What question did this study set out to answer?

To evaluate the reproducibility and robustness of an AI-based pipeline for assessing stromal tumor-infiltrating lymphocytes in triple-negative breast cancer.

February 22, 2026

Abstract PS3-04-26: Reproducibility and Robustness of an AI-based Stromal Tumor-Infiltrating Lymphocyte Pipeline in Triple-Negative Breast Cancer

Key Points

To evaluate the reproducibility and robustness of an AI-based pipeline for assessing stromal tumor-infiltrating lymphocytes in triple-negative breast cancer.
Validated an AI-sTIL pipeline using H&E-stained slides from the ARTEMIS trial.
Compared AI-generated sTIL scores with manual assessments by pathologists.
Examined effects of input variability through rescanning slides with different scanners and operators.
AI-sTIL pipeline showed high concordance with manual assessments (ρ=0.768-0.793).
Excellent reproducibility across different scanner models and magnifications (ρ=0.972-0.992).
Identified systematic bias towards higher scores for one scanner model, indicating need for calibration.

Abstract

Abstract Introduction: Triple-negative breast cancer (TNBC) has limited treatment options and significant treatment-related toxicity. Recent studies have shown that quantifying stromal tumor-infiltrating lymphocytes (sTILs) can aid in identifying early-stage TNBC patients likely to achieve a pathological complete response to neoadjuvant chemotherapy (Abuhadra et al., 2023), suggesting its utility in treatment stratification. However, manual assessment of sTILs is prone to interobserver variability (Van Bockstal et al., 2021). Artificial intelligence (AI) promises a more consistent alternative, but its reproducibility and robustness to input variability remains insufficiently evaluated, limiting its clinical adoption (Park et al., 2020). Methods: We aimed to validate the reproducibility and robustness of an in-house AI-sTIL pipeline (AbdulJabbar et al., 2020; Pan et al., 2025) developed using H AT2 vs. Hamamatsu: ρ=0.987, R2=0.975), though a systematic bias toward higher scores for AT2 (regression slopes of 0.853 and 0.885, when compared with GT450 and Hamamatsu scores) suggests calibration may be necessary to enable accurate cross-platform comparisons. Similarly, scores were consistent across scanning magnification (20x vs. 40x: AT2: ρ=0.984, R2=0.964; Hamamatsu: ρ=0.992, R2=0.979). Interestingly, we observed lower scores for rescanned images of older slides (median slide age: 6.3 years), likely due to stain fading. However, this is unlikely to impact clinical deployment, as such delays between tissue sectioning and scanning are rare in routine clinical practice. The pipeline demonstrated excellent inter-run consistency with 100% concordance (ρ=0.991, R2=0.988), confirming the repeatability and stability of the pipeline. Conclusion: This study highlights the reproducibility and robustness of our in-house developed AI-sTIL pipeline across common variations in pathology workflows. Through controlled data acquisition experiments, we identified the appropriate data acquisition setup to pair with the pipeline. Our approach provides a practical framework for institutions seeking to assess deployment readiness of pathology AI tools before formal validation and clinical integration./abstract Citation Format: S. Ranjbar, A. San Lucas, P. Chen, K. Shah, M. Suchko, B. Guerrouahen, C. Ercan, E. Barrientos Toro, K. Sweeney, X. Pan, J. DeLa Cruz, B. Rodriguez, N. Reddy, C. Yam, G. Mantha, M. Riben, L. Huo, Y. Yuan. Reproducibility and Robustness of an AI-based Stromal Tumor-Infiltrating Lymphocyte Pipeline in Triple-Negative Breast Cancer abstract. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-04-26.

Perguntar à IA

Bookmark