Lung Cancer

Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography.

PURPOSE: Meticulous manual delineations of the prostate and the surrounding organs at risk are necessary for prostate cancer radiation therapy to avoid side effects to the latter. This process is time consuming and hampered by inter- and intraobserver variability, all of which could be alleviated by artificial intelligence (AI). This study aimed to evaluate the performance of AI compared with manu...

The utility of automatic segmentation of kidney MRI in chronic kidney disease using a 3D convolutional neural network.

We developed a 3D convolutional neural network (CNN)-based automatic kidney segmentation method for patients with chronic kidney disease (CKD) using MRI Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images. The dataset comprised 100 participants with renal dysfunction (RD; eGFR < 45 mL/min/1.73 m) and 70 without (non-RD; eGFR ≥ 45 mL/min/1.73 m). The model was applied to...

Differences Between Attending and Trainee Surgeon Performance Using Objective Performance Indicators During Robot-Assisted Lobectomy.

OBJECTIVE: Existing approaches for assessing surgical performance are subjective and prone to bias. In contrast, utilizing digital kinematic and syste...

Volumetric tumor tracking from a single cone-beam X-ray projection image enabled by deep learning.

Radiotherapy serves as a pivotal treatment modality for malignant tumors. However, the accuracy of radiotherapy is significantly compromised due to re...

Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma.

BACKGROUND: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid com...

Automation and artificial intelligence in radiation therapy treatment planning.

Automation and artificial intelligence (AI) is already possible for many radiation therapy planning and treatment processes with the aim of improving ...

A look at radiation detectors and their applications in medical imaging.

The effectiveness and precision of disease diagnosis and treatment have increased, thanks to developments in clinical imaging over the past few decade...

A comparison of 18 F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study.

OBJECTIVE: The performance of 18 F-FDG PET-based radiomics and deep learning in detecting pathological regional nodal metastasis (pN+) in resectable l...

β-D-Glucan promotes NF-κB activation and ameliorates high-LET carbon-ion irradiation-induced human umbilical vein endothelial cell injury.

BACKGROUND/AIM: Heavy-ion irradiation seriously perturbs cellular homeostasis and thus damages cells. Vascular endothelial cells (ECs) play an importa...

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.

PURPOSE: No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grad...

Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network.

. In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important ...

Dose Optimization Using a Deep Learning Tool in Various CT Protocols for Urolithiasis: A Physical Human Phantom Study.

We attempted to determine the optimal radiation dose to maintain image quality using a deep learning application in a physical human phantom. Three ...

Comparison of Global Evaluative Assessment of Robotic Surgery with objective performance indicators for the assessment of skill during robotic-assisted thoracic surgery.

BACKGROUND: The Global Evaluative Assessment of Robotic Skills is a popular but ultimately subjective assessment tool in robotic-assisted surgery. An ...

Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas.

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease with five predominant histologic subtypes. Fully supervised convolutional neural...

Deep learning in MRI-guided radiation therapy: A systematic review.

Recent advances in MRI-guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real-time MRI m...

Automating Ground Truth Annotations for Gland Segmentation Through Immunohistochemistry.

Microscopic evaluation of glands in the colon is of utmost importance in the diagnosis of inflammatory bowel disease and cancer. When properly trained...

75% radiation dose reduction using deep learning reconstruction on low-dose chest CT.

OBJECTIVE: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to co...

Impact of Iron Supplementation on Hospital Length of Stay for Pneumonia or Skin and Skin Structure Infections: A Retrospective Cohort Study.

Pathogenic organisms utilize iron to survive and replicate and have evolved many processes to extract iron from human hosts. The goal of this study w...

Artificial intelligence and Italian culture: an understanding of how artificial intelligence can transform the radiation therapy landscape.

The aim is to support the perception of artificial intelligence in the radiation therapy landscape.

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

PURPOSE: Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has b...