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Robust subtyping of non-small cell lung cancer whole sections through MALDI mass spectrometry imaging.
Janßen, Charlotte; Boskamp, Tobias; Hauberg-Lotte, Lena; Behrmann, Jens; Deininger, Sören-Oliver; Kriegsmann, Mark; Kriegsmann, Katharina; Steinbuß, Georg; Winter, Hauke; Muley, Thomas; Casadonte, Rita; Kriegsmann, Jörg; Maaß, Peter.
Afiliación
  • Janßen C; Center for Industrial Mathematics (ZeTeM), University of Bremen, Bremen, Germany.
  • Boskamp T; Center for Industrial Mathematics (ZeTeM), University of Bremen, Bremen, Germany.
  • Hauberg-Lotte L; Bruker Daltonics GmbH, Bremen, Germany.
  • Behrmann J; Center for Industrial Mathematics (ZeTeM), University of Bremen, Bremen, Germany.
  • Deininger SO; Center for Industrial Mathematics (ZeTeM), University of Bremen, Bremen, Germany.
  • Kriegsmann M; Bruker Daltonics GmbH, Bremen, Germany.
  • Kriegsmann K; Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
  • Steinbuß G; Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.
  • Winter H; Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany.
  • Muley T; Department of Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany.
  • Casadonte R; Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.
  • Kriegsmann J; Department of Thoracic Surgery, Thoraxklinik, Heidelberg University Hospital, Heidelberg, Germany.
  • Maaß P; Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany.
Proteomics Clin Appl ; 16(4): e2100068, 2022 07.
Article en En | MEDLINE | ID: mdl-35238465
Subtyping of the most common non-small cell lung cancer (NSCLC) tumor types adenocarcinoma (ADC) and squamous cell carcinoma (SqCC) is still a challenge in the clinical routine and a correct diagnosis is crucial for an adequate therapy selection. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has shown potential for NSCLC subtyping but is subject to strong technical variability and has only been applied to tissue samples assembled in tissue microarrays (TMAs). To our knowledge, a successful transfer of a classifier from TMAs to whole sections, which are generated in the standard clinical routine, has not been presented in the literature as of yet. We introduce a classification algorithm using extensive preprocessing and a classifier (either a neural network or a linear discriminant analysis (LDA)) to robustly classify whole sections of ADC and SqCC lung tissue. The classifiers were trained on TMAs and validated and tested on whole sections. Vital for a successful application on whole sections is the extensive preprocessing and the use of whole sections for hyperparameter selection. The classification system with the neural network/LDA results in 99.0%/98.3% test accuracy on spectra level and 100.0%/100.0% test accuracy on whole section level, respectively, and, therefore, provides a powerful tool to support the pathologist's decision making process. The presented method is a step further towards a clinical application of MALDI MSI and artificial intelligence for subtyping of NSCLC tissue sections.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Células Escamosas / Adenocarcinoma / Carcinoma de Pulmón de Células no Pequeñas / Neoplasias Pulmonares Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Proteomics Clin Appl Asunto de la revista: BIOQUIMICA Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Alemania
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Células Escamosas / Adenocarcinoma / Carcinoma de Pulmón de Células no Pequeñas / Neoplasias Pulmonares Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Proteomics Clin Appl Asunto de la revista: BIOQUIMICA Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Alemania