Browsing by Subject "cytomorphology"

Acute myeloid leukemia (AML) is a disease in which blood cell production is severely disrupted. Cell count and morphological analysis from bone marrow (BM) samples are key in the diag- nosis of AML. Recent advances in computer vision have led to algorithms developed at the Hematoscope Lab that can automatically classify cells from these BM samples and calculate various cell-level statistics. This thesis investigated the use of cytomorphological data along with standard clinical data to predict progression-free survival (PFS). A benchmark study using penalized Cox regression, random survival forests, and survival support vector machines was conducted to study the utility of cytomorphology data. As features greatly outnumber samples, the methods are further compared over three feature filtering methods based on Spearman’s correlation coefficient, conditional Cox screening, and mutual information. In a dataset from the national VenEx trial, the penalized Cox regression method with ElasticNet penalization supplemented with Cox conditional screening was found to perform best in the nested CV benchmarking. A post-hoc dissection of two best-performing Cox models revealed potentially predictive cytomorphological features, while disease etiology and patient age were likewise important.