scholarly journals Multi-Omics Analysis in Initiation and Progression of Meningiomas: From Pathogenesis to Diagnosis

2020 ◽  
Vol 10 ◽  
Author(s):  
Jiachen Liu ◽  
Congcong Xia ◽  
Gaiqing Wang
Keyword(s):  
Early Diagnosis ◽  
Poor Prognosis ◽  
Scientific Basis ◽  
High Grade ◽  
Intracranial Tumors ◽  
Pathological Mechanism ◽  
Growing Body ◽  
Diagnosis And Classification ◽  
Subsequent Intervention

Meningiomas are common intracranial tumors that can be cured by surgical resection in most cases. However, the most disconcerting is high-grade meningiomas, which frequently recur despite initial successful treatment, eventually conferring poor prognosis. Therefore, the early diagnosis and classification of meningioma is necessary for the subsequent intervention and an improved prognosis. A growing body of evidence demonstrates the potential of multi-omics study (including genomics, transcriptomics, epigenomics, proteomics) for meningioma diagnosis and mechanistic links to potential pathological mechanism. This thesis addresses a neglected aspect of recent advances in the field of meningiomas at multiple omics levels, highlighting that the integration of multi-omics can reveal the mechanism of meningiomas, which provides a timely and necessary scientific basis for the treatment of meningiomas.

Working Formulation (WF) and Revised European-American classification of Lymphoma (REAL): Inter-observer agreement in conflicting lymphoma cases

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 17567-17567
Author(s):  
S. Latifzadeh ◽  
T. Riahi ◽  
V. Entezari
Keyword(s):  
Kappa Statistic ◽  
Clinical Findings ◽  
Observer Agreement ◽  
Aggressive Lymphoma ◽  
Low Grade ◽  
High Grade ◽  
Diagnosis And Classification ◽  
Clinical Risks ◽  
High Level

17567 Background: Immunophenotypic and genetic studies play an increasingly important role in diagnosis and classification of lymphoid neoplasm. This study tried to re-evaluate a number of conflicting lymphoma cases which were reported by WF previously, with REAL classification and to measure the agreement between these two methods of classification. Methods: In a three year period, a panel of expert pathologists evaluated referral cases by WF. Those cases (n = 60, Mean age = 40.9 ± 16.4) whose evaluations did not reached to a definitive pathologic diagnosis or there was a discrepancy between their pathologic and clinical findings were reviewed in Keil institute of hematopathology in Germany based on REAL classification. The primary and secondary diagnoses each were classified in five subgroups with equivalent clinical risks (see Table ). Results: Disagreement was detected in 23 cases (38%), while exact kappa statistic was 0.50. Sixteen cases (70%) of difference belonged to group of low grade lymphoma (kappa = 0.35) in which 11 cases (69%) changed to aggressive lymphoma and one case changed to highly aggressive subgroups. Four cases (25%) of difference occurred in the group of low probability lymphoma in which neoplasia was documented. High grade and Hodgkin lymphoma subgroups showed a high level of agreement (kappa = 0.84 and 0.74 respectively). Conclusions: Based on this study’s results, it can be concluded that there is a moderate agreement between WF and REAL classifications in conflicting lymphoma cases. WF underestimates clinical risk of low grade lymphoma in a considerable amount of patients but in high grade lymphoma the disagreement is not so high. [Table: see text] No significant financial relationships to disclose.

Electron Microscopy of Carcinomas of the Lung

1974 ◽  
Vol 32 ◽  
pp. 16-17
Author(s):  
B. Mackay ◽  
M. Mandavia ◽  
J. M. Lukeman ◽  
C. F. Mountain
Keyword(s):  
Electron Microscopy ◽  
Poor Prognosis ◽  
Light And Electron Microscopy ◽  
Malignant Neoplasm ◽  
Therapeutic Modalities ◽  
The Poor ◽  
Diagnosis And Classification ◽  
Methods Of Treatment ◽  
Insight Into

Carcinoma of the lung is the most common malignant neoplasm in males and the incidence continues to rise. The inadequacy of current methods of treatment is reflected in the poor prognosis: only 5% of patients survive for more than five years following diagnosis.In order to assess the effectiveness of new therapeutic modalities, accurate pathologic diagnosis is essential, and it is recognized that a proportion of these tumors can not be accurately classified by light microscopy alone. We have now studied over one hundred lung carcinomas with correlated light and electron microscopy, and our findings indicate that electron microscopy can be an invaluable aid in the diagnosis and classification of the tumors. Study of the fine structure of the tumor cells can provide the basis for a more precise classification than is currently used in clinical studies, and additionally give insight into problems of histogenesis.

scholarly journals Questions of Early Diagnosis and Classification of Ankylosing Spondylitis

2014 ◽  
Vol 0 (1-2.13-14) ◽  
pp. 14-18
Author(s):  
Sh.F. Erdes ◽  
A.G. Bochkova ◽  
T.V. Dubinina ◽  
O.A. Rumyantseva ◽  
A.V. Smirnov ◽  
...  
Keyword(s):  
Ankylosing Spondylitis ◽  
Early Diagnosis ◽  
Diagnosis And Classification

scholarly journals Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ching-Wei Wang ◽  
Yi-An Liou ◽  
Yi-Jia Lin ◽  
Cheng-Chang Chang ◽  
Pei-Hsuan Chu ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Squamous Cell Carcinoma ◽  
Pap Smear ◽  
Jaccard Index ◽  
High Grade ◽  
Cervical Lesions ◽  
Diagnosis And Classification ◽  
F Measure ◽  
Whole Slide Images

AbstractEvery year cervical cancer affects more than 300,000 people, and on average one woman is diagnosed with cervical cancer every minute. Early diagnosis and classification of cervical lesions greatly boosts up the chance of successful treatments of patients, and automated diagnosis and classification of cervical lesions from Papanicolaou (Pap) smear images have become highly demanded. To the authors’ best knowledge, this is the first study of fully automated cervical lesions analysis on whole slide images (WSIs) of conventional Pap smear samples. The presented deep learning-based cervical lesions diagnosis system is demonstrated to be able to detect high grade squamous intraepithelial lesions (HSILs) or higher (squamous cell carcinoma; SQCC), which usually immediately indicate patients must be referred to colposcopy, but also to rapidly process WSIs in seconds for practical clinical usage. We evaluate this framework at scale on a dataset of 143 whole slide images, and the proposed method achieves a high precision 0.93, recall 0.90, F-measure 0.88, and Jaccard index 0.84, showing that the proposed system is capable of segmenting HSILs or higher (SQCC) with high precision and reaches sensitivity comparable to the referenced standard produced by pathologists. Based on Fisher’s Least Significant Difference (LSD) test (P < 0.0001), the proposed method performs significantly better than the two state-of-the-art benchmark methods (U-Net and SegNet) in precision, F-Measure, Jaccard index. For the run time analysis, the proposed method takes only 210 seconds to process a WSI and is 20 times faster than U-Net and 19 times faster than SegNet, respectively. In summary, the proposed method is demonstrated to be able to both detect HSILs or higher (SQCC), which indicate patients for further treatments, including colposcopy and surgery to remove the lesion, and rapidly processing WSIs in seconds for practical clinical usages.

scholarly journals Biocatalytic Amplification of UV Signal in Capillary Electrophoresis of MicroRNA

2019 ◽  
Vol 21 (1) ◽  
pp. 51 ◽  
Author(s):  
Ruibin Hu ◽  
Yi Chen
Keyword(s):  
Capillary Electrophoresis ◽  
Detection Limit ◽  
Early Diagnosis ◽  
Large Scale ◽  
Cost Effective ◽  
Related Research ◽  
Diagnosis And Classification ◽  
Large Scale Screening ◽  
Potential Biomarkers

MicroRNAs (miRNAs) are new potential biomarkers for early diagnosis and classification of cancer. This study is the first attempt to use biocatalytic amplification reactions combined with capillary electrophoresis to detect multiple miRNAs simultaneously. In this way, miRNAs, as catalysts, can catalyze two single strands of DNA to form double-strand DNA. Feasibility was demonstrated by non-gel capillary electrophoresis coupled with UV detection (NGCE-UV). The detection limit was improved down to 1.0 nM, having ca. 103-fold improvement. This method has a good linear range of between 3.0 nM and 300 nM, with R2 at 0.99, recovery at 88–115%, and peak area precision at 1–12.7%. Using three target miRNAs as a model can achieve the baseline separation and good selectivity. The proposed biocatalysis coupled with a capillary electrophoresis-based method is simple, rapid, multiplexed, and cost-effective, making it potentially applicable for simultaneous, large-scale screening for other nucleic acids biomarkers and related research.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

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