Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma

J Luo, M Pan, K Mo, Y Mao, D Zou - Seminars in Cancer Biology, 2023 - Elsevier
Glioma represents a dominant primary intracranial malignancy in the central nervous
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 …

Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease

J Lee, E Warner, S Shaikhouni, M Bitzer, M Kretzler… - Scientific reports, 2022 - nature.com
Pathologists use visual classification to assess patient kidney biopsy samples when
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

M Zhu, S Li, Y Kuang, VB Hill, AB Heimberger… - Frontiers in …, 2022 - frontiersin.org
Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron
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

TC Booth, M Grzeda, A Chelliah, A Roman… - Frontiers in …, 2022 - frontiersin.org
Objective Monitoring biomarkers using machine learning (ML) may determine glioblastoma
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 …

Diagnosis of glioblastoma multiforme progression via interpretable structure-constrained graph neural networks

X Song, J Li, X Qian - IEEE Transactions on Medical Imaging, 2022 - ieeexplore.ieee.org
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 …

Advanced neuroimaging approaches to pediatric brain tumors

RM Nikam, X Yue, G Kaur, V Kandula, A Khair… - Cancers, 2022 - mdpi.com
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 …

A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients

M Moassefi, S Faghani, GM Conte… - Journal of neuro …, 2022 - Springer
Abstract Introduction Glioblastomas (GBMs) are highly aggressive tumors. A common
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 …