Predicting Nottingham grade in breast cancer digital pathology using a foundation model

Abstract

Background: The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide. Traditional grading systems rely on subjective expert judgment and require extensive pathological expertise, are time-consuming, and often lead to inter-observer variability.

Methods: To address these limitations, we develop an AI-based model to predict Nottingham grade from whole-slide images of hematoxylin and eosin (H&E)-stained breast cancer tissue using a pathology foundation model. From TCGA database, we trained and evaluated using 521 H&E breast cancer slide images with available Nottingham scores through internal split validation, and further validated its clinical utility using an additional set of 597 cases without Nottingham scores. The model leveraged deep features extracted from a pathology foundation model (UNI) and incorporated 14 distinct multiple instance learning (MIL) algorithms.

Results: The best-performing model achieved an F1 score of 0.731 and a multiclass average AUC of 0.835. The top 300 genes correlated with model predictions were significantly enriched in pathways related to cell division and chromosome segregation, supporting the model's biological relevance. The predicted grades demonstrated statistically significant association with 5-year overall survival (p < 0.05).

Conclusion: Our AI-based automated Nottingham grading system provides an efficient and reproducible tool for breast cancer assessment, offering potential for standardization of histologic grade in clinical practice.

Keywords: Biological processes; Breast cancer; Gene expression data; Gene ontology; Multiple instance learning; Nottingham grade; TCGA.

Fig. 1

Flowchart of patient and slide selection from the TCGA-BRCA and BRACS dataset. (A) TCGA-BRCA cohort selection for model development. (B) BRACS cohort selection for external validation

Fig. 2

Comprehensive Workflow for Histopathological Analysis. (A) Displays segmentation of histopathological images using the CLAM model, isolating tissue-only tiles and dividing them into 224 × 224 pixel patches. (B) The UNI model with DINO, pretrained using self-supervised learning (SSL), extracts 1,024-dimensional features at the patch level, which are subsequently aggregated within the MIL framework using attention-based selection. (C) The model predicts the Nottingham grade using a combination of multi-branch learning, stochastic top-K instance masking, and attention mechanisms. The results are visualized using survival analysis, heatmap visualization, and gene ontology analysis

Fig. 3

ROC Curves for Nottingham Grade Classification. (A) ROC curves of model performance in the internal TCGA-BRCA test set. (B) ROC curves of model performance in the external BRACS validation set

Fig. 4

Confusion Matrices for Nottingham Grade Classification in TCGA-BRCA and BRACS Datasets. (A) Confusion matrix from internal TCGA test set, showing the model’s classification performance across Nottingham grades 1, 2, and 3. (B) Confusion matrix from external BRACS validation set, illustrating the model’s generalizability in predicting Nottingham grades in an independent cohort

Fig. 5

Kaplan-Meier Survival Curves for Breast Cancer Patients by Nottingham Grades: Pathologist vs. Deep Learning. (A) Kaplan-Meier survival curves for pathologist-classified Nottingham grades show a clear trend, with grade 1 having the highest survival probability, followed by grades 2 and 3, reflecting the expected relationship between grades and survival outcomes. (B) Kaplan-Meier survival curves for AI-predicted Nottingham grades align with clinical expectations, showing grade 1 with the highest survival probability, grade 2 in the middle, and grade 3 with the lowest. Both graphs demonstrate consistent survival trends across Nottingham grades, with AI predictions closely matching clinical classifications

Fig. 6

Visualization of slides, heatmaps, and key regions for Nottingham grade. this figure illustrates the progression of histopathological features for Nottingham grades 1, 2, and 3 through original slides, corresponding heatmaps, and magnified grade-related regions: (A) Nottingham Grade 1: The original slide shows uniform cell structures. The heatmap highlights low-attention areas, reflecting minimal irregularities. The zoomed-in region confirms orderly tubular formations and low mitotic activity. (B) Nottingham Grade 2: The slide reveals moderately irregular structures. The heatmap shows mixed attention areas, indicating regions with moderate cellular pleomorphism and mitotic activity. The zoomed-in view corroborates an intermediate level of tubularity and nuclear atypia. (C) Nottingham Grade 3: The slide depicts highly irregular structures and significant cellular proliferation. The heatmap highlights intense attention areas, aligning with severe nuclear pleomorphism and high mitotic activity observed in the zoomed-in region. These visualizations demonstrate the AI model’s ability to focus on histologically relevant regions that align with Nottingham grading criteria

Fig. 7

Key Biological Processes Associated with Nottingham Grade 3 Breast Cancer. The bar chart illustrates the key biological processes associated with Nottingham Grade 3 breast cancer, identified through gene ontology (GO) analysis. The most significant processes include cell division, chromosome segregation, and the mitotic cell cycle, reflecting the rapid and aggressive proliferation characteristic of Grade 3 tumors. Other notable processes, such as mitotic spindle organization, G2/M transition of the mitotic cell cycle, and mitotic cytokinesis, further highlight the enhanced mitotic activity observed in high-grade tumors. Additionally, processes like DNA replication and regulation of cyclin-dependent kinase activity underscore the genomic instability and dysregulated cell cycle mechanisms typical of advanced breast cancer. These results provide biological insights into the distinct and aggressive nature of Nottingham Grade 3