Lung Cancer

Novel Deep Learning Denoising Enhances Image Quality and Lowers Radiation Exposure in Interventional Bronchial Artery Embolization Cone Beam CT.

OBJECTIVES: In interventional bronchial artery embolization (BAE), periprocedural cone beam CT (CBCT) improves guiding and localization. However, a trade-off exists between 6-second runs (high radiation dose and motion artifacts, but low noise) and 3-second runs (vice versa). This study aimed to determine the efficacy of an advanced deep learning denoising (DLD) technique in mitigating the trade-o...

Large-scale pancreatic cancer detection via non-contrast CT and deep learning.

Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely...

Oncologic outcomes of robot-assisted laparoscopy versus conventional laparoscopy for the treatment of apparent early-stage endometrioid adenocarcinoma of the uterus.

OBJECTIVE: To compare long-term oncologic outcomes in patients with clinically uterine-confined endometrioid endometrial cancer who underwent surgical...

PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer.

Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a...

Development and Validation of a Machine Learning-Based Model Using CT Radiomics for Predicting Immune Checkpoint Inhibitor-related Pneumonitis in Patients With NSCLC Receiving Anti-PD1 Immunotherapy: A Multicenter Retrospective CaseControl Study.

RATIONALE AND OBJECTIVES: This study aimed to develop and evaluate a radiomics-based model combined with clinical and qualitative radiological (semant...

CT-Based Deep-Learning Model for Spread-Through-Air-Spaces Prediction in Ground Glass-Predominant Lung Adenocarcinoma.

BACKGROUND: Sublobar resection is strongly associated with poor prognosis in early-stage lung adenocarcinoma, with the presence of tumor spread throug...

Deep learning catheter tip locations for photoacoustic-guided cardiac interventions.

SIGNIFICANCE: Interventional cardiac procedures often require ionizing radiation to guide cardiac catheters to the heart. To reduce the associated ris...

Biochemical outcome after curative treatment for localized prostate cancer with external beam radiotherapy: a cross-sectional study.

Although many patients who receive definitive radiotherapy (RT) for localised prostate cancer (CaP) experience long-term disease-free survival and bet...

A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis.

BACKGROUND: The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial go...

Framework for Radiation Oncology Department-wide Evaluation and Implementation of Commercial Artificial Intelligence Autocontouring.

PURPOSE: Artificial intelligence (AI)-based autocontouring in radiation oncology has potential benefits such as standardization and time savings. Howe...

Exploring Capabilities of Large Language Models such as ChatGPT in Radiation Oncology.

PURPOSE: Technological progress of machine learning and natural language processing has led to the development of large language models (LLMs), capabl...

An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning.

PURPOSE: The purpose of this study was to develop a model for predicting chemoradiation response in non-small cell lung cancer (NSCLC) patients by int...

Enhancing Safety in AI-Driven Cone Beam CT-based Online Adaptive Radiation Therapy: Development and Implementation of an Interdisciplinary Workflow.

PURPOSE: The emerging online adaptive radiation therapy (OART) treatment strategy based on cone beam computed tomography allows for real-time replanni...

Current Strengths and Weaknesses of ChatGPT as a Resource for Radiation Oncology Patients and Providers.

PURPOSE: Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence program that uses natural language processing to generate conve...

Screening the phytotoxicity of micro/nanoplastics through non-targeted metallomics with synchrotron radiation X-ray fluorescence and deep learning: Taking micro/nano polyethylene terephthalate as an example.

Microplastics (MPs) and nanoplastics (NPs) are global pollutants with emerging concerns. Methods to predict and screen their toxicity are crucial. Ele...

Healthcare Professional Association Agency in Preparing for Artificial Intelligence: A Multiple-Case Study of Radiation Medicine and Medical Imaging in the Canadian context.

As artificial intelligence (AI) is increasingly integrated in healthcare, it is incumbent on healthcare professional associations (HPAs) to assist the...

The Histological Detection of Ulcerative Colitis Using a No-Code Artificial Intelligence Model.

Ulcerative colitis (UC) is an intractable disease that affects young adults. Histological findings are essential for its diagnosis; however, the numbe...

Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.

PURPOSE: To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung...

Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.

OBJECTIVES: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma...

Enlightening the path to NSCLC biomarkers: Utilizing the power of XAI-guided deep learning.

BACKGROUND AND OBJECTIVE: The early diagnosis of Non-small cell lung cancer (NSCLC) is of prime importance to improve the patient's survivability and ...