Computational pathology
Digital pathology opens new opportunities for artificial intelligence applications in disease diagnosis and treatment. Major benefits come with extraction and quantification of novel features of pathology, often not visible by usual microscopy examination. The field is rapidly developing in many areas of medicine.
We started digital pathology research in 2010 with image analysis-based quantification1, 2 and exploring the benefits of multivariate analytics of image analysis data to generate combined image-based biomarkers3, 4. Further, we performed accuracy and calibration experiments for image analysis-based quantification5-8.
In 2015, we proposed a method based on hexagonal tiling of image analysis data to quantify intra-tumoral heterogeneity of biomarker expression9; this allowed to generate rich data set to compute spatial indicators. We conceptualized this approach as “comprehensive immunohistochemistry”10. The heterogeneity indicators were demonstrated in later studies as independent prognostic factors, often exceeding the informative value of the average level of the biomarker expression11, 12.
In 2020, we published another hexagonal grid-based method to automatically detect tumor-host interface zone and compute immune cell density profile across the zone13; this actually measures the “willingness” of immune cells to enter the tumor (immunogradient) and provides independent prognostic value12-14. Compared to other methods proposed to measure immune response in the tumor microenvironment, the Interface Zone Immunogradient provides quantitative directional assessment in the very frontline of tumor-host interaction. The methods were recently reviewed in the context of artificial intelligence applications for tumor pathology15, 16.
Watch the animation Immunogradient - Computational Biomarkers of Anti-Tumour Responses
Guided tour @NPC „What we do in computational pathology“
Prof. Arvydas Laurinavičius with a team of researchers digital@vpc.lt
[1] Brazdziute E, Laurinavicius A: Digital pathology evaluation of complement C4d component deposition in the kidney allograft biopsies is a useful tool to improve reproducibility of the scoring. Diagnostic Pathology 2011. http://www.diagnosticpathology.org/content/pdf/1746-1596-6-S1-S5.pdf
[2] Laurinaviciene A, Dasevicius D, Ostapenko V, Jarmalaite S, Lazutka J, Laurinavicius A: Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays. Diagnostic Pathology 2011. http://www.diagnosticpathology.org/content/pdf/1746-1596-6-87.pdf
[3] Laurinavicius A, Laurinaviciene A, Ostapenko V, Dasevicius D, Jarmalaite S, Lazutka J: Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data. Diagnostic Pathology 2012. http://www.diagnosticpathology.org/content/pdf/1746-1596-7-27.pdf
[4] Laurinavicius A, Green AR, Laurinaviciene A, Smailyte G, Ostapenko V, Meskauskas R, Ellis IO: Ki67/SATB1 ratio is an independent prognostic factor of overall survival in patients with early hormone receptor-positive invasive ductal breast carcinoma. Oncotarget 2015. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4747395/pdf/oncotarget-06-41134.pdf
[5] Laurinavicius A, Plancoulaine B, Laurinaviciene A, Herlin P, Meskauskas R, Baltrusaityte I, Besusparis J, Dasevicius D, Elie N, Iqbal Y, Bor C, Ellis IO: A methodology to ensure and improve accuracy of Ki67 labelling index estimation by automated digital image analysis in breast cancer tissue. Breast Cancer Research 2014. http://www.breast-cancer-research.com/content/pdf/bcr3639.pdf
[6] Laurinaviciene A, Plancoulaine B, Baltrusaityte I, Meskauskas R, Besusparis J, Lesciute-Krilaviciene D, Raudeliunas D, Iqbal Y, Herlin P, Laurinavicius A: Digital immunohistochemistry platform for the staining variation monitoring based on integration of image and statistical analyses with laboratory information system. Diagn Pathol 2014. http://www.ncbi.nlm.nih.gov/pubmed/25565007
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305968/pdf/1746-1596-9-S1-S10.pdf
[7] Plancoulaine B, Laurinaviciene A, Meskauskas R, Baltrusaityte I, Besusparis J, Herlin P, Laurinavicius A: Digital immunohistochemistry wizard: image analysis-assisted stereology tool to produce reference data set for calibration and quality control. Diagn Pathol 2014. http://www.ncbi.nlm.nih.gov/pubmed/25565221
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305978/pdf/1746-1596-9-S1-S8.pdf
[8] Besusparis J, Plancoulaine B, Rasmusson A, Augulis R, Green AR, Ellis IO, Laurinaviciene A, Herlin P, Laurinavicius A: Impact of tissue sampling on accuracy of Ki67 immunohistochemistry evaluation in breast cancer. Diagnostic Pathology 2016. https://pubmed.ncbi.nlm.nih.gov/27576949/
[9] Plancoulaine B, Laurinaviciene A, Herlin P, Besusparis J, Meskauskas R, Baltrusaityte I, Iqbal Y, Laurinavicius A: A methodology for comprehensive breast cancer Ki67 labeling index with intra-tumor heterogeneity appraisal based on hexagonal tiling of digital image analysis data. Virchows Archiv 2015. https://link.springer.com/article/10.1007/s00428-015-1865-x
[10] Laurinavicius A, Plancoulaine B, Herlin P, Laurinaviciene A: Comprehensive Immunohistochemistry: Digital, Analytical and Integrated. Pathobiology 2016. <Go to ISI>://WOS:000375025100012
https://www.karger.com/Article/Pdf/442389
[11] Laurinavicius A, Plancoulaine B, Rasmusson A, Besusparis J, Augulis R, Meskauskas R, Herlin P, Laurinaviciene A, Muftah AAA, Miligy I, Aleskandarany M, Rakha EA, Green AR, Ellis IO: Bimodality of intratumor Ki67 expression is an independent prognostic factor of overall survival in patients with invasive breast carcinoma. Virchows Archiv 2016. https://link.springer.com/article/10.1007%2Fs00428-016-1907-z
[12] Zilenaite D, Rasmusson A, Augulis R, Besusparis J, Laurinaviciene A, Plancoulaine B, Ostapenko V, Laurinavicius A: Independent Prognostic Value of Intratumoral Heterogeneity and Immune Response Features by Automated Digital Immunohistochemistry Analysis in Early Hormone Receptor-Positive Breast Carcinoma. Front Oncol 2020. https://www.ncbi.nlm.nih.gov/pubmed/32612954
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308549/pdf/fonc-10-00950.pdf
[13] Rasmusson A, Zilenaite D, Nestarenkaite A, Augulis R, Laurinaviciene A, Ostapenko V, Poskus T, Laurinavicius A: Immunogradient indicators for anti-tumor response assessment by automated tumor-stroma interface zone detection. Am J Pathol 2020. https://www.sciencedirect.com/science/article/pii/S0002944020301267#appsec1 https://www.ncbi.nlm.nih.gov/pubmed/32194048
[14] Nestarenkaite A, Fadhil W, Rasmusson A, Susanti S, Hadjimichael E, Laurinaviciene A, Ilyas M, Laurinavicius A: Immuno-Interface Score to Predict Outcome in Colorectal Cancer Independent of Microsatellite Instability Status. Cancers (Basel) 2020. https://www.ncbi.nlm.nih.gov/pubmed/33050344
https://res.mdpi.com/d_attachment/cancers/cancers-12-02902/article_deploy/cancers-12-02902-v2.pdf
[15] Laurinavicius A, Rasmusson A, Plancoulaine B, Shribak M, Levenson R: Machine-learning-based evaluation of intratumoral heterogeneity and tumor-stroma interface for clinical guidance. Am J Pathol 2021. https://www.ncbi.nlm.nih.gov/pubmed/33895120
https://ajp.amjpathol.org/article/S0002-9440(21)00165-6/pdf
[16] Yousif M, van Diest PJ, Laurinavicius A, Rimm D, van der Laak J, Madabhushi A, Schnitt S, Pantanowitz L: Artificial intelligence applied to breast pathology. Virchows Arch 2021. https://www.ncbi.nlm.nih.gov/pubmed/34791536
https://link.springer.com/content/pdf/10.1007/s00428-021-03213-3.pdf