The impact of radiomics in diagnosis and staging of pancreatic cancer

Abstract

Introduction: Pancreatic cancer (PC) is one of the most aggressive tumours, and better risk stratification among patients is required to provide tailored treatment. The meaning of radiomics and texture analysis as predictive techniques are not already systematically assessed. The aim of this study is to assess the role of radiomics in PC.

Methods: A PubMed/MEDLINE and Embase systematic review was conducted to assess the role of radiomics in PC. The search strategy was 'radiomics [All Fields] AND ("pancreas" [MeSH Terms] OR "pancreas" [All Fields] OR "pancreatic" [All Fields])' and only original articles referred to PC in humans in the English language were considered.

Results: A total of 123 studies and 183 studies were obtained using the mentioned search strategy on PubMed and Embase, respectively. After the complete selection process, a total of 56 papers were considered eligible for the analysis of the results. Radiomics methods were applied in PC for assessment technical feasibility and reproducibility aspects analysis, risk stratification, biologic or genomic status prediction and treatment response prediction.

Discussion: Radiomics seems to be a promising approach to evaluate PC from diagnosis to treatment response prediction. Further and larger studies are required to confirm the role and allowed to include radiomics parameter in a comprehensive decision support system.

Keywords: decision supporting system; pancreatic cancer; radiomics.

Figure 1.

Flowchart of the systematic literature search process.

Figure 2.

Example of clinical implementation of a delta radiomic model during a stereotactic radiotherapy treatment prescribed in five fractions. Before the start of the treatment, a radiomic model able to predict the LC 1 year from the end to the treatment was trained and tested on a retrospective cohort of patients. The model was based on the radiomic analysis of the MR images acquired during simulation and during fractions 1 and 2. Using the radiomic model, the RO can have a prediction of 1 year LC at the end of the fraction 2, so having the possibility to modify the radiation treatment for the remaining three fractions, increasing the dose or moving towards alternative approaches. MR, magnetic resonance.