A Systematic Review of PET Textural Analysis and Radiomics in Cancer

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.

NSCLC Biomarkers to Predict Response to Immunotherapy with Checkpoint Inhibitors (ICI): From the Cells to In Vivo Images

“…More recently, AI-based approaches have been applied to medical imaging in combination with radiomics or alone, allowing relevant features to be identified with several fully automatic or semi-automatic approaches (machine learning and deep learning) [ 161 , 162 , 163 , 164 ]. These new approaches could implement the assessment of different aspects and stages of the lung cancer history: to differentiate the histological subtypes of cancer, particularly, adenocarcinoma and squamous cell carcinoma; to predict EGFR mutation status or PD-L1 expression for the risk stratification of NSCLC patients; and to predict response to different kind of therapies [ 165 ].…”

NSCLC Biomarkers to Predict Response to Immunotherapy with Checkpoint Inhibitors (ICI): From the Cells to in-Vivo Images

“…More recently, AI-based approaches have been applied to medical imaging in combination with radiomics or alone, allowing relevant features to be identified with several fully automatic or semi-automatic approaches (machine learning and deep learning) [149][150][151][152]. These new approaches could implement the assessment of different aspects and stages of the lung cancer history: to differentiate histological subtypes of cancer, particularly, adenocarcinoma and squamous cell carcinoma, to predict EGFR mutation status or PD-L1 expression, for the risk stratification of NSCLC patients and to predict response to different kind of therapies [153].…”

Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features

“…Radiomics are an emerging means in image analysis [ 1 , 2 , 3 , 4 ] that allow quantitative image assessment beyond morphologic and macroscopic characteristics [ 5 ]. For this, statistics of the grey level composition in a region of interest (ROI) are calculated, resulting in many different quantitative texture features that can be statistically analyzed and linked to an outcome [ 5 ].…”