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

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 int...

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Vydané v:	Breast cancer research : BCR Ročník 27; číslo 1; s. 58 - 14
Hlavní autori:	Kim, Jun Seo, Lee, Jeong Hoon, Yeon, Yousung, An, Doyeon, Kim, Seok Jun, Noh, Myung-Giun, Lee, Suehyun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 19.04.2025 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Algorithms Artificial intelligence Artificial intelligence in breast imaging Automation Biological processes Biomedical and Life Sciences Biomedicine Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - genetics Breast Neoplasms - mortality Breast Neoplasms - pathology Cancer Cancer Research Cell division Female Gene expression Gene expression data Gene ontology Humans Multiple instance learning Neoplasm Grading - methods Nottingham grade Oncology Pathology Prognosis Statistical analysis Surgical Oncology United Kingdom Gene ontology Biological processes Nottingham grade Breast cancer Gene expression data Multiple instance learning TCGA
ISSN:	1465-542X, 1465-5411, 1465-542X
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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.
AbstractList	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 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. 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. 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). 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. BackgroundThe 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.MethodsTo 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.ResultsThe 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).ConclusionOur 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. 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. 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. 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). 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. 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. 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. 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.BACKGROUNDThe 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.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.METHODSTo 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.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).RESULTSThe 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).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.CONCLUSIONOur 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.
ArticleNumber	58
Audience	Academic
Author	An, Doyeon Noh, Myung-Giun Yeon, Yousung Kim, Jun Seo Kim, Seok Jun Lee, Suehyun Lee, Jeong Hoon
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Keywords	Gene ontology Biological processes Nottingham grade Breast cancer Gene expression data Multiple instance learning TCGA
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Snippet	Background The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide.... The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide. Traditional grading... Background The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide.... BackgroundThe Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide.... Abstract Background The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide....
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SubjectTerms	Algorithms Artificial intelligence Artificial intelligence in breast imaging Automation Biological processes Biomedical and Life Sciences Biomedicine Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - genetics Breast Neoplasms - mortality Breast Neoplasms - pathology Cancer Cancer Research Cell division Female Gene expression Gene expression data Gene ontology Humans Multiple instance learning Neoplasm Grading - methods Nottingham grade Oncology Pathology Prognosis Statistical analysis Surgical Oncology
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Title	Predicting Nottingham grade in breast cancer digital pathology using a foundation model
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