MALDI‐Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods

Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix‐assisted laser desorption/ionization (MALDI) imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray.

[1]  Pramodita Sharma.  2012 , 2013, Les 25 ans de l’OMC: Une rétrospective en photos.

[2]  J. H. Kobarg,et al.  Unraveling local tissue changes within severely injured skeletal muscles in response to MSC-based intervention using MALDI Imaging mass spectrometry , 2018, Scientific Reports.

[3]  J. Prat,et al.  Ovarian carcinomas: at least five different diseases with distinct histological features and molecular genetics. , 2018, Human pathology.

[4]  Christian Etmann,et al.  Deep learning for tumor classification in imaging mass spectrometry , 2017, Bioinform..

[5]  R. Casadonte,et al.  A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples. , 2017, Biochimica et Biophysica Acta - Proteins and Proteomics.

[6]  Kirill Veselkov,et al.  Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6sc03738k Click here for additional data file. Click here for additional data file. Click here for additional data file. , 2017, Chemical science.

[7]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[8]  R. Casadonte,et al.  MALDI IMS and Cancer Tissue Microarrays. , 2017, Advances in cancer research.

[9]  A. Jemal,et al.  Cancer statistics, 2017 , 2017, CA: a cancer journal for clinicians.

[10]  S. Ganesan,et al.  Molecular Characterization of Epithelial Ovarian Cancer: Implications for Diagnosis and Treatment , 2016, International journal of molecular sciences.

[11]  Stefan Kommoss,et al.  Ovarian carcinoma diagnosis: the clinical impact of 15 years of change , 2016, British Journal of Cancer.

[12]  R. Casadonte,et al.  Reliable Entity Subtyping in Non-small Cell Lung Cancer by Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry on Formalin-fixed Paraffin-embedded Tissue Specimens* , 2016, Molecular & Cellular Proteomics.

[13]  N. Packer,et al.  N-glycan MALDI Imaging Mass Spectrometry on Formalin-Fixed Paraffin-Embedded Tissue Enables the Delineation of Ovarian Cancer Tissues * , 2016, Molecular & Cellular Proteomics.

[14]  Inge Koch,et al.  Classification of MALDI‐MS imaging data of tissue microarrays using canonical correlation analysis‐based variable selection , 2016, Proteomics.

[15]  Wentao Yang,et al.  A clinically applicable molecular classification for high-grade serous ovarian cancer based on hormone receptor expression , 2016, Scientific Reports.

[16]  S. Hauptmann,et al.  The new WHO classification of ovarian, fallopian tube, and primary peritoneal cancer and its clinical implications , 2016, Archives of Gynecology and Obstetrics.

[17]  S. Mabuchi,et al.  Clear cell carcinoma of the ovary: molecular insights and future therapeutic perspectives , 2016, Journal of gynecologic oncology.

[18]  Kylie L. Gorringe,et al.  Molecular profiling of low grade serous ovarian tumours identifies novel candidate driver genes , 2015, Oncotarget.

[19]  Vladimir Jojic,et al.  Deconvolving molecular signatures of interactions between microbial colonies , 2015, Bioinform..

[20]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[21]  R. Casadonte,et al.  MALDI TOF imaging mass spectrometry in clinical pathology: a valuable tool for cancer diagnostics (review). , 2015, International journal of oncology.

[22]  S. Pignata,et al.  Low Grade Serous Ovarian Carcinoma: from the molecular characterization to the best therapeutic strategy. , 2015, Cancer treatment reviews.

[23]  A. Walch,et al.  MALDI Imaging mass spectrometry: current frontiers and perspectives in pathology research and practice , 2015, Laboratory Investigation.

[24]  J. Oetjen,et al.  MALDI imaging mass spectrometry: Discrimination of pathophysiological regions in traumatized skeletal muscle by characteristic peptide signatures , 2014, Proteomics.

[25]  Zhaohui Lu,et al.  [Introduction of WHO classification of tumours of female reproductive organs, fourth edition]. , 2014, Zhonghua bing li xue za zhi = Chinese journal of pathology.

[26]  M. Köbel,et al.  Ovarian carcinoma histotype determination is highly reproducible, and is improved through the use of immunohistochemistry , 2014, Histopathology.

[27]  Peter Hoffmann,et al.  Tryptic peptide reference data sets for MALDI imaging mass spectrometry on formalin-fixed ovarian cancer tissues. , 2013, Journal of proteome research.

[28]  Theodore Alexandrov,et al.  New analysis workflow for MALDI imaging mass spectrometry: application to the discovery and identification of potential markers of childhood absence epilepsy. , 2012, Journal of proteome research.

[29]  Marius Ueffing,et al.  MALDI imaging mass spectrometry reveals COX7A2, TAGLN2 and S100-A10 as novel prognostic markers in Barrett's adenocarcinoma. , 2012, Journal of proteomics.

[30]  Horst Zitzelsberger,et al.  Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging. , 2012, Journal of proteome research.

[31]  R. Casadonte,et al.  Proteomic analysis of formalin-fixed paraffin-embedded tissue by MALDI imaging mass spectrometry , 2011, Nature Protocols.

[32]  I. Shih,et al.  Molecular pathogenesis and extraovarian origin of epithelial ovarian cancer--shifting the paradigm. , 2011, Human pathology.

[33]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[34]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[35]  Michelle L. Reyzer,et al.  Gastric cancer-specific protein profile identified using endoscopic biopsy samples via MALDI mass spectrometry. , 2010, Journal of proteome research.

[36]  Michael Becker,et al.  Tutorial: multivariate statistical treatment of imaging data for clinical biomarker discovery. , 2010, Methods in molecular biology.

[37]  Carsten Denkert,et al.  Estrogen receptor 1 mRNA is a prognostic factor in ovarian carcinoma: determination by kinetic PCR in formalin-fixed paraffin-embedded tissue. , 2009, Endocrine-related cancer.

[38]  U. Köthe,et al.  Toward digital staining using imaging mass spectrometry and random forests. , 2009, Journal of proteome research.

[39]  Fred A Hamprecht,et al.  Concise representation of mass spectrometry images by probabilistic latent semantic analysis. , 2008, Analytical chemistry.

[40]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[41]  Sandra Rauser,et al.  MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology , 2008, Histochemistry and Cell Biology.