Clinico-hematological study of leukemias in a hospital-based setup

Background: Leukemia, the malignant proliferation of hematopoietic cells, accounts for a major portion of cancer globally. Types of leukemia are necessary for effective therapy as prognosis, and survival rates are different for each type of leukemia. The objective of the study was to know the relative incidence of leukemia in Silchar Medical College and Hospital, Assam. This study also aims to know the clinical manifestations of leukemia and their hematological correlation.Methods: It was a retrospective study of 60 patients carried out in the Department of Pathology in SMCH, Assam, over a period of 2 years from April 2019 to March 2021. Diagnosis was based on peripheral blood count, peripheral blood smear and bone marrow examination for morphology, along with cytochemistry study whenever required.Results: In this study, acute leukemia was more prevalent than chronic leukemia. The most common form was CML followed by AML, ALL and then CLL. Male predominance was observed in this study with male: female ratio = 1.7:1. Conclusions: In our study, Acute leukemia was more prevalent than chronic leukemia. Leukemia affected male more than female. In this study, the frequency of AML is more than that of ALL but number of cases of CML exceeds that of AML.

Characteristics of Dental Resin-Based Composites in Leukemia Saliva: An In Vitro Analysis

Background: The aim was to analyze, in vitro, four resin based composite systems (RBCs) immersed in saliva of leukemia patients before starting chemotherapy regiments. Material and methods: Saliva was collected from 20 patients (4 healthy patients, 16 leukemia patients). Resin disks were made for each RBC and were immersed in the acute leukemia (acute lymphocytic (ALL), acute myeloid (AML)), chronic leukemia (chronic lymphocytic (CLL), chronic myeloid (CML)), Artificial saliva and Control environment, and maintained for seven days. At the end of the experiment, the characteristics and the effective response of saliva from the studied salivas’ on RBCs was assessed using water sorption, water solubility, residual monomer and scanning electron microscopy (SEM). Data analysis was performed and a p-value under 0.05 was considered statistically significant. Results: The behaviour of RBCs in different immersion environments varies according to the characteristics of the RBCs. RBCs with a higher filler ratio have a lower water sorption. The solubility is also deteriorated by the types of organic matrix and filler; the results of solubility being inversely proportional on the scale of negative values compared to sorption values. Chromatograms of residual monomers showed the highest amount of unreacted monomers in ALL and AML, and the Control and artificial saliva environments had the smallest residual monomer peaks. Because of the low number of differences between the experimental conditions, we further considered that there were no important statistical differences between experimental conditions and analysed them as a single group. Conclusion: The influence of saliva on RBCs depends on the type of leukemia; acute leukemia influenced the most RBCs by changing their properties compared to chronic leukemia.

Modeling Medicare’s Oncology Care Model bundles at more clinically granular levels: Evaluating the impact on provider performance.

e18854 Background: Aggregating different subtypes of cancers into bundles is an important methodology in oncology payment reform as an alternative to fee for service. However, expected resource utilization can vary significantly across cancer subtypes. We evaluated the impact that modeling Chronic Leukemia into a more clinically granular two part framework of chronic myelogenous leukemia (CML) and chronic lymphocytic leukemia would have on OCM results and the risk that the distribution of clinical subtypes at a practice would influence overall performance in the bundle. Methods: OCM episodes of chronic leukemia initiating between July 2016 and June 2019 were subdivided on the basis of individual ICD-10 coded diagnoses on cancer-related E&M visits. From a total of 4,658 episodes, we randomly sampled with replacement 3,500 episodes from 16 practices using empirical data distributions. Data models and mappings were developed based on clinical knowledge and exploratory data analyses to subdivide the OCM bundle of Chronic Leukemias into CLL and CML. Total cost of care and episode target prices were calculated through implementation of the OCM methodology. The distributional consistencies of episode target, cost, cost above target, and percent above target for the two diseases were evaluated by two-sample Kolmogorov-Smirnov (KS) tests. Results: The CML and CLL subtypes modeled from the aggregate OCM bundle demonstrated significantly different cost distributions relative to each other. As anticipated, treatments used in each subtype varied significantly marking different patterns of expected resource utilization. In our model, CLL episodes were on average 13.7% over target. Average CLL episode costs were $52.2K vs. an average target of $47.6K with 54% of episodes running over target. In contrast, CML episodes were 6.1% under target. Average CML episode costs were $45.2K vs. an average target of $50.3K with 43% of episodes running over target. Conclusions: Value based payment models in oncology such as OCM can be improved by modeling cancer bundles in more clinically granular ways that better reflect expected resource utilization for appropriate, standard of care. Insufficient clinical granularity in bundle construction can lead to provider performance being influenced by the distribution of patient subtypes at the practice. This can lead to inappropriate shifts of risk from payers to providers in value based models. Aggregate vs. subtype episode costs (mean, 5th, and 95th percentiles).[Table: see text]

Deep Learning for Discovering and Identifying Morphological Heterogeneity of Neutrophils in Primary Hematological Diseases Based on Bone Marrow Neutrophils Analysis

It has been known that neutrophils play an important role in regulating homeostasis and disease. Tumor-associated neutrophils (TANs), as an important member of the tumor microenvironment, have gradually been proved their roles in a variety of solid tumors. It is generally believed that the changes in blood cell morphology (including neutrophils) are the phenotype of hematological diseases (such as in myelodysplastic syndromes) or tumor cells themselves. However, whether there is a possibility that the accumulation of abnormal neutrophils function leads to the change of hematopoietic stem cells and this is just the reason of hematological diseases? Do neutrophils play a key role in the pathogenesis and development of hematological tumors, especially acquired or age-related blood diseases, such as most acute and chronic leukemia, multiple myeloma and other diseases? TAN also has polarization, which is similar to tumor-associated macrophages (TAM), suggesting that the function and morphology of neutrophils are closely associated. Therefore, we assumed that there are function-related morphological differences in neutrophils in different hematological diseases. Finding these differences may provide clues for the functional research of neutrophils in hematological diseases. Artificial intelligence represented by deep learning can distinguish images efficiently and accurately (such as face recognition). Here we try to apply deep learning to discovery and recognize the morphological difference among neutrophils in different hematological diseases. We obtained whole slide images (WSI) from 4 types of malignant hematological diseases, which is chronic myelogenous leukemia (CML), multiple myeloma (MM), acute myeloblastic leukemia with maturation (AML-M2), acute monocytic leukemia (AML-M5) and normal bone marrow. Neutrophils were segmented from WSI by two diagnostic physicians (one with more than 40 years of diagnostic experience and the other with 13 years of diagnostic experience) There are 6115 neutrophils, and the number of cells in each disease and normal bone marrow is 593, 1404, 2509, 850, and 759, respectively. We trained these neutrophils using the transfer learning algorithm and the ratio of training and verification groups is 80:20. We established a convolutional neural network (CNN) model based on the morphological phenotype of neutrophils to judge their disease classification and used confusion matrix and receiver operator characteristic (ROC) curve for model evaluation. We found that neutrophils from different diseases can be classified into different categories, and the deep learning model has a high accuracy rate for judging the neutrophils from different diseases. Moreover, according to the obtained mixed matrix results, it is found that some M2 and M5 neutrophils are prone to misjudgment, while M2 and M5 is rarely confused with other diseases. The reason for this may be that M2 and M5 are both acute myeloid leukemia. Neutrophils from MM and normal bone marrow are prone to misjudge each other or judged as CML neutrophils, and MM often involves the plasma cell system, so some neutrophils of MM may be similar to normal bone marrow. Compared with acute leukemia, some chronic leukemia neutrophils are close to MM or normal bone marrow. Based on these results, we can further confirm that there are morphological and phenotypic differences between different types of hematological diseases. According to the ROC curve results, it is suggested that the deep learning model constructed based on the feature extraction of the CNN model can more accurately determine different hematological diseases according to morphological phenotypes of neutrophils. These findings suggest that neutrophils in different hematological diseases have their own features. These features may provide more evidence for the diagnosis of the disease and also provide clues for further research on the function of TAN in primary hematological diseases. Disclosures No relevant conflicts of interest to declare.