dc.contributor.author | Makhlouf, Y | |
dc.contributor.author | Singh, VK | |
dc.contributor.author | Craig, S | |
dc.contributor.author | McArdle, A | |
dc.contributor.author | French, D | |
dc.contributor.author | Loughrey, MB | |
dc.contributor.author | Oliver, N | |
dc.contributor.author | Acevedo, JB | |
dc.contributor.author | O'Reilly, P | |
dc.contributor.author | James, JA | |
dc.contributor.author | Maxwell, P | |
dc.contributor.author | Salto-Tellez, M | |
dc.coverage.spatial | Netherlands | |
dc.date.accessioned | 2024-01-12T15:54:19Z | |
dc.date.available | 2024-01-12T15:54:19Z | |
dc.date.issued | 2024-12-01 | |
dc.identifier | S2001-0370(23)00464-6 | |
dc.identifier.citation | Computational and Structural Biotechnology Journal, 2024, 23 pp. 174 - 185 | |
dc.identifier.issn | 2001-0370 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/6105 | |
dc.identifier.eissn | 2001-0370 | |
dc.identifier.eissn | 2001-0370 | |
dc.identifier.doi | 10.1016/j.csbj.2023.11.048 | |
dc.identifier.doi | 10.1016/j.csbj.2023.11.048 | |
dc.description.abstract | The immune response associated with oncogenesis and potential oncological ther- apeutic interventions has dominated the field of cancer research over the last decade. T-cell lymphocytes in the tumor microenvironment are a crucial aspect of cancer's adaptive immunity, and the quantification of T-cells in specific can- cer types has been suggested as a potential diagnostic aid. However, this is cur- rently not part of routine diagnostics. To address this challenge, we present a new method called True-T, which employs artificial intelligence-based techniques to quantify T-cells in colorectal cancer (CRC) using immunohistochemistry (IHC) images. True-T analyses the chromogenic tissue hybridization signal of three widely recognized T-cell markers (CD3, CD4, and CD8). Our method employs a pipeline consisting of three stages: T-cell segmentation, density estimation from the segmented mask, and prediction of individual five-year survival rates. In the first stage, we utilize the U-Net method, where a pre-trained ResNet-34 is em- ployed as an encoder to extract clinically relevant T-cell features. The segmenta- tion model is trained and evaluated individually, demonstrating its generalization in detecting the CD3, CD4, and CD8 biomarkers in IHC images. In the second stage, the density of T-cells is estimated using the predicted mask, which serves as a crucial indicator for patient survival statistics in the third stage. This ap- proach was developed and tested in 1041 patients from four reference diagnostic institutions, ensuring broad applicability. The clinical effectiveness of True-T is demonstrated in stages II-IV CRC by offering valuable prognostic information that surpasses previous quantitative gold standards, opening possibilities for po- tential clinical applications. Finally, to evaluate the robustness and broader ap- plicability of our approach without additional training, we assessed the universal accuracy of the CD3 component of the True-T algorithm across 13 distinct solid tumors. | |
dc.format | Electronic-eCollection | |
dc.format.extent | 174 - 185 | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | ELSEVIER | |
dc.relation.ispartof | Computational and Structural Biotechnology Journal | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Artificial intelligence | |
dc.subject | Computational biotechnology | |
dc.subject | Digital pathology | |
dc.subject | Immune | |
dc.subject | Response | |
dc.title | True-T - Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistochemistry images. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2023-11-24 | |
dc.date.updated | 2024-01-12T12:42:05Z | |
rioxxterms.version | VoR | |
rioxxterms.versionofrecord | 10.1016/j.csbj.2023.11.048 | |
rioxxterms.licenseref.startdate | 2024-12-01 | |
rioxxterms.type | Journal Article/Review | |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/38146436 | |
pubs.organisational-group | ICR | |
pubs.organisational-group | ICR/Primary Group | |
pubs.organisational-group | ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | ICR/Primary Group/ICR Divisions/Molecular Pathology | |
pubs.organisational-group | ICR/Primary Group/ICR Divisions/Molecular Pathology/Integrated Pathology | |
pubs.organisational-group | ICR/ImmNet | |
pubs.publication-status | Published online | |
pubs.publisher-url | http://dx.doi.org/10.1016/j.csbj.2023.11.048 | |
pubs.volume | 23 | |
icr.researchteam | Integrated Pathology | |
dc.contributor.icrauthor | Salto-Tellez, Manuel | |
icr.provenance | Deposited by Mr Arek Surman (impersonating Prof Manuel Salto-Tellez) on 2024-01-12. Deposit type is initial. No. of files: 1. Files: iTrue-Ti - Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistoche.pdf | |