AIMC Topic: Lymphocyte Count

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Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning.

BMC cancer Dec 18, 2024
BACKGROUND: Inflammation-related biomarkers, such as systemic inflammation score (SIS) and neutrophil-lymphocyte ratio (NLR), are associated with colorectal cancer prognosis. However, the combined role of SIS, NLR, and clinicopathological factors in ...
Inflammation Nomograms Neutrophils Neoplasm Staging Lymphocytes Humans Aged Machine Learning Colorectal Neoplasms Prognosis Biomarkers, Tumor Retrospective Studies Male Female Middle Aged Lymphocyte Count
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Artificial intelligence-based quantification of lymphocytes in feline small intestinal biopsies.

Veterinary pathology Oct 14, 2024
Feline chronic enteropathy is a poorly defined condition of older cats that encompasses chronic enteritis to low-grade intestinal lymphoma. The histological evaluation of lymphocyte numbers and distribution in small intestinal biopsies is crucial for...
Biopsy Artificial Intelligence Lymphocytes Cats Reproducibility of Results Lymphocyte Count Cat Diseases Sensitivity and Specificity Intestine, Small Animals
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Development of a Fully Automated Method to Obtain Reproducible Lymphocyte Counts in Patients With Colorectal Cancer.

Applied immunohistochemistry & molecular morphology : AIMM Jun 15, 2022
Colorectal cancer (CRC) is the third most common cancer worldwide. Although clinical outcome varies among patients diagnosed within the same TNM stage it is the cornerstone in treatment decisions as well as follow-up programmes. Tumor-infiltrating ly...
Artificial Intelligence Colorectal Neoplasms Lymphocytes, Tumor-Infiltrating CD8-Positive T-Lymphocytes Humans Lymphocyte Count
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Correlation between lung infection severity and clinical laboratory indicators in patients with COVID-19: a cross-sectional study based on machine learning.

BMC infectious diseases Feb 18, 2021
BACKGROUND: Coronavirus disease 2019 (COVID-19) has caused a global pandemic that has raised worldwide concern. This study aims to investigate the correlation between the extent of lung infection and relevant clinical laboratory testing indicators in...
Clinical Laboratory Techniques Severity of Illness Index Adult Neutrophils Cross-Sectional Studies Lymphocytes Male COVID-19 Testing Humans Middle Aged Lymphocyte Count Lung Prognosis Machine Learning Tomography, X-Ray Computed Pandemics Female COVID-19
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A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.

Scientific reports Feb 8, 2021
The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countrie...
Respiration, Artificial Length of Stay Cohort Studies Reverse Transcriptase Polymerase Chain Reaction Computational Biology Aged, 80 and over Adult Male Middle Aged Brazil C-Reactive Protein Lymphocyte Count Humans Machine Learning COVID-19 Algorithms Prognosis Intensive Care Units Female SARS-CoV-2 Aged
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Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.

Scientific reports Feb 3, 2021
COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 ...
Female SARS-CoV-2 Risk Factors Adult Male Humans Machine Learning COVID-19 Respiratory Distress Syndrome Middle Aged Area Under Curve Comorbidity Sex Factors Severity of Illness Index Lymphocyte Count ROC Curve Body Mass Index
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A Machine Learning Approach Yields a Multiparameter Prognostic Marker in Liver Cancer.

Cancer immunology research Jan 11, 2021
A number of staging systems have been developed to predict clinical outcomes in hepatocellular carcinoma (HCC). However, no general consensus has been reached regarding the optimal model. New approaches such as machine learning (ML) strategies are po...
Machine Learning Prognosis Retrospective Studies Female Risk Factors Young Adult Adult T-Lymphocytes Male Middle Aged Humans Neoplasm Invasiveness Risk Assessment Lymphocyte Count Liver Adolescent Kaplan-Meier Estimate Neoplasm Staging Liver Neoplasms Models, Immunological Hepatectomy Immune Checkpoint Proteins Carcinoma, Hepatocellular Portal Vein Venous Thrombosis
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Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Oct 1, 2020
Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been considered for inclusion in the American Joint Committee on Cancer (AJCC) staging criteria. However, interobserver discordance continues to prevent the ado...
Databases, Factual Retrospective Studies Progression-Free Survival Female Microscopy Melanoma Image Interpretation, Computer-Assisted Male Lymphocytes, Tumor-Infiltrating Skin Neoplasms Reproducibility of Results Lymphocyte Count Humans Middle Aged Risk Factors Predictive Value of Tests Machine Learning Risk Assessment Aged Neoplasm Staging Automation, Laboratory Disease Progression Biopsy Adult Neoplasm Recurrence, Local Tumor Microenvironment Diagnosis, Computer-Assisted
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Multilevel approach to male fertility by machine learning highlights a hidden link between haematological and spermatogenetic cells.

Andrology Jun 21, 2020
BACKGROUND: Male infertility represents a complex clinical condition requiring an accurate multilevel assessment, in which machine learning technology, combining large data series in non-linear and highly interactive ways, could be innovatively appli...
Retrospective Studies Hematopoiesis Humans Female Environment Semen Analysis Machine Learning Adult Infertility, Male Male Algorithms Lymphocyte Count Longitudinal Studies Databases, Factual Spermatogenesis Erythrocyte Count
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Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning.

Computational intelligence and neuroscience May 14, 2020
Intelligent medical diagnosis has become common in the era of big data, although this technique has been applied to asthma only in limited contexts. Using routine blood biomarkers to identify asthma patients would make clinical diagnosis easier to im...
Lymphocyte Count Humans Machine Learning Biomarkers Asthma ROC Curve
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