Myeloproliferative neoplasms: Diagnostic workup of the cythemic patient

Histopathology imagery dataset of Ph-negative myeloproliferative neoplasm

Tumorous cancer has been a widely known and well-studied medical phenomenon; however, rare diseases like Myeloproliferative Neoplasm (MPN) have received less attention, leading to delayed diagnosis. Despite the availability of advanced technology in diagnostic tools that can boost the procedure, the morphological assessment of bone marrow trephine (BMT) images remains critical to confirm and differentiate MPN subtypes. This paper reports a histopathological imagery dataset that was created to focus on the most common MPN from the Philadelphia Chromosome (Ph)-negative type, namely Essential Thrombocythemia (ET), Polycythemia Vera (PV), and Primary Myelofibrosis (MF). The dataset consisted of 300 BMT images that can be used to enable computer vision applications, such as image segmentation, disease classification, and object recognition, in assisting the classification of the MPN disease. Ethical approval was obtained from the Ministry of Health, Malaysia and the bone marrow trephine images were captured using a digital microscope from the Olympus model (BX41 Dual head microscope) with x10, x20, and x40 lens types. The development of comprehensive tools deployed from this dataset can assist medical practitioners in diagnosing diseases, thus overcoming the current challenges.

Designing a Scoring System for Differential Diagnosis From Reactive Thrombocytosis and Essential Thrombocytosis

Background: Thrombocytosis is a common finding in hospitalized patients and is of two main types, essential thrombocytosis (ET) and reactive thrombocytosis (RT). It is important to distinguish the two due to increased risk of developing marrow fibrosis, acute leukemia, and thrombosis in the former. Molecular studies are the main tools to differentiate the two but are not available in all hospitals. We aimed to design a highly sensitive scoring system using routine lab data to classify thrombocytosis as essential or reactive.

Methods: A total of 145 patients were enrolled in this study. Potential predictors included patient demographics and clinical laboratory parameters. Receiver operating characteristic curve analysis was used to decide the optimal cutoff level. Multivariate logistic regression with forward model selection method was performed to decide the predictors.

Results: The risk scores by multivariate analysis were as follows: 1 point for WBC > 13,500/μL; 2.5 points for Hb > 10.9 g/dL; 3 points for platelet count > 659,000/μL; and 2 points for MPV > 9.3 fL. The cut off value was set as 4.5 points, and sensitivity of 91.1% and specificity of 75.8% were noted.

Conclusion: In this study, we investigated lab data and developed a high-sensitivity convenient-to-use scoring system to differentiate ET from RT. The scoring system was assigned to the resulting model to make it more economical, simple, and convenient for clinical practice.

Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen

Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis are characterized by abnormal proliferation of mature bone marrow cell lineages. Since various non-hematologic disorders can also cause leukocytosis, thrombocytosis and polycythemia, the detection of abnormal peripheral blood cells is essential for the diagnostic screening of Ph-negative MPNs. We sought to develop an automated diagnostic support system of Ph-negative MPNs. Our strategy was to combine the complete blood cell count and research parameters obtained by an automated hematology analyzer (Sysmex XN-9000) with morphological parameters that were extracted using a convolutional neural network deep learning system equipped with an Extreme Gradient Boosting (XGBoost)-based decision-making algorithm. The developed system showed promising performance in the differentiation of PV, ET, and MF with high accuracy when compared with those of the human diagnoses, namely: > 90% sensitivity and > 90% specificity. The calculated area under the curve of the ROC curves were 0.990, 0.967, and 0.974 for PV, ET, MF, respectively. This study is a step toward establishing a universal automated diagnostic system for all types of hematology disorders.