Lung Cancer

Deep-learning based fast and accurate 3D CT deformable image registration in lung cancer.

BACKGROUND: Deformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application.

Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features.

To investigate whether the combination scheme of deep learning score (DL-score) and radiomics can improve preoperative diagnosis in the presence of micropapillary/solid (MPP/SOL) patterns in lung adenocarcinoma (ADC). A retrospective cohort of 514 confirmed pathologically lung ADC in 512 patients after surgery was enrolled. The clinicoradiographic model (model 1) and radiomics model (model 2) were...

A deep-learning model using enhanced chest CT images to predict PD-L1 expression in non-small-cell lung cancer patients.

AIM: To develop a deep-learning model using contrast-enhanced chest computed tomography (CT) images to predict programmed death-ligand 1 (PD-L1) expre...

Plasma Exosome Analysis for Protein Mutation Identification Using a Combination of Raman Spectroscopy and Deep Learning.

Protein mutation detection using liquid biopsy can be simply performed periodically, making it easy to detect the occurrence of newly emerging mutatio...

Robot-assisted partial nephrectomy in morbidly obese patients: a VCQI database study.

To compare perioperative outcomes following robot-assisted partial nephrectomy (RAPN) in patients with morbid obesity (body mass index (BMI > 40 kg/m)...

Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy.

BACKGROUND: Segmentation of the Gross Tumor Volume (GTV) is a crucial step in the brachytherapy (BT) treatment planning workflow. Currently, radiation...

Deep learning-based markerless lung tumor tracking in stereotactic radiotherapy using Siamese networks.

BACKGROUND: Radiotherapy (RT) is involved in about 50% of all cancer patients, making it a very important treatment modality. The most common type of ...

Effects of remote ischemic preconditioning on renal protection in patients undergoing robot-assisted laparoscopic partial nephrectomy.

We aimed to evaluate the renoprotective effects of remote ischemic preconditioning (RIPC) in patients undergoing robot-assisted laparoscopic partial n...

Coronary Computed Tomography Angiography with Deep Learning Image Reconstruction: A Preliminary Study to Evaluate Radiation Exposure Reduction.

Coronary computed tomography angiography (CCTA) is a medical imaging technique that produces detailed images of the coronary arteries. Our work focuse...

Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.

Deep learning (DL) models for radiation therapy (RT) image segmentation require accurately annotated training data. Multiple organ delineation guideli...

Neural network and spline-based regression for the prediction of salivary hypofunction in patients undergoing radiation therapy.

BACKGROUND: This study leverages a large retrospective cohort of head and neck cancer patients in order to develop machine learning models to predict ...

Outcome-Supervised Deep Learning on Pathologic Whole Slide Images for Survival Prediction of Immunotherapy in Patients with Non-Small Cell Lung Cancer.

Although programmed death-(ligand) 1 (PD-(L)1) inhibitors are marked by durable efficacy in patients with non-small cell lung cancer (NSCLC), approxim...

Implementation and evaluation of an intelligent automatic treatment planning robot for prostate cancer stereotactic body radiation therapy.

PURPOSE: We previously developed a virtual treatment planner (VTP), an artificial intelligence robot, operating a treatment planning system (TPS). Usi...

Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational status.

The detection of tumour gene mutations by DNA or RNA sequencing is crucial for the prescription of effective targeted therapies. Recent developments s...

Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment F-FDG PET/CT Using Deep Learning.

RATIONALE AND OBJECTIVES: To develop an end-to-end deep learning (DL) model for non-invasively predicting non-small cell lung cancer (NSCLC) pathologi...

Deep learning enables MV-based real-time image guided radiation therapy for prostate cancer patients.

. Using MV images for real-time image guided radiation therapy (IGRT) is ideal as it does not require additional imaging equipment, adds no additional...

Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.

. Radiation therapy for head and neck (H&N) cancer relies on accurate segmentation of the primary tumor. A robust, accurate, and automated gross tumor...

Robot-assisted Laparoscopic Prostatectomy After SpaceOAR Placement.

BACKGROUND: Patients with prostate cancer are offered a wide range of treatments from radiation therapy to chemotherapy to surgery. When giving radiat...

Opportunistic Extraction of Quantitative CT Biomarkers: Turning the Incidental Into Prognostic Information.

The past two decades have seen a significant increase in the use of CT, with a corresponding rise in the mean population radiation dose. This rise in ...

Deep learning-based classification and spatial prognosis risk score on whole-slide images of lung adenocarcinoma.

AIMS: Classification of histological patterns in lung adenocarcinoma (LUAD) is critical for clinical decision-making, especially in the early stage. H...