Acute lymphoblastic leukemia (ALL) in adults: disparities in treatment intervention based on access to treatment facility

Khullar, Karishma; Plascak, Jesse J.

doi:10.1080/10428194.2021.1975187

Cited by 7 publications

(1 citation statement)

References 38 publications

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“…These dangerously formed cells are known as “leukemia cells” or “blasts” because they are severely undeveloped. Leukemia is defined by Khullar et al [ 4 ] as an abnormal hyper-proliferation of polymorphonuclear leukocytes which do not produce solid tumor aggregates, i.e., liquid cancer. Acute leukemia cells mature fast and can be lethal if not detected early.…”

Section: Introductionmentioning

confidence: 99%

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

et al. 2022

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Acute lymphoblastic leukemia (ALL) is a rare type of blood cancer caused due to the overproduction of lymphocytes by the bone marrow in the human body. It is one of the common types of cancer in children, which has a fair chance of being cured. However, this may even occur in adults, and the chances of a cure are slim if diagnosed at a later stage. To aid in the early detection of this deadly disease, an intelligent method to screen the white blood cells is proposed in this study. The proposed intelligent deep learning algorithm uses the microscopic images of blood smears as the input data. This algorithm is implemented with a convolutional neural network (CNN) to predict the leukemic cells from the healthy blood cells. The custom ALLNET model was trained and tested using the microscopic images available as open-source data. The model training was carried out on Google Collaboratory using the Nvidia Tesla P-100 GPU method. Maximum accuracy of 95.54%, specificity of 95.81%, sensitivity of 95.91%, F1-score of 95.43%, and precision of 96% were obtained by this accurate classifier. The proposed technique may be used during the pre-screening to detect the leukemia cells during complete blood count (CBC) and peripheral blood tests.

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Section: Introductionmentioning

confidence: 99%

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

et al. 2022

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Survival Outcomes in Older Adult Acute Lymphoblastic Leukemia Patients Analyzed by Facility Volume and Type: A National Cancer Database Analysis

Dykes,

Chou,

Taylor

et al. 2024

Cancer Reports

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BakgroundIt is important to understand the outcomes of adult acute lymphoblastic leukemia (ALL) patients at different facilities as treatment paradigms change.AimsOur primary objective was to determine adult ALL overall survival (OS) by facility volume and type. Secondary objectives included identifying sociodemographic factors that may have impacted outcomes and analyzing treatment patterns by facility volume and type.MethodsThis was a retrospective analysis of the National Cancer Database (NCDB) that included patients ≥40 years diagnosed with ALL between 2004 and 2016.ResultsA total of 14 593 patients were included in this study. Univariate OS was greatest at low volume (LV) and community programs (CPs) and the least at high volume (HV) and academic programs (AP). This difference was lost after multivariable Cox proportional hazards model analysis, which found no difference in survival by facility volume or type, however, survival was significantly influenced by age, race, Hispanic ethnicity, insurance, and residence location (p < 0.05). Patients treated at HV and APs compared to LV and CP received more anti‐neoplastic directed therapy.ConclusionOur results suggest treatment facility volume and type do not impact older adult ALL patient (≥40 years) survival, however confounding sociodemographic differences do impact survival outcomes, despite more aggressive and novel treatment approaches provided at HV and APs.

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Effect of Age and Socioeconomic Factors in the Utilization of Chemotherapy in Acute Lymphoblastic Leukemia (ALL): A SEER Database Study of 16,196 Patients

Joshi

Adhikari

Bhetuwal

et al. 2022

Clinical Lymphoma Myeloma and Leukemia

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Acute lymphoblastic leukemia (ALL) in adults: disparities in treatment intervention based on access to treatment facility

Cited by 7 publications

References 38 publications

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

Survival Outcomes in Older Adult Acute Lymphoblastic Leukemia Patients Analyzed by Facility Volume and Type: A National Cancer Database Analysis

Effect of Age and Socioeconomic Factors in the Utilization of Chemotherapy in Acute Lymphoblastic Leukemia (ALL): A SEER Database Study of 16,196 Patients

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