11P Tumor-infiltrating lymphocytes on routine H&E staining with automated quantification predict outcomes in resectable non-small cell lung cancer
Year: 2024
Type: article
Study design: Observational Study
Abstract: Adjuvant treatment has become more personalized in resectable NSCLC. New biomarkers are needed to predict treatment outcomes. In this study, we looked for a broadly applicable biomarker using deep learning on hematoxylin and eosin (H&E) histology. Based on recommendations [Salgado et al., Ann Oncol 2015], we focused on analyzing tumor infiltrating lymphocytes in the stromal (sTILs) and the intratumoral (iTILs) compartment. We assessed TILs fully automated at slide and tumor bulk level, guided by manual annotations of the tumor bulk.
Source: Immuno-Oncology Technology
Authors: G. van den Heuvel, F. Ciompi, M. van den Heuvel, J. Spronck, S. Vos, L.V. Eekelen, E. Aarntzen, I. Walraven
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,170
OpenAlex ID: W4405374873