Lymphoma

Neural networks for the prediction of bacterial and fungal infections: current evidence and implications.

In the present narrative review, we discuss the use of artificial neural networks (ANNs) for predicting bacterial and fungal infections based on commonly available clinical and laboratory data, focusing on promises and challenges of these machine learning models. For predicting different bacterial or fungal infections from data commonly found in electronical medical records, ANN models may reach, ...

Ordinal Sleep Depth: A Data-Driven Continuous Measurement of Sleep Depth.

Conventional sleep staging categorises sleep into discrete stages, which may not capture the continuous nature of sleep depth. We aimed to develop a data-driven continuous measure of sleep depth-ordinal sleep depth (OSD)-using a deep learning framework, and to evaluate its correlation with arousal probability and its association with age, sex, sleep-disordered breathing (SDB) and cognitive impairm...

A novel cancer-associated fibroblast-related gene signature for predicting diffuse large B cell lymphoma prognosis using weighted gene co-expression network analysis and machine learning.

ObjectiveOur objective was to investigate a novel cancer-associated fibroblast-related gene signature for predicting clinical outcomes in patients wit...

Clinical performance of a machine learning-based model for detecting lymph node metastasis in papillary thyroid carcinoma: A multicenter study.

Papillary thyroid carcinoma (PTC) is a common endocrine malignancy with a generally favorable prognosis, but lymph node metastasis (LNM) complicates t...

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

The diagnosis and prognosis of Primary Central Nervous System Lymphoma (PCNSL) present significant challenges. In this study, the potential use of mac...

Machine learning-based identification of cuproptosis-related lncRNA biomarkers in diffuse large B-cell lymphoma.

Multiple machine learning techniques were employed to identify key long non-coding RNA (lncRNA) biomarkers associated with cuproptosis in Diffuse Larg...

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

OBJECTIVES: To evaluate the role of multimodal magnetic resonance imaging radiomics features in predicting early recurrence of primary central nervous...

Updated perspectives on visceral pleural invasion in non-small cell lung cancer: A propensity score-matched analysis of the SEER database.

BACKGROUND: Visceral pleural invasion (VPI), including PL1 (the tumor invades beyond the elastic layer) and PL2 (the tumor extends to the surface of t...

Artificial neural network model enhancing the accuracy of clinical evaluation for high-risk population of lymph node metastasis in non-intestinal type early gastric cancer: a multicenter real-world study in China.

BACKGROUND: Recent years have witnessed a proliferation of studies aimed at developing clinical models capable of predicting lymph node metastasis (LN...

Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.

BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can ...

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

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 hea...

5-5-5 ABRT (Dose of 5 Gy per Fraction for up to 5 Fractions Over 5 Weeks Adaptive Bridging Radiation Therapy)-Artificial Intelligence Enters the CAR (-T) (Chimeric Antigen Receptor-T) in Relapsed/Refractory Large B Cell Lymphoma.

PURPOSE: Bridging radiation therapy (BRT) is effective for local control in patients with relapsed or refractory large B cell lymphoma who are undergo...

Prediction of PD-L1 expression in NSCLC patients using PET/CT radiomics and prognostic modelling for immunotherapy in PD-L1-positive NSCLC patients.

AIM: To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model for predicting programmed cell death ligand 1 (PD-L1...

Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence.

The integration of artificial intelligence (AI) into medical diagnostics is transforming the landscape of healthcare, particularly in hematology. AI t...

Predictive models of epidermal growth factor receptor mutation in lung adenocarcinoma using PET/CT-based radiomics features.

BACKGROUND: Lung adenocarcinoma (LAC) comprises a substantial subset of non-small cell lung cancer (NSCLC) diagnoses, where epidermal growth factor re...

Patient-specific MRI super-resolution via implicit neural representations and knowledge transfer.

Magnetic resonance imaging (MRI) is a non-invasive imaging technique that provides high soft tissue contrast, playing a vital role in disease diagnosi...

Automated detection of metastatic lymph nodes in head and neck malignant tumors on high - resolution MRI images using an improved convolutional neural network.

PURPOSE: To develop an AI-based diagnostic model for assessing cervical lymph nodes in head and neck malignant tumors using MRI, enabling non-invasive...

Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based on F-FDG PET radiomics and clinical factors.

OBJECTIVE: This study aimed to develop and validate a nomogram combining F-FDG PET radiomics and clinical factors to non-invasively predict bone marro...

F-FDG PET radiomics score construction by automatic machine learning for treatment response prediction in elderly patients with diffuse large B-cell lymphoma: a multicenter study.

PURPOSE: To explore the development and validation of automated machine learning (AutoML) models for F-FDG PET imaging-based radiomics signatures to p...

Stability of Jurkat cells during short-term liquid storage analyzed by flow imaging microscopy.

The viability of cell-based medicinal products (CBMPs) is a critical quality attribute and must be assessed throughout the product lifecycle to contri...