16 Automated tertiary lymphoid structure detection and prognostic value in colorectal liver metastases
Bibliographic record
Abstract
Background Tertiary lymphoid structures (TLS) are organized lymphoid cell aggregates that can form within solid cancers and are generally associated with more favorable prognosis. 1 TLS significance in colorectal liver metastases (CRLM) remains unclear,2 while reproducible and scalable tools for TLS characterization on routine histopathology are lacking. We developed an automated machine-learning pipeline to detect and classify TLS from hematoxylin and eosin (H&E) WSI and evaluated association with overall survival (OS) in patients who underwent CRLM resection with curative intent.Methods As ground truth, a TCGA gastric adenocarcinoma cohort (n = 76) with a dataset of 235 TLS expert-annotated in three morphologic classes on H&E images was used to train and tune the pipeline, then adapted to our internal cohort of 1286 CRLM resected in 314 patients. After stain-normalization of H&E slides, a multiresolution latent hypergraph learning framework was applied, incorporating a diffusion-based model at two magnifications to capture tissue-level context (10x) and cell-level graphs (40x). TLS were categorized into three morphologic classes: TLS1, lymphoid aggregates; TLS2, immature; TLS3, with germinal center ( figure 1). Each patient received a score computed as a weighted sum of the three TLS subtypes, and assigned to groups: TLS score = 0 (Absent), TLS score > 0 to ≤ Q3 (Intermediate), and TLS score > Q3 (High), where Q3 represented the 75th percentile of TLS scores across the cohort. Association with OS was assessed using KM analysis and the log-rank test.Results The pipeline developed with the TCGA dataset correctly assigned TLS to morphological classes with an accuracy of 91% , and TLS scoring stratified patients by overall survival. Out of a total of 4855 TLS detected in CRLM, 3056 (62.9%), 1328 (27.4%), and 471 (9.7%) were TLS1, TLS2, and TLS3, respectively. No TLS were found in 215 CRLM, representing 16.7% patients. The median OS of patients with Absent TLS, Intermediate, and High scores were 30, 45, and 65 months, respectively (p < .001) ( figure 1F). Pairwise comparisons confirmed significant differences across all strata (0 vs. intermediate, p < .001; 0 vs. high, p < .001; intermediate vs. high, p = .03). A greater proportion of patients who received pre-operative chemotherapy were found in TLS High compared to TLS Absent and Intermediate groups (p = 0.01).Conclusions We developed a graph-based computational pathology pipeline for automated TLS scoring from H&E-stained CRLM. Validation of its prognostic value in independent cohorts could ultimately help clinical decision-making for adjuvant therapy.Acknowledgements We are grateful to the staff of the CRCHUM Hepatopancreatotobliliary and Colorectal Biobank and Database for patient recruitment, pathological slide acquisition, and prospective outcome data maintenance, and to all contributing patients.References Fridman WH, Meylan M, Pupier G, Calvez A, Hernandez I, Sautès-Fridman C. Tertiary lymphoid structures and B cells: An intratumoral immunity cycle. Immunity 2023;56:2254–69.Zhang C, Wang X-Y, Zuo J-L, et al. Localization and density of tertiary lymphoid structures associate with molecular subtype and clinical outcome in colorectal cancer liver metastases. J Immunother Cancer 2023;11:e006425.Ethics Approval This study was approved by the Centre hospitalier de l’Université de Montréal Ethics Board; approval numbers 09.237 and 18.023. All patients consented to provide data and pathological images to this research.Abstract 16 Figure 1Automated detection and classification of tertiary lymphoid structures (TLS) in hematoxylin and eosin (H&E)-stained slides of colorectal liver metastases (CRLM). (A, B) Low-magnification whole-slide images showing automated TLS detection (red boxes) in two CRLM samples, one with 6 TLS (A) and another with 23 TLS (B). (C-E) High-magnification examples of TLS subtypes identified by the pipeline: immature TLS1 (C, ye
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".