Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging
This article is a comprehensive review of the basic background, technique, and clinical
applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A …
applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A …
Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis
Purpose To systematically review and evaluate the methodological quality of studies using
radiomics for diagnostic and predictive purposes in patients with intracranial meningioma …
radiomics for diagnostic and predictive purposes in patients with intracranial meningioma …
Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma
Background Deep learning is promising to predict treatment response. We aimed to
evaluate and validate the predictive performance of the CT-based model using deep …
evaluate and validate the predictive performance of the CT-based model using deep …
A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study
Background Prediction of brain invasion pre-operatively rather than postoperatively would
contribute to the selection of surgical techniques, predicting meningioma grading and …
contribute to the selection of surgical techniques, predicting meningioma grading and …
Artificial intelligence in brain tumour surgery—an emerging paradigm
S Williams, H Layard Horsfall, JP Funnell… - Cancers, 2021 - mdpi.com
Simple Summary Artificial intelligence (AI) is the branch of computer science that enables
machines to learn, reason, and problem solve. In recent decades, AI has been developed …
machines to learn, reason, and problem solve. In recent decades, AI has been developed …
Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study
Background and purpose Advanced imaging analysis for the prediction of tumor biology and
modelling of clinically relevant parameters using computed imaging features is part of the …
modelling of clinically relevant parameters using computed imaging features is part of the …
Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning …
Background Deep learning methods have great potential to predict tumor characterization,
such as histological diagnosis and genetic aberration. The objective of this study was to …
such as histological diagnosis and genetic aberration. The objective of this study was to …
Fully automated MRI segmentation and volumetric measurement of intracranial meningioma using deep learning
Background Accurate and rapid measurement of the MRI volume of meningiomas is
essential in clinical practice to determine the growth rate of the tumor. Imperfect automation …
essential in clinical practice to determine the growth rate of the tumor. Imperfect automation …
A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy
S Tanaka, N Kadoya, Y Sugai, M Umeda… - Scientific Reports, 2022 - nature.com
Early regression—the regression in tumor volume during the initial phase of radiotherapy
(approximately 2 weeks after treatment initiation)—is a common occurrence during …
(approximately 2 weeks after treatment initiation)—is a common occurrence during …
A spotlight on the role of radiomics and machine-learning applications in the management of intracranial meningiomas: a new perspective in neuro-oncology: a review
L Brunasso, G Ferini, L Bonosi, R Costanzo, S Musso… - Life, 2022 - mdpi.com
Background: In recent decades, the application of machine learning technologies to medical
imaging has opened up new perspectives in neuro-oncology, in the so-called radiomics …
imaging has opened up new perspectives in neuro-oncology, in the so-called radiomics …