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[Preprint]. 2020 Nov 6:2020.11.04.20226159.
doi: 10.1101/2020.11.04.20226159.

Comparison of Radiomic Feature Aggregation Methods for Patients with Multiple Tumors

Enoch Chang  1 , Marina Joel  2 , Hannah Y Chang  3 , Justin Du  2 , Omaditya Khanna  4 , Antonio Omuro  5 , Veronica Chiang  6 , Sanjay Aneja  1   7
Affiliations

Comparison of Radiomic Feature Aggregation Methods for Patients with Multiple Tumors

Enoch Chang et al. medRxiv. .

Update in

Abstract

Background: Radiomic feature analysis has been shown to be effective at modeling cancer outcomes. It has not yet been established how to best combine these radiomic features in patients with multifocal disease. As the number of patients with multifocal metastatic cancer continues to rise, there is a need for improving personalized patient-level prognostication to better inform treatment.

Methods: We compared six mathematical methods of combining radiomic features of 3596 tumors in 831 patients with multiple brain metastases and evaluated the performance of these aggregation methods using three survival models: a standard Cox proportional hazards model, a Cox proportional hazards model with LASSO regression, and a random survival forest.

Results: Across all three survival models, the weighted average of the largest three metastases had the highest concordance index (95% confidence interval) of 0.627 (0.595-0.661) for the Cox proportional hazards model, 0.628 (0.591-0.666) for the Cox proportional hazards model with LASSO regression, and 0.652 (0.565-0.727) for the random survival forest model.

Conclusions: Radiomic features can be effectively combined to establish patient-level outcomes in patients with multifocal brain metastases. Future studies are needed to confirm that the volume-weighted average of the largest three tumors is an effective method for combining radiomic features across other imaging modalities and disease sites.

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Figures

Figure 1.
Figure 1.
Preprocessing Workflow. (A) Input: slices of pre-treatment T1-post contrast brain MRI scans. (B) Identification of the region of interest from manual segmentations. (C) Output: extracted tumors with pixel resampling, N4ITK bias field correction, and z-score normalization.
See this image and copyright information in PMC

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