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 computational 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 expression11-13.

 

In 2020, we published another hexagonal grid-based method to automatically detect tumor-host interface zone and compute immune cell density profile across the zone14; this actually measures the “willingness” of immune cells to enter the tumor (immunogradient) and provides independent prognostic value12-17. 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.

 

We demonstrated that intratumoral heterogeneity- and immunogradient-based computational biomarkers could be combined to predict patient survival without requirement for any conventional clinical or pathology data12. Also, computational augmentation of a single CD8 immunohistochemistry image data generated three independent prognostic features18.

 

We are further exploring clinical utility of tissue microarchitecture patterns both in tumor- and non-tumor pathology19-22.

 

These approaches were reviewed in the context of artificial intelligence applications 23,24.

 

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 10.1186/1746-1596-6-S1-S5 http://www.diagnosticpathology.org/content/pdf/1746-1596-6-S1-S5.pdf

 

  1. 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 10.1186/1746-1596-6-87 http://www.diagnosticpathology.org/content/pdf/1746-1596-6-87.pdf

 

  1. 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 10.1186/1746-1596-7-27 http://www.diagnosticpathology.org/content/pdf/1746-1596-7-27.pdf

 

  1. 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. pp. 41134-45

  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4747395/pdf/oncotarget-06-41134.pdf

 

  1. 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 10.1186/bcr3639 http://www.breast-cancer-research.com/content/pdf/bcr3639.pdf

 

  1. 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. p. S10

 10.1186/1746-1596-9-S1-S10  http://www.ncbi.nlm.nih.gov/pubmed/25565007

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305968/pdf/1746-1596-9-S1-S10.pdf

 

  1. 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. p. S8

 10.1186/1746-1596-9-S1-S8  http://www.ncbi.nlm.nih.gov/pubmed/25565221

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305978/pdf/1746-1596-9-S1-S8.pdf

 

  1. 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 10.1186/s13000-016-0525-z https://pubmed.ncbi.nlm.nih.gov/27576949/

 

  1. 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. pp. 711-22

 10.1007/s00428-015-1865-x  https://link.springer.com/article/10.1007/s00428-015-1865-x

 

  1. Laurinavicius A, Plancoulaine B, Herlin P, Laurinaviciene A: Comprehensive Immunohistochemistry: Digital, Analytical and Integrated. Pathobiology 2016. pp. 156-63

 10.1159/000442389  <Go to ISI>://WOS:000375025100012

https://www.karger.com/Article/Pdf/442389

 

  1. 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. pp. 493-502

 10.1007/s00428-016-1907-z  https://link.springer.com/article/10.1007%2Fs00428-016-1907-z

 

  1. 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. p. 950

 10.3389/fonc.2020.00950  https://www.ncbi.nlm.nih.gov/pubmed/32612954

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308549/pdf/fonc-10-00950.pdf

 

  1. Zilenaite-Petrulaitiene D, Rasmusson A, Besusparis J, Valkiuniene RB, Augulis R, Laurinaviciene A, Plancoulaine B, Petkevicius L, Laurinavicius A: Intratumoral heterogeneity of Ki67 proliferation index outperforms conventional immunohistochemistry prognostic factors in estrogen receptor-positive HER2-negative breast cancer. Virchows Arch 2024 10.1007/s00428-024-03737-4 https://www.ncbi.nlm.nih.gov/pubmed/38217716

https://link.springer.com/article/10.1007/s00428-024-03737-4

 

  1. 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 10.1016/j.ajpath.2020.01.018 https://www.sciencedirect.com/science/article/pii/S0002944020301267#appsec1 https://www.ncbi.nlm.nih.gov/pubmed/32194048

 

  1. 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 10.3390/cancers12102902 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

 

  1. Stulpinas R, Zilenaite-Petrulaitiene D, Rasmusson A, Gulla A, Grigonyte A, Strupas K, Laurinavicius A: Prognostic Value of CD8+ Lymphocytes in Hepatocellular Carcinoma and Perineoplastic Parenchyma Assessed by Interface Density Profiles in Liver Resection Samples. Cancers (Basel) 2023 10.3390/cancers15020366 https://www.ncbi.nlm.nih.gov/pubmed/36672317

https://mdpi-res.com/d_attachment/cancers/cancers-15-00366/article_deploy/cancers-15-00366-v2.pdf?version=1673921305

 

  1. Drachneris J, Rasmusson A, Morkunas M, Fabijonavicius M, Cekauskas A, Jankevicius F, Laurinavicius A: CD8+ Cell Density Gradient across the Tumor Epithelium-Stromal Interface of Non-Muscle Invasive Papillary Urothelial Carcinoma Predicts Recurrence-Free Survival after BCG Immunotherapy. Cancers (Basel) 2023 10.3390/cancers15041205 https://www.ncbi.nlm.nih.gov/pubmed/36831546

https://mdpi-res.com/d_attachment/cancers/cancers-15-01205/article_deploy/cancers-15-01205-v2.pdf?version=1676427029

 

  1. Radziuviene G, Rasmusson A, Augulis R, Grineviciute RB, Zilenaite D, Laurinaviciene A, Ostapenko V, Laurinavicius A: Intratumoral Heterogeneity and Immune Response Indicators to Predict Overall Survival in a Retrospective Study of HER2-Borderline (IHC 2+) Breast Cancer Patients. Front Oncol 2021. p. 774088

 10.3389/fonc.2021.774088  https://www.ncbi.nlm.nih.gov/pubmed/34858854

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8631965/pdf/fonc-11-774088.pdf

 

  1. Morkunas M, Zilenaite D, Laurinaviciene A, Treigys P, Laurinavicius A: Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients. Sci Rep 2021. p. 15474

 10.1038/s41598-021-94862-6  https://www.ncbi.nlm.nih.gov/pubmed/34326378

https://www.nature.com/articles/s41598-021-94862-6.pdf

 

  1. Drachneris J, Morkunas M, Fabijonavicius M, Cekauskas A, Jankevicius F, Laurinavicius A: Prediction of Non-Muscle Invasive Papillary Urothelial Carcinoma Relapse from Hematoxylin-Eosin Images Using Deep Multiple Instance Learning in Patients Treated with Bacille Calmette-Guerin Immunotherapy. Biomedicines 2024 10.3390/biomedicines12020360 https://www.ncbi.nlm.nih.gov/pubmed/38397962

https://mdpi-res.com/d_attachment/biomedicines/biomedicines-12-00360/article_deploy/biomedicines-12-00360.pdf?version=1706952598

 

  1. Augulis R, Rasmusson A, Laurinaviciene A, Jen KY, Laurinavicius A: Computational pathology model to assess acute and chronic transformations of the tubulointerstitial compartment in renal allograft biopsies. Sci Rep 2024. p. 5345

 10.1038/s41598-024-55936-3  https://www.ncbi.nlm.nih.gov/pubmed/38438513

https://www.nature.com/articles/s41598-024-55936-3.pdf

 

  1. Stulpinas R, Morkunas M, Rasmusson A, Drachneris J, Augulis R, Gulla A, Strupas K, Laurinavicius A: Improving HCC Prognostic Models after Liver Resection by AI-Extracted Tissue Fiber Framework Analytics. Cancers (Basel) 2023 10.3390/cancers16010106 https://www.ncbi.nlm.nih.gov/pubmed/38201532

https://mdpi-res.com/d_attachment/cancers/cancers-16-00106/article_deploy/cancers-16-00106.pdf?version=1703409446

 

  1. 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 10.1016/j.ajpath.2021.04.008 https://www.ncbi.nlm.nih.gov/pubmed/33895120

https://ajp.amjpathol.org/article/S0002-9440(21)00165-6/pdf

 

  1. 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 10.1007/s00428-021-03213-3 https://www.ncbi.nlm.nih.gov/pubmed/34791536

https://link.springer.com/content/pdf/10.1007/s00428-021-03213-3.pdf

 

 

    Privacy & Cookie Policy
(c) 2016 All rigths reserved. Solution: TEXUS

Our website uses cookies. We use these cookies for statistical and marketing purposes.

If you consent to the use of these cookies, please click “accept” and continue browsing our website.

Agree
Settings