AIMC Topic: Brain Neoplasms

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Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning.

Current computer-aided drug design
BACKGROUND: Diagnosis and treatment planning play a very vital role in improving the survival of oncological patients. However, there is high variability in the shape, size, and structure of the tumor, making automatic segmentation difficult. The aut...

Deciphering the Role of SLFN12: A Novel Biomarker for Predicting Immunotherapy Outcomes in Glioma Patients Through Artificial Intelligence.

Journal of cellular and molecular medicine
Gliomas are the most prevalent form of primary brain tumours. Recently, targeting the PD-1 pathway with immunotherapies has shown promise as a novel glioma treatment. However, not all patients experience long-lasting benefits, underscoring the necess...

Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.

Briefings in bioinformatics
This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencin...

Enhancing neuro-oncology care through equity-driven applications of artificial intelligence.

Neuro-oncology
The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient's demographics and social determinants of health. While current investigations in neuro-o...

Automated segmentation of brain metastases with deep learning: A multi-center, randomized crossover, multi-reader evaluation study.

Neuro-oncology
BACKGROUND: Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation.

Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma.

Neuro-oncology
BACKGROUND: Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the backgro...

Leveraging machine learning for preoperative prediction of supramaximal ablation in laser interstitial thermal therapy for brain tumors.

Neurosurgical focus
OBJECTIVE: Maximizing safe resection in neuro-oncology has become paramount to improving patient survival and outcomes. Laser interstitial thermal therapy (LITT) offers similar survival benefits to traditional resection, alongside shorter hospital st...

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.

The Lancet. Oncology
Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarke...

Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.

The Lancet. Oncology
The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment ...

Diffusion-weighted MRI precisely predicts telomerase reverse transcriptase promoter mutation status in World Health Organization grade IV gliomas using a residual convolutional neural network.

The British journal of radiology
OBJECTIVES: Telomerase reverse transcriptase promoter (pTERT) mutation status plays a key role in making decisions and predicting prognoses for patients with World Health Organization (WHO) grade IV glioma. This study was conducted to assess the valu...