Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma
Glioma represents a dominant primary intracranial malignancy in the central nervous
system. Artificial intelligence that mainly includes machine learning, and deep learning …
system. Artificial intelligence that mainly includes machine learning, and deep learning …
Oncolytic viro-immunotherapy: an emerging option in the treatment of gliomas
J Zeng, X Li, M Sander, H Zhang, G Yan… - Frontiers in …, 2021 - frontiersin.org
The prognosis of malignant gliomas remains poor, with median survival fewer than 20
months and a 5-year survival rate merely 5%. Their primary location in the central nervous …
months and a 5-year survival rate merely 5%. Their primary location in the central nervous …
Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease
Pathologists use visual classification to assess patient kidney biopsy samples when
diagnosing the underlying cause of kidney disease. However, the assessment is qualitative …
diagnosing the underlying cause of kidney disease. However, the assessment is qualitative …
Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective
Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron
emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches …
emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches …
Imaging biomarkers of glioblastoma treatment response: a systematic review and meta-analysis of recent machine learning studies
Objective Monitoring biomarkers using machine learning (ML) may determine glioblastoma
treatment response. We systematically reviewed quality and performance accuracy of …
treatment response. We systematically reviewed quality and performance accuracy of …
The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists
A Urushibara, T Saida, K Mori, T Ishiguro, K Inoue… - BMC Medical …, 2022 - Springer
Purpose To compare the diagnostic performance of deep learning models using
convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial …
convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial …
Diagnosis of glioblastoma multiforme progression via interpretable structure-constrained graph neural networks
Glioblastoma multiforme (GBM) is the most common type of brain tumors with high
recurrence and mortality rates. After chemotherapy treatment, GBM patients still show a high …
recurrence and mortality rates. After chemotherapy treatment, GBM patients still show a high …
Advanced neuroimaging approaches to pediatric brain tumors
Simple Summary After leukemias, brain tumors are the most common cancers in children,
and early, accurate diagnosis is critical to improve patient outcomes. Beyond the …
and early, accurate diagnosis is critical to improve patient outcomes. Beyond the …
A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients
Abstract Introduction Glioblastomas (GBMs) are highly aggressive tumors. A common
clinical challenge after standard of care treatment is differentiating tumor progression from …
clinical challenge after standard of care treatment is differentiating tumor progression from …
When artificial intelligence meets PD-1/PD-L1 inhibitors: Population screening, response prediction and efficacy evaluation
W Jin, Q Luo - Computers in Biology and Medicine, 2022 - Elsevier
Programmed cell death protein-1 (PD-1) and its ligand (programmed death ligand 1, PD-L1)
inhibitors, as the rising stars of immunotherapy, have been widely used in clinical practice …
inhibitors, as the rising stars of immunotherapy, have been widely used in clinical practice …