AIMC Topic: Lymphoma

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A novel hybrid convolutional and transformer network for lymphoma classification.

Scientific reports
Lymphoma poses a critical health challenge worldwide, demanding computer aided solutions towards diagnosis, treatment, and research to significantly enhance patient outcomes and combat this pervasive disease. Accurate classification of lymphoma subty...

An interpretable machine learning model for predicting bone marrow invasion in patients with lymphoma via F-FDG PET/CT: a multicenter study.

BMC medical informatics and decision making
PURPOSE: Accurate identification of bone marrow invasion (BMI) is critical for determining the prognosis of and treatment strategies for lymphoma. Although bone marrow biopsy (BMB) is the current gold standard, its invasive nature and sampling errors...

Simultaneous determination of 7 thiols associated proteins in lymphoma patients'serum and cerebrospinal fluid by UHPLC-HRMS technique.

Scientific reports
Thiol compounds can serve as markers for the antioxidant and prognostic status of lymphoma, playing a crucial role in early tumor diagnosis. However, their high polarity and lack of chromophores pose challenges for multivariate analysis. This study a...

A novel UHPLC-HRMS method for simultaneous determination of 20 amino metabolites and proteins in lymphoma patients' cells and serum.

Scientific reports
Highly sensitive and selective monitoring of amino metabolites such as glutamine, arginine, tryptophan and related proteins played significant roles in early diagnosis and warning of lymphoma. But those limited abundance and lacked chromophore group ...

B lymphocyte subset-based stratification in primary Sjögren's syndrome: implications for lymphoma risk and personalized treatment.

Clinical rheumatology
OBJECTIVE: This study aimed to perform a detailed stratification analysis of B lymphocyte subsets in patients with primary Sjögren's syndrome (pSS) and to investigate their associations with lymphoma risk, clinical phenotypes, and disease activity.

Prediction of Lymphoma Aggressiveness Using Machine Learning Algorithms.

International journal of laboratory hematology
INTRODUCTION: Lymph nodes are essential to diagnose lymphoid neoplasms, metastases, and infections. Some lymphomas, particularly aggressive non-Hodgkin lymphomas (NHL), need urgent diagnosis. Combining lymph node cytology (LNC) and flow cytometry (FC...

Machine Learning-Enhanced Cerebrospinal Fluid N-Glycome for the Diagnosis and Prognosis of Primary Central Nervous System Lymphoma.

Journal of proteome research
The diagnosis and prognosis of Primary Central Nervous System Lymphoma (PCNSL) present significant challenges. In this study, the potential use of machine learning algorithms in diagnosing and predicting the prognosis for PCNSL based on cerebrospinal...

Prediction of early recurrence in primary central nervous system lymphoma based on multimodal MRI-based radiomics: A preliminary study.

European journal of radiology
OBJECTIVES: To evaluate the role of multimodal magnetic resonance imaging radiomics features in predicting early recurrence of primary central nervous system lymphoma (PCNSL) and to investigate their correlation with patient prognosis.

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...