Artificial intelligence in cancer imaging: clinical challenges and applications
Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered
data with nuanced decision making. Cancer offers a unique context for medical decisions …
data with nuanced decision making. Cancer offers a unique context for medical decisions …
Radiomics: from qualitative to quantitative imaging
W Rogers, S Thulasi Seetha… - The British journal of …, 2020 - academic.oup.com
Historically, medical imaging has been a qualitative or semi-quantitative modality. It is
difficult to quantify what can be seen in an image, and to turn it into valuable predictive …
difficult to quantify what can be seen in an image, and to turn it into valuable predictive …
Residual Convolutional Neural Network for the Determination of IDH Status in Low- and High-Grade Gliomas from MR Imaging
Purpose: Isocitrate dehydrogenase (IDH) mutations in glioma patients confer longer survival
and may guide treatment decision making. We aimed to predict the IDH status of gliomas …
and may guide treatment decision making. We aimed to predict the IDH status of gliomas …
Radiomic MRI phenotyping of glioblastoma: improving survival prediction
Purpose To investigate whether radiomic features at MRI improve survival prediction in
patients with glioblastoma multiforme (GBM) when they are integrated with clinical and …
patients with glioblastoma multiforme (GBM) when they are integrated with clinical and …
Natural and artificial intelligence in neurosurgery: a systematic review
JT Senders, O Arnaout, AV Karhade… - …, 2018 - journals.lww.com
BACKGROUND Machine learning (ML) is a domain of artificial intelligence that allows
computer algorithms to learn from experience without being explicitly programmed …
computer algorithms to learn from experience without being explicitly programmed …
Identification of non–small cell lung cancer sensitive to systemic cancer therapies using radiomics
Purpose: Using standard-of-care CT images obtained from patients with a diagnosis of non–
small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of …
small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of …
Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
Prognostic values of systemic inflammatory immunological markers in glioblastoma: A systematic review and meta-analysis
P Jarmuzek, K Kozlowska, P Defort, M Kot… - Cancers, 2023 - mdpi.com
Simple Summary The authors report the most up-to-date review and a thorough meta-
analysis of inflammatory, immunological markers such as neutrophil-to-lymphocyte ratio …
analysis of inflammatory, immunological markers such as neutrophil-to-lymphocyte ratio …
Overall survival prediction in glioblastoma with radiomic features using machine learning
Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of
patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) …
patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) …
Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low-and high-grade gliomas
Purpose Isocitrate dehydrogenase (IDH) and 1p19q codeletion status are importantin
providing prognostic information as well as prediction of treatment response in gliomas …
providing prognostic information as well as prediction of treatment response in gliomas …