AIMC Topic: Glioblastoma

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Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.

Radiology. Artificial intelligence
Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting ...

Enhancing Spatial Transcriptomics Analysis by Integrating Image-Aware Deep Learning Methods.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Spatial transcriptomics (ST) represents a pivotal advancement in biomedical research, enabling the transcriptional profiling of cells within their morphological context and providing a pivotal tool for understanding spatial heterogeneity in cancer ti...

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19.

Briefings in bioinformatics
Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification...

THE SAFETY AND EFFICACY OF ROBOT-ASSISTED STEREOTACTIC BIOPSY FOR BRAIN GLIOMA: EARLIEST INSTITUTIONAL EXPERIENCES AND EVALUATION OF LITERATURE.

Acta clinica Croatica
Robot-assisted brain tumor biopsy is becoming one of the most important innovative technologies in neurosurgical practice. The idea behind its engagement is to advance the safety and efficacy of the biopsy procedure, which is much in demand when plan...

Challenges in Building of Deep Learning Models for Glioblastoma Segmentation: Evidence from Clinical Data.

Studies in health technology and informatics
In this article, we compare the performance of a state-of-the-art segmentation network (UNet) on two different glioblastoma (GB) segmentation datasets. Our experiments show that the same training procedure yields almost twice as bad results on the re...

Differentiation of rare brain tumors through unsupervised machine learning: Clinical significance of in-depth methylation and copy number profiling illustrated through an unusual case of IDH wildtype glioblastoma.

Clinical neuropathology
Methylation profiling has become a mainstay in brain tumor diagnostics since the introduction of the first publicly available classification tool by the German Cancer Research Center in 2017. We demonstrate the capability of this system through an ex...

Deep Learning AI Applications in the Imaging of Glioma.

Topics in magnetic resonance imaging : TMRI
This manuscript will review emerging applications of artificial intelligence, specifically deep learning, and its application to glioblastoma multiforme (GBM), the most common primary malignant brain tumor. Current deep learning approaches, commonly ...

An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning.

Neurosurgery
BACKGROUND: Although survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata.