Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy

Abstract

Background and purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of metastatic environments. To investigate the feasibility of analyzing CT texture, we sought to build an automated model to predict progression-free survival using CT radiomics and artificial intelligence (AI).

Materials and methods: Liver CT scans and outcomes for N = 97 CLM patients treated with radiotherapy were retrospectively obtained. A survival model was built by extracting 108 radiomic features from liver and tumor CT volumes for a random survival forest (RSF) to predict local progression. Accuracies were measured by concordance indices (C-index) and integrated Brier scores (IBS) with 4-fold cross-validation. This was repeated with different liver segmentations and radiotherapy clinical variables as inputs to the RSF. Predictive features were identified by perturbation importances.

Results: The AI radiomics model achieved a C-index of 0.68 (CI: 0.62-0.74) and IBS below 0.25 and the most predictive radiomic feature was gray tone difference matrix strength (importance: 1.90 CI: 0.93-2.86) and most predictive treatment feature was maximum dose (importance: 3.83, CI: 1.05-6.62). The clinical data only model achieved a similar C-index of 0.62 (CI: 0.56-0.69), suggesting that predictive signals exist in radiomics and clinical data.

Conclusions: The AI model achieved good prediction accuracy for progression-free survival of CLM, providing support that radiomics or clinical data combined with machine learning may aid prognostic assessment and management.

Keywords: Artificial intelligence; Computer vision; Machine learning; Radiomics; Survival analysis.

Fig. 1

A visualization of the survival prediction system. The system contains two stages. The first is a training stage, where radiomic features are extracted from a set of computed tomography liver scans. Variance inflation factor and hazard ratio ranking is then used to filter out low information yielding features. The remaining features are used to train a random survival forest prediction model. Once the survival model has been built, it can be exported to a real-time prediction environment, where liver scans of new patients can be fed as input to the survival model to obtain a predicted survival for the new patient. In this way, most of the computation required is done beforehand to build the model and prediction can occur in real-time for new patients.

Fig. 2

Comparison of the predicted local progression-free survival (red), defined as freedom from local progression, from the random survival forest compared to the actual survival (red) from a Kaplan-Meier model of the outcome data. Comparisons include the best k-fold (left) and worst k-fold (right) during cross-validation from using radiomics using liver and tumor volumes and treatment data (top), radiomics data only (middle), or treatment data only (bottom). All models a higher C-index greater than 0.50 and the usage of radiomic features enhances the accuracy of the model compared to with treatment data alone. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)