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Towards automatic protein co-expression quantification in immunohistochemical TMA slides

Solorzano, Leslie, 1989- (författare)
Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab
Pereira, Carla (författare)
I3S, Porto, Portugal
Martins, Diana (författare)
Deptartment of Biomedical Laboratory Sciences, Polytechnic Institute of Coimbra, Portugal
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Almeida, Raquel (författare)
Faculty of Medicine, University of Porto, Portugal
Carneiro, Fatima (författare)
Department of Pathology, Faculty of Medicine, University of Porto, Portugal
Almeida, Gabriela (författare)
I3S, Porto, Portugal
Oliveira, Carla (författare)
I3S, Porto, Portugal
Wählby, Carolina, professor, 1974- (författare)
Uppsala universitet,Avdelningen för visuell information och interaktion,Bildanalys och människa-datorinteraktion,Science for Life Laboratory, SciLifeLab
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 (creator_code:org_t)
Institute of Electrical and Electronics Engineers (IEEE), 2021
2021
Engelska.
Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 25:2, s. 393-402
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Immunohistochemical (IHC) analysis of tissue biopsies is currently used for clinical screening of solid cancers to assess protein expression. The large amount of image data produced from these tissue samples requires specialized computational pathology methods to perform integrative analysis. Even though proteins are traditionally studied independently, the study of protein co-expression may offer new insights towards patients' clinical and therapeutic decisions. To explore protein co-expression, we constructed a modular image analysis pipeline to spatially align tissue microarray (TMA) image slides, evaluate alignment quality, define tumor regions, and ultimately quantify protein expression, before and after tumor segmentation. The pipeline was built with open-source tools that can manage gigapixel slides. To evaluate the consensus between pathologist and computer, we characterized a cohort of 142 gastric cancer (GC) cases regarding the extent of E-cadherin and CD44v6 expression. We performed IHC analysis in consecutive TMA slides and compared the automated quantification with the pathologists' manual assessment. Our results show that automated quantification within tumor regions improves agreement with the pathologists' classification. A co-expression map was created to identify the cores co-expressing both proteins. The proposed pipeline provides not only computational tools forwarding current pathology practices to explore co-expression, but also a framework for merging data and transferring information in learning-based approaches to pathology.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Medical Engineering -- Medical Image Processing (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

Nyckelord

Co-expression
computational pathology
gastric cancer
image analysis
immunohistochemistry
protein
registration
Computerized Image Processing
Datoriserad bildbehandling

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