Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review
Year: 2018
Type: review
Study design: Systematic Review
Abstract: Background: Radiomics has been widely investigated for non-invasive acquisition of quantita-tive textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images obtained from computed tomography (CT), magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), how-ever, attempts to perform consistent investigations are sparse, and it is unclear whether the result-ing textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC. Methods: Literature search was conducted in accordance with guidelines established by Pre-ferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Electronic data-bases were examined from January 1990 through November 2017 for common radiomic key-words. Eligible completed studies were then scored using a standardized checklist that we de-veloped from Enhancing the Quality and Transparency of Health Research (EQUATOR) guide-lines for reporting machine learning predictive model specifications and results in biomedical re-search, defined by Luo et al.(1). Descriptive statistics of checklist scores were populated, and a sub-group analysis of methodology items alone was conducted in comparison to overall scores. Results: Sixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (27.5%).MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13-118 patients (median of 20), and final radiomic signatures ranged from 2-279 features. Analyzed end-points included either segmentation or his-topathological classification parameters (44%) or prognostic and predictive biomarkers (56%) . Liu et al.(39) addressed the highest number of our checklist items (Total Score [TS]: 48), and a sub-group analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies (Spearman’s ρ = 0.94 [p <.0001]). Conclusions: Although MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data.
Source: Frontiers in Oncology
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funders: National Science Foundation, Elekta, Andrew Sabin Family Foundation, National Institutes of Health, Health Science Center, University of Tennessee, University of Texas MD Anderson Cancer Center, National Cancer Institute, National Institute of Dental and Craniofacial Research, Division of Mathematical Sciences
Awards: G111615301, G1442369977, G1858909031, G203557288, G2164129353, G2326049341, M D Anderson Cancer Center Head & Neck SPORE, G2816209679, G293715275, G3027680094, G3670257500, G380820829, G458618165, G4660434062, G5499366301, G7087022923, G7400934742, QuBBD: Collaborative Research: SMART -- Spatial-Nonspatial Multidimensional Adaptive Radiotherapy Treatment, G816801620, G8401789219, G9000012603, G9000019672, G9000025114, G9000034222, G9000091839, G9000101031, G9000117382, G9000146054, G9000297587, G941367620, G9625747
OpenAlex ID: W2802099355