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Clinical Chemistry and Laboratory Medicine (CCLM)

Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

Editor-in-Chief: Plebani, Mario

Ed. by Gillery, Philippe / Greaves, Ronda / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter


IMPACT FACTOR 2018: 3.638

CiteScore 2018: 2.44

SCImago Journal Rank (SJR) 2018: 1.191
Source Normalized Impact per Paper (SNIP) 2018: 1.205

Online
ISSN
1437-4331
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Volume 48, Issue 7

Issues

Integrated diagnostics: a conceptual framework with examples

Anant Madabhushi
  • Laboratory for Computational Imaging and Bioinformatics, Department of Biomedical Engineering, Rutgers University, NJ, USA
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/ Scott Doyle
  • Laboratory for Computational Imaging and Bioinformatics, Department of Biomedical Engineering, Rutgers University, NJ, USA
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/ George Lee
  • Laboratory for Computational Imaging and Bioinformatics, Department of Biomedical Engineering, Rutgers University, NJ, USA
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/ Ajay Basavanhally
  • Laboratory for Computational Imaging and Bioinformatics, Department of Biomedical Engineering, Rutgers University, NJ, USA
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/ James Monaco
  • Laboratory for Computational Imaging and Bioinformatics, Department of Biomedical Engineering, Rutgers University, NJ, USA
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/ Steve Masters
  • Department of Pathology and Laboratory Medicine, University of Pennsylvania Health System, Philadelphia, PA, USA
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/ John Tomaszewski
  • Department of Pathology and Laboratory Medicine, University of Pennsylvania Health System, Philadelphia, PA, USA
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/ Michael Feldman
  • Department of Pathology and Laboratory Medicine, University of Pennsylvania Health System, Philadelphia, PA, USA
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Published Online: 2010-05-23 | DOI: https://doi.org/10.1515/CCLM.2010.193

Abstract

With the advent of digital pathology, imaging scientists have begun to develop computerized image analysis algorithms for making diagnostic (disease presence), prognostic (outcome prediction), and theragnostic (choice of therapy) predictions from high resolution images of digitized histopathology. One of the caveats to developing image analysis algorithms for digitized histopathology is the ability to deal with highly dense, information rich datasets; datasets that would overwhelm most computer vision and image processing algorithms. Over the last decade, manifold learning and non-linear dimensionality reduction schemes have emerged as popular and powerful machine learning tools for pattern recognition problems. However, these techniques have thus far been applied primarily to classification and analysis of computer vision problems (e.g., face detection). In this paper, we discuss recent work by a few groups in the application of manifold learning methods to problems in computer aided diagnosis, prognosis, and theragnosis of digitized histopathology. In addition, we discuss some exciting recent developments in the application of these methods for multi-modal data fusion and classification; specifically the building of meta-classifiers by fusion of histological image and proteomic signatures for prostate cancer outcome prediction.

Clin Chem Lab Med 2010;48:989–98.

Keywords: diagnostic pathology; digitalized histopathology; image analysis algorithms; pattern recognition; proteomic signature; theragnostic

About the article

Corresponding author: Michael Feldman, MD, PhD, Department of Pathology and Laboratory Medicine, University of Pennsylvania Health System, Founders Bldg, Rm 6.058, 3400 Spruce St Philadelphia, PA 19104, USA Phone: +215-662-6503, Fax: +215-349-5910,


Published Online: 2010-05-23

Published in Print: 2010-07-01


Citation Information: Clinical Chemistry and Laboratory Medicine, Volume 48, Issue 7, Pages 989–998, ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: https://doi.org/10.1515/CCLM.2010.193.

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Scott Doyle, Michael D Feldman, Natalie Shih, John Tomaszewski, and Anant Madabhushi
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[2]
Patrick Leo, Robin Elliott, Natalie N. C. Shih, Sanjay Gupta, Michael Feldman, and Anant Madabhushi
Scientific Reports, 2018, Volume 8, Number 1
[3]
Gregory Penzias, Asha Singanamalli, Robin Elliott, Jay Gollamudi, Natalie Shih, Michael Feldman, Phillip D. Stricker, Warick Delprado, Sarita Tiwari, Maret Böhm, Anne-Maree Haynes, Lee Ponsky, Pingfu Fu, Pallavi Tiwari, Satish Viswanath, Anant Madabhushi, and Aamir Ahmad
PLOS ONE, 2018, Volume 13, Number 8, Page e0200730
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Federico Cabitza and Giuseppe Banfi
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Journal of Zhejiang University-SCIENCE B, 2017, Volume 18, Number 5, Page 393
[7]
Cheng Lu, Hongming Xu, Jun Xu, Hannah Gilmore, Mrinal Mandal, and Anant Madabhushi
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John M. Nicholls and Glenn D. Francis
Pathology, 2011, Volume 43, Number 6, Page 635
[9]
David N. Louis, Michael Feldman, Alexis B. Carter, Anand S. Dighe, John D. Pfeifer, Lynn Bry, Jonas S. Almeida, Joel Saltz, Jonathan Braun, John E. Tomaszewski, John R. Gilbertson, John H. Sinard, Georg K. Gerber, Stephen J. Galli, Jeffrey A. Golden, and Michael J. Becich
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[10]
George Lee, Asha Singanamalli, Haibo Wang, Michael D. Feldman, Stephen R. Master, Natalie N. C. Shih, Elaine Spangler, Timothy Rebbeck, John E. Tomaszewski, and Anant Madabhushi
IEEE Transactions on Medical Imaging, 2015, Volume 34, Number 1, Page 284
[11]
Zachary R. Stephen, Forrest M. Kievit, Omid Veiseh, Peter A. Chiarelli, Chen Fang, Kui Wang, Shelby J. Hatzinger, Richard G. Ellenbogen, John R. Silber, and Miqin Zhang
ACS Nano, 2014, Volume 8, Number 10, Page 10383
[12]
Farzad Ghaznavi, Andrew Evans, Anant Madabhushi, and Michael Feldman
Annual Review of Pathology: Mechanisms of Disease, 2013, Volume 8, Number 1, Page 331
[13]
Robert W Veltri, Christhunesa S Christudass, and Sumit Isharwal
Asian Journal of Andrology, 2012, Volume 14, Number 3, Page 375
[14]
P. Tiwari, S. Viswanath, J. Kurhanewicz, A. Sridhar, and A. Madabhushi
NMR in Biomedicine, 2012, Volume 25, Number 4, Page 607
[15]
Anant Madabhushi, Shannon Agner, Ajay Basavanhally, Scott Doyle, and George Lee
Computerized Medical Imaging and Graphics, 2011, Volume 35, Number 7-8, Page 506

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