scholarly journals Computer aided system for lymphoblast classification to detect acute lymphoblastic leukemia

2019 ◽  
Vol 14 (2) ◽  
pp. 597
Author(s):  
Syadia Nabilah Mohd Safuan ◽  
Mohd Razali Md Tomari ◽  
Wan Nurshazwani Wan Zakaria ◽  
Mohd Norzali Haji Mohd ◽  
Nor Surayahani Suriani
Keyword(s):  
Feature Extraction ◽  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Color Space ◽  
Support Vector ◽  
Early Prevention ◽  
Computer Aided ◽  
Watershed Method ◽  
The Individual ◽  
Inaccurate Result

Acute lymphoblastic leukemia (ALL) is a disease that is detected by the presence of lymphoblast cell. Basically, lymphoblast cell is the abnormal cell of lymphocyte which is one of the White Blood Cell (WBC) types. Early prevention is suggested as this disease can be fatal and caused death. Traditionally, ALL is detected by using manual analysis which is challenging and time consuming. It can also yield inaccurate result as it is highly dependent on the pathologist’s skills. Industry has come out with hematology counter which is fast, accurate and automated. However, these machines are costly and cannot be afforded by some countries. For that reason, Computer Aided System (CAS) will be a great help to the pathologist for assisting purposes and it also can act as second opinion for the pathologist. This system contains six main steps which are color space correction, WBC segmentation, post processing, clumped area extraction, feature extraction and lymphoblast classification. Firstly, color space correction is apply by using l*a*b* color space to standardize the image’s intensity. Next, WBC segmentation is made to prune out WBC region using color space analysis with Otsu thresholding. However, segmented image contains noises that need to be eliminated and it is accomplished by applying morphological filter with Connected Component Labelling (CCL). There is an overlapping WBC which need to be separated by using Watershed method to extract the individual cells. Next, feature extraction is made to collect the cell’s data to be fed into the classifier. Classifier used in this system to classify lymphoblast is Support Vector Machine (SVM) and this system is able to achieve 96.69% of accuracy.

Meningeal Leukemia following Lymphoid Blast Crisis in Chronic Myeloid Leukemia: Therapeutic Implications

1982 ◽  
Vol 68 (3) ◽  
pp. 257-263 ◽  
Author(s):  
Mario Cazzola ◽  
Giulio Nalli ◽  
Ercole Brusamolino ◽  
Maurizio Daccò ◽  
Angela Ghizzi ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Chronic Myeloid Leukemia ◽  
Complete Remission ◽  
Myeloid Leukemia ◽  
Lymphoblastic Leukemia ◽  
Blast Crisis ◽  
Early Prevention ◽  
Therapeutic Implications ◽  
Therapeutic Protocol ◽  
Meningeal Leukemia

Five of 40 patients with chronic myeloid leukemia (CML) had lymphoid blast crisis and 4 of them achieved complete remission of metamorphosis with vincristine and prednisone. While in hematologic remission, two of these subjects developed meningeal leukemia. Clinical and biologic data indicated that the course of the disease after lymphoid blast crisis was very similar to that of acute lymphoblastic leukemia (ALL). It is suggested that patients with CML who develop lymphoid blast crisis should be treated with an intensive therapeutic protocol including early prevention of meningeal leukemia.

Clinical significance of translocation t(1;19) in childhood acute lymphoblastic leukemia in the context of contemporary therapies: a report from the Children's Cancer Group.

1998 ◽  
Vol 16 (2) ◽  
pp. 527-535 ◽  
Author(s):  
F M Uckun ◽  
M G Sensel ◽  
H N Sather ◽  
P S Gaynon ◽  
D C Arthur ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Prognostic Significance ◽  
Newly Diagnosed ◽  
Event Free Survival ◽  
Free Survival ◽  
Cancer Group ◽  
Improved Outcomes ◽  
B Lineage ◽  
The Individual

PURPOSE The nonrandom translocation t(1;19) has been associated with poor outcome in pediatric B-lineage acute lymphoblastic leukemia (ALL). Because most patients treated by contemporary therapies now achieve improved outcomes, we have reassessed the prognostic significance of t(1;19). PATIENTS AND METHODS Cytogenetic data were accepted for 1,322 children (<21 years old) with newly diagnosed ALL enrolled between 1988 and 1994 on risk-adjusted studies of the Children's Cancer Group (CCG). Forty-seven patients (3.6%) were t(1;19) positive (+); 1,275 (96.4%) were t(1;19) negative (-). Clinical characteristics and treatment outcome were compared using standard methods. RESULTS Translocation (1;19)+ patients were more likely than t(1;19)- patients to be 10 years of age or greater (P < .001) or CD10+ CD19+ CD34- (P < .0001), or nonwhite (P = .02). Patients with a balanced t(1;19) were less likely to be hyperdiploid than patients with an unbalanced der(19)t(1;19). Event-free survival (EFS) was similar for the overall group of t(1;19)+ and t(1;19)- patients, with 4-year estimates of 69.5% (SD, 6.8%) and 74.8% (SD, 1.3%; P = .48), respectively. However, patients with unbalanced der(19)t(1;19) had significantly better outcomes than patients with balanced t(1;19): 4-year EFS were 80.6% (SD, 7.1%) and 41.7% (SD, 13.5%), respectively (P = .003). These differences were maintained within the individual studies analyses and after exclusion of t(1;19)+ patients whose cells were hyperdiploid with more than 50 chromosomes. CONCLUSION The overall group of t(1;19)+ patients, as well as the subgroup with an unbalanced der(19)+ (1;19) had outcomes similar to that of t(1;19)- patients, whereas patients with balanced t(1;19) had poorer outcomes. Thus, although the overall prognostic significance of t(1;19) has been obviated by contemporary risk-adjusted protocols, the balanced t(1;19) translocation remains an adverse prognostic factor.

scholarly journals Content Based Image Retrieval using Collaborative Color, Texture and Shape Features

2020 ◽  
Vol 9 (3) ◽  
pp. 1466-1469
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Attribute Extraction ◽  
The Individual ◽  
Selection Of ◽  
Color Texture

Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques

Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia

2018 ◽  
pp. 162-183
Author(s):  
Vanika Singhal ◽  
Preety Singh
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Principal Component ◽  
Texture Features ◽  
Automatic Detection ◽  
Support Vector ◽  
Shape Features ◽  
Geometrical Properties ◽  
Correlation Based Feature Selection ◽  
Human Interpretation

Acute Lymphoblastic Leukemia is a cancer of blood caused due to increase in number of immature lymphocyte cells. Detection is done manually by skilled pathologists which is time consuming and depends on the skills of the pathologist. The authors propose a methodology for discrimination of a normal lymphocyte cell from a malignant one by processing the blood sample image. Automatic detection process will reduce the diagnosis time and not be limited by human interpretation. The lymphocyte images are classified based on two types of extracted features: shape and texture. To identify prominent shape features, Correlation based Feature Selection is applied. Principal Component Analysis is applied on the texture features to reduce their dimensionality. Support Vector Machine is used for classification. It is observed that 16 shape features are able to give a classification accuracy of 92.3% and that changes in the geometrical properties of the nucleus emerge as significant features contributing towards detecting a malignant lymphocyte.

scholarly journals Genetic and Epigenetic Targeting Therapy for Pediatric Acute Lymphoblastic Leukemia

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3349
Author(s):  
Huan Xu ◽  
Hui Yu ◽  
Runming Jin ◽  
Xiaoyan Wu ◽  
Hongbo Chen
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Histone Modifications ◽  
Lymphoblastic Leukemia ◽  
Genetic Alterations ◽  
Epigenetic Alterations ◽  
Targeting Therapy ◽  
Precision Therapy ◽  
Dna Methylations ◽  
The Individual

Acute lymphoblastic leukemia is the most common malignancy in children and is characterized by numerous genetic and epigenetic abnormalities. Epigenetic mechanisms, including DNA methylations and histone modifications, result in the heritable silencing of genes without a change in their coding sequence. Emerging studies are increasing our understanding of the epigenetic role of leukemogenesis and have demonstrated the potential of DNA methylations and histone modifications as a biomarker for lineage and subtypes classification, predicting relapse, and disease progression in acute lymphoblastic leukemia. Epigenetic abnormalities are relatively reversible when treated with some small molecule-based agents compared to genetic alterations. In this review, we conclude the genetic and epigenetic characteristics in ALL and discuss the future role of DNA methylation and histone modifications in predicting relapse, finally focus on the individual and precision therapy targeting epigenetic alterations.

scholarly journals The NOTCH1 Driven Long Non-Coding RNA Repertoire in T-Cell Acute Lymphoblastic Leukemia

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 900-900
Author(s):  
Annelynn Wallaert ◽  
Kaat Durinck ◽  
Pieter Rondou ◽  
Inge van de Walle ◽  
Wouter Vanloocke ◽  
...  
Keyword(s):  
T Cells ◽  
Acute Lymphoblastic Leukemia ◽  
T Cell ◽  
Lymphoblastic Leukemia ◽  
Gene Set Enrichment Analysis ◽  
Protein Coding ◽  
Gene Set ◽  
Notch1 Signaling ◽  
Cell Acute Lymphoblastic Leukemia ◽  
The Individual

Abstract Introduction: T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive cancer resulting from the malignant transformation of T-cell precursors. Genetic studies in T-cell acute lymphoblastic leukemia (T-ALL) have uncovered a remarkable heterogeneity of genetic defects. Amongst these, NOTCH1activating mutations are the most frequently occurring events (>50%) in T-ALL. Since long non-coding RNAs (lncRNAs) are emerging as important players in oncogenesis, we decided to decode the NOTCH1 driven lncRNA transcriptional landscape in T-ALL and normal T-cell development. Methods and Results: RNA-sequencing was performed following pharmacological inhibition (GSI) of the NOTCH1 mutant and gamma secretase inhibitor (GSI) sensitive T-ALL cell line CUTLL1 in a time series experiment as well as for human CD34+ thymic progenitor T-cells cultured on an OP9 feeder layer with or without DL1-triggered NOTCH1 stimulation. First, we validated both model systems by confirming robust regulation of multiple canonical known protein coding NOTCH1 target genes including DTX1, NOTCH3 and NRARP. Next, we identified distinct subsets of NOTCH1 regulated lncRNAs in both experiments with an overlap of 27 commonly regulated NOTCH1 driven and previously annotated lncRNAs. An even larger number of novel, unannotated T-ALL/T-cell specific lncRNAs was found to be NOTCH1 regulated. Next, we took advantage of publically available ChIP-sequencing data for ICN1 and enhancer specific chromatin marks in CUTLL1 (Wang et al., PNAS, 2013), allowing the selection for direct regulated lncRNAs with enhancer properties. Amongst these lncRNAs, the recently described LUNAR1 lncRNA (Trimarchi et al., Cell, 2014) was present as top candidate in our dataset, thus validating this approach for further selecting bona fide NOTCH1 regulated lncRNAs. In a first step towards functional annotation of this subset of selected lncRNAs, we performed so-called guilt-by-association analysis through correlating expression levels of the individual lncRNAs with transcriptome data for all protein coding genes followed by gene set enrichment analysis in a large cohort of primary T-ALL patients. Subsequent enrichment mapping of significant gene sets yielded markedly different gene set clustering patterns for each of the individual analyzed lncRNAs, as well as distinct annotated related functionalities such as cytokine signaling, TCA-cycle, DNA replication and repair and translation. Prioritarization of lncRNAs for further functional validation was performed by measuring their expression in an extended panel of GSI-treated T-ALL cell lines (HPB-ALL, DND-41, T-ALL1 and ALL-SIL), sorted subsets of CD34+ and CD4+/CD8+ double positive thymocytes and an independent T-ALL patient cohort. Conclusion: We present the landscaping of an integrated lncRNA network acting downstream of NOTCH1 signaling in T-ALL and normal T-cells. These data pave the way towards the development of novel therapeutic strategies impacting on hyperactive NOTCH1 signaling. Disclosures No relevant conflicts of interest to declare.

scholarly journals Validation of Minimal Residual Disease as Surrogate Endpoint for Event-Free Survival in Childhood Acute Lymphoblastic Leukemia

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
Stefania Galimberti ◽  
Meenakshi Devidas ◽  
Ausiliatrice Lucenti ◽  
Giovanni Cazzaniga ◽  
Anja Möricke ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Minimal Residual Disease ◽  
Residual Disease ◽  
Lymphoblastic Leukemia ◽  
Surrogate Endpoint ◽  
Phase Iii ◽  
Event Free Survival ◽  
Free Survival ◽  
The Individual ◽  
Minimal Residual

Abstract Background The aim of this study was to assess whether minimal residual disease (MRD) at the end of induction front-line treatment can serve as a surrogate endpoint for event-free survival (EFS) in childhood B-lineage acute lymphoblastic leukemia. Methods The analysis was based on individual data of 4830 patients from two large phase III trials that asked a randomized question on the effect of different corticosteroids (dexamethasone vs prednisone) during induction chemotherapy on EFS. The association between MRD classified in three ordered categories [negative = 0, low positive = (>0 and <5 × 10−4), and positive = (≥5 × 10-4)] and EFS at the individual and trial levels was evaluated with the meta-analytic approach based on the Plackett copula model. Centers within trial were grouped according to geographical area, and a total of 28 units were identified for the analysis. Results MRD at the end of induction was a poor surrogate for treatment effect on EFS at the trial level, with Rtrial2 = 0.09 (95% confidence interval [CI] = 0.00 to 0.29), whereas at the individual level it was strongly associated with EFS, with an odds ratio of 3.90 (95% CI = 3.35 to 4.44) of failure for patients with higher compared with lower MRD levels. Additional sensitivity and relevant subgroup analyses confirmed these findings at both trial- and patient-level association. Conclusions Although MRD is a robust biomarker highly predictive of outcome for individual patients, clinicians and regulatory bodies should be cautious in using early MRD response in the context of complex multiagent acute lymphoblastic leukemia therapy as an early surrogate endpoint to predict the effect of a randomized treatment intervention on long-term EFS.

scholarly journals Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Li ◽  
Rui Zhu ◽  
Lei Mi ◽  
Yihui Cao ◽  
Di Yao
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Blood Cell ◽  
White Blood Cell ◽  
Lymphoblastic Leukemia ◽  
Golden Section ◽  
Color Space ◽  
Good Prospect ◽  
Threshold Method ◽  
Threshold Segmentation ◽  
Hsv Color Space

We propose a dual-threshold method based on a strategic combination of RGB and HSV color space for white blood cell (WBC) segmentation. The proposed method consists of three main parts: preprocessing, threshold segmentation, and postprocessing. In the preprocessing part, we get two images for further processing: one contrast-stretched gray image and one H component image from transformed HSV color space. In the threshold segmentation part, a dual-threshold method is proposed for improving the conventional single-threshold approaches and a golden section search method is used for determining the optimal thresholds. For the postprocessing part, mathematical morphology and median filtering are utilized to denoise and remove incomplete WBCs. The proposed method was tested in segmenting the lymphoblasts on a public Acute Lymphoblastic Leukemia (ALL) image dataset. The results show that the performance of the proposed method is better than single-threshold approach independently performed in RGB and HSV color space and the overall single WBC segmentation accuracy reaches 97.85%, showing a good prospect in subsequent lymphoblast classification and ALL diagnosis.

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