Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.

Background: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation burden.

Purpose: This work tests the viability of semi-supervision for brain metastases segmentation.

Mathematical modelling of stem and progenitor cell dynamics during ruxolitinib treatment of patients with myeloproliferative neoplasms.

Introduction: The Philadelphia chromosome-negative myeloproliferative neoplasms are a group of slowly progressing haematological malignancies primarily characterised by an overproduction of myeloid blood cells. Patients are treated with various drugs, including the JAK1/2 inhibitor ruxolitinib. Mathematical modelling can help propose and test hypotheses of how the treatment works.

Neutrophil-to-lymphocyte ratio and all-cause mortality with and without myeloproliferative neoplasms-a Danish longitudinal study.

The neutrophil-to-lymphocyte ratio(NLR) is increased in chronic inflammation and myeloproliferative neoplasms (MPN). We hypothesize that NLR is associated with all-cause mortality and mortality by comorbidity burden in the general population and individuals with MPN. We included 835,430 individuals from The Danish General Suburban Population Study, general practitioners, and outpatient clinics.

Aging, Inflammation, and Comorbidity in Cancers-A General In Silico Study Exemplified by Myeloproliferative Malignancies.

(1) Background: We consider dormant, pre-cancerous states prevented from developing into cancer by the immune system. Inflammatory morbidity may compromise the immune system and cause the pre-cancer to escape into an actual cancerous development. The immune deficiency described is general, but the results may vary across specific cancers due to different variances (2) Methods: We formulate a general conceptual model to perform rigorous in silico consequence analysis.