True-T - Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistochemistry images.
Date
2024-12-01ICR Author
Author
Makhlouf, Y
Singh, VK
Craig, S
McArdle, A
French, D
Loughrey, MB
Oliver, N
Acevedo, JB
O'Reilly, P
James, JA
Maxwell, P
Salto-Tellez, M
Type
Journal Article
Metadata
Show full item recordAbstract
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.
Collections
Subject
Artificial intelligence
Computational biotechnology
Digital pathology
Immune
Response
Research team
Integrated Pathology
Language
eng
Date accepted
2023-11-24
License start date
2024-12-01
Citation
Computational and Structural Biotechnology Journal, 2024, 23 pp. 174 - 185
Publisher
ELSEVIER