Essential thrombocythemia (ET) is one of the classical Philadelphia-negative myeloproliferative neoplasms with different mutations that can be associated with it, like Janus kinase 2 (JAK2), myeloproliferative leukemia protein (MPL), and Calreticulin (CALR) (types 1 and 2). However, there is a lack in the literature concerning other types of CALR mutations and their clinical significance and prognosis. Here we report a 42-year-old male with type 2 diabetes who presented with an inferior ST-elevation myocardial infarction and thrombocytosis. The diagnosis of ET with CALR (neither type 1 nor type 2) was confirmed, which suggests the pathognomonic feature of this mutation.

Philadelphia-negative (Ph-) myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies identified by clonal proliferation of blood cell lineages and encompasses polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The clinical and laboratory features of Philadelphia-negative MPNs are similar, making them difficult to diagnose, especially in the preliminary stages. Because treatment goals and progression risk differ amongst MPNs, accurate classification and prognostication are critical for optimal management. Artificial intelligence (AI) and machine learning (ML) algorithms provide a plethora of possible tools to clinicians in general, and particularly in the field of malignant hematology, to better improve diagnosis, prognosis, therapy planning, and fundamental knowledge. In this review, we summarize the literature discussing the application of AI and ML algorithms in patients with diagnosed or suspected Philadelphia-negative MPNs. A literature search was conducted on PubMed/MEDLINE, Embase, Scopus, and Web of Science databases and yielded 125 studies, out of which 17 studies were included after screening. The included studies demonstrated the potential for the practical use of ML and AI in the diagnosis, prognosis, and genomic landscaping of patients with Philadelphia-negative MPNs.

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