TILseg：TIL（肿瘤浸润淋巴细胞）全切片级自动空间评分揭示三阴性乳腺癌预后模式

Stromal tumor-infiltrating lymphocytes (sTILs) are promising biomarkers for predicting therapeutic outcomes in triple-negative breast cancer (TNBC), with higher sTIL levels correlating with improved chemotherapy response and survival outcomes. Currently, sTILs are manually evaluated by pathologists, which is prone to inter-reader variability. In this study, we have developed an AI-driven TIL segmentation pipeline to process entire diagnostic hematoxylin-and-eosin-stained whole slide images for reproducible scoring (global TILseg scoring) and reliable prognostication.

This pipeline was optimized and tested using two independent TNBC patient cohorts (n = 57 in the discovery cohort, n = 43 in the validation cohort) with clinical outcomes and follow-up data. The global scores generated by TILseg showed moderate to high concordance with expert scoring (Spearman R = 0. 84-0. 89) and improved patient stratification (p-value = 0. 0191) as compared to manual scoring (p-value = 0. 0663).

Additionally, we investigate how the spatial localization of sTILs (spatial TILseg) impact survival outcomes by identifying TILs in selected stromal subsets (0. 02-2 mm from the epithelial clusters).

Our findings have shown that TILs up to 50 m from epithelial regions prove to be most prognostic in predicting recurrence-free survival post-neoadjuvant chemotherapy with higher statistical significance than both manual and global TILseg scoring.

Further, spatial TILseg scoring was more significantly associated with pathological complete response status in both patient cohorts. In summary, we present an AI-based digital tool for robust sTIL scoring and spatial mapping to enhance its potential as both a diagnostic and prognostic biomarker, particularly in TNBC patients.