Lymphoma

A strategy for the automatic diagnostic pipeline towards feature-based models: a primer with pleural invasion prediction from preoperative PET/CT images.

BACKGROUND: This study aims to explore the feasibility to automate the application process of nomograms in clinical medicine, demonstrated through the task of preoperative pleural invasion prediction in non-small cell lung cancer patients using PET/CT imaging.

Detecting genetic interactions with visible neural networks.

Non-linear interactions among single nucleotide polymorphisms (SNPs), genes, and pathways play an important role in human diseases, but identifying these interactions is a challenging task. Neural networks are state-of-the-art predictors in many domains due to their ability to analyze big data and model complex patterns, including non-linear interactions. In genetics, visible neural networks are p...

Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis.

Diabetes Mellitus is a chronic metabolic disorder affecting a substantial global population leading to complications such as retinopathy, nephropathy,...

Performance of AI methods in PET-based imaging for outcome prediction in lymphoma: A systematic review and meta-analysis.

OBJECTIVES: To evaluate the predictive performance of artificial intelligence (AI) methods using pre-treatment PET-based imaging for outcome predictio...

Tumor-specific draining lymph node CD8 T cells orchestrate an anti-tumor response to neoadjuvant PD-1 immune checkpoint blockade.

Elucidating the anti-tumor role of tumor-draining lymph nodes (tdLNs) in patients could offer critical mechanistic insight and shift therapeutic strat...

Optimizing Attenuation Correction in Ga-PSMA PET Imaging Using Deep Learning and Artifact-Free Dataset Refinement.

Attenuation correction (AC) is essential for achieving quantitatively accurate PET imaging. In Ga-PSMA PET, however, artifacts such as respiratory mo...

Prediction of Lymph Node Metastasis in Non-Small Cell Lung Carcinoma Using Primary Tumor Somatic Mutation Data.

PURPOSE: Lymph node metastasis (LNM) significantly affects prognosis and treatment strategies in non-small cell lung cancer (NSCLC). Current diagnosti...

Heterogeneous III-V/Si micro-ring laser array with multi-state non-volatile memory for ternary content-addressable memories.

In this work, we introduce programmable memory elements embedded within III-V/Si light sources which facilitate non-volatile wavelength tuning. These ...

18F-FDG PET/CT Radiomics for Predicting Therapy Response in Primary Mediastinal B-Cell Lymphoma: A Bi-Centric Pilot Study.

PURPOSE: This bi-centric pilot study investigates the predictive value of pre-treatment [F]FDG PET/CT radiomics for assessing therapy response in prim...

Ultrasound image-based contrastive fusion non-invasive liver fibrosis staging algorithm.

OBJECTIVE: The diagnosis of liver fibrosis is usually based on histopathological examination of liver puncture specimens. Although liver puncture is a...

Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.

Musculoskeletal tumors present a diagnostic challenge due to their rarity, histological diversity, and overlapping imaging features. Accurate characte...

Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer.

OBJECTIVE: This study aimed to evaluate the value of habitat analysis based bi-modal ultrasound radiomics in predicting axillary lymph node (ALN) stat...

Large Scale MRI Collection and Segmentation of Cirrhotic Liver.

Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly inc...

The Value of PET/CT-Based Radiomics in Predicting Adrenal Metastases in Patients with Cancer.

Differentiation of adrenal incidentalomas (AIs) remains a challenge in the oncological setting. The aim of the study was to explore the diagnostic ef...

Efficient field correction of low-cost particulate matter sensors using machine learning, mixed multiplicative/additive scaling and extended calibration inputs.

Particulate matter (PM) stands out as a highly perilous form of atmospheric pollution, posing significant risks to human health by triggering or worse...

Advances in Diagnostic Approaches for Alzheimer's Disease: From Biomarkers to Deep Learning Technology.

Alzheimer's disease (AD) is a devastating neurological disorder that affects humans and is a major contributor to dementia. It is characterized by cog...

Multiple Instance Learning for the Detection of Lymph Node and Omental Metastases in Carcinoma of the Ovaries, Fallopian Tubes and Peritoneum.

: Surgical pathology of tubo-ovarian and peritoneal cancer carries a well-recognised diagnostic workload, partly due to the large amount of non-primar...

Determining time since deposition of latent fingerprints on forensic adhesive tape using ultrafast DESI-MS and machine learning.

Fingerprints provide indisputable forensic evidence for establishing identity. Latent fingerprints, often visualized with black magnetic powder and re...

Computational Risk Stratification of Preclinical Alzheimer's in Younger Adults.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that often begins decades before clinical symptoms manifest. Early detection rem...

Machine learning-based radiomics analysis in enhancing CT for predicting pathological subtypes and WHO staging of thymic epithelial tumors: a multicenter study.

This study is aimed to develop predictive models for classifying thymic epithelial tumor (TET) histological subtypes (A/AB/B1, B2/B3, C) and WHO stage...