Journal
DIAGNOSTICS
Volume 12, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/diagnostics12061329
Keywords
radiomics; artificial intelligence; brain tumors; head and neck tumors; lung tumors; breast tumors; thyroid nodules; thymic tumors
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Radiomics is a promising but technically challenging field in nuclear oncology. This study conducted a literature search and evaluation to summarize the research on supradiaphragmatic neoplasia, revealing various factors associated with radiomics analysis such as tumor type, number of patients, and use of validation sets.
Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.
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