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Application of an Artificial Neural Network in the Diagnosis of Chronic Lymphocytic Leukemia.

ABSTRACT: Introduction Chronic lymphocytic leukemia (CLL) is one of the most common types of leukemia, and the early diagnosis of patients coincides with their proper treatment and survival. If patients are diagnosed late or proper treatment is not applied, it may lead to harmful results. Several methods could be used for the diagnosis of leukemia; some of these include complete blood count (CBC), immunophenotyping, lymph node biopsy, chest X-ray, computerized tomography (CT) scan, and ultrasound. Most of these methods are time-consuming and an application of more than one method will result as intended. This acknowledgment stresses the necessity of rapid and proper diagnosis for leukemia based on clinical and medical findings, inasmuch as it was decided to apply the artificial neural network (ANN) in order to identify a molecular biomarker for rapid leukemia diagnosis from blood samples and evaluate its potential for the detection of cancer. Materials & methods The independent sample t-test was applied with the Statistical Package for the Social Sciences (SPSS; IBM Corp, Armonk, NY, US) software on the microarray gene expression data of Gene Expression Omnibus (GEO) datasets (GSE22529); 12 genes that had shown the highest differences (among parameters whose p-value was less than 0.01) were selected for further ANN analysis. The selected genes of 53 patients were applied to the training network algorithm, with a learning rate of 0.1. Results The results showed a high accuracy of the relationship between the output of the trained network and the test data. The area under the receiver operating characteristic (ROC) curve was 0.991, which provides proof of the precision and the relationship with identifying Gelsolin as a potential biomarker for this research. Conclusions With these results, it was concluded that the training process of the ANN could be applied to rapid CLL diagnosis and finding a potential biomarker. Besides, it is suggested that this method could be performed to diagnose other forms of cancer in order to get a rapid and reliable outcome.

SUBMITTER: Shaabanpour Aghamaleki F

PROVIDER: S-EPMC6450593 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Application of an Artificial Neural Network in the Diagnosis of Chronic Lymphocytic Leukemia.

Shaabanpour Aghamaleki Fateme F Mollashahi Behrouz B Nosrati Mokhtar M Moradi Afshin A Sheikhpour Mojgan M Movafagh Abolfazl A

Cureus 20190204 2

Introduction Chronic lymphocytic leukemia (CLL) is one of the most common types of leukemia, and the early diagnosis of patients coincides with their proper treatment and survival. If patients are diagnosed late or proper treatment is not applied, it may lead to harmful results. Several methods could be used for the diagnosis of leukemia; some of these include complete blood count (CBC), immunophenotyping, lymph node biopsy, chest X-ray, computerized tomography (CT) scan, and ultrasound. Most of ...[more]

PMID: 31001458

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Project description:BACKGROUND:Chronic lymphocytic leukemia (CLL) is incurable through conventional chemoimmunotherapy regimens. Despite durable responses to front-line therapy and sustained remission rates in patients with CLL, a majority of patients eventually relapse in 5 years of initial treatment. The depth of the response may affect the length of response. Maintenance therapies were aimed to deep remissions and extend the period of disease quiescence. Lenalidomide, rituximab and ofatumumab had demonstrated some efficacy as a maintenance therapy compared to no intervention for CLL patients. The relative effect on disease control and safety between different maintenance therapies were unclear. METHODS:We performed a systematic literature review and network meta-analysis to evaluate relative effect on disease control and safety of current available maintenance therapies. We searched PubMed, Embase and Cochrane database up to March 6, 2019. Relevant reference of review article and conference abstract including European Hematology Association Annual Meeting (EHA 2018), American Society of Hematology Annual Meeting (ASH 2018) and American Society of Clinical Oncology Annual Meeting (ASCO 2018) were searched. Randomized controlled trials (RCT) involving current available maintenance therapy including "Lenalidomide", "Rituximab", "Ofatumumab", "Ibrutinib", "Idelalisib", "Venetoclax"and "Obinutuzumab"were eligible. Outcomes of interest included progression-free survival (PFS), overall survival (OS) and serious adverse events (SAE) in CLL patients received subsequent maintenance therapy. Two authors CHL and CL) independently assessed eligibility for all identified citations and extracted data from the original trial reports. The selected studies' risk of bias was assessed following the guidelines of Cochrane Collaboration Handbook. RESULTS:In total, six phase III RCTs with total 1,615 CLL patients were identified. Maintenance therapy using lenalidomide, rituximab, and ofatumumab demonstrated a statistically significant effect in prolongation of progression-free survival (HR:0.37, 95% CI: 0.27-0.50 of lenalidomide; HR:0.50, 95% CI: 0.38-0.66 of rituximab; HR:0.52, 95% CI:0.41-0.66 of ofatumumab, separately) compared with no intervention; however, for overall survival, the effect of maintenance therapy showed no significant difference versus no intervention (HR: 0.89, 95% CI: 0.70-1.14). Lenalidomide showed the best efficacy for PFS (HR: 0.37, 95% CI: 0.27-0.50, Probability of being best treatment: 96%). CONCLUSIONS:Our network meta-analysis provided an integrated overview of relative efficacy and safety of different maintenance therapies in CLL. All maintenance therapies were effective in reducing the risk of disease progression versus no intervention. Based on current best evidence, maintenance therapy with lenalidomide is the most efficacious option.

Project description:In many cancers, cells undergo re-programming of metabolism, cell survival and anti-apoptotic defense strategies, with the proteins mediating this reprogramming representing potential biomarkers. Here, we searched for novel biomarker proteins in chronic lymphocytic leukemia (CLL) that can impact diagnosis, treatment and prognosis by comparing the protein expression profiles of peripheral blood mononuclear cells from CLL patients and healthy donors using specific antibodies, mass spectrometry and binary logistic regression analyses and other bioinformatics tools. Mass spectrometry (LC-HR-MS/MS) analysis identified 1,360 proteins whose expression levels were modified in CLL-derived lymphocytes. Some of these proteins were previously connected to different cancer types, including CLL, while four other highly expressed proteins were not previously reported to be associated with cancer, and here, for the first time, DDX46 and AK3 are linked to CLL. Down-regulation expression of two of these proteins resulted in cell growth inhibition. High DDX46 expression levels were associated with shorter survival of CLL patients and thus can serve as a prognosis marker. The proteins with modified expression include proteins involved in RNA splicing and translation and particularly mitochondrial proteins involved in apoptosis and metabolism. Thus, we focused on several metabolism- and apoptosis-modulating proteins, particularly on the voltage-dependent anion channel 1 (VDAC1), regulating both metabolism and apoptosis. Expression levels of Bcl-2, VDAC1, MAVS, AIF and SMAC/Diablo were markedly increased in CLL-derived lymphocytes. VDAC1 levels were highly correlated with the amount of CLL-cancerous CD19+/CD5+ cells and with the levels of all other apoptosis-modulating proteins tested. Binary logistic regression analysis demonstrated the ability to predict probability of disease with over 90% accuracy. Finally, based on the changes in the levels of several proteins in CLL patients, as revealed from LC-HR-MS/MS, we could distinguish between patients in a stable disease state and those who would be later transferred to anti-cancer treatments. The over-expressed proteins can thus serve as potential biomarkers for early diagnosis, prognosis, new targets for CLL therapy, and treatment guidance of CLL, forming the basis for personalized therapy.

Dataset Information

Application of an Artificial Neural Network in the Diagnosis of Chronic Lymphocytic Leukemia.

Publications

Application of an Artificial Neural Network in the Diagnosis of Chronic Lymphocytic Leukemia.

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