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Review
. 2021 Feb 23;11(2):380.
doi: 10.3390/diagnostics11020380.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Manuel Piñeiro-Fiel  1   2 , Alexis Moscoso  1   2 , Virginia Pubul  2 , Álvaro Ruibal  1   2   3 , Jesús Silva-Rodríguez  2 , Pablo Aguiar  1   2
Affiliations
Review

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Manuel Piñeiro-Fiel et al. Diagnostics (Basel). .

Abstract

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies.

Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted.

Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20-1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1-286).

Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

Keywords: PET; cancer; heterogeneity; radiomics; textural analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Example of image processing for radiomics. (a) Image acquisition and tumor segmentation. (b) Extraction of different shape, intensity, and textural features from the segmented tumor. (c) Development of prediction models using imaging features.
Figure 2
Figure 2
Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram.
Figure 3
Figure 3
The number of publications per year. In green, the interannual increase.
Figure 4
Figure 4
The number of articles associated with each cancer type group. (a) Number of patients per number of groups studied in the present review. (b) Distribution of cancer types in the “Other” group. In green, the percentage of publications corresponding to that group. * Percentages in panel (b) represent the percentage in the “Other” group.
Figure 5
Figure 5
Studies targeting each of the defined objectives segmented by cancer type. (a) Lung, (b) head and neck, (c) breast, (d) gynecologic, (e) blood, (f) brain cancer.
Figure 6
Figure 6
Per item and overall risk of bias.
See this image and copyright information in PMC

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