Radiomics insight into the neurodegenerative “hot” brain: A narrative review from the nuclear medicine perspective

Abstract

The application of radiomics for non-oncologic diseases is currently emerging. Despite its relative infancy state, the evidence highlights the potential of radiomics approaches to serve as neuroimaging biomarkers in the field of the neurodegenerative brain. This systematic review presents the last progress and potential application of radiomics in the field of neurodegenerative nuclear imaging applied to positron-emission tomography (PET) and single-photon emission computed tomography (SPECT) by focusing mainly on the two most common neurodegenerative disorders, Alzheimer's (AD) and Parkinson's disease (PD). A comprehensive review of the current literature was performed using the PubMed and Web of Science databases up to November 2022. The final collection of eighteen relevant publications was grouped as AD-related and PD-related. The main efforts in the field of AD dealt with radiomics-based early diagnosis of preclinical AD and the prediction of MCI to AD conversion, meanwhile, in the setting of PD, the radiomics techniques have been used in the attempt to improve the assessment of PD diagnosis, the differential diagnosis between PD and other parkinsonism, severity assessment, and outcome prediction. Although limited evidence with relatively small cohort studies, it seems that radiomics-based analysis using nuclear medicine tools, mainly [18F]Fluorodeoxyglucose (FDG) and β-amyloid (Aβ) PET, and dopamine transporter (DAT) SPECT, can be used for computer-aided diagnoses in AD-continuum and parkinsonian disorders. Combining nuclear radiomics analysis with clinical factors and introducing a multimodality approach can significantly improve classification and prediction efficiency in neurodegenerative disorders.

Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment

“…The escalating interest in radiomics research has led to an increase in its applications across various medical imaging domains, particularly in the field of oncology [ 10 , 11 , 12 , 13 , 14 , 15 ]. Nevertheless, the translation of radiomics within clinical practice is hindered by actual limitations of current research, due to the heterogeneity in all the above-mentioned key steps of the radiomic pipeline, but also the lack of external validation, prospective design, and large-scale multicenter datasets [ 16 , 17 ].…”

Delta-radiomics in cancer immunotherapy response prediction: A systematic review

“…Radiomics is based on the extraction from medical images of high-dimensional quantitative features, which has been widely used to develop predictive models [22] , [23] .…”