Lymphoma

Epidemiology and survival of patients with primary lymphoma of bone: a large retrospective cohort study.

Primary lymphoma of bone (PLB) is a rare extranodal lymphoma, and its epidemiology and prognosis remain controversial. We conducted a retrospective analysis of 1,222 patients with PLB in the Surveillance, Epidemiology, and End Results (SEER) database to investigate its epidemiology and prognostic factors. The incidence of PLB peaked in 1992 with an average annual percent change of 1.72 after a sig...

The predictive value of F-FDG PET/CT radiomics for pleural invasion in non-small cell lung cancer.

OBJECTIVE: This study aims to develop and validate a PET/CT radiomics fusion model for preoperative predicting pleural invasion (PI) in non-small cell lung cancer (NSCLC) patients.

Non-invasive arterial input function estimation using an MRA atlas and machine learning.

BACKGROUND: Quantifying biological parameters of interest through dynamic positron emission tomography (PET) requires an arterial input function (AIF)...

Optical coherence tomography angiography as a tool for diagnosis and monitoring of sickle cell related eye disease: a systematic review and meta-analysis.

Sickle cell retinopathy (SCR) is an ocular manifestation of sickle cell disease (SCD). In SCR abnormal sickling of erythrocytes is associated with sig...

Predicting the availability of power line communication nodes using semi-supervised learning algorithms.

Power Line Communication (PLC) facilitates the usage of power cables to transmit data. The issue is that sending data to unavailable nodes is time-con...

Imaging patterns and recommendations for diagnosis, staging, and management of lung cancer.

Lung cancer is the second most commonly diagnosed cancer worldwide. In the present era of targeted therapy for various lung cancer mutations, it is es...

Deep learning-based interpretable prediction of recurrence of diffuse large B-cell lymphoma.

BACKGROUND: The heterogeneous and aggressive nature of diffuse large B-cell lymphoma (DLBCL) presents significant treatment challenges as up to 50% of...

Dual-structure community preserving network embedding.

Network embedding, an effective method for learning low-dimensional representations of nodes, plays a crucial role in various network learning scenari...

Advancements in Hematologic Malignancy Detection: A Comprehensive Survey of Methodologies and Emerging Trends.

The investigation and diagnosis of hematologic malignancy using blood cell image analysis are major and emerging subjects that lie at the intersection...

Artificial Intelligence Assisted 18F-FDG PET Radiomics in Classifying Histological Subtypes of Lung Cancer: Systematic Review and Meta-analysis.

UNLABELLED: AI-assisted radiomics is an emerging tool for precision oncology, and many studies have recently shown promising results. However, there a...

ProtFun: A Protein Function Prediction Model Using Graph Attention Networks with a Protein Large Language Model.

Understanding protein functions facilitates the identification of the underlying causes of many diseases and guides the research for discovering new t...

2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction.

Positron Emission Tomography (PET) is an important clinical imaging tool but inevitably introduces radiation exposure to patients and healthcare provi...

Replacing non-biomedical concepts improves embedding of biomedical concepts.

Embeddings are semantically meaningful representations of words in a vector space, commonly used to enhance downstream machine learning applications. ...

Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.

BACKGROUND: Patients with early-stage non-small cell lung cancer (NSCLC) typically receive surgery as their primary form of treatment. However, studie...

Forecasting optimal treatments in relapsed/refractory mature T- and NK-cell lymphomas: A global PETAL Consortium study.

There is no standard of care in relapsed/refractory T-cell/natural killer-cell lymphomas. Patients often cycle through cytotoxic chemotherapy (CC), ep...

The Use of Maximum-Intensity Projections and Deep Learning Adds Value to the Fully Automatic Segmentation of Lesions Avid for [F]FDG and [Ga]Ga-PSMA in PET/CT.

This study investigated the added value of using maximum-intensity projection (MIP) images for fully automatic segmentation of lesions using deep lear...

Global burden of non-melanoma skin cancers among older adults: a comprehensive analysis using machine learning approaches.

Non-melanoma skin cancers (NMSCs), including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), have shown significant global increases in ...

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

OBJECTIVE: This study aimed to perform a detailed stratification analysis of B lymphocyte subsets in patients with primary Sjögren's syndrome (pSS) an...

Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches.

M2 macrophages play a crucial role in the initiation and progression of various tumors, including diffuse large B-cell lymphoma (DLBCL). However, the ...

Using artificial intelligence-based software for an unbiased discrimination of immune cell subtypes in the fracture hematoma and bone marrow of non-osteoporotic and osteoporotic mice.

It is well established that the early inflammatory response following fracture is essential for initiating subsequent bone regeneration. An imbalance ...