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Updated: Nov 21, 2025
Heterogeneity Mapping of Protein Expression in Tumors using Quantitative Immunofluorescence
Published on: October 25, 2011
Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with
Dania Daye1, Azadeh Tabari2, Hyunji Kim2,3
1Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, GRB #290, Boston, MA, 02114, USA. ddaye@mgh.harvard.edu.
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.
