Radiomics in Oncological PET Imaging: A Systematic Review—Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

Abstract

The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative

“…However, in practice, this tool has become the de facto standard for systematic reviews of the literature focused on radiomics quality assessment as confirmed by our findings. In any case, it should be acknowledged that some alternatives have been proposed, even though their use is usually sporadic [66,67]. Additionally, several checklists checklists does not allow a formal methodological quality score, but rather an unweighted assessment of overall adherence to the included items.…”

Machine learning approach using 18F-FDG PET-based radiomics in differentiation of lung adenocarcinoma with bronchoalveolar distribution and infection

“…Using mathematical methods, texture analysis evaluates the relationship of pixel density and position within the region of interest and creates secondary parameters that provide a quantitative representation of tumor heterogeneity [10,11]. Several studies have suggested that texture analysis has predictive value in diagnostic imaging in different cancer types [12][13][14][15][16][17].…”

Baseline 18F-FDG PET/CT Radiomics in Classical Hodgkin’s Lymphoma: The Predictive Role of the Largest and the Hottest Lesions

“…Radiomics, an emerging field of research aiming to extract mineable, quantitative, high-dimensional data from clinical images, has been suggested as a solution [ 12 , 13 , 14 ]. Various studies have explored the possibilities offered by radiomics in HL [ 15 ] whether for computer-aided histological classification [ 16 ], staging [ 17 , 18 ], early metabolic response assessment [ 19 , 20 , 21 ], or refractory disease prediction [ 22 , 23 ].…”