Dr. Peter J. Schüffler

Dr. Peter J. Schüffler

Dr. Peter J. Schüffler

Memorial Sloan Kettering Cancer Center
417 East 68th St., ZRC-682 area
New York, NY 10065
schueffp@mskcc.org
+1-646-888-3810

Postdoctoral Research Fellow

Personal Website

I received my PhD in 2014 in the Machine Learning Laboratory of Prof. Joachim Buhmann at the ETH Zurich, Switzerland. During these studies, we investigated different machine learning pipelines for the automatic staining estimation on histological images. Also, the grading of Crohn's Disease in abdominal MRI was part of my PhD. Before that, I graduated my MSc in Bioinformatics at the Saarland University and MPI Saarbruecken, Germany.

I am generally interested in medical data analysis, preferrably with machine learning methods (i.e. learning from labeled and unlabeled medical data). The impact of successful methods in this field undoubted, and the research questions are very manifold. I like the multidisciplinary environment of computer science, computer vision, machine learning, medicine, pathology and biology.


Selected Papers

Can't render Publication.
BibTeXKey missing from Bibliography: schueffler_mitochondria-based_2016
Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry.
C. Giesen, H. A. Wang, D. Schapiro, N. Zivanovic, A. Jacobs, B. Hattendorf, P. J. Schüffler, D. Grolimund, J. M. Buhmann, S. Brandt, Z. Varga, P. J. Wild, D. Gunther and B. Bodenmiller.
Nature methods, vol. 11, p. 417-22, 2014
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@article{giesen_highly_2014,
    title = {Highly {Multiplexed} {Imaging} of {Tumor} {Tissues} with {Subcellular} {Resolution} by {Mass} {Cytometry}},
    volume = {11},
    issn = {1548-7105 (Electronic) 1548-7091 (Linking)},
    url = {http://www.nature.com/nmeth/journal/v11/n4/full/nmeth.2869.html},
    doi = {10.1038/nmeth.2869},
    abstract = {Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.},
    journal = {Nat Methods},
    author = {Giesen, C. and Wang, H. A. and Schapiro, D. and Zivanovic, N. and Jacobs, A. and Hattendorf, B. and Sch\"uffler, P. J. and Grolimund, D. and Buhmann, J. M. and Brandt, S. and Varga, Z. and Wild, P. J. and Gunther, D. and Bodenmiller, B.},
    year = {2014},
    pages = {417--22}
}
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TY - JOUR
TI - Highly Multiplexed Imaging of Tumor Tissues with Subcellular Resolution by Mass Cytometry
AU - Giesen, C.
AU - Wang, H. A.
AU - Schapiro, D.
AU - Zivanovic, N.
AU - Jacobs, A.
AU - Hattendorf, B.
AU - Schüffler, P. J.
AU - Grolimund, D.
AU - Buhmann, J. M.
AU - Brandt, S.
AU - Varga, Z.
AU - Wild, P. J.
AU - Gunther, D.
AU - Bodenmiller, B.
T2 - Nat Methods
AB - Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.
DA - 2014///
PY - 2014
DO - 10.1038/nmeth.2869
VL - 11
SP - 417
EP - 22
J2 - Nature methods
SN - 1548-7105 (Electronic) 1548-7091 (Linking)
UR - http://www.nature.com/nmeth/journal/v11/n4/full/nmeth.2869.html
ER -
Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.
Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging.
P. J. Schüffler, D. Mahapatra, R. E. Naziroglu, Z. Li, C. A. J. Puylaert, R. Andriantsimiavona, F. M. Vos, D. A. Pendsé, C. Yung Nio, J. Stoker, S. A. Taylor and J. M. Buhmann.
6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications, 2014
PDF   URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schuffler_semi-automatic_2014,
    title = {Semi-{Automatic} {Crohn}'s {Disease} {Severity} {Estimation} on {MR} {Imaging}},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12},
    abstract = {Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).},
    author = {Sch\"uffler, P. J. and Mahapatra, D. and Naziroglu, R. E. and Li, Z. and Puylaert, C. A. J. and Andriantsimiavona, R. and Vos, F. M. and Pends\'e, D. A. and Nio, C. Yung and Stoker, J. and Taylor, S. A. and Buhmann, J. M.},
    year = {2014},
}
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TY - CONF
TI - Semi-Automatic Crohn's Disease Severity Estimation on MR Imaging
AU - Schüffler, P. J.
AU - Mahapatra, D.
AU - Naziroglu, R. E.
AU - Li, Z.
AU - Puylaert, C. A. J.
AU - Andriantsimiavona, R.
AU - Vos, F. M.
AU - Pendsé, D. A.
AU - Nio, C. Yung
AU - Stoker, J.
AU - Taylor, S. A.
AU - Buhmann, J. M.
T2 - 6th MICCAI Workshop on Abdominal Imaging - Computational and Clinical Applications
AB - Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).
DA - 2014///
PY - 2014
UR - http://link.springer.com/chapter/10.1007%2F978-3-319-13692-9_12
ER -
Crohn’s disease (CD) is a chronic inflammatory bowel disease which can be visualized by magnetic resonance imaging (MRI). For CD grading, several non-invasive MRI based severity scores are known, most prominent the MaRIA and AIS. As these scores rely on manual MRI readings for individual bowel segments by trained radiologists, automated MRI assessment has been more and more focused in recent research. We show on a dataset of 27 CD patients that semi-automatically measured bowel wall thickness (ABWT) and dynamic contrast enhancement (DCE) completely outperform manual scorings: the segmental correlation to the Crohn’s Disease Endoscopic Index of Severity (CDEIS) of ABWT and DCE is significantly higher (r = .78) than that of MaRIA (r = .45) or AIS (r = .51). Also on a per-patient basis, the models with ABWT and DCE show significantly higher correlation (r = .69) to global CDEIS than MaRIA (r = .46).
Computer Aided Crohn's Disease Severity Assessment in MRI.
Peter J. Schüffler, Dwarikanath Mahapatra, Franciscus M. Vos and Joachim M. Buhmann.
VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook, 2014
Best Poster Award
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@misc{schuffler_computer_2014,
    address = {London},
    type = {Poster},
    title = {Computer {Aided} {Crohn}'s {Disease} {Severity} {Assessment} in {MRI}},
    url = {https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Vos, Franciscus M. and Buhmann, Joachim M.},
    year = {2014},
}
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TY - SLIDE
TI - Computer Aided Crohn's Disease Severity Assessment in MRI
T2 - VIGOR++ Workshop 2014 - Showcase of Research Outcomes and Future Outlook
A2 - Schüffler, Peter J.
A2 - Mahapatra, Dwarikanath
A2 - Vos, Franciscus M.
A2 - Buhmann, Joachim M.
CY - London
DA - 2014///
PY - 2014
M3 - Poster
UR - https://www.researchgate.net/publication/262198303_Computer_Aided_Crohns_Disease_Severity_Assessment_in_MRI
ER -
TMARKER: A Free Software Toolkit for Histopathological Cell Counting and Staining Estimation.
Peter J. Schüffler, Thomas J. Fuchs, Cheng S. Ong, Peter Wild and Joachim M. Buhmann.
Journal of Pathology Informatics, vol. 4, 2, p. 2, 2013
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@article{schuffler_tmarker:_2013,
    title = {{TMARKER}: {A} {Free} {Software} {Toolkit} for {Histopathological} {Cell} {Counting} and {Staining} {Estimation}},
    volume = {4},
    url = {http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=2;spage=2;epage=2;aulast=Sch%FCffler;t=6},
    doi = {10.4103/2153-3539.109804},
    abstract = {Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.},
    number = {2},
    journal = {Journal of Pathology Informatics},
    author = {Sch\"uffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng S. and Wild, Peter and Buhmann, Joachim M.},
    year = {2013},
    pages = {2}
}
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TY - JOUR
TI - TMARKER: A Free Software Toolkit for Histopathological Cell Counting and Staining Estimation
AU - Schüffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng S.
AU - Wild, Peter
AU - Buhmann, Joachim M.
T2 - Journal of Pathology Informatics
AB - Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.
DA - 2013///
PY - 2013
DO - 10.4103/2153-3539.109804
VL - 4
IS - 2
SP - 2
UR - http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2013;volume=4;issue=2;spage=2;epage=2;aulast=Sch%FCffler;t=6
ER -
Background: Histological tissue analysis often involves manual cell counting and staining estimation of cancerous cells. These assessments are extremely time consuming, highly subjective and prone to error, since immunohistochemically stained cancer tissues usually show high variability in cell sizes, morphological structures and staining quality. To facilitate reproducible analysis in clinical practice as well as for cancer research, objective computer assisted staining estimation is highly desirable. Methods: We employ machine learning algorithms as randomized decision trees and support vector machines for nucleus detection and classification. Superpixels as segmentation over the tissue image are classified into foreground and background and thereafter into malignant and benign, learning from the user's feedback. As a fast alternative without nucleus classification, the existing color deconvolution method is incorporated. Results: Our program TMARKER connects already available workflows for computational pathology and immunohistochemical tissue rating with modern active learning algorithms from machine learning and computer vision. On a test dataset of human renal clear cell carcinoma and prostate carcinoma, the performance of the used algorithms is equivalent to two independent pathologists for nucleus detection and classification. Conclusion: We present a novel, free and operating system independent software package for computational cell counting and staining estimation, supporting IHC stained tissue analysis in clinic and for research. Proprietary toolboxes for similar tasks are expensive, bound to specific commercial hardware (e.g. a microscope) and mostly not quantitatively validated in terms of performance and reproducibility. We are confident that the presented software package will proof valuable for the scientific community and we anticipate a broader application domain due to the possibility to interactively learn models for new image types.
TMARKER: A Robust and Free Software Toolkit for Histopathological Cell Counting and Immunohistochemical Staining Estimations.
Peter J. Schueffler, Niels Rupp, Cheng S. Ong, Joachim M. Buhmann, Thomas J. Fuchs and Peter J. Wild.
German Society of Pathology 97th Annual Meeting, 2013
PDF    URL   BibTeX   Endnote / RIS   Abstract
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@inproceedings{schueffler_tmarker:_2013,
    title = {{TMARKER}: {A} {Robust} and {Free} {Software} {Toolkit} for {Histopathological} {Cell} {Counting} and {Immunohistochemical} {Staining} {Estimations}},
    url = {http://link.springer.com/article/10.1007%2Fs00292-013-1765-2},
    abstract = {Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming.
    Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision.
    Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas.
    Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.},
    booktitle = {German {Society} of {Pathology} 97th {Annual} {Meeting}},
    author = {Schueffler, Peter J. and Rupp, Niels and Ong, Cheng S. and Buhmann, Joachim M. and Fuchs, Thomas J. and Wild, Peter J.},
    year = {2013},
}
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TY - CONF
TI - TMARKER: A Robust and Free Software Toolkit for Histopathological Cell Counting and Immunohistochemical Staining Estimations
AU - Schueffler, Peter J.
AU - Rupp, Niels
AU - Ong, Cheng S.
AU - Buhmann, Joachim M.
AU - Fuchs, Thomas J.
AU - Wild, Peter J.
AB - Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming.
Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision.
Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas.
Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.
C3 - German Society of Pathology 97th Annual Meeting
DA - 2013///
PY - 2013
UR - http://link.springer.com/article/10.1007%2Fs00292-013-1765-2
ER -
Aims. Assessment of immunhistochemical staining intensity and percentage of positive cells generally suffers from high inter- and intra-observer variability. Besides, estimation of the intensity and percentage of immunostained cells often involves manual cell counting and is therefore time consuming. Methods. A novel, free, and open-source software toolkit was developed, connecting already available work flows for computational pathology and immunohistochemical tissue assessment with modern active learning algorithms from machine learning and computer vision. Results. A new and platform independent software program, called TMARKER, was developed and introduced. The validity and robustness of the used algorithms has been shown on a test dataset of human renal clear cell carcinomas and prostate carcinomas. Conclusions. This new software program together with a user-friendly Java-based graphical user interface enabled comprehensive computational assistance in pathological tissue rating. Routine use of this software toolkit may provide more reliable and robust biomarkers for treatment decision.
TMARKER: A User-Friendly Open-Source Assistance for Tma Grading and Cell Counting.
Peter J. Schueffler, Thomas J. Fuchs, Cheng S. Ong, Peter Wild and Joachim M. Buhmann.
Histopathology Image Analysis (HIMA) Workshop at the 15th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI, 2012
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@inproceedings{schueffler_tmarker:_2012,
    title = {{TMARKER}: {A} {User}-{Friendly} {Open}-{Source} {Assistance} for {Tma} {Grading} and {Cell} {Counting}},
    booktitle = {Histopathology {Image} {Analysis} ({HIMA}) {Workshop} at the 15th {International} {Conference} on {Medical} {Image} {Computing} and {Computer} {Assisted} {Intervention} {MICCAI}},
    author = {Schueffler, Peter J. and Fuchs, Thomas J. and Ong, Cheng S. and Wild, Peter and Buhmann, Joachim M.},
    year = {2012}
}
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TY - CONF
TI - TMARKER: A User-Friendly Open-Source Assistance for Tma Grading and Cell Counting
AU - Schueffler, Peter J.
AU - Fuchs, Thomas J.
AU - Ong, Cheng S.
AU - Wild, Peter
AU - Buhmann, Joachim M.
C3 - Histopathology Image Analysis (HIMA) Workshop at the 15th International Conference on Medical Image Computing and Computer Assisted Intervention MICCAI
DA - 2012///
PY - 2012
ER -
A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images.
Peter J. Schüffler, Dwarikanath Mahapatra, Jeroen A. W. Tielbeek, Franciscus M. Vos, Jesica Makanyanga, Doug A. Pendsé, C. Yung Nio, Jaap Stoker, Stuart A. Taylor and Joachim M. Buhmann.
In: Hiroyuki Yoshida, Simon Warfield and Michael Vannier (eds.) Abdominal Imaging. Computation and Clinical Applications, vol. 8198, p. 1-10, Lecture Notes in Computer Science, Springer Berlin Heidelberg, ISBN 978-3-642-41082-6, 2013
Outstanding Paper Award
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@incollection{schuffler_model_2013,
    series = {Lecture {Notes} in {Computer} {Science}},
    title = {A {Model} {Development} {Pipeline} for {Crohn}'s {Disease} {Severity} {Assessment} from {Magnetic} {Resonance} {Images}},
    volume = {8198},
    isbn = {978-3-642-41082-6},
    url = {http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1},
    abstract = {Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.},
    booktitle = {Abdominal {Imaging}. {Computation} and {Clinical} {Applications}},
    publisher = {Springer Berlin Heidelberg},
    author = {Sch\"uffler, Peter J. and Mahapatra, Dwarikanath and Tielbeek, Jeroen A. W. and Vos, Franciscus M. and Makanyanga, Jesica and Pends\'e, Doug A. and Nio, C. Yung and Stoker, Jaap and Taylor, Stuart A. and Buhmann, Joachim M.},
    editor = {Yoshida, Hiroyuki and Warfield, Simon and Vannier, Michael},
    year = {2013},
    keywords = {AIS, CDEIS, Crohn’s Disease, MaRIA, abdominal MRI},
    pages = {1--10}
}
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TY - CHAP
TI - A Model Development Pipeline for Crohn's Disease Severity Assessment from Magnetic Resonance Images
AU - Schüffler, Peter J.
AU - Mahapatra, Dwarikanath
AU - Tielbeek, Jeroen A. W.
AU - Vos, Franciscus M.
AU - Makanyanga, Jesica
AU - Pendsé, Doug A.
AU - Nio, C. Yung
AU - Stoker, Jaap
AU - Taylor, Stuart A.
AU - Buhmann, Joachim M.
T2 - Abdominal Imaging. Computation and Clinical Applications
A2 - Yoshida, Hiroyuki
A2 - Warfield, Simon
A2 - Vannier, Michael
T3 - Lecture Notes in Computer Science
AB - Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.
DA - 2013///
PY - 2013
VL - 8198
SP - 1
EP - 10
PB - Springer Berlin Heidelberg
SN - 978-3-642-41082-6
UR - http://link.springer.com/chapter/10.1007%2F978-3-642-41083-3_1
KW - AIS
KW - CDEIS
KW - Crohn’s Disease
KW - MaRIA
KW - abdominal MRI
ER -
Crohn's Disease affects the intestinal tract of a patient and can have varying severity which influences treatment strategy. The clinical severity score CDEIS (Crohn’s Disease Endoscopic Index of severity) ranges from 0 to 44 and is measured by endoscopy. In this paper we investigate the potential of non-invasive magnetic resonance imaging to assess this severity, together with the underlying question which features are most relevant for this estimation task. We propose a new general and modular pipeline that uses machine learning techniques to quantify disease severity from MR images and show its value on Crohn’s Disease severity assessment on 30 patients scored by 4 medical experts. With the pipeline, we can obtain a magnetic resonance imaging score which outperforms two existing reference scores MaRIA and AIS.
Cancer Genetics-Guided Discovery of Serum Biomarker Signatures for Diagnosis and Prognosis of Prostate Cancer.
Igor Cima, Ralph Schiess, Peter Wild, Martin Kaelin, Peter Schueffler, Vinzenz Lange, Paola Picotti, Reto Ossola, Arnoud Templeton, Olga Schubert, Thomas J. Fuchs, Thomas Leippold, Stephen Wyler, Jens Zehetner, Wolfram Jochum, Joachim Buhmann, Thomas Cerny, Holger Moch, Silke Gillessen, Ruedi Aebersold and Wilhelm Krek.
PNAS: Proceedings of the National Academy of Sciences, vol. 108, 8, p. 3342-3347, 2011
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@article{cima_cancer_2011,
    title = {Cancer {Genetics}-{Guided} {Discovery} of {Serum} {Biomarker} {Signatures} for {Diagnosis} and {Prognosis} of {Prostate} {Cancer}},
    volume = {108},
    url = {http://www.pnas.org/content/108/8/3342.abstract},
    doi = {10.1073/pnas.1013699108},
    abstract = {A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.},
    number = {8},
    journal = {PNAS: Proceedings of the National Academy of Sciences},
    author = {Cima, Igor and Schiess, Ralph and Wild, Peter and Kaelin, Martin and Schueffler, Peter and Lange, Vinzenz and Picotti, Paola and Ossola, Reto and Templeton, Arnoud and Schubert, Olga and Fuchs, Thomas J. and Leippold, Thomas and Wyler, Stephen and Zehetner, Jens and Jochum, Wolfram and Buhmann, Joachim and Cerny, Thomas and Moch, Holger and Gillessen, Silke and Aebersold, Ruedi and Krek, Wilhelm},
    year = {2011},
    pages = {3342--3347}
}
Download Endnote/RIS citation
TY - JOUR
TI - Cancer Genetics-Guided Discovery of Serum Biomarker Signatures for Diagnosis and Prognosis of Prostate Cancer
AU - Cima, Igor
AU - Schiess, Ralph
AU - Wild, Peter
AU - Kaelin, Martin
AU - Schueffler, Peter
AU - Lange, Vinzenz
AU - Picotti, Paola
AU - Ossola, Reto
AU - Templeton, Arnoud
AU - Schubert, Olga
AU - Fuchs, Thomas J.
AU - Leippold, Thomas
AU - Wyler, Stephen
AU - Zehetner, Jens
AU - Jochum, Wolfram
AU - Buhmann, Joachim
AU - Cerny, Thomas
AU - Moch, Holger
AU - Gillessen, Silke
AU - Aebersold, Ruedi
AU - Krek, Wilhelm
T2 - PNAS: Proceedings of the National Academy of Sciences
AB - A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.
DA - 2011///
PY - 2011
DO - 10.1073/pnas.1013699108
VL - 108
IS - 8
SP - 3342
EP - 3347
UR - http://www.pnas.org/content/108/8/3342.abstract
ER -
A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.