Abstract B048: Large Scale, AI-Enabled, Spatial Signal Processing of Breast Cancer Pathology Identifies Consensus Tissue Structures Related to Biology and Outcomes
Notice bibliographique
Résumé
Abstract Breast cancer remains a leading cause of cancer-related mortality worldwide, underscoring the critical need for innovative diagnostic and prognostic approaches. Spatial evaluation of tissue structure in breast cancer provides valuable insights into the tumor microenvironment, including cellular organization, stromal interactions, and molecular heterogeneity. However, large-scale, high-resolution profiling of tumor structure is not financially nor logistically feasible. In this study, we sought to interrogate spatial signal organization in a large set of breast pathology images to identify common structural features and associated biology, in an unsupervised manner, and connect the presence of these features to clinically relevant endpoints. To accomplish this objective, we utilized a large cohort (n=1988) of normal breast and breast cancer biopsy digital, whole-slide, pathology images (WSI), from TCGA (n=1058), OSU’s TCC (n=401), and GTEx (n=529). WSI processing and QC included color normalization and artifact segmentation. Quality tissue areas were tiled into non-overlapping 224mm x 224mm boxes of 1mm/px and feature embeddings were subsequently extracted from tiles using a deep learning pathology foundation model (CTransPath). Each embedding was subsequently converted to spatial Fourier coefficients (FCs), specific to each sample, using spatial graph Fourier transform, and converted to feature spatial-coordination maps generated from cosine similarity of embedding FCs. To identify conserved tissue structures, we developed a customized graph-neural-network (GNN), trained to identify a common latent feature space of all tile embeddings, based on spatial similarity among all samples, and performed Louvain clustering of the resultant feature latent space. We identified 33 conserved tissue structures (FTU) consisting of 15 to 78 spatially coordinated image features each. We quantified the feature prominence in each sample through feature spatial aggregation and compared FTU quantitation to gene expression programs and clinical features in 1459 breast cancers from TCGA and TCC. Almost half of the FTU’s correlated strongly with molecular subtypes (ER, PR, HER2 status, p<0.05) and every identified FTU associated with a gene expression module. This included gene expression modules associated with lymphocyte infiltration, which paired immune related gene expression modules with at least two FTUs. Additionally, one FTU (FTU-14) correlated significantly (p=0.03) with microbial abundance, derived from RNA-Seq, suggesting bacteria may impact local tissue structure, detectable in H&E images. This work highlights the growing appreciation for spatial ecology in tumors and particularly breast cancer tissue structure. We also demonstrates that computationally-identifiable, conserved tissue structures in breast cancer, derived from digital pathology, can serve as a biomarker for diagnosis and prognosis, with direct relationship to biology and clinical course. Citation Format: Jordan E. Krull, Mirage Modi, Yi Jiang, Karthik Chakravarthy, Daniel Spakowicz, Qin Ma. Large Scale, AI-Enabled, Spatial Signal Processing of Breast Cancer Pathology Identifies Consensus Tissue Structures Related to Biology and Outcomes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B048.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».