Early MPN detection in laboratory setting

Abstract

Myeloproliferative Neoplasms (MPNs) are a group of bone marrow diseases with potentially lethal cardio-vascular complications. Two sub-diseases of MPN are Essential Thrombocytosis (ET) and Polycythemia Vera (PV), which are recognised by an abnormal blood count of respectively thrombocytes and red blood cells.

If an MPN is treated appropriately, complications for patients are reduced, leading to a relative increase of patients life expectancy. However, MPN is often recognised long after the first clinical signs. 1/4 of MPN patients already had abnormal blood measurements for longer than 1 year in advance of their diagnosis.

Therefore, there is the call for methods for earlier recognition of MPN. Screening like methods could be useful to alert clinicians in case of a suspected case. Although genetic testing is conclusive in recognising MPN, high costs make that they are only applied in case of already clinically suspected MPN.

In this thesis, the outlines of a method are proposed for early detection of MPN patients based on blood measurements in the general hospital laboratory workflow. A two stage solution is proposed:
• Stage 1: Filter on regular blood measurements (combined with demographic data);
• Stage 2: Filter based on microscopy imaging of blood.

The primary scope of this thesis is the development of the first stage for ET and PV subtypes of MPN. A machine learning algorithm called XGBoost is utilized to develop classification algorithms for ET and PV in this stage. Patients with elevated blood platelet counts (ET marker) or elevated red blood cell indicators (PV marker) were separately included in a nested cross validation setup for training and testing of the algorithms. For ET vs control classification, mean metrics obtained during cross validation are AUC: 0.87, recall (sensitivity): 0.74 and specificity: 0.84. For PV vs control corresponding metrics are respectively 0.86, 0.66 and 0.87.

Regarding the development of methods for stage 2, a first step is set. A XGBoost model using cell counts from microscopy images as features results in mean AUC, recall and specificity scores of 0.67, 0.78 and 0.80 respectively when trained and tested using nested cross validation. Training a Convolutional Neural Network (CNN) to take microscopy images as input and return MPN vs control classification resulted in an algorithm which only predicted control cases. These results give an indication of the potential of microscopy for automated MPN recognition, calling for further development of the stage 2 filter.

With this proposed laboratory population screening method and the developed blood measurement based filtering, a next step is set toward early detection of MPN in order to prevent (lethal) MPN related complications.