AIMC Topic: Brain Neoplasms

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Chat-GPT on brain tumors: An examination of Artificial Intelligence/Machine Learning's ability to provide diagnoses and treatment plans for example neuro-oncology cases.

Clinical neurology and neurosurgery
OBJECTIVE: Assess the capabilities of ChatGPT-3.5 and 4 to provide accurate diagnoses, treatment options, and treatment plans for brain tumors in example neuro-oncology cases.

"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.

Radiation oncology (London, England)
BACKGROUND: Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumo...

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.

Academic radiology
BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this ci...

Synthesizing Contrast-Enhanced MR Images from Noncontrast MR Images Using Deep Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Recent developments in deep learning methods offer a potential solution to the need for alternative imaging methods due to concerns about the toxicity of gadolinium-based contrast agents. The purpose of the study was to synthe...

Using a deep learning prior for accelerating hyperpolarized C MRSI on synthetic cancer datasets.

Magnetic resonance in medicine
PURPOSE: We aimed to incorporate a deep learning prior with k-space data fidelity for accelerating hyperpolarized carbon-13 MRSI, demonstrated on synthetic cancer datasets.

A Prospective Study of Machine Learning-Assisted Radiation Therapy Planning for Patients Receiving 54 Gy to the Brain.

International journal of radiation oncology, biology, physics
PURPOSE: The capacity for machine learning (ML) to facilitate radiation therapy (RT) planning for primary brain tumors has not been described. We evaluated ML-assisted RT planning with regard to clinical acceptability, dosimetric outcomes, and planni...

Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: a multi-center study.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVES: To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting.

Synthesis of gadolinium-enhanced glioma images on multisequence magnetic resonance images using contrastive learning.

Medical physics
BACKGROUND: Gadolinium-based contrast agents are commonly used in brain magnetic resonance imaging (MRI), however, they cannot be used by patients with allergic reactions or poor renal function. For long-term follow-up patients, gadolinium deposition...

Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features.

Journal of imaging informatics in medicine
Gliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and molecular markers. Gliomas are infiltrated by myeloid cells that accumu...

Quantitative and Visual Analysis of Data Augmentation and Hyperparameter Optimization in Deep Learning-Based Segmentation of Low-Grade Glioma Tumors Using Grad-CAM.

Annals of biomedical engineering
This study executes a quantitative and visual investigation on the effectiveness of data augmentation and hyperparameter optimization on the accuracy of deep learning-based segmentation of LGG tumors. The study employed the MobileNetV2 and ResNet bac...