Lymphoma

Development of a deep learning method for CT-free correction for an ultra-long axial field of view PET scanner.

INTRODUCTION: The possibility of low-dose positron emission tomography (PET) imaging using high sensitivity long axial field of view (FOV) PET/computed tomography (CT) scanners makes CT a critical radiation burden in clinical applications. Artificial intelligence has shown the potential to generate PET images from non-corrected PET images. Our aim in this work is to develop a CT-free correction fo...

Upstaging and Survival Outcomes for Non-Muscle Invasive Bladder Cancer After Radical Cystectomy: Results from the International Robotic Cystectomy Consortium.

We sought to describe the incidence, risk factors, and survival outcomes associated with pathologic upstaging from non-muscle invasive bladder cancer (NMIBC) to muscle invasive bladder cancer (MIBC) after robot-assisted radical cystectomy (RARC). We reviewed the International Robotic Cystectomy Consortium database between 2004 and 2020. Upstaging was defined as ≥pT or pathologic node positive (p...

End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography.

PURPOSE: Clinical TNM staging is a key prognostic factor for patients with lung cancer and is used to inform treatment and monitoring. Computed tomogr...

External and Internal Validation of a Computer Assisted Diagnostic Model for Detecting Multi-Organ Mass Lesions in CT images.

Objective We developed a universal lesion detector (ULDor) which showed good performance in in-lab experiments. The study aims to evaluate the perform...

Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images.

BACKGROUND: Currently, only a fraction of patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs) experience...

Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation.

Kinases play important roles in diverse cellular processes, including signaling, differentiation, proliferation, and metabolism. They are frequently m...

Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma.

Differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma (ML) remains challenging on cross-sectional images. The aim of thi...

Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease.

Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic class...

Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods.

BACKGROUND: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. Ho...

Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features.

Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this...

Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography.

This study aimed to explore the ability of texture parameters combining with machine learning methods in distinguishing intrahepatic cholangiocarcino...

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.

PURPOSE: Considerable progress has been made in the assessment and management of non-small cell lung cancer (NSCLC) patients based on mutation status ...

HONEM: Learning Embedding for Higher Order Networks.

Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and, as a result, has ...

A refined cell-of-origin classifier with targeted NGS and artificial intelligence shows robust predictive value in DLBCL.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routin...

3-To-1 Pipeline: Restructuring Transfer Learning Pipelines for Medical Imaging Classification via Optimized GAN Synthetic Images.

The difficulty of applying deep learning algorithms to biomedical imaging systems arises from a lack of training images. An existing workaround to the...

Prediction of Patient Demographics using 3D Craniofacial Scans and Multi-view CNNs.

3D data is becoming increasingly popular and accessible for computer vision tasks. A popular format for 3D data is the mesh format, which can depict a...

Assisting the Non-invasive Diagnosis of Liver Fibrosis Stages using Machine Learning Methods.

Fibrosis is a significant indication of chronic liver diseases often due to hepatitis C Virus. It is becoming a global concern as a result of the rapi...

Analysis of Four Types of Leukemia Using Gene Ontology Term and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Scores.

AIM AND OBJECTIVE: Leukemia is the second common blood cancer after lymphoma, and its incidence rate has an increasing trend in recent years. Leukemia...

Radiomics and artificial Intelligence for PET imaging analysis.

In recent years, processing of the imaging signal derived from CT, MR or positron emission has proven to be able to predict outcome parameters in canc...

Lung Nodule Detection in CT Images Using a Raw Patch-Based Convolutional Neural Network.

Remarkable progress has been made in image classification and segmentation, due to the recent study of deep convolutional neural networks (CNNs). To s...