DIAPath (Digital Image Analysis in Pathology) is a platform oriented towards the validation of tissue-based biomarkers using standardized immunohistochemistry (IHC) and slide digitalization processes. It proposes services that range from sample preparation to image and data analysis:
- Cell and tissue processing (fixation, paraffin embedding)
- Tissue microarray production
- Slide sectioning and automated staining (H&E, PAS, Masson trichrome, …)
- Standardized immunohistochemistry (IHC), sequential IHC, chromogenic in situ hybridization (CISH) (Complementary information)
- Whole slide high-resolution imaging
- Qualitative and semi-quantitative scoring by experts (double blind test)
- Quantitative image analysis (morphometry, staining characterization, …) (Complementary information)
- Tissue-based biomarker colocalization (Complementary information)
- Data analysis and reporting
- Customized services related to pathology, histology and IHC
- Imaging and digital pathology: macro/microscopy imaging, whole slide scanning, customized image analysis
- Screening: drug target, protein expression
- Validation: animal model, biomarker, antibody, companion diagnostics
- Automated immunohistochemistry system (Ventana Discovery XT)
- Automated tissue micro-arrayer (Alphelys MiniCore)
- Slide scanner (Hamamatsu Nanozoomer C96000-01)
DIAPath is a multidisciplinary and inter-faculty unit (ULB Schools of Medicine and of Engineering) which offers an integrated solution to histological analysis as well as the identification, characterization and validation of tissue-based biomarkers. The methodology is based on standardized laboratory procedures and quality controls ensuring reproducibility and traceability. It involves the following technologies : (i) macroscopic analysis (organs, tissues, etc.), (ii) microscopic histological analysis (using haematoxilin-eosin and special staining), (iii) tissue microarray (TMA) and cell-block (for cell line analysis), (iv) immunohistochemistry (IHC), (v) whole slide imaging and image analysis, (vi) statistical data analysis. IHC has the advantage to preserve tissue morphology and thus antigen location at histological and cell levels. By simultaneously processing thousands of samples, the TMA technology allows standardized screening of protein expression using IHC and thus provides a very efficient way for biomarker validation. Slide scanning and image analysis enable archiving, sharing and quantitative staining characterization. Finally, data analysis enables biomarkers to be statistically validated and compared.
- Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: a deep learning approach.
Van Eycke et al. Medical Image Analysis. In press (2018)
- Identification of the tumour transition states occurring during EMT.
Pastushenko I, Brisebarre A, Sifrim A, Fioramonti M, Revenco T, Boumahdi S, Van Keymeulen A, Brown D, Moers V, Lemaire S, De Clercq S, Minguijón E, Balsat C, Sokolow Y, Dubois C, De Cock F, Scozzaro S, Sopena F, Lanas A, D’Haene N, Salmon I, Marine JC, Voet T, Sotiropoulou PA, Blanpain C. Nature. 2018 Apr;556(7702):463-468.
- Image processing in digital pathology: an opportunity to solve inter-batch variability of immunohistochemical staining.
Van Eycke YR, Allard J, Salmon I, Debeir O, Decaestecker C. Sci Rep. 2017 Feb 21;7:42964.
- High-throughput analysis of tissue-based biomarkers in digital pathology.
Van Eycke YR, Debeir O, Verset L, Demetter P, Salmon I, Decaestecker C. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:7732-5.
- IGF-IR: a new prognostic biomarker for human glioblastoma.
Maris C, D’Haene N, Trépant AL, Le Mercier M, Sauvage S, Allard J, Rorive S, Demetter P, Decaestecker C, Salmon I. Br J Cancer. 2015 Sep 1;113(5):729-37. doi: 10.1038/bjc.2015.242
- Registration of whole immunohistochemical slide images: an efficient way to characterize biomarker colocalization.
Moles Lopez X, Barbot P, Van Eycke YR, Verset L, Trépant AL, Larbanoix L, Salmon I, Decaestecker C. J Am Med Inform Assoc. 2015 Jan;22(1):86-99.
- Immunohistochemical toolkit for tracking and quantifying xenotransplanted human stem cells.
Allard J, Li K, Moles Lopez X, Blanchard S, Barbot P, Rorive S, Decaestecker C, Pochet R, Bohl D, Lepore AC, Salmon I, Nicaise C. Regen Med. 2014;9(4):437-52
- Moles Lopez X, Debeir O, Salmon I, Decaestecker C. Whole slide imaging and analysis for biomarker evaluation in digital pathology. In: A. Mendez-Vilas (ed.) “Microscopy: advances in scientific research and education”, Microscopy Book Series – 2014 Edition, Formatex Research Center, Vol. 2, pp. 776-787.
- SOX2 controls tumour initiation and cancer stem-cell functions in squamous-cell carcinoma.
Boumahdi S, Driessens G, Lapouge G, Rorive S, Nassar D, Le Mercier M, Delatte B, Caauwe A, Lenglez S, Nkusi E, Brohée S, Salmon I, Dubois C, del Marmol V, Fuks F, Beck B, Blanpain C. Nature. 2014 Jul 10;511(7508):246-50
- An automated blur detection method for histological whole slide imaging.
Moles Lopez X, D’Andrea E, Barbot P, Bridoux AS, Rorive S, Salmon I, Debeir O, Decaestecker C. PLoS One. 2013 Dec 13;8(12):e82710
- Clustering methods applied in the detection of Ki67 hot-spots in whole tumor slide images: an efficient way to characterize heterogeneous tissue-based biomarkers.
Moles Lopez X, Debeir O, Maris C, Rorive S, Roland I, Saerens M, Salmon I, Decaestecker C. Cytometry A. 2012 Sep;81(9):765-75
- A simplified approach for the molecular classification of glioblastomas.
Le Mercier M, Hastir D, Moles Lopez X, De Nève N, Maris C, Trepant AL, Rorive S, Decaestecker C, Salmon I. PLoS One. 2012;7(9):e45475
- Expression of endoplasmic reticulum stress markers in the islets of patients with type 1 diabetes.
Marhfour I, Moles Lopez X, Lefkaditis D, Salmon I, Allagnat F, Richardson SJ, Morgan NG, Eizirik DL. Diabetologia 2012 Sep;55(9):2417-20.
- Epithelial expression of FHL2 is negatively associated with metastasis-free and overall survival in colorectal cancer.
Verset L, Tommelein J, Moles Lopez X, Decaestecker C, Mareel M, Bracke M, Salmon I, De Wever O, Demetter P. Br J Cancer 2013;109(1):114-20.
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