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 …
[HTML][HTML] Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review
Radiomics research is rapidly growing in recent years, but more concerns on radiomics
reliability are also raised. This review attempts to update and overview the current status of …
reliability are also raised. This review attempts to update and overview the current status of …
Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast …
Objective The aim of this study was to develop and validate a deep learning-based radiomic
(DLR) model combined with clinical characteristics for predicting pathological complete …
(DLR) model combined with clinical characteristics for predicting pathological complete …
Artificial Intelligence-based Radiomics in the Era of Immuno-oncology
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and
spurred further research into tumor biology. Yet, cancer patients respond variably to …
spurred further research into tumor biology. Yet, cancer patients respond variably to …
Treatment response and prognosis evaluation in high‐grade glioma: an imaging review based on MRI
Q Zhou, C Xue, X Ke, J Zhou - Journal of Magnetic Resonance …, 2022 - Wiley Online Library
In recent years, the development of advanced magnetic resonance imaging (MRI)
technology and machine learning (ML) have created new tools for evaluating treatment …
technology and machine learning (ML) have created new tools for evaluating treatment …
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 …
Achieving imaging and computational reproducibility on multiparametric MRI radiomics features in brain tumor diagnosis: Phantom and clinical validation
Abstract Objectives The Image Biomarker Standardization Initiative has helped improve the
computational reproducibility of MRI radiomics features. Nonetheless, the MRI sequences …
computational reproducibility of MRI radiomics features. Nonetheless, the MRI sequences …
Robustness of radiomics to variations in segmentation methods in multimodal brain MRI
Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical
areas for which radiomic features are computed. However, differences among these …
areas for which radiomic features are computed. However, differences among these …
[HTML][HTML] Current applications of deep-learning in neuro-oncological MRI
Abstract Purpose Magnetic Resonance Imaging (MRI) provides an essential contribution in
the screening, detection, diagnosis, staging, treatment and follow-up in patients with a …
the screening, detection, diagnosis, staging, treatment and follow-up in patients with a …
Comparable Performance of Deep Learning–Based to Manual-Based Tumor Segmentation in KRAS/NRAS/BRAF Mutation Prediction With MR-Based Radiomics in …
G Zhang, L Chen, A Liu, X Pan, J Shu, Y Han… - Frontiers in …, 2021 - frontiersin.org
Radiomic features extracted from segmented tumor regions have shown great power in
gene mutation prediction, while deep learning–based (DL-based) segmentation helps to …
gene mutation prediction, while deep learning–based (DL-based) segmentation helps to …