Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Abstract

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Keywords: deep learning; digital pathology; guidelines; image analysis; machine learning; pitfalls; prognostic biomarker; triple-negative breast cancer; tumor-infiltrating lymphocytes.

Figure 1

Lymphocyte‐dense regions associated with other structures should be excluded as the inflammation is not necessarily an immune response to the tumor. (A) TLS. (B) Lymphocytes surrounding vessels. These areas are reported [24] as possible false‐positive areas in CTA at much higher levels than VTA. Images by Elisabeth Specht Stovgaard from Herlev cohort used in [22].

Figure 2

Examples of discrepant cases from Herlev cohort used in [22]; purple areas: tumor nests, heatmap areas: sTIL regions. (A) A case of high sTIL density at tumor margin compared to central area. As the stroma is scarce inside the tumor, sTIL density is reported to be very high in CTA as mostly the margin contributes to the score. (B) The tumor grows irregularly with small tumor nests between larger invasive tumor areas. In these cases, the CTA includes more stroma than VTA, resulting in a lower sTIL density score (larger denominator) than the manual score.