Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review

F.C.R. Staal, D.J. van der Reijd, M. Taghavi, D.M.J. Lambregts, R.G.H. Beets-Tan, M. Maas*

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review

Abstract

Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on F-18-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models. (C) 2020 Elsevier Inc. All rights reserved.
Original languageEnglish
Pages (from-to)52-71
Number of pages20
JournalClinical Colorectal Cancer
Volume20
Issue number1
DOIs
Publication statusPublished - 1 Mar 2021

Keywords

  • Artificial intelligence
  • Metastasis
  • Neoadjuvant chemotherapy
  • Quantitative imaging analysis
  • Response
  • CT TEXTURE ANALYSIS
  • CONTRAST-ENHANCED CT
  • INTRA-TUMOR HETEROGENEITY
  • RECTAL-CANCER
  • NEOADJUVANT CHEMORADIOTHERAPY
  • LIVER METASTASES
  • PREOPERATIVE CHEMORADIOTHERAPY
  • PATHOLOGICAL RESPONSE
  • PERSONALIZED APPROACH
  • THERAPEUTIC RESPONSE

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