Brain Cancer

Deep Learning-Based Estimation of Radiographic Position to Automatically Set Up the X-Ray Prime Factors.

Radiation dose and image quality in radiology are influenced by the X-ray prime factors: KVp, mAs, and source-detector distance. These parameters are set by the X-ray technician prior to the acquisition considering the radiographic position. A wrong setting of these parameters may result in exposure errors, forcing the test to be repeated with the increase of the radiation dose delivered to the pa...

Artificial Intelligence-Driven Skin Aging Simulation as a Novel Skin Cancer Prevention.

INTRODUCTION: Skin cancer, a prevalent cancer type among fair-skinned patients globally, poses a relevant public health concern due to rising incidence rates. Ultraviolet (UV) radiation poses a major risk factor for skin cancer. However, intentional tanning associated with sunburns remains a common practice, notably among female adults. Appropriate prevention campaigns targeting children and adole...

Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning.

BACKGROUND: Despite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of...

A Lightweight Method for Breast Cancer Detection Using Thermography Images with Optimized CNN Feature and Efficient Classification.

Breast cancer is a prominent cause of death among women worldwide. Infrared thermography, due to its cost-effectiveness and non-ionizing radiation, ha...

Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy.

PURPOSE: Magnetic resonance (MR)-guided radiation therapy enables online adaptation to address intra- and interfractional changes. To address the need...

Development of learning-based predictive models for radiation-induced atrial fibrillation in non-small cell lung cancer patients by integrating patient-specific clinical, dosimetry, and diagnostic information.

BACKGROUND AND PURPOSE: Radiotherapy (RT) in non-small cell lung cancer (NSCLC) can induce cardiac adverse events, including atrial fibrillation (AF),...

A Flow-based Truncated Denoising Diffusion Model for super-resolution Magnetic Resonance Spectroscopic Imaging.

Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying metabolism and has become a crucial tool for understa...

Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas.

Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and inf...

Development and validation of a deep learning-based survival prediction model for pediatric glioma patients: A retrospective study using the SEER database and Chinese data.

OBJECTIVE: Develop a time-dependent deep learning model to accurately predict the prognosis of pediatric glioma patients, which can assist clinicians ...

Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet.

To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined with 3D nnUNet for rapid and consistent autosegmentat...

Machine learning predicts cuproptosis-related lncRNAs and survival in glioma patients.

Gliomas are the most common tumor in the central nervous system in adults, with glioblastoma (GBM) representing the most malignant form, while low-gra...

Deep Learning and Habitat Radiomics for the Prediction of Glioma Pathology Using Multiparametric MRI: A Multicenter Study.

RATIONALE AND OBJECTIVES: Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive...

Quantifying and visualising uncertainty in deep learning-based segmentation for radiation therapy treatment planning: What do radiation oncologists and therapists want?

BACKGROUND AND PURPOSE: During the ESTRO 2023 physics workshop on "AI for the fully automated radiotherapy treatment chain", the topic of deep learnin...

Machine learning-based discovery of UPP1 as a key oncogene in tumorigenesis and immune escape in gliomas.

INTRODUCTION: Gliomas are the most common and aggressive type of primary brain tumor, with a poor prognosis despite current treatment approaches. Unde...

Advances in spatial resolution and radiation dose reduction using super-resolution deep learning-based reconstruction for abdominal computed tomography: A phantom study.

RATIONALE AND OBJECTIVES: This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it th...

Artificial intelligence in paediatric head trauma: enhancing diagnostic accuracy for skull fractures and brain haemorrhages.

Pediatric head trauma is a significant cause of morbidity and mortality, with children, particularly those under two years old, being more susceptible...

Deep Learning-Based Denoising Enables High-Quality, Fully Diagnostic Neuroradiological Trauma CT at 25% Radiation Dose.

RATIONALE AND OBJECTIVES: Traumatic neuroradiological emergencies necessitate rapid and accurate diagnosis, often relying on computed tomography (CT)....

Benchmarking deep learning-based low-dose CT image denoising algorithms.

BACKGROUND: Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography ...

The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma.

PURPOSE: To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanc...

An enhanced Garter Snake Optimization-assisted deep learning model for lung cancer segmentation and classification using CT images.

An early detection of lung tumors is critical for better treatment results, and CT scans can reveal lumps in the lungs which are too small to be picke...