AIMC Topic: Lymphoma, Non-Hodgkin

Clear Filters Showing 1 to 10 of 10 articles

Computational screening and in vitro evaluation of sphingosine-1-phosphate analogues as therapeutics for Non-Hodgkin's lymphoma.

Scientific reports
Non-Hodgkin's lymphoma (NHL) is a prevalent hematological malignancy that includes a variety of B-cell and T-cell proliferations. The S1P (sphingosine-1-phosphate) pathway, involved in cell survival, proliferation, and migration, plays a critical rol...

Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.

BMC medical imaging
PURPOSE: To apply quantitative imaging analysis for noninvasive classification of the most frequent subtypes of Non-Hodgkin Lymphoma (NHL) as a basis for a clinical imaging genomic model to support therapeutic monitoring and clinical decision making.

AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma.

Journal of nanobiotechnology
BACKGROUND: Lymphoma is a malignant tumor of the immune system and its incidence is increasing year after year, causing a major threat to people's health. Conventional diagnosis of lymphoma basically depends on histological images consuming long-time...

Deep transfer learning radiomics for distinguishing sinonasal malignancies: a preliminary MRI study.

Future oncology (London, England)
PURPOSE: This study aimed to assess the diagnostic accuracy of combining MRI hand-crafted (HC) radiomics features with deep transfer learning (DTL) in identifying sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC), and non-Hodgki...

Survival trend and outcome prediction for pediatric Hodgkin and non-Hodgkin lymphomas based on machine learning.

Clinical and experimental medicine
Pediatric Hodgkin and non-Hodgkin lymphomas differ from adult cases in biology and management, yet there is a lack of survival analysis tailored to pediatric lymphoma. We analyzed lymphoma data from 1975 to 2018, comparing survival trends between 7,8...

Automated Lugano Metabolic Response Assessment in F-Fluorodeoxyglucose-Avid Non-Hodgkin Lymphoma With Deep Learning on F-Fluorodeoxyglucose-Positron Emission Tomography.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: Artificial intelligence can reduce the time used by physicians on radiological assessments. For F-fluorodeoxyglucose-avid lymphomas, obtaining complete metabolic response (CMR) by end of treatment is prognostic.

Low-value care and excess out-of-pocket expenditure among older adults with incident cancer - A machine learning approach.

Journal of cancer policy
OBJECTIVE: To evaluate the association of low-value care with excess out-of-pocket expenditure among older adults diagnosed with incident breast, prostate, colorectal cancers, and Non-Hodgkin's Lymphoma.

Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis.

Biomolecules
Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymp...

[Radiotherapy of non-Hodgkin lymphoma-discussion of modern treatment concepts and innovations].

Radiologie (Heidelberg, Germany)
BACKGROUND: Radiotherapy is an established treatment modality for malignant non-Hodgkin lymphoma. However, the complexity of the treatment situations demands precise treatment indication and execution. The following review presents modern radiooncolo...