Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms
The evaluation of bone marrow pathology is essential for diagnosing and classifying myeloproliferative neoplasms (MPNs). However, morphological assessments of bone marrow trephine (BMT) sections by hematopathologists are inherently subjective; thus, an accurate and objective diagnostic system is ne...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ferrata Storti Foundation
2025-06-01
|
| Series: | Haematologica |
| Online Access: | https://haematologica.org/article/view/12100 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850161330316640256 |
|---|---|
| author | Dandan Yu Hongju Zhang Yanyan Song Yuan Tao Fengyuan Zhou Ziyi Wang Rongfeng Fu Ting Sun Huan Dong Wenjing Gu Renchi Yang Zhijian Xiao Qi Sun Lei Zhang |
| author_facet | Dandan Yu Hongju Zhang Yanyan Song Yuan Tao Fengyuan Zhou Ziyi Wang Rongfeng Fu Ting Sun Huan Dong Wenjing Gu Renchi Yang Zhijian Xiao Qi Sun Lei Zhang |
| author_sort | Dandan Yu |
| collection | DOAJ |
| description |
The evaluation of bone marrow pathology is essential for diagnosing and classifying myeloproliferative neoplasms (MPNs). However, morphological assessments of bone marrow trephine (BMT) sections by hematopathologists are inherently subjective; thus, an accurate and objective diagnostic system is needed. Based on U2-Net, UNeXt, and ResNet, we developed an automatic quantitative analysis platform of BMT sections from MPNs patients and nonneoplastic cases (n=342 total) to enhance the accuracy of diagnosis and classification of MPNs. Bone marrow metrics, including marrow cellularity, the myeloid-to-erythroid (M: E) ratio, megakaryocyte morphology and distribution, and the grading of marrow fibrosis (MF), were quantitatively analyzed (with an accuracy of approximately 0.9) based on the accuracy segmentation and identification of various cells and tissues (with an intersection over union (IoU) of roughly 0.8). A bone marrow classification model incorporating bone marrow metrics, a clinical classification model utilizing clinical features, and a comprehensive classification model that includes both bone marrow metrics and clinical features were developed using random forest classifiers to differentiate MPN subtypes and nonneoplastic conditions. The bone marrow and comprehensive classification models reached a macro-average area under the curve (AUC) of 0.96 for differentiating MPN subtypes and nonneoplastic cases. The clinical classification model attained a macro-average AUC of 0.92. This platform is highly accurate for quantitatively analyzing bone marrow pathology and classifying MPN subtypes and nonneoplastic cases. It can be a potentially auxiliary diagnostic tool for hematopathologists when dealing with patients with suspected MPNs.
|
| format | Article |
| id | doaj-art-dcc2eeac7029451da779923d845c12a3 |
| institution | OA Journals |
| issn | 0390-6078 1592-8721 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Ferrata Storti Foundation |
| record_format | Article |
| series | Haematologica |
| spelling | doaj-art-dcc2eeac7029451da779923d845c12a32025-08-20T02:22:54ZengFerrata Storti FoundationHaematologica0390-60781592-87212025-06-01999110.3324/haematol.2024.286123Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasmsDandan Yu0Hongju Zhang1Yanyan Song2Yuan Tao3Fengyuan Zhou4Ziyi Wang5Rongfeng Fu6Ting Sun7Huan Dong8Wenjing Gu9Renchi Yang10Zhijian Xiao11Qi Sun12Lei Zhang13State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600XY AI Technologies (Su Zhou) Limited, Jiangsu 215422XY AI Technologies (Su Zhou) Limited, Jiangsu 215422State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Tianjin Key Laboratory of Gene Therapy for Blood Diseases, CAMS Key Laboratory of Gene Therapy for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China; Tianjin Institutes of Health Science, Tianjin 301600, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730 The evaluation of bone marrow pathology is essential for diagnosing and classifying myeloproliferative neoplasms (MPNs). However, morphological assessments of bone marrow trephine (BMT) sections by hematopathologists are inherently subjective; thus, an accurate and objective diagnostic system is needed. Based on U2-Net, UNeXt, and ResNet, we developed an automatic quantitative analysis platform of BMT sections from MPNs patients and nonneoplastic cases (n=342 total) to enhance the accuracy of diagnosis and classification of MPNs. Bone marrow metrics, including marrow cellularity, the myeloid-to-erythroid (M: E) ratio, megakaryocyte morphology and distribution, and the grading of marrow fibrosis (MF), were quantitatively analyzed (with an accuracy of approximately 0.9) based on the accuracy segmentation and identification of various cells and tissues (with an intersection over union (IoU) of roughly 0.8). A bone marrow classification model incorporating bone marrow metrics, a clinical classification model utilizing clinical features, and a comprehensive classification model that includes both bone marrow metrics and clinical features were developed using random forest classifiers to differentiate MPN subtypes and nonneoplastic conditions. The bone marrow and comprehensive classification models reached a macro-average area under the curve (AUC) of 0.96 for differentiating MPN subtypes and nonneoplastic cases. The clinical classification model attained a macro-average AUC of 0.92. This platform is highly accurate for quantitatively analyzing bone marrow pathology and classifying MPN subtypes and nonneoplastic cases. It can be a potentially auxiliary diagnostic tool for hematopathologists when dealing with patients with suspected MPNs. https://haematologica.org/article/view/12100 |
| spellingShingle | Dandan Yu Hongju Zhang Yanyan Song Yuan Tao Fengyuan Zhou Ziyi Wang Rongfeng Fu Ting Sun Huan Dong Wenjing Gu Renchi Yang Zhijian Xiao Qi Sun Lei Zhang Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms Haematologica |
| title | Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms |
| title_full | Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms |
| title_fullStr | Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms |
| title_full_unstemmed | Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms |
| title_short | Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms |
| title_sort | artificial intelligence based quantitative bone marrow pathology analysis for myeloproliferative neoplasms |
| url | https://haematologica.org/article/view/12100 |
| work_keys_str_mv | AT dandanyu artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT hongjuzhang artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT yanyansong artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT yuantao artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT fengyuanzhou artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT ziyiwang artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT rongfengfu artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT tingsun artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT huandong artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT wenjinggu artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT renchiyang artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT zhijianxiao artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT qisun artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms AT leizhang artificialintelligencebasedquantitativebonemarrowpathologyanalysisformyeloproliferativeneoplasms |