True-T - Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistochemistry images.

Autor:	Makhlouf Y; Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK., Singh VK; Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK., Craig S; Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK., McArdle A; Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK., French D; Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK., Loughrey MB; Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK.; Cellular Pathology, Belfast Health and Social Care Trust, Belfast City Hospital, Lisburn Road, Belfast BT9 7AB, UK., Oliver N; Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK., Acevedo JB; Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK., O'Reilly P; Sonrai Analytics, Belfast BT9 7AE, UK., James JA; Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK.; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast BT9 7AE, UK., Maxwell P; Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK., Salto-Tellez M; Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK.; Sonrai Analytics, Belfast BT9 7AE, UK.; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast BT9 7AE, UK.; Integrated Pathology Unit, Institute of Cancer Research and Royal Marsden Hospital, London SW7 3RP, UK.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2023 Dec 02; Vol. 23, pp. 174-185. Date of Electronic Publication: 2023 Dec 02 (Print Publication: 2024).
DOI:	10.1016/j.csbj.2023.11.048
Abstrakt:	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. Competing Interests: Manuel Salto-Tellez is a scientific advisor to Mindpeak and Sonrai Analytics, and has received honoraria recently from BMS, MSD, Roche, Sanofi and Incyte. He has received grant support from Phillips, Roche, MSD and Akoya. None of these disclosures are related to this work.. (© 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)
Databáze:	MEDLINE
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