Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology

The subjectivity of morphological assessment and the overlapping pathological features of different subtypes of myeloproliferative neoplasms (MPNs) make accurate diagnosis challenging. To improve the pathological assessment of MPNs, we developed a diagnosis model (fusion model) based on the combination of bone marrow whole-slide images (deep learning [DL] model) and clinical parameters (clinical model). Thousand and fifty-one MPN and non-MPN patients were divided into the training, internal testing and one internal and two external validation cohorts (the combined validation cohort). In the combined validation cohort, fusion model achieved higher areas under curve (AUCs) than clinical or DL model or both for MPNs and subtype identification. Compared with haematopathologists with different experience, clinical model achieved AUC which was comparable to seniors and higher than juniors (p = 0.0208) for polycythaemia vera. The AUCs of fusion model were comparable to seniors and higher than juniors for essential thrombocytosis (p = 0.0141), prefibrotic primary myelofibrosis (p = 0.0085) and overt primary myelofibrosis (p = 0.0330) identification. In conclusion, the performances of our proposed models are equivalent to senior haematopathologists and better than juniors, providing a new perspective on the utilization of DL algorithms in MPN morphological assessment.

Authors

Institutions

Publication Details

Journal
British Journal of Haematology
Published
2024-12-10
DOI
https://doi.org/10.1111/bjh.19938
Citations
3
Primary Topic
Myeloproliferative Neoplasms: Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
1.31

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology

Qian Wang, Lei Fan, Liangmin Wei, Minghui Duan et al.
3 citations
British Journal of Haematology
Myeloproliferative Neoplasms: Diagnosis and Treatment
1.31
article

Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology

Qian Wang, Lei Fan, Liangmin Wei, Minghui Duan, Guanyu Yang, Jinhong Mei, Min Xiao, Jianyao Huang, Yongyue Wei, Rong Wang, Feng Chen, Yuan Zhang, Jianyong Li, Xiaomei Hu, Wenyi Shen, Jieyu He, Suning Chen, Zhongxun Shi, Jin Wang
article en
3 citations

Abstract

The subjectivity of morphological assessment and the overlapping pathological features of different subtypes of myeloproliferative neoplasms (MPNs) make accurate diagnosis challenging. To improve the pathological assessment of MPNs, we developed a diagnosis model (fusion model) based on the combination of bone marrow whole-slide images (deep learning [DL] model) and clinical parameters (clinical model). Thousand and fifty-one MPN and non-MPN patients were divided into the training, internal testing and one internal and two external validation cohorts (the combined validation cohort). In the combined validation cohort, fusion model achieved higher areas under curve (AUCs) than clinical or DL model or both for MPNs and subtype identification. Compared with haematopathologists with different experience, clinical model achieved AUC which was comparable to seniors and higher than juniors (p = 0.0208) for polycythaemia vera. The AUCs of fusion model were comparable to seniors and higher than juniors for essential thrombocytosis (p = 0.0141), prefibrotic primary myelofibrosis (p = 0.0085) and overt primary myelofibrosis (p = 0.0330) identification. In conclusion, the performances of our proposed models are equivalent to senior haematopathologists and better than juniors, providing a new perspective on the utilization of DL algorithms in MPN morphological assessment.

British Journal of HaematologyVol. 206(2)
Fujian Medical University (CN), Nanjing University of Chinese Medicine (CN), Nanchang University (CN), Anhui Medical University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Peking University (CN), Peking Union Medical College Hospital (CN), Soochow University (CN), First Affiliated Hospital of Anhui Medical University (CN), Jiangsu Province Hospital (CN), First Affiliated Hospital of Soochow University (CN), Tongji Hospital (CN), Union Hospital (CN), First Affiliated Hospital of Nanchang University (CN), Huazhong University of Science and Technology (CN), Southeast University (CN), Nanjing Medical University (CN)
National Natural Science Foundation of China, Government of Jiangsu Province
Openalex Percentile: Top 17%
Myeloproliferative Neoplasms: Diagnosis and Treatment
1.31
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.