• International Journal of Computer Science & Network Security
• /
• v.24 no.9
• /
• pp.97-104
• /
• 2024

Leukemia induced death has been listed in the top ten most dangerous mortality basis for human being. Some of the reason is due to slow decision-making process which caused suitable medical treatment cannot be applied on time. Therefore, good clinical decision support for acute leukemia type classification has become a necessity. In this paper, the author proposed a novel approach to perform acute leukemia type classification using sequential neural network classifier. Our experimental result only covers the first classification process which shows an excellent performance in differentiating normal and abnormal cells. Further development is needed to prove the effectiveness of second neural network classifier.

• Journal of Korea Multimedia Society
• /
• v.21 no.10
• /
• pp.1150-1161
• /
• 2018

Classification is widely used in medical images to categorize patients and non-patients. However, conventional classification requires a complex procedure, including some rigid steps such as pre-processing, segmentation, feature extraction, detection, and classification. In this paper, we propose a novel convolutional neural network (CNN), called LeukemiaNet, to specifically classify two different types of leukemia, including acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), and non-cancerous patients. To extend the limited dataset, a PCA color augmentation process is utilized before images are input into the LeukemiaNet. This augmentation method enhances the accuracy of our proposed CNN architecture from 96.9% to 97.2% for distinguishing ALL, AML, and normal cell images.

• Journal of Korea Multimedia Society
• /
• v.23 no.2
• /
• pp.174-185
• /
• 2020

Leukemia induced death has been listed in the top ten most dangerous mortality basis for human being. Some of the reason is due to slow decision-making process which caused suitable medical treatment cannot be applied on time. Therefore, good clinical decision support for acute leukemia type classification has become a necessity. In this paper, the author proposed a novel approach to perform acute leukemia type classification using sequential neural network classifier. Our experimental result only cover the first classification process which shows an excellent performance in differentiating normal and abnormal cells. Further development is needed to prove the effectiveness of second neural network classifier.

• Journal of Korean Biological Nursing Science
• /
• v.10 no.2
• /
• pp.170-175
• /
• 2008

Purpose: The purpose of this study was to predict treatment outcome of chemotherapy compared with and bone marrow transplantation in acute leukemia patients. Methods: We respectively reviewed the characteristics of subjects, cytokine, complete remission time and survival time of 111 patients with acute leukemia, admitted in St. Mary's hospital, between July 2007 and August 2008. Results: The complete remission rate with chemotherapy group was 70.8% and bone marrow transplantation group was 54.3% but without statistically significance. The prognostic factors related with survival is classification of acute leukemia and complete remission time. Conclusion: This study suggests a need for nursing research and nursing intervention for acute leukemia patients.

• Genomics & Informatics
• /
• v.2 no.1
• /
• pp.19-29
• /
• 2004

Gene expression profiles may offer more information than morphology and provide an alternative to morphology- based tumor classification systems. Informative gene selection is finding gene subsets that are able to discriminate between tumor types, and may have clear biological interpretation. Gene selection is a fundamental issue in gene expression based tumor classification. In this report, techniques for selecting informative genes are illustrated and supervised shaving introduced as a gene selection method in the place of a clustering algorithm. The supervised shaving method showed good performance in gene selection and classification, even though it is a clustering algorithm. Almost selected genes are related to leukemia disease. The expression profiles of 3051 genes were analyzed in 27 acute lymphoblastic leukemia and 11 myeloid leukemia samples. Through these examples, the supervised shaving method has been shown to produce biologically significant genes of more than $94\%$ accuracy of classification. In this report, SVM has also been shown to be a practicable method for gene expression-based classification.

• Maxillofacial Plastic and Reconstructive Surgery
• /
• v.31 no.6
• /
• pp.535-540
• /
• 2009

Proliferation of abnormal hematopoietic cells with impaired differentiation, regulation and programmed cell death leads to leukemia. AML(acute myeloid leukemia) is a malignancy with malfunction of myeloid hematopoietic cells with acute behavior. The oral manifestations of the disease are posterior palate hemorrhage, gingival bleeding and gingival ulceration as a result of infection by normal oral flora and gingival infiltration by leukemic cells. A 49-year-old male patient was referred from local dental clinic. The patient was diagnosed with AML FAB M1 (acute myeloid leukemia French-American-British classification M1 myeloblastic leukemia without maturation). The oral infection focus was removed by a conservative treatment. 2 days after the dental treatment, the patient underwent chemotherapy. At 8-month follow-up, the overall outcome was excellent. Oral manifestations of AML are often the first indications of the malignancy. Therefore it is essential for dentists, especially oral and maxillofacial surgeons, to be aware of the diagnostic signs and complications associated with leukemia for better diagnosis and subsequent treatment and management.

• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.1
• /
• pp.369-372
• /
• 2016

Background: Acute myeloid leukemia is an acquired clonal heterogeneous stem cell disorder. Hence, various parameters are sought out to categorize this disease into subtypes, so that as a consequence specific treatment modalities can be offered. Conventionally, the practically used method for classification utilizes French American British (FAB) criteria based on morphology and cytochemistry. The aim of present study was to determine the current spectrum of AML sub types in patients in Karachi. Materials and Methods: This single centre cross sectional study was conducted at Liaquat National Hospital, Karachi, extending from January 2010 to December 2014. Data were retrieved from archives were analyzed with SPSS version 22. Results: A total of 125 patients were diagnosed at our institution with de novo AML during five years period, 76 males and 49 females. Median age was 34.5 years. AML-M1 was the predominant FAB subtype (23.2%) followed by M2 (18.4%), M3 and M4 (16% each), M0 (14.4%), M5 (7.2%), M6 (3.2%) and M7 (1.6%). Conclusions: AML in Pakistani patients is seen in a relatively young population. The most common FAB subtype observed in our study was acute myeloblastic leukemia, without maturation (M1).

• The Korean journal of internal medicine
• /
• v.39 no.1
• /
• pp.34-56
• /
• 2024

Acute lymphoblastic leukemia (ALL) is one of the most rapidly changing hematological malignancies with advanced understanding of the genetic landscape, detection methods of minimal residual disease (MRD), and the development of immunotherapeutic agents with good clinical outcomes. The annual incidence of adult ALL in Korea is 300-350 patients per year. The WHO classification of ALL was revised in 2022 to reflect the molecular cytogenetic features and suggest new adverse-risk subgroups, such as Ph-like ALL and ETP-ALL. We continue to use traditional adverse-risk features and cytogenetics, with MRD-directed post-remission therapy including allogeneic hematopoietic cell transplantation. However, with the introduction of novel agents, such as ponatinib, blinatumomab, and inotuzumab ozogamicin incorporated into frontline therapy, good MRD responses have been achieved, and overall survival outcomes are improving. Accordingly, some clinical trials have suggested a possible era of chemotherapy-free or transplantation-free approaches in the near future. Nevertheless, relapse of refractory ALL still occurs, and some poor ALL subtypes, such as Ph-like ALL and ETP-ALL, are unsolved problems for which novel agents and treatment strategies are needed. In this review, we summarize the currently applied diagnostic and therapeutic practices in the era of advanced genetic analysis and targeted immunotherapies in United States and Europe and introduce real-world Korean data.

• Child Health Nursing Research
• /
• v.29 no.4
• /
• pp.280-289
• /
• 2023

Purpose: This study investigated weight status in survivors of childhood acute lymphocytic leukemia (ALL) and identified related factors. Methods: A retrospective review of the electronic medical records of survivors of childhood ALL (n=230) was conducted. We analyzed the survivors' characteristics, including sex, age, weight status at diagnosis, central nervous system involvement, risk classification, length of treatment, radiation therapy, and hematopoietic stem cell transplantation. Analysis of variance and the chi-squared test were applied to investigate influencing factors. Results: The weight status distribution was as follows: 23 individuals (10.0%) were classified as underweight, 151 individuals (65.7%) were healthy weight, and 56 individuals (24.3%) were overweight/obese. Age at diagnosis (F=10.03, p<.001), weight status at diagnosis (x²=43.41, p<.001), and risk classification (F=10.98, p=0.027) showed significant differences among the weight status groups. Survivors who were older at diagnosis and those in the very high-risk category had a higher likelihood of experiencing underweight status during their survivorship, while survivors who were overweight/obese at diagnosis were more likely to remain overweight/obese at the time of survival. Conclusion: Considering the potential health implications related to an unhealthy weight status in survivors of ALL, it is imperative to undertake early identification and implement interventions for at-risk individuals.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.10
• /
• pp.830-836
• /
• 2005

Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.