Lung Cancer

Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography.

OBJECTIVES: Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification.

The Long-Term Effect of Intensity Modulated Radiation Therapy for Prostate Cancer on Testosterone Levels.

PURPOSE: Concern about a long-term effect of the delivery of intensity modulated radiation therapy (IMRT) for prostate cancer on serum testosterone levels remains unelucidated. We evaluated how IMRT for localized prostate cancer affects serum testosterone levels during a follow-up period of up to 10 years.

Development and validation of a supervised deep learning algorithm for automated whole-slide programmed death-ligand 1 tumour proportion score assessment in non-small cell lung cancer.

AIMS: Immunohistochemical programmed death-ligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small ce...

Evaluation of the Postoperative Nursing Effect of Thoracic Surgery Assisted by Artificial Intelligence Robot.

In order to evaluate the postoperative nursing effect of artificial intelligence robot-assisted thoracic surgery, this study proposed the Da Vinci rob...

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

OBJECTIVE: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminati...

Deep learning-based motion tracking using ultrasound images.

PURPOSE: Ultrasound (US) imaging is an established imaging modality capable of offering video-rate volumetric images without ionizing radiation. It ha...

Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans.

Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained...

Integration of Deep Learning Radiomics and Counts of Circulating Tumor Cells Improves Prediction of Outcomes of Early Stage NSCLC Patients Treated With Stereotactic Body Radiation Therapy.

PURPOSE: We develop a deep learning (DL) radiomics model and integrate it with circulating tumor cell (CTC) counts as a clinically useful prognostic m...

Integrated Cells and Collagen Fibers Spatial Image Analysis.

Modern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and ...

Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.

PURPOSE: This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subt...

Cascaded deep learning-based auto-segmentation for head and neck cancer patients: Organs at risk on T2-weighted magnetic resonance imaging.

PURPOSE: To investigate multiple deep learning methods for automated segmentation (auto-segmentation) of the parotid glands, submandibular glands, and...

Deep learning-based thoracic CBCT correction with histogram matching.

Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especial...

A deep learning- and CT image-based prognostic model for the prediction of survival in non-small cell lung cancer.

OBJECTIVE: To assist clinicians in arranging personalized treatment, planning follow-up programs and extending survival times for non-small cell lung ...

The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis.

Although significant advancements in computer-aided diagnostics using artificial intelligence (AI) have been made, to date, no viable method for radia...

Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images.

COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, wides...

Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer.

Background Preoperative mediastinal staging is crucial for the optimal management of clinical stage I non-small cell lung cancer (NSCLC). Purpose To d...

DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays.

The global pandemic of coronavirus disease 2019 (COVID-19) is continuing to have a significant effect on the well-being of the global population, thus...

A Wavelet-Based Learning Model Enhances Molecular Prognosis in Pancreatic Adenocarcinoma.

Genome-wide omics technology boosts deep interrogation into the clinical prognosis and inherent mechanism of pancreatic oncology. Classic LASSO method...

Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma.

PURPOSE: Diagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal adenocarcinoma (PDAC). We aimed to build deep learni...

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.

One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representat...