1932

Abstract

Pathology is essential for research in disease and development, as well as for clinical decision making. For more than 100 years, pathology practice has involved analyzing images of stained, thin tissue sections by a trained human using an optical microscope. Technological advances are now driving major changes in this paradigm toward digital pathology (DP). The digital transformation of pathology goes beyond recording, archiving, and retrieving images, providing new computational tools to inform better decision making for precision medicine. First, we discuss some emerging innovations in both computational image analytics and imaging instrumentation in DP. Second, we discuss molecular contrast in pathology. Molecular DP has traditionally been an extension of pathology with molecularly specific dyes. Label-free, spectroscopic images are rapidly emerging as another important information source, and we describe the benefits and potential of this evolution. Third, we describe multimodal DP, which is enabled by computational algorithms and combines the best characteristics of structural and molecular pathology. Finally, we provide examples of application areas in telepathology, education, and precision medicine. We conclude by discussing challenges and emerging opportunities in this area.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-bioeng-112415-114722
2016-07-11
2024-05-26
Loading full text...

Full text loading...

/deliver/fulltext/bioeng/18/1/annurev-bioeng-112415-114722.html?itemId=/content/journals/10.1146/annurev-bioeng-112415-114722&mimeType=html&fmt=ahah

Literature Cited

  1. Ghaznavi F, Evans A, Madabhushi A, Feldman M. 1.  2013. Digital imaging in pathology: whole-slide imaging and beyond. Annu. Rev. Pathol. Mech. Dis. 8:331–59 [Google Scholar]
  2. Madabhushi A.2.  2009. Digital pathology image analysis: opportunities and challenges. Imaging Med. 1:7–10 [Google Scholar]
  3. Pantanowitz L, Sinard JH, Henricks WH, Fatheree LA, Carter AB. 3.  et al. 2013. Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center. Arch. Pathol. Lab. Med. 137:1710–22 [Google Scholar]
  4. Snead DR, Tsang Y-W, Meskiri A, Kimani PK, Crossman R. 4.  et al. 2015. Validation of digital pathology imaging for primary histopathological diagnosis. Histopathology. In press. doi: 10.1111/his.12879
  5. Farahani N, Pantanowitz L. 5.  2015. Overview of telepathology. Surg. Pathol. Clin. 8:223–31 [Google Scholar]
  6. Pantanowitz L, Dickinson K, Evans AJ, Hassell LA, Henricks WH. 6.  et al. 2014. American Telemedicine Association clinical guidelines for telepathology. J. Pathol. Inform. 5:39 [Google Scholar]
  7. Pantanowitz L, McHugh J, Cable W, Zhao C, Parwani AV. 7.  2015. Imaging file management to support international telepathology. J. Pathol. Inform. 6:17 [Google Scholar]
  8. Prichard JW, Davison JM, Campbell BB, Repa KA, Reese LM. 8.  et al. 2015. TissueCypher™: a systems biology approach to anatomic pathology. J. Pathol. Inform. 6:48 [Google Scholar]
  9. Ali S, Veltri R, Epstein JI, Christudass C, Madabhushi A. 9.  2015. Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays. Comput. Med. Imaging Graph. 41:3–13 [Google Scholar]
  10. Ali S, Madabhushi A. 10.  2012. An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery. IEEE Trans. Med. Imaging 31:1448–60 [Google Scholar]
  11. Ali S, Madabhushi A. 11.  2011. Graphical processing unit implementation of an integrated shape-based active contour: application to digital pathology. J. Pathol. Inform. 2:S13 [Google Scholar]
  12. Fatakdawala H, Xu J, Basavanhally A, Bhanot G, Ganesan S. 12.  et al. 2010. Expectation-maximization-driven geodesic active contour with overlap resolution (EMaGACOR): application to lymphocyte segmentation on breast cancer histopathology. IEEE Trans. Biomed. Eng. 57:1676–89 [Google Scholar]
  13. Monaco J, Hipp J, Lucas D, Smith S, Balis U, Madabhushi A. 13.  2012. Image segmentation with implicit color standardization using spatially constrained expectation maximization: detection of nuclei. Med. Image Comput. Comput. Assist. Interv. 15:365–72 [Google Scholar]
  14. Veta M, van Diest PJ, Willems SM, Wang H, Madabhushi A. 14.  et al. 205. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med. Image Anal. 20:237–48 [Google Scholar]
  15. Wang AH, Cruz A, Basavanhally A, Gilmore H, Shih N. 15.  et al. 2014. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J. Med. Imaging 1:034003 [Google Scholar]
  16. Basavanhally AN, Ganesan S, Agner S, Monaco JP, Feldman MD. 16.  et al. 2010. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE Trans. Biomed. Eng. 57:642–53 [Google Scholar]
  17. Chen J-M, Qu A-P, Wang L-W, Yuan J-P, Yang F. 17.  et al. 2015. New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images. Sci. Rep. 5:10690 [Google Scholar]
  18. Chu P, Weiss L. 18.  2014. Modern Immunohistochemistry Cambridge, UK: Cambridge Univ. Press, 2nd ed.
  19. Camp RL, Chung GG, Rimm DL. 19.  2002. Automated subcellular localization and quantification of protein expression in tissue microarrays. Nat. Med. 8:1323–27 [Google Scholar]
  20. Angelo M, Bendall SC, Finck R, Hale MB, Hitzman C. 20.  et al. 2014. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20:436–42 [Google Scholar]
  21. Xing Y, Chaudry Q, Shen C, Kong KY, Zhau HE. 21.  et al. 2007. Bioconjugated quantum dots for multiplexed and quantitative immunohistochemistry. Nat. Protoc. 2:1152–65 [Google Scholar]
  22. Evans CL, Potma EO, Puoris'haag M, Côté D, Lin CP, Xie XS. 22.  2005. Chemical imaging of tissue in vivo with video-rate coherent anti-Stokes Raman scattering microscopy. PNAS 102:16807–12 [Google Scholar]
  23. Freudiger CW, Min W, Saar BG, Lu S, Holtom GR. 23.  et al. 2008. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science 322:1857–61 [Google Scholar]
  24. Nandakumar P, Kovalev A, Volkmer A. 24.  2009. Vibrational imaging based on stimulated Raman scattering microscopy. New J. Phys. 11:033026 [Google Scholar]
  25. Yeh K, Kenkel S, Liu J-N, Bhargava R. 25.  2015. Fast infrared chemical imaging with a quantum cascade laser. Anal. Chem. 87:485–93 [Google Scholar]
  26. Lasch P, Haensch W, Naumann D, Diem M. 26.  2004. Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis. Biochim. Biophys. Acta 1688:176–86 [Google Scholar]
  27. Al-Janabi S, Huisman A, Van Diest PJ. 27.  2012. Digital pathology: current status and future perspectives. Histopathology 61:1–9 [Google Scholar]
  28. Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B. 28.  2009. Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2:147–71 [Google Scholar]
  29. Irshad H, Veillard A, Roux L, Racoceanu D. 29.  2014. Methods for nuclei detection, segmentation, and classification in digital histopathology: a review—current status and future potential. IEEE Rev. Biomed. Eng. 7:97–114 [Google Scholar]
  30. Pantanowitz L, Valenstein PN, Evans AJ, Kaplan KJ, Pfeifer JD. 30.  et al. 2011. Review of the current state of whole slide imaging in pathology. J. Pathol. Inform. 2:36 [Google Scholar]
  31. Rocha R, Vassallo J, Soares F, Miller K, Gobbi H. 31.  2009. Digital slides: present status of a tool for consultation, teaching, and quality control in pathology. Pathol. Res. Pract. 205:735–41 [Google Scholar]
  32. Kanade T.32.  1973. Picture processing system by computer complex and recognition of human faces PhD thesis, Dep. Inf. Sci., Kyoto Univ., Kyoto, Jpn.
  33. Nishikawa RM, Giger ML, Doi K, Vyborny CJ, Schmidt RA. 33.  1993. Computer-aided detection of clustered microcalcifications: an improved method for grouping detected signals. Med. Phys. 20:1661–66 [Google Scholar]
  34. Prewitt JM, Mendelsohn ML. 34.  1966. The analysis of cell images. Ann. N.Y. Acad. Sci. 128:1035–53 [Google Scholar]
  35. Jack CR Jr., Bernstein MA, Fox NC, Thompson P, Alexander G. 35.  et al. 2008. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27:685–91 [Google Scholar]
  36. Mincione G, Di Nicola M, Di Marcantonio MC, Muraro R, Piattelli A. 36.  et al. 2015. Nuclear fractal dimension in oral squamous cell carcinoma: a novel method for the evaluation of grading, staging, and survival. J. Oral Pathol. Med. 44:680–84 [Google Scholar]
  37. Khan AM, El-Daly H, Rajpoot N. 37.  2012. RanPEC: random projections with ensemble clustering for segmentation of tumor areas in breast histology images. Proceedings of the 16th Conference on Medical Image Understanding and Analysis X Xie 17–23 Swansea, UK: Dep. Comput. Sci., Univ. Swansea
  38. Hipp JD, Cheng JY, Toner M, Tompkins RG, Balis UJ. 38.  2011. Spatially invariant vector quantization: a pattern matching algorithm for multiple classes of image subject matter including pathology. J. Pathol. Inform. 2:13 [Google Scholar]
  39. Kong J, Sertel O, Shimada H, Boyer K, Saltz J, Gurcan M. 39.  2007. Computer-aided grading of neuroblastic differentiation: multi-resolution and multi-classifier approach. Proceedings of the IEEE International Conference on Image Processing 5525–28 Piscataway, NJ: IEEE
  40. Wang Y-Y, Chang S-C, Wu L-W, Tsai S-T, Sun Y-N. 40.  2007. A color-based approach for automated segmentation in tumor tissue classification. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society6577–80 Piscataway, NJ: IEEE
  41. Kwak JT, Hewitt SM, Bhargava R. 41.  2011. Multimodal microscopy for automated histologic analysis of prostate cancer. BMC Cancer 11:62 [Google Scholar]
  42. Guo Y, Xu X, Wang Y, Wang Y, Xia S, Yang Z. 42.  2014. An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images. Microsc. Res. Tech. 77:547–59 [Google Scholar]
  43. Gurcan MN, Madabhushi A, Rajpoot N. 43.  2010. Pattern recognition in histopathological images: an ICPR 2010 contest. Recognizing Patterns in Signals, Speech, Images and Videos D Ünay, Z Çataltepe, S Aksoy 226–34 Berlin: Springer [Google Scholar]
  44. Basavanhally A, Ganesan S, Feldman M, Shih N, Mies C. 44.  et al. 2013. Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides. IEEE Trans. Biomed. Eng. 60:2089–99 [Google Scholar]
  45. Doyle S, Feldman MD, Shih N, Tomaszewski J, Madabhushi A. 45.  2012. Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer. BMC Bioinform. 13:282 [Google Scholar]
  46. Sparks R, Madabhushi A. 46.  2013. Statistical shape model for manifold regularization: Gleason grading of prostate histology. Comput. Vis. Image Underst. 117:1138–46 [Google Scholar]
  47. Sparks R, Madabhushi A. 47.  2013. Explicit shape descriptors: novel morphologic features for histopathology classification. Med. Image Anal. 17:997–1009 [Google Scholar]
  48. Kass M, Witkin A, Terzopoulos D. 48.  1988. Snakes: active contour models. Int. J. Comput. Vis. 1:321–31 [Google Scholar]
  49. Osher S, Sethian JA. 49.  1988. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys. 79:12–49 [Google Scholar]
  50. van Ginneken B, Frangi AF, Staal JJ, ter Haar Romeny BM, Viergever MA. 50.  2002. Active shape model segmentation with optimal features. IEEE Trans. Med. Imaging 21:924–33 [Google Scholar]
  51. Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. 51.  2013. Mitosis detection in breast cancer histology images with deep neural networks. Med. Image Comput. Comput. Assist. Interv. 16:411–18 [Google Scholar]
  52. Cruz-Roa A, Basavanhally A, González F, Gilmore H, Feldman M. 52.  et al. 2014. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. Proc. SPIE 9041:904103 [Google Scholar]
  53. Bilgin C, Demir C, Nagi C, Yener B. 53.  2007. Cell-graph mining for breast tissue modeling and classification. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society5311–14 Piscataway, NJ: IEEE
  54. Marcelpoil R.54.  1993. Normalization of the minimum spanning tree. Anal. Cell Pathol. 5:177–86 [Google Scholar]
  55. Ali S, Lewis J, Madabhushi A. 55.  2013. Spatially aware cell cluster (spACC1) graphs: predicting outcome in oropharyngeal pl6+ tumors. Med. Image Comput. Comput. Assist. Interv. 16:412–19 [Google Scholar]
  56. Ali S, Veltri R, Epstein JA, Christudass C, Madabhushi A. 56.  2013. Cell cluster graph for prediction of biochemical recurrence in prostate cancer patients from tissue microarrays. Proc. SPIE 8676:H86760 [Google Scholar]
  57. Lewis JS Jr., Ali S, Luo J, Thorstad WL, Madabhushi A. 57.  2014. A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma. Am. J. Surg. Pathol. 38:128–37 [Google Scholar]
  58. Lee G, Sparks R, Ali S, Shih NNC, Feldman MD. 58.  et al. 2014. Co-occurring gland angularity in localized subgraphs: predicting biochemical recurrence in intermediate-risk prostate cancer patients. PLOS ONE 9:e97954 [Google Scholar]
  59. Yu E, Monaco JP, Tomaszewski J, Shih N, Feldman M, Madabhushi A. 59.  2011. Detection of prostate cancer on histopathology using color fractals and probabilistic pairwise Markov models. Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society3427–30 Piscataway, NJ: IEEE
  60. Lee G, Ali S, Veltri R, Epstein JI, Christudass C, Madabhushi A. 60.  2013. Cell orientation entropy (COrE): predicting biochemical recurrence from prostate cancer tissue microarrays. Med. Image Comput. Comput. Assist. Interv. 16:396–403 [Google Scholar]
  61. Jain AK, Farrokhnia F. 61.  1991. Unsupervised texture segmentation using Gabor filters. Pattern Recognit. 24:1167–86 [Google Scholar]
  62. Ojala T, Pietikainen M, Harwood D. 62.  1994. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. Proceedings of the 12th IAPR International Conference on Pattern Recognition 1582–85 Los Alamitos, CA: IEEE Comput. Soc. Press
  63. Laws KI.63.  1980. Textured image segmentation Univ. South. Calif. Image Proc. Inst. (USCIPI) rep. 940, Dep. Electr. Eng., USCIPI, Los Angeles
  64. Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO. 64.  et al. 2011. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci. Transl. Med. 3:108ra113 [Google Scholar]
  65. Gertych A, Ing N, Ma Z, Fuchs TJ, Salman S. 65.  et al. 2015. Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput. Med. Imaging Graph. 46:197–208 [Google Scholar]
  66. Doyle S, Feldman M, Tomaszewski J, Madabhushi A. 66.  2012. A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans. Biomed. Eng. 59:1205–18 [Google Scholar]
  67. Bellman R. 67.  1957. Dynamic Programming Princeton, NJ: Princeton Univ. Press
  68. Lee G, Rodriguez C, Madabhushi A. 68.  2008. Investigating the efficacy of nonlinear dimensionality reduction schemes in classifying gene and protein expression studies. IEEE/ACM Trans. Comput. Biol. Bioinform. 5:368–84 [Google Scholar]
  69. Ginsburg S, Lee G, Ali S, Madabhushi A. 69.  2015. Feature importance in nonlinear embeddings (FINE): applications in digital pathology. IEEE Trans. Med. Imaging 99:1 [Google Scholar]
  70. Paik S, Shak S, Tang G, Kim C, Baker J. 70.  et al. 2004. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351:2817–26 [Google Scholar]
  71. Ali S, Lewis JS, Madabhushi A. 71.  2012. Use of quantitative histomorphometrics to classify disease progression in HPV-positive squamous cell carcinoma. J. Clin. Oncol. 30:73 (abstr.) [Google Scholar]
  72. Boyce BF.72.  2015. Whole slide imaging: uses and limitations for surgical pathology and teaching. Biotech. Histochem. 90:321–30 [Google Scholar]
  73. Hartman DJ.73.  2015. Mobile technologies for the surgical pathologist. Surg. Pathol. Clin. 8:233–38 [Google Scholar]
  74. Jones NC, Nazarian RM, Duncan LM, Kamionek M, Lauwers GY. 74.  et al. 2015. Interinstitutional whole slide imaging teleconsultation service development: assessment using internal training and clinical consultation cases. Arch. Pathol. Lab. Med. 139:627–35 [Google Scholar]
  75. Desai S, Patil R, Kothari A, Shet T, Kane S. 75.  et al. 2004. Static telepathology consultation service between Tata Memorial Centre, Mumbai and Nargis Dutt Memorial Charitable Hospital, Barshi, Solapur, Maharashtra: an analysis of the first 100 cases. Indian J. Pathol. Microbiol. 47:480–85 [Google Scholar]
  76. Leifer Z.76.  2015. The use of virtual microscopy and a wiki in pathology education: tracking student use, involvement, and response. J. Pathol. Inform. 6:30 [Google Scholar]
  77. Van Es SL, Kumar RK, Pryor WM, Salisbury EL, Velan GM. 77.  2015. Cytopathology whole slide images and adaptive tutorials for postgraduate pathology trainees: a randomized crossover trial. Hum. Pathol. 46:1297–305 [Google Scholar]
  78. Kaplan KJ.78.  2012. PathXchange case of the week. Digital Pathology Blog June 20. http://tissuepathology.com/2012/06/20/pathxchange-case-of-the-week-1#axzz3pyCcAMuX
  79. Sağol Ö, Yörükoğlu K, Lebe B, Durak MG, Ulukuş Ç. 79.  et al. 2015. Transition to virtual microscopy in medical undergraduate pathology education: first experience of Turkey in Dokuz Eylül University Hospital. Türk Patol. Derg. 31:175–80 [Google Scholar]
  80. Engelberg JA, Retallack H, Balassanian R, Dowsett M, Zabaglo L. 80.  et al. 2015. “Score the Core” web-based pathologist training tool improves the accuracy of breast cancer IHC4 scoring. Hum. Pathol. 46:1694–704 [Google Scholar]
  81. Walkowski S, Lundin M, Szymas J, Lundin J. 81.  2014. Students’ performance during practical examination on whole slide images using view path tracking. Diagn. Pathol. 9:208 [Google Scholar]
  82. Bhargava R.82.  2012. Infrared spectroscopic imaging: the next generation. Appl. Spectrosc. 66:1091–120 [Google Scholar]
  83. Diem M, Mazur A, Lenau K, Schubert J, Bird B. 83.  et al. 2013. Molecular pathology via IR and Raman spectral imaging. J. Biophotonics 6:855–86 [Google Scholar]
  84. Levin IW, Bhargava R. 84.  2005. Fourier transform infrared vibrational spectroscopic imaging: integrating microscopy and molecular recognition. Annu. Rev. Phys. Chem. 56:429–74 [Google Scholar]
  85. Bhargava R, Fernandez DC, Hewitt SM, Levin IW. 85.  2006. High throughput assessment of cells and tissues: Bayesian classification of spectral metrics from infrared vibrational spectroscopic imaging data. Biochim. Biophys. Acta 1758:830–45 [Google Scholar]
  86. Kodali AK, Schulmerich M, Ip J, Yen G, Cunningham BT, Bhargava R. 86.  2010. Narrowband midinfrared reflectance filters using guided mode resonance. Anal. Chem. 82:5697–706 [Google Scholar]
  87. Liu J-N, Schulmerich MV, Bhargava R, Cunningham BT. 87.  2011. Optimally designed narrowband guided-mode resonance reflectance filters for mid-infrared spectroscopy. Opt. Express 19:24182–97 [Google Scholar]
  88. Geiger FB, Koerdel M, Schick A, Heimann A, Matiasek K, Herkommer AM. 88.  2015. Concept and setup for intraoperative imaging of tumorous tissue via attenuated total reflection spectrosocopy with quantum cascade lasers. Proc. SPIE 9412:F94125 [Google Scholar]
  89. Chan KLA, Kazarian SG. 89.  2007. Chemical imaging of the stratum corneum under controlled humidity with the attenuated total reflection Fourier transform infrared spectroscopy method. J. Biomed. Opt. 12:044010 [Google Scholar]
  90. Kazarian SG, Chan KLA. 90.  2006. Applications of ATR-FTIR spectroscopic imaging to biomedical samples. Biochim. Biophys. Acta 1758:858–67 [Google Scholar]
  91. Marcott C, Lo M, Kjoller K, Domanov Y, Balooch G, Luengo GS. 91.  2013. Nanoscale infrared (IR) spectroscopy and imaging of structural lipids in human stratum corneum using an atomic force microscope to directly detect absorbed light from a tunable IR laser source. Exp. Dermatol. 22:419–21 [Google Scholar]
  92. Centrone A.92.  2015. Infrared imaging and spectroscopy beyond the diffraction limit. Annu. Rev. Anal. Chem. 8:101–26 [Google Scholar]
  93. Nasse MJ, Walsh MJ, Mattson EC, Reininger R, Kajdacsy-Balla A. 93.  et al. 2011. High-resolution Fourier-transform infrared chemical imaging with multiple synchrotron beams. Nat. Methods 8:413–16 [Google Scholar]
  94. Reddy RK, Walsh MJ, Schulmerich MV, Carney PS, Bhargava R. 94.  2013. High-definition infrared spectroscopic imaging. Appl. Spectrosc. 67:93–105 [Google Scholar]
  95. van Dijk T, Mayerich D, Bhargava R, Carney PS. 95.  2013. Rapid spectral-domain localization. Opt. Express 21:12822–30 [Google Scholar]
  96. van Dijk Mayerich T D, Carney PS, Bhargava R. 96.  2016. Understanding the optics-sample interaction in infrared spectroscopic imaging. Analyst.In press
  97. Reddy RK, Bhargava R. 97.  2010. Accurate histopathology from low signal-to-noise ratio spectroscopic imaging data. Analyst 135:2818–25 [Google Scholar]
  98. Phillips MC, N. 98.  2008. Infrared hyperspectral imaging using a broadly tunable external cavity quantum cascade laser and microbolometer focal plane array. Opt. Express 16:1836–45 [Google Scholar]
  99. Serrano AL, Ghosh A, Ostrander JS, Zanni MT. 99.  2015. Wide-field FTIR microscopy using mid-IR pulse shaping. Opt. Express 23:17815–27 [Google Scholar]
  100. Capasso F.100.  2010. High-performance midinfrared quantum cascade lasers. Opt. Eng. 49:111102 [Google Scholar]
  101. Faist J, Capasso F, Sivco DL, Sirtori C, Hutchinson AL, Cho AY. 101.  1994. Quantum cascade laser. Science 264:553–56 [Google Scholar]
  102. Luo G, Peng C, Le H, Pei S, Hwang W-Y. 102.  et al. 2001. Grating-tuned external-cavity quantum-cascade semiconductor lasers. Appl. Phys. Lett. 78:2834–36 [Google Scholar]
  103. Leslie LS, Wrobel TP, Mayerich D, Bindra S, Emmadi R, Bhargava R. 103.  2015. High definition infrared spectroscopic imaging for lymph node histopathology. PLOS ONE 10:e0127238 [Google Scholar]
  104. Sreedhar H, Varma VK, Nguyen PL, Davidson B, Akkina S. 104.  et al. 2015. High-definition Fourier transform infrared (FT-IR) spectroscopic imaging of human tissue sections towards improving pathology. J. Vis. Exp. 95:52332 [Google Scholar]
  105. Kole MR, Reddy RK, Schulmerich MV, Gelber MK, Bhargava R. 105.  2012. Discrete frequency infrared microspectroscopy and imaging with a tunable quantum cascade laser. Anal. Chem. 84:10366–72 [Google Scholar]
  106. Bassan P, Weida MJ, Rowlette J, Gardner P. 106.  2014. Large scale infrared imaging of tissue micro arrays (TMAs) using a tunable quantum cascade laser (QCL) based microscope. Analyst 139:3856–59 [Google Scholar]
  107. Kröger N, Egl A, Engel M, Gretz N, Haase K. 107.  et al. 2014. Quantum cascade laser–based hyperspectral imaging of biological tissue. J. Biomed. Opt. 19:111607 [Google Scholar]
  108. Mohlenhoff B, Romeo M, Diem M, Wood BR. 108.  2005. Mie-type scattering and non-Beer-Lambert absorption behavior of human cells in infrared microspectroscopy. Biophys. J. 88:3635–40 [Google Scholar]
  109. Kwak JT, Reddy R, Sinha S, Bhargava R. 109.  2012. Analysis of variance in spectroscopic imaging data from human tissues. Anal. Chem. 84:1063–69 [Google Scholar]
  110. Nallala J, Piot O, Diebold M-D, Gobinet C, Bouché O. 110.  et al. 2013. Infrared imaging as a cancer diagnostic tool: introducing a new concept of spectral barcodes for identifying molecular changes in colon tumors. Cytometry A 83:294–300 [Google Scholar]
  111. Bassan P, Kohler A, Martens H, Lee J, Byrne HJ. 111.  et al. 2010. Resonant Mie scattering (RMieS) correction of infrared spectra from highly scattering biological samples. Analyst 135:268–77 [Google Scholar]
  112. Bassan P, Kohler A, Martens H, Lee J, Jackson E. 112.  et al. 2010. RMieS-EMSC correction for infrared spectra of biological cells: extension using full Mie theory and GPU computing. J. Biophotonics 3:609–20 [Google Scholar]
  113. van Dijk T, Mayerich D, Carney PS, Bhargava R. 113.  2013. Recovery of absorption spectra from Fourier transform infrared (FT-IR) microspectroscopic measurements of intact spheres. Appl. Spectrosc. 67:546–52 [Google Scholar]
  114. Holton SE, Bergamaschi A, Katzenellenbogen BS, Bhargava R. 114.  2014. Integration of molecular profiling and chemical imaging to elucidate fibroblast–microenvironment impact on cancer cell phenotype and endocrine resistance in breast cancer. PLOS ONE 9:e96878 [Google Scholar]
  115. Holton SE, Walsh MJ, Kajdacsy-Balla A, Bhargava R. 115.  2011. Label-free characterization of cancer-activated fibroblasts using infrared spectroscopic imaging. Biophys. J. 101:1513–21 [Google Scholar]
  116. Kumar S, Shabi TS, Goormaghtigh E. 116.  2014. A FTIR imaging characterization of fibroblasts stimulated by various breast cancer cell lines. PLOS ONE 9:e111137 [Google Scholar]
  117. Wald N, Goormaghtigh E. 117.  2015. Infrared imaging of primary melanomas reveals hints of regional and distant metastases. Analyst 140:2144–55 [Google Scholar]
  118. Baker MJ, Trevisan J, Bassan P, Bhargava R, Butler HJ. 118.  et al. 2014. Using Fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc. 9:1771–91 [Google Scholar]
  119. Tiwari S, Bhargava R. 119.  2015. Extracting knowledge from chemical imaging data using computational algorithms for digital cancer diagnosis. Yale J. Biol. Med. 88:131–43 [Google Scholar]
  120. Brady SP, Do MN, Bhargava R. 120.  2009. Reconstructing FT-IR spectroscopic imaging data with a sparse prior. Proceedings of the 16th International IEEE Conference on Image Processing829–32 Piscataway, NJ: IEEE
  121. Mayerich D, RB Walsh MJ, Schulmerich MV. 121.  2013. Real-time interactive data mining for chemical imaging information: application to automated histopathology. BMC Bioinform. 14:156 [Google Scholar]
  122. Stack EC, Wang C, Roman KA, Hoyt CC. 122.  2014. Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of Tyramide signal amplification, multispectral imaging and multiplex analysis. Methods 70:46–58 [Google Scholar]
  123. Sweeney E, Ward TH, Gray N, Womack C, Jayson G. 123.  et al. 2008. Quantitative multiplexed quantum dot immunohistochemistry. Biochem. Biophys. Res. Commun. 374:181–86 [Google Scholar]
  124. Teverovskiy M, Vengrenyuk Y, Tabesh A, Sapir M, Fogarasi S. 124.  et al. 2008. Automated localization and quantification of protein multiplexes via multispectral fluorescence imaging. Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro300–3 Piscataway, NJ: IEEE
  125. Diem M.125.  2015. Modern Vibrational Spectroscopy and Micro-Spectroscopy: Theory, Instrumentation and Biomedical Applications New York: Wiley
  126. Hughes C, Baker MJ. 126.  2016. Can mid-infrared biomedical spectroscopy of cells, fluids and tissue aid improvements in cancer survival? A patient paradigm. Analyst 141:467–75 [Google Scholar]
  127. Keith FN, Reddy RK, Bhargava R. 127.  2016. Development of a practical spatial-spectral analysis protocol for breast histopathology using Fourier transform infrared spectroscopic imaging. Faraday Discuss. doi: 10.1039/C5FD00199D. 27 pp.
  128. Akalin A, Mu X, Kon MA, Ergin A, Remiszewski SH. 128.  et al. 2015. Corrigendum: Classification of malignant and benign tumors of the lung by infrared spectral histopathology (SHP). Lab. Investig. 95:697 [Google Scholar]
  129. Kwak JT, Kajdacsy-Balla A, Macias V, Walsh M, Sinha S, Bhargava R. 129.  2015. Improving prediction of prostate cancer recurrence using chemical imaging. Sci. Rep. 5:8758 [Google Scholar]
  130. Beleites C, Salzer R. 130.  2008. Assessing and improving the stability of chemometric models in small sample size situations. Anal. Bioanal. Chem. 390:1261–71 [Google Scholar]
  131. Bhargava R.131.  2007. Towards a practical Fourier transform infrared chemical imaging protocol for cancer histopathology. Anal. Bioanal. Chem. 389:1155–69 [Google Scholar]
  132. Fernandez DC, Bhargava R, Hewitt SM, Levin IW. 132.  2005. Infrared spectroscopic imaging for histopathologic recognition. Nat. Biotechnol. 23:469–74 [Google Scholar]
  133. Petibois C, Déléris G. 133.  2006. Chemical mapping of tumor progression by FT-IR imaging: towards molecular histopathology. Trends Biotechnol. 24:455–62 [Google Scholar]
  134. Kumar S, Desmedt C, Larsimont D, Sotiriou C, Goormaghtigh E. 134.  2013. Change in the microenvironment of breast cancer studied by FTIR imaging. Analyst 138:4058–65 [Google Scholar]
  135. Travo A, Piot O, Wolthuis R, Gobinet C, Manfait M. 135.  et al. 2010. IR spectral imaging of secreted mucus: a promising new tool for the histopathological recognition of human colonic adenocarcinomas. Histopathology 56:921–31 [Google Scholar]
  136. Hughes C, Iqbal-Wahid J, Brown M, Shanks JH, Eustace A. 136.  et al. 2013. FTIR microspectroscopy of selected rare diverse sub-variants of carcinoma of the urinary bladder. J. Biophotonics 6:73–87 [Google Scholar]
  137. Kong R, Reddy RK, Bhargava R. 137.  2010. Characterization of tumor progression in engineered tissue using infrared spectroscopic imaging. Analyst 135:1569–78 [Google Scholar]
  138. Kumar V, Abbas AK, Fausto N, Aster JC. 138.  2014. Robbins and Cotran Pathologic Basis of Disease, Professional Edition Philadelphia: Saunders Elsevier, 8th ed.
  139. Bratthauer GL, Moinfar F, Stamatakos MD, Mezzetti TP, Shekitka KM. 139.  et al. 2002. Combined E-cadherin and high molecular weight cytokeratin immunoprofile differentiates lobular, ductal, and hybrid mammary intraepithelial neoplasias. Hum. Pathol. 33:620–27 [Google Scholar]
  140. Mayerich D, Walsh MJ, Kadjacsy-Balla A, Ray PS, Hewitt SM, Bhargava R. 140.  2015. Stain-less staining for computed histopathology. Technology 3:27–31 [Google Scholar]
  141. Boutros PC, Fraser M, Harding NJ, de Borja R, Trudel D. 141.  et al. 2015. Spatial genomic heterogeneity within localized, multifocal prostate cancer. Nat. Genet. 47:736–45 [Google Scholar]
  142. Gallee MPW, Ten Kate FJW, Mulder PGH, Blom JHM, Van der Heul RO. 142.  1990. Histological grading of prostatic carcinoma in prostatectomy specimens. Comparison of prognostic accuracy of five grading systems. Br. J. Urol. 65:368–75 [Google Scholar]
  143. Albertsen PC.143.  2008. Predicting survival for men with clinically localized prostate cancer: What do we need in contemporary practice?. Cancer 112:1–3 [Google Scholar]
  144. Albertsen PC.144.  2010. Treatment of localized prostate cancer: When is active surveillance appropriate?. Nat. Rev. Clin. Oncol. 7:394–400 [Google Scholar]
  145. Albertsen PC, Hanley JA, Fine J. 145.  2005. 20-year outcomes following conservative management of clinically localized prostate cancer. JAMA 293:2095–101 [Google Scholar]
  146. Albertsen PC, Hanley JA, Penson DF, Barrows G, Fine J. 146.  2007. 13-year outcomes following treatment for clinically localized prostate cancer in a population based cohort. J. Urol. 177:932–36 [Google Scholar]
  147. Epstein JI.147.  2010. An update of the Gleason grading system. J. Urol. 183:433–40 [Google Scholar]
  148. Fine SW, Epstein JI. 148.  2008. A contemporary study correlating prostate needle biopsy and radical prostatectomy Gleason score. J. Urol. 179:1335–38 [Google Scholar]
  149. Singanamalli A, Wang H, Lee G, Shih N, Ziober A. 149.  et al. 2014. Supervised multi-view canonical correlation analysis: fused multimodal prediction of disease prognosis. Proc. SPIE 9038:903805 [Google Scholar]
  150. Viswanath S, Madabhushi A. 150.  2012. Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data. BMC Bioinform. 13:26 [Google Scholar]
  151. Golugula A, Lee G, Master SR, Feldman MD, Tomaszewski JE. 151.  et al. 2011. Supervised regularized canonical correlation analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery. BMC Bioinform. 12:483 [Google Scholar]
  152. Lee G, Singanamalli A, Wang H, Feldman M, Master S. 152.  et al. 2014. Supervised multi-view canonical correlation analysis (SMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer. IEEE Trans. Med. Imaging 34:284–97 [Google Scholar]
  153. Tiwari P, Kurhanewicz J, Madabhushi A. 153.  2013. Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS. Med. Image Anal. 17:219–35 [Google Scholar]
  154. Chang H, Han J, Borowsky A, Loss L, Gray JW. 154.  et al. 2013. Invariant delineation of nuclear architecture in glioblastoma multiforme for clinical and molecular association. IEEE Trans. Med. Imaging 32:670–82 [Google Scholar]
  155. Großerueschkamp F, Kallenbach-Thieltges A, Behrens T, Brüning T, Altmayer M. 155.  et al. 2015. Marker-free automated histopathological annotation of lung tumour subtypes by FTIR imaging. Analyst 140:2114–20 [Google Scholar]
  156. Kallenbach-Thieltges A, Großerüschkamp F, Mosig A, Diem M, Tannapfel A, Gerwert K. 156.  2013. Immunohistochemistry, histopathology and infrared spectral histopathology of colon cancer tissue sections. J. Biophotonics 6:88–100 [Google Scholar]
  157. Gawlik A, Lee G, Whitney J, Epstein J, Veltri R, Madabhushi A. 157.  2015. Computer extracted nuclear features from Feulgen and H&E images predict prostate cancer outcomes Presemted at Annu. Meet. Biomed. Eng. Soc., Tampa, Oct. 7–10
  158. 158. US Food Drug Admin 2015. Draft guidance for industry and Food and Drug Administration staff: technical performance assessment of digital pathology whole slide imaging devices. Draft rep., US Food Drug Admin., Rockville, MD
  159. Bellis M, Metias S, Naugler C, Pollett A, Jothy S, Yousef GM. 159.  2013. Digital pathology: attitudes and practices in the Canadian pathology community. J. Pathol. Inform. 4:3 [Google Scholar]
  160. Webster JD, Dunstan RW. 160.  2014. Whole-slide imaging and automated image analysis: considerations and opportunities in the practice of pathology. Vet. Pathol. 51:211–23 [Google Scholar]
  161. Ergin A, Großerüschkamp F, Theisen O, Gerwert K, Remiszewski S. 161.  et al. 2015. A method for the comparison of multi-platform spectral histopathology (SHP) data sets. Analyst 140:2465–72 [Google Scholar]
  162. Steiner G, Kirsch M. 162.  2014. Optical spectroscopic methods for intraoperative diagnosis. Anal. Bioanal. Chem. 406:21–25 [Google Scholar]
/content/journals/10.1146/annurev-bioeng-112415-114722
Loading
/content/journals/10.1146/annurev-bioeng-112415-114722
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error