AIMC Topic: Lymphoma

Clear Filters Showing 61 to 70 of 89 articles

Radiomics based on F-FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine-learning approach: A preliminary study.

Cancer medicine Nov 25, 2019
PURPOSE: Our study assessed the ability F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics to differentiate breast carcinoma from breast lymphoma using machine-learning approach.
Retrospective Studies Female Aged Aged, 80 and over Adult Humans Machine Learning Prognosis Lymphoma Diagnosis, Differential Positron Emission Tomography Computed Tomography Radiopharmaceuticals Fluorodeoxyglucose F18 Breast Neoplasms Middle Aged ROC Curve Image Processing, Computer-Assisted Follow-Up Studies
View on PubMed DOI

Texture analysis and multiple-instance learning for the classification of malignant lymphomas.

Computer methods and programs in biomedicine Oct 23, 2019
BACKGROUND AND OBJECTIVES: Malignant lymphomas are cancers of the immune system and are characterized by enlarged lymph nodes that typically spread across many different sites. Many different histological subtypes exist, whose diagnosis is typically ...
Humans Tomography, X-Ray Computed Datasets as Topic Machine Learning Sensitivity and Specificity Algorithms Support Vector Machine Lymphoma Positron-Emission Tomography
View on PubMed DOI

F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.

NeuroImage. Clinical Jun 27, 2019
The differential diagnosis of primary central nervous system lymphoma from glioblastoma multiforme (GBM) is essential due to the difference in treatment strategies. This study retrospectively reviewed 77 patients (24 with lymphoma and 53 with GBM) to...
Machine Learning Humans Radiopharmaceuticals Retrospective Studies Male Positron-Emission Tomography Female Middle Aged Fluorodeoxyglucose F18 Aged Neuroimaging Brain Neoplasms Lymphoma Diagnosis, Differential Glioblastoma
View on PubMed DOI

Robust identification of molecular phenotypes using semi-supervised learning.

BMC bioinformatics May 28, 2019
BACKGROUND: Modern molecular profiling techniques are yielding vast amounts of data from patient samples that could be utilized with machine learning methods to provide important biological insights and improvements in patient outcomes. Unsupervised ...
Humans Algorithms Databases as Topic Chemotherapy, Adjuvant RNA, Messenger Phenotype Gene Expression Regulation, Neoplastic Prognosis Lymphoma Female Supervised Machine Learning Breast Neoplasms Disease-Free Survival
View on PubMed DOI

Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.

Neuroradiology Apr 4, 2019
PURPOSE: While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this...
Aged, 80 and over Adult Sensitivity and Specificity Brain Neoplasms Male Machine Learning Prospective Studies Female Aged Glioblastoma Image Interpretation, Computer-Assisted Meningioma Middle Aged Adolescent Lymphoma Magnetic Resonance Imaging Diagnosis, Differential Humans
View on PubMed DOI

Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

European journal of radiology Sep 14, 2018
PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to differentiate a glioblastoma multiforme (GBM) from a primary cerebral nervous system lymphoma (PCNSL).
Diagnosis, Differential Glioblastoma Image Enhancement Female Aged Adult Humans Brain Neoplasms Middle Aged Machine Learning Male ROC Curve Brain Logistic Models Retrospective Studies Lymphoma Magnetic Resonance Imaging
View on PubMed DOI

Artificial neural networks help to identify disease subsets and to predict lymphoma in primary Sjögren's syndrome.

Clinical and experimental rheumatology Aug 14, 2018
OBJECTIVES: Primary Sjögren's syndrome (pSS) is a complex chronic systemic disorder, for which specific and effective therapeutic interventions are still lacking. In this era of precision medicine, there is a clear need for a better definition of dis...
Data Mining Serologic Tests Sjogren's Syndrome Lymphoma Diagnosis, Differential Humans Predictive Value of Tests Female Principal Component Analysis Reproducibility of Results Autoantibodies Middle Aged Risk Factors Neural Networks, Computer Disease Progression Aged Decision Support Techniques Severity of Illness Index Male Phenotype Diagnosis, Computer-Assisted Biomarkers Prognosis
View on PubMed

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine May 16, 2018
PURPOSE: Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classificatio...
Humans Machine Learning Retrospective Studies ROC Curve Support Vector Machine Lymphoma Contrast Media Central Nervous System Neoplasms Magnetic Resonance Imaging Diagnosis, Differential Glioblastoma Image Interpretation, Computer-Assisted
View on PubMed DOI

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

AJNR. American journal of neuroradiology Apr 27, 2017
BACKGROUND AND PURPOSE: Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purp...
Diagnosis, Differential Image Interpretation, Computer-Assisted Image Enhancement Humans Central Nervous System Neoplasms Algorithms Glioma Adult Female Support Vector Machine Sensitivity and Specificity Lymphoma Male Magnetic Resonance Imaging Middle Aged ROC Curve
View on PubMed DOI

Gradient Boosted Decision Tree Classification of Endophthalmitis Versus Uveitis and Lymphoma from Aqueous and Vitreous IL-6 and IL-10 Levels.

Journal of ocular pharmacology and therapeutics : the official journal of the Association for Ocular Pharmacology and Therapeutics Feb 3, 2017
PURPOSE: To investigate the effectiveness of gradient boosting to classify endophthalmitis versus uveitis and lymphoma by intraocular cytokine levels.
Uveitis Endophthalmitis Water ROC Curve Lymphoma Humans Decision Trees Machine Learning Data Interpretation, Statistical Interleukin-10 Interleukin-6
View on PubMed DOI
Popular Topics
Recent Journals