Brain Cancer

Noise reduction performance of a deep learning-based reconstruction in brain computed tomography images acquired with organ-based tube current modulation.

We aimed to evaluate the image quality of brain computed tomography (CT) images reconstructed using ...

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 impo...

Large language model artificial intelligence: the current state and future of ChatGPT in neuro-oncology publishing.

Current state of radiomics in pediatric neuro-oncology practice: a systematic review.

BACKGROUND: Radiomics is the process of converting radiological images into high-dimensional data th...

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 detaile...

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...

Precise Brain-shift Prediction by New Combination of W-Net Deep Learning for Neurosurgical Navigation.

Brain tissue deformation during surgery significantly reduces the accuracy of image-guided neurosurg...

Deployment of an electrocorticography system with a soft robotic actuator.

Electrocorticography (ECoG) is a minimally invasive approach frequently used clinically to map epile...

Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.

This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma fro...

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 or...

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...

Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma.

BACKGROUND AND PURPOSE: An autoencoder can learn representative time-signal intensity patterns to pr...

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 requir...

Contribution of whole slide imaging-based deep learning in the assessment of intraoperative and postoperative sections in neuropathology.

The pathological diagnosis of intracranial germinoma (IG), oligodendroglioma, and low-grade astrocyt...

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 tu...

Deep Learning Approaches for Glioblastoma Prognosis in Resource-Limited Settings: A Study Using Basic Patient Demographic, Clinical, and Surgical Inputs.

BACKGROUND: Glioblastoma (GBM) is the most common brain tumor in the United States, with an annual i...

Robot-assisted Laparoscopic Prostatectomy After SpaceOAR Placement.

BACKGROUND: Patients with prostate cancer are offered a wide range of treatments from radiation ther...

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...

Standardizing analysis of intra-tumoral heterogeneity with computational pathology.

Many malignant cancers like glioblastoma are highly adaptive diseases that dynamically change their ...

Realistic CT data augmentation for accurate deep-learning based segmentation of head and neck tumors in kV images acquired during radiation therapy.

BACKGROUND: Using radiation therapy (RT) to treat head and neck (H&N) cancers requires precise targe...

Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.

OBJECTIVES: To establish a robust interpretable multiparametric deep learning (DL) model for automat...