Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients

Remy Klaassen*, Ruben T. H. M. Larue, Banafsche Mearadji, Stephanie O. van der Woude, Jaap Stoker, Philippe Lambin, Hanneke W. M. van Laarhoven

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

23 Citations (Web of Science)

Abstract

In this study we investigate a CT radiomics approach to predict response to chemotherapy of individual liver metastases in patients with esophagogastric cancer (EGC). In eighteen patients with metastatic EGC treated with chemotherapy, all liver metastases were manually delineated in 3D on the pre-treatment and evaluation CT. From the pre-treatment CT scans 370 radiomics features were extracted per lesion. Random forest (RF) models were generated to discriminate partial responding (PR, >65% volume decrease, including 100% volume decrease), and complete remission (CR, only 100% volume decrease) lesions from other lesions. RF-models were build using a leave one out strategy where all lesions of a single patient were removed from the dataset and used as validation set for a model trained on the lesions of the remaining patients. This process was repeated for all patients, resulting in 18 trained models and one validation set for both the PR and CR datasets. Model performance was evaluated by receiver operating characteristics with corresponding area under the curve (AUC). In total 196 liver metastases were delineated on the pre-treatment CT, of which 99 (51%) lesions showed a decrease in size of more than 65% (PR). From the PR set a total of 47 (47% of RL, 24% of initial) lesions were no longer detected in CT scan 2 (CR). The RF-model for PR lesions showed an average training AUC of 0.79 (range: 0.74-0.83) and 0.65 (95% ci: 0.57-0.73) for the combined validation set. The RF-model for CR lesions had an average training AUC of 0.87 (range: 0.83-0.90) and 0.79 (95% ci 0.72-0.87) for the validation set. Our findings show that individual response of liver metastases varies greatly within and between patients. A CT radiomics approach shows potential in discriminating responding from non-responding liver metastases based on the pre-treatment CT scan, although further validation in an independent patient cohort is needed to validate these findings.

Original languageEnglish
Article number0207362
Number of pages13
JournalPLOS ONE
Volume13
Issue number11
DOIs
Publication statusPublished - 15 Nov 2018

Keywords

  • PRIMARY ESOPHAGEAL CANCER
  • TEXTURE ANALYSIS
  • TUMOR HETEROGENEITY
  • COLORECTAL-CANCER
  • FEATURE STABILITY
  • GASTRIC-CANCER
  • CHEMOTHERAPY
  • SURVIVAL
  • FEATURES
  • ADENOCARCINOMA

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