Abstract RF3-01: Clinical outcomes of invasive lobular carcinoma (ILC) versus non-lobular breast cancer (NLC) assessed by expert pathologists, an artificial intelligence (AI) CDH1 classifier, and AI-derived tumor microenvironment (TME) biomarkers in TAILORx

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Authors: R. Salgado, R. J. Gray, Glenn Broeckx, C. Desmedt, Annie Li, GG Van den Eynden, Zuzana Kos, Balázs Ács, T. Tramm, E. Stovgaard, Cornelia M. Focke, L. Comerma Blesa
+14 more Akira I. Hida, Magali Lacroix‐Triki, Elena Provenzano, Fresia G. Pareja, Sai Kumar Maley, N. Villena, H. Montgomery, E. Li-Ning-Tapia, Alexander J. Lazar, Sunil Badve, Sandra Loi, Joseph A. Sparano, The International Immuno-Oncology Biomarker Working Group, TAILORx Investigators
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Abstract

Abstract Background: Approximately15% of invasive breast cancers are Invasive Lobular Cancer (ILC) with differingclinical outcomes than non-lobular breast cancer (NLC). Digital pathologyenables high-throughput biomarkers and morphology analysis. The TME,particularly TILs is a robust prognostic factor in localized breast cancer.Here, we detail the prognostic importance of ductal vs lobular histology,manually and with AI, combined with manual and novel AI-derived TME metrics inearly (5y) recurrence with 15-years of follow-up inTAILORx. Methods: H&E whole slideimages (WSI) from 8,422 patients were reviewed by 16 breast pathologists who completedcentral review for grade, TILs (www.tilsinbreastcancer.org) and histology (WHOclassification). The same H&E dataset was evaluated with an AI-based CDH1-classifier(Paige.AI). In addition, a zero-shot AI model (Case45) generated a panel of TMEbiomarkers (TIL abundance, spatial TIL levels with proximity to cancer cellsand degree of cancer cell-fibroblast contact) combined into a single TME-riskscore. Clinical outcomes were evaluated using multivariable Cox models adjustedfor age, tumor size, Oncotype DX 21-gene recurrence score (RS; Exact Sciences),adjuvant therapy (endocrine vs chemo-endocrine), and centrally determined grade,histology (ILC vs NLC), and manual TILs. Similar analysis were performed usingthe CDH1-classifier rather than central histology, and TME risk score. Results: Centralpathology review revealed ILC in 11.9% of cases, and NLC in 88.1% (77.7% ductal,10.4% non-ductal). Concordances ratesbetween the pathologists were 0.837 and 0.494 (Fleiss’ Kappa) for histology andgrade, respectively, with ICC of 0.968 for TILs. Both centralized pathologyreview (11.9%) and the CDH1-AI classifier (11.2%) demonstrated that ILC has consistentlyhigher risk of recurrence than NLC between years 5-15, but not before; there was a 4.9% overall survival (OS)-difference between ILC vs NLC at 15years. For manual TILs, estimated hazard ratio (HR) for a 10-point differencein TILs was 1.06 (95% CI 1.00, 1.13 p=0.04) for distance recurrence freeinterval (DRFI). AI-derived TME-risk stratified DRFI from 95.7% to 90.9% at 10years, and from 92.1% to 86.9% at 15 years (HR per standard deviation (SD)1.27, 95% CI 1.15-1.40, p

Fields of Study & Subjects

Breast Cancer Treatment Studies AI in cancer detection Breast Lesions and Carcinomas

Publication details

Journal / source
Clinical Cancer Research
Volume
32
Issue
4_Supplement
Pages
RF3–01
Publication date
17 February 2026
Language
EN