Treatment of blast phase chronic myeloid leukaemia: A rare and challenging entity.

Despite the success of BCR-ABL-specific tyrosine kinase inhibitors (TKIs) such as imatinib in chronic phase (CP) chronic myeloid leukaemia (CML), patients with blast phase (BP)-CML continue to have a dismal outcome with median survival of less than one year from diagnosis. Thus BP-CML remains a critical unmet clinical need in the management of CML. Our understanding of the biology of BP-CML continues to grow; genomic instability leads to acquisition of mutations which drive leukaemic progenitor cells to develop self-renewal properties, resulting in differentiation block and a poor-prognosis acute leukaemia which may be myeloid, lymphoid or bi-phenotypic. Similar advances in therapy are urgently needed to improve patient outcomes; however, this is challenging given the rarity and heterogeneity of BP-CML, leading to difficulty in designing and recruiting to prospective clinical trials. This review will explore the treatment of BP-CML, evaluating the data for TKI therapy alone, combinations with intensive chemotherapy, the role of allogeneic haemopoietic stem cell transplantation, the use of novel agents and clinical trials, as well as discussing the most appropriate methods for diagnosing BP and assessing response to therapy, and factors predicting outcome.

COVID-19 in chronic myeloid leukemia patients in Latin America.

This observational, multicenter study aimed to report the clinical evolution of COVID-19 in patients with chronic myeloid leukemia in Latin America. A total of 92 patients presented with COVID-19 between March and December 2020, 26% of whom were severe or critical. The median age at COVID-19 diagnosis was 48 years (22-79 years), 32% were 60 years or older, and 61% were male. Thirty-nine patients presented with at least one comorbidity (42.3%). Eighty-one patients recovered (88%), and 11 (11.9%) died from COVID-19. There was one case of reinfection. Patients with a major molecular response presented superior overall survival compared to patients with no major molecular response (91 vs. 61%, respectively; p = 0.004). Patients in treatment-free remission and receiving tyrosine kinase inhibitors showed higher survival rates than patients who underwent hematopoietic stem cell transplantation and those who did not receive tyrosine kinase inhibitors (100, 89, 50, and 33%, respectively; p < 0.001).

IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning.

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.