Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms

Abstract

The evaluation of bone marrow pathology is essential for diagnosing and classifying myeloproliferative neoplasms (MPN). 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 automated quantitative analysis platform for BMT sections from MPN patients and non-neoplastic cases (N=342 total) to enhance the accuracy of diagnosis and classification of MPN. Bone marrow metrics, including marrow cellularity, the myeloid-to-erythroid ratio, megakaryocyte morphology and distribution, and the grading of marrow fibrosis, 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 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 non-neoplastic 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 non-neoplastic 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 non-neoplastic cases. It can be a potentially auxiliary diagnostic tool for hematopathologists when dealing with patients with suspected MPN.