scholar

  • 81.

    Basavanhally AN, Ganesan S, Agner S. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE Trans Biomed Eng 2010;57:642–53.CrossrefPubMedGoogle Scholar

  • 82.

    Janowczyk A, Chandran S, Madabhushi A. Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral from stromal regions. J Pathol Inform 2013;4:S8.PubMedCrossrefGoogle Scholar

  • 83.

    Setiadi AF, Ray NC, Kohrt HE. Quantitative, architectural analysis of immune cell subsets in tumor-draining lymph nodes from breast cancer patients and healthy lymph nodes. PLoS One 2010;5:e12420.PubMedCrossrefGoogle Scholar

  • 84.

    Kruger JM, Wemmert C, Sternberger L. Combat or surveillance? Evaluation of the heterogeneous inflammatory breast cancer microenvironment. J Pathol 2013;229:569–78.CrossrefPubMedGoogle Scholar

  • 85.

    Mansfield J, Nelson L, van der Loos C. Abstract C286: phenotyping TILs in situ: automated enumeration of Tregs and Tacts in solid tumors. Mol Cancer Ther 2013;12:C286.CrossrefGoogle Scholar

  • 86.

    Denkert C, Loibl S, Noske A. Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J Clin Oncol 2010;28:105–13.CrossrefPubMedGoogle Scholar

  • 87.

    Loi S, Sirtaine N, Piette F. Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98. J Clin Oncol 2013;31:860–7.CrossrefGoogle Scholar

  • 88.

    Mahmoud SM, Paish EC, Powe DG. Tumor-infiltrating CD8+ lymphocytes predict clinical outcome in breast cancer. J Clin Oncol 2011;29:1949–55.PubMedCrossrefGoogle Scholar

  • 89.

    Zhang L, Conejo-Garcia JR, Katsaros D. Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. N Engl J Med 2003;348:203–13.PubMedCrossrefGoogle Scholar

  • 90.

    Bindea G, Mlecnik B, Tosolini M. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 2013;39:782–95.PubMedCrossrefGoogle Scholar

  • 91.

    Chang AY, Bhattacharya N, Mu J. Spatial organization of dendritic cells within tumor draining lymph nodes impacts clinical outcome in breast cancer patients. J Transl Med 2013;11:242.CrossrefPubMedGoogle Scholar

  • 92.

    Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 2011;474:609–15.PubMedGoogle Scholar

  • 93.

    Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012;490:61–70.PubMedGoogle Scholar

  • 94.

    Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012;487:330–7.PubMedGoogle Scholar

  • 95.

    Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 2012;489:519–25.PubMedGoogle Scholar

  • 96.

    Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 2013;499:43–9.PubMedGoogle Scholar

  • 97.

    Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 2014;507:315–22.PubMedGoogle Scholar

  • 98.

    Rutledge WC, Kong J, Gao J. Tumor-infiltrating lymphocytes in glioblastoma are associated with specific genomic alterations and related to transcriptional class. Clin Cancer Res 2013;19:4951–60.PubMedCrossrefGoogle Scholar

  • 99.

    Davnall F, Yip CS, Ljungqvist G. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 2012;3:573–89.CrossrefPubMedGoogle Scholar

  • 100.

    Ganeshan B, Miles KA. Quantifying tumour heterogeneity with CT. Cancer Imaging 2013;13:140–9.CrossrefPubMedGoogle Scholar

  • 101.

    Liang Y, Wang F, Treanor D, Magee D, Roberts N, Teodoro G, et al. A framework for 3D vessel analysis using whole slide images of liver tissue sections. Int J Comput Biol Drug Des 2016;9:102–19.PubMedCrossrefGoogle Scholar

  • 102.

    Roberts N, Magee D, Song Y, Brabazon K, Shires M, Crellin D, et al. Toward routine use of 3D histopathology as a research tool. Am J Pathol 2012;180:1835–42.PubMedCrossrefGoogle Scholar

  • 103.

    Tot T, Pekár G, Hofmeyer S, Sollie T, Gere M, Tarján M. The distribution of lesions in 1–14-mm invasive breast carcinomas and its relation to metastatic potential. Virchows Archiv 2009;455:109–15.CrossrefGoogle Scholar

  • 104.

    Clarke GM, Peressotti C, Constantinou P, Hosseinzadeh D, Martel A, Yaffe MJ. Increasing specimen coverage using digital whole-mount breast pathology: implementation, clinical feasibility and application in research. Comput Med Imaging Graph 2011;35:531–41.PubMedCrossrefGoogle Scholar

  • 105.

    Clarke GM, Zubovits JT, Katic M, Peressotti C, Yaffe MJ. Spatial resolution requirements for acquisition of the virtual screening slide for digital whole-specimen breast histopathology. Human Pathol 2007;38:1764–71.CrossrefGoogle Scholar

  • 106.

    Liang Y, Wang F, Zhang P, Saltz JH, Brat DJ, Kong J. Development of a framework for large scale three-dimensional pathology and biomarker imaging and spatial analytics. AMIA Jt Summits Transl Sci Proc 2017;2017:75–84.PubMedGoogle Scholar

  • 107.

    Gevaert O, Xu J, Hoang CD. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data[mdash]methods and preliminary results. Radiology 2012;264:387–96.CrossrefGoogle Scholar

  • 108.

    Gutman DA, Cooper LA, Hwang SN. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 2013;267:560–9.PubMedCrossrefGoogle Scholar

  • 109.

    Jain R, Poisson L, Narang J. Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology 2013;267:212–20.PubMedCrossrefGoogle Scholar

  • 110.

    Zinn PO, Mahajan B, Sathyan P. Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. PLoS One 2011;6:e25451.PubMedCrossrefGoogle Scholar

  • 111.

    Bautista PA, Hashimoto N, Yagi Y. Color standardization in whole slide imaging using a color calibration slide. J Pathol Inform 2014;5:4.CrossrefPubMedGoogle Scholar

  • 112.

    Khan AM, Rajpoot N, Treanor D. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng 2014;61:1729–38.PubMedCrossrefGoogle Scholar

  • 113.

    Murakami Y, Abe T, Hashiguchi A. Color correction for automatic fibrosis quantification in liver biopsy specimens. J Pathol Inform 2013;4:36.PubMedCrossrefGoogle Scholar

  • 114.

    Chappelow J, Tomaszewski JE, Feldman M. HistoStitcher((c)): an interactive program for accurate and rapid reconstruction of digitized whole histological sections from tissue fragments. Comput Med Imaging Graph 2011;35:557–67.CrossrefGoogle Scholar

  • 115.

    Kothari S, Phan JH, Wang MD. Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade. J Pathol Inform 2013;4:22.CrossrefPubMedGoogle Scholar

  • 116.

    Tuominen VJ, Ruotoistenmaki S, Viitanen A. ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Res 2010;12:R56.CrossrefPubMedGoogle Scholar

  • 117.

    Varghese F, Bukhari AB, Malhotra R. IHC Profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples. PLoS One 2014;9:e96801.PubMedCrossrefGoogle Scholar

  • 118.

    Wang F, Kong J, Cooper L. A data model and database for high-resolution pathology analytical image informatics. J Pathol Inform 2011;2:32.PubMedCrossrefGoogle Scholar

  • 119.

    Wang F, Kong J, Gao J. A high-performance spatial database based approach for pathology imaging algorithm evaluation. J Pathol Inform 2013;4:5.CrossrefPubMedGoogle Scholar

  • 120.

    Gutman DA, Cobb J, Somanna D. Cancer digital slide archive: an informatics resource to support integrated in silico analysis of TCGA pathology data. J Am Med Inform Assoc 2013;20:1091–8.CrossrefPubMedGoogle Scholar

  • 121.

    Kong J, Cooper LA, Wang F, Gutman DA, Gao J, Chisolm C, et al. Integrative, multimodal analysis of glioblastoma using TCGA molecular data, pathology images, and clinical outcomes. IEEE Trans Biomed Eng 2011;58:3469–74.CrossrefPubMedGoogle Scholar

  • 122.

    Jara-Lazaro AR, Thamboo TP, Teh M, Tan PH. Digital pathology: exploring its applications in diagnostic surgical pathology practice. Pathology 2010;42:512–8.PubMedCrossrefGoogle Scholar

About the article

Corresponding author: George M. Yousef, MD, PhD, FRCPC (Path), MSc, MBBCh, Department of Pediatric Laboratory Medicine, The Hospital for Sick Children, 555 University Avenue, Toronto, ON M5G 1X8, Canada; Department of Laboratory Medicine, and the Keenan Research Centre for Biomedical Science at the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada; and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada, Phone: +416-813-5990, Fax: +416-813-8674


Received: 2018-07-25

Accepted: 2018-09-28

Published Online: 2018-12-20


Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: This work was supported by grants from the Kidney Cancer Research Network of Canada and the Canadian Urology Oncology group (KCRNC/CUOG) research trainee award, the Ontario Institute of Cancer Research (Transformative Pathology Fellowship award RFTP-004), and Ontario Molecular Pathology Research Network (OMPRN stream 1 grant #CPTRG-004).

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.


Citation Information: Diagnosis, 20180064, ISSN (Online) 2194-802X, ISSN (Print) 2194-8011, DOI: https://doi.org/10.1515/dx-2018-0064.

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