Published in:
01-04-2019 | Original Article
External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy
Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 4/2019
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Purpose
The aim of this study was to validate previously developed radiomics models relying on just two radiomics features from 18F-fluorodeoxyglucose positron emission tomography (PET) and magnetic resonance imaging (MRI) images for prediction of disease free survival (DFS) and locoregional control (LRC) in locally advanced cervical cancer (LACC).
Methods
Patients with LACC receiving chemoradiotherapy were enrolled in two French and one Canadian center. Pre-treatment imaging was performed for each patient. Multicentric harmonization of the two radiomics features was performed with the ComBat method. The models for DFS (using the feature from apparent diffusion coefficient (ADC) MRI) and LRC (adding one PET feature to the DFS model) were tuned using one of the French cohorts (n = 112) and applied to the other French (n = 50) and the Canadian (n = 28) external validation cohorts.
Results
The DFS model reached an accuracy of 90% (95% CI [79–98%]) (sensitivity 92–93%, specificity 87–89%) in both the French and the Canadian cohorts. The LRC model reached an accuracy of 98% (95% CI [90–99%]) (sensitivity 86%, specificity 100%) in the French cohort and 96% (95% CI [80–99%]) (sensitivity 83%, specificity 100%) in the Canadian cohort. Accuracy was significantly lower without ComBat harmonization (82–85% and 71–86% for DFS and LRC, respectively). The best prediction using standard clinical variables was 56–60% only.
Conclusions
The previously developed PET/MRI radiomics predictive models were successfully validated in two independent external cohorts. A proposed flowchart for improved management of patients based on these models should now be confirmed in future larger prospective studies.