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Erschienen in:
16.01.2021 | Gastrointestinal
Quantitative tumor heterogeneity MRI profiling improves machine learning–based prognostication in patients with metastatic colon cancer
Erschienen in: European Radiology | Ausgabe 8/2021
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Objectives
Intra-tumor heterogeneity has been previously shown to be an independent predictor of patient survival. The goal of this study is to assess the role of quantitative MRI-based measures of intra-tumor heterogeneity as predictors of survival in patients with metastatic colorectal cancer.
Methods
In this IRB-approved retrospective study, we identified 55 patients with stage 4 colon cancer with known hepatic metastasis on MRI. Ninety-four metastatic hepatic lesions were identified on post-contrast images and manually volumetrically segmented. A heterogeneity phenotype vector was extracted from each lesion. Univariate regression analysis was used to assess the contribution of 110 extracted features to survival prediction. A random forest–based machine learning technique was applied to the feature vector and to the standard prognostic clinical and pathologic variables. The dataset was divided into a training and test set at a ratio of 4:1. ROC analysis and confusion matrix analysis were used to assess classification performance.
Results
Mean survival time was 39 ± 3.9 months for the study population. A total of 22 texture features were associated with patient survival (p < 0.05). The trained random forest machine learning model that included standard clinical and pathological prognostic variables resulted in an area under the ROC curve of 0.83. A model that adds imaging-based heterogeneity features to the clinical and pathological variables resulted in improved model performance for survival prediction with an AUC of 0.94.
Conclusions
MRI-based texture features are associated with patient outcomes and improve the performance of standard clinical and pathological variables for predicting patient survival in metastatic colorectal cancer.
Key Points
• MRI-based tumor heterogeneity texture features are associated with patient survival outcomes.
• MRI-based tumor texture features complement standard clinical and pathological variables for prognosis prediction in metastatic colorectal cancer.
• Agglomerative hierarchical clustering shows that patient survival outcomes are associated with different MRI tumor profiles.