Lung Cancer

Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM).

PURPOSE: Frequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize imaging properties at lower radiation dosages. The aim of this study is to investigate whether use of SilverBeam filter and deep learning reconstruction algorithm allows for reduced radiation dose chest CT scanning in patients with lymphangioleiomyom...

A Histopathologic Image Analysis for the Classification of Endocervical Adenocarcinoma Silva Patterns Depend on Weakly Supervised Deep Learning.

Twenty-five percent of cervical cancers are classified as endocervical adenocarcinomas (EACs), which comprise a highly heterogeneous group of tumors. A histopathologic risk stratification system known as the Silva pattern system was developed based on morphology. However, accurately classifying such patterns can be challenging. The study objective was to develop a deep learning pipeline (Silva3-AI...

A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images.

PURPOSE: Deep learning-based auto-segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual...

Comparing Robotic, Thoracoscopic, and Open Segmentectomy: A National Cancer Database Analysis.

INTRODUCTION: Minimally invasive approaches to lung resection have become widely acceptable and more recently, segmentectomy has demonstrated equivale...

A Review of deep learning methods for denoising of medical low-dose CT images.

To prevent patients from being exposed to excess of radiation in CT imaging, the most common solution is to decrease the radiation dose by reducing th...

Exploring the role of large language models in radiation emergency response.

In recent times, the field of artificial intelligence (AI) has been transformed by the introduction of large language models (LLMs). These models, pop...

Deciphering the fibrotic process: mechanism of chronic radiation skin injury fibrosis.

This review explores the mechanisms of chronic radiation-induced skin injury fibrosis, focusing on the transition from acute radiation damage to a chr...

Single-Port Multiport Robot-Assisted Partial Nephrectomy: A Meta-Analysis.

Several centers have reported their experience with single-port robot-assisted partial nephrectomy (SP-RAPN); however, it is uncertain if utilization...

METnet: A novel deep learning model predicting MET dysregulation in non-small-cell lung cancer on computed tomography images.

BACKGROUND: Mesenchymal epithelial transformation (MET) is a key molecular target for diagnosis and treatment of non-small cell lung cancer (NSCLC). T...

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes...

Effect of Febuxostat versus Allopurinol on the Glomerular Filtration Rate and Hyperuricemia in Patients with Chronic Kidney Disease.

Hyperuricemia is a risk factor for the progression of chronic kidney disease (CKD). We compared febuxostat versus allopurinol in the progression of CK...

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BACKGROUND:: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep lear...

Deep learning-based algorithms for low-dose CT imaging: A review.

The computed tomography (CT) technique is extensively employed as an imaging modality in clinical settings. The radiation dose of CT, however, is sign...

Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy.

OBJECTIVE: Fast progression (FP) represents a desperate situation for advanced non-small cell lung cancer (NSCLC) patients undergoing immune checkpoin...

Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus.

Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between...

GlioPredictor: a deep learning model for identification of high-risk adult IDH-mutant glioma towards adjuvant treatment planning.

Identification of isocitrate dehydrogenase (IDH)-mutant glioma patients at high risk of early progression is critical for radiotherapy treatment plann...

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation.

. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinica...

Prescription trend and lactic acidosis in patients prescribed metformin before and after the revision of package insert for allowing metformin administration to patients with moderately decreased kidney function based on real-world data from MID-NET in Japan.

INTRODUCTION: This study was conducted to understand the impact of package insert (PI) revision in Japan on 18 June 2019 to allow metformin use for pa...

DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery.

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibit...

Deep Learning Auto-Segmentation Network for Pediatric Computed Tomography Data Sets: Can We Extrapolate From Adults?

PURPOSE: Artificial intelligence (AI)-based auto-segmentation models hold promise for enhanced efficiency and consistency in organ contouring for adap...