Brain Cancer

Deep-learning Method for the Prediction of Three-Dimensional Dose Distribution for Left Breast Cancer Conformal Radiation Therapy.

AIMS: An increase in the demand of a new generation of radiotherapy planning systems based on learni...

Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods.

PURPOSE: Convolutional Neural Networks (ConvNets) have quickly become popular machine learning techn...

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 ...

Natural language processing deep learning models for the differential between high-grade gliomas and metastasis: what if the key is how we report them?

OBJECTIVES: The differential between high-grade glioma (HGG) and metastasis remains challenging in c...

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.

Real-Time Motion Analysis With 4D Deep Learning for Ultrasound-Guided Radiotherapy.

Motion compensation in radiation therapy is a challenging scenario that requires estimating and fore...

AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods.

PURPOSE: To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-...

Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy.

Deep-learning auto-contouring (DL-AC) promises standardisation of organ-at-risk (OAR) contouring, en...

Deep learning-based detection and classification of multi-leaf collimator modeling errors in volumetric modulated radiation therapy.

PURPOSE: The purpose of this study was to create and evaluate deep learning-based models to detect a...

Infrastructure tools to support an effective Radiation Oncology Learning Health System.

PURPOSE: Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the q...

Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4).

Adult-type diffuse glioma (grade 4) has infiltrating nature, and therefore local progression is like...

Unlocking the Power of ChatGPT, Artificial Intelligence, and Large Language Models: Practical Suggestions for Radiation Oncologists.

Recent advances in artificial intelligence (AI), such as generative AI and large language models (LL...

Circulating Plasma Exosomal PD-L1 Predicts Prognosis of Head and Neck Squamous Cell Carcinoma After Radiation Therapy.

PURPOSE: Radiation therapy is widely used to treat head and neck squamous cell carcinoma (HNSCC). Th...

Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis.

BACKGROUND: Meningiomas are common intracranial tumors. Machine learning (ML) algorithms are emergin...

An overview of artificial intelligence in medical physics and radiation oncology.

Artificial intelligence (AI) is developing rapidly and has found widespread applications in medicine...

Preoperative Discrimination of CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase-Mutant Astrocytoma: A Deep Learning-Based Radiomics Model Using MRI.

BACKGROUND: Cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) homozygous deletion has been verified ...

A novel MRI-based deep learning networks combined with attention mechanism for predicting CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma.

OBJECTIVES: To develop a high-accuracy MRI-based deep learning method for predicting cyclin-dependen...

Deep learning-based scan range optimization can reduce radiation exposure in coronary CT angiography.

OBJECTIVES: Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. Howe...

The Clinical Added Value of Breast Cancer Imaging Using Hybrid PET/MR Imaging.

Dedicated MR imaging is highly performant for the evaluation of the primary lesion and should regula...

Reducing radiation dose in routine CT scans: an AI-driven approach with deep learning-based dual-energy CT reconstruction.

Suitability of DNN-based vessel segmentation for SIRT planning.

PURPOSE: The segmentation of the hepatic arteries (HA) is essential for state-of-the-art pre-interve...