ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte (TIL) assessment in breast cancer: Multicentre validation in 2,340 patients with breast cancer
Journal:
arXiv
Published Date:
Jan 24, 2025
Abstract
The level of tumour-infiltrating lymphocytes (TILs) is a prognostic factor
for patients with (triple-negative) breast cancer (BC). Computational TIL
assessment (CTA) has the potential to assist pathologists in this
labour-intensive task, but current CTA models rely heavily on many detailed
annotations. We propose and validate a fundamentally simpler deep learning
based CTA that can be trained in only ten minutes on hundredfold fewer
pathologist annotations. We collected whole slide images (WSIs) with TILs
scores and clinical data of 2,340 patients with BC from six cohorts including
three randomised clinical trials. Morphological features were extracted from
whole slide images (WSIs) using a pathology foundation model. Our
label-efficient Computational stromal TIL assessment model (ECTIL) directly
regresses the TILs score from these features. ECTIL trained on only a few
hundred samples (ECTIL-TCGA) showed concordance with the pathologist over five
heterogeneous external cohorts (r=0.54-0.74, AUROC=0.80-0.94). Training on all
slides of five cohorts (ECTIL-combined) improved results on a held-out test set
(r=0.69, AUROC=0.85). Multivariable Cox regression analyses indicated that
every 10% increase of ECTIL scores was associated with improved overall
survival independent of clinicopathological variables (HR 0.86, p<0.01),
similar to the pathologist score (HR 0.87, p<0.001). We demonstrate that ECTIL
is highly concordant with an expert pathologist and obtains a similar hazard
ratio. ECTIL has a fundamentally simpler design than existing methods and can
be trained on orders of magnitude fewer annotations. Such a CTA may be used to
pre-screen patients for, e.g., immunotherapy clinical trial inclusion, or as a
tool to assist clinicians in the diagnostic work-up of patients with BC. Our
model is available under an open source licence
(https://github.com/nki-ai/ectil).
