Impact of Tumour Segmentation Accuracy on Efficacy of Quantitative MRI Biomarkers of Radiotherapy Outcome in Brain Metastasis.

Abstract

Simple SummaryRadiotherapy is a major treatment option for patients with brain metastasis. However, response to radiotherapy is highly varied among the patients, and it may take months before the response of brain metastasis to radiotherapy is apparent on standard follow-up imaging. This is not desirable, especially given the fact that patients diagnosed with brain metastasis suffer from a short median survival. Recent studies have shown the high potential of machine learning methods for analyzing quantitative imaging features (biomarkers) to predict the response of brain metastasis before or early after radiotherapy. However, these methods require manual delineation of individual tumours on imaging that is tedious and time-consuming, hindering further development and widespread application of these techniques. Here, we investigated the impact of using less accurate but automatically generated tumour outlines on the efficacy of the derived imaging biomarkers for radiotherapy response prediction. Our findings demonstrate that while the effect of tumour delineation accuracy is considerable for automatic contours with low accuracy, imaging biomarkers and prediction models are rather robust to imperfections in the produced tumour masks. The results of this study open the avenue to utilizing automatically generated tumour contours for discovering imaging biomarkers without sacrificing their accuracy.Significantly affecting patients’ clinical course and quality of life, a growing number of cancer cases are diagnosed with brain metastasis (BM) annually. Stereotactic radiotherapy is now a major treatment option for patients with BM. However, it may take months before the local response of BM to stereotactic radiation treatment is apparent on standard follow-up imaging. While machine learning in conjunction with radiomics has shown great promise in predicting the local response of BM before or early after radiotherapy, further development and widespread application of such techniques has been hindered by their dependency on manual tumour delineation. In this study, we explored the impact of using less-accurate automatically generated segmentation masks on the efficacy of radiomic features for radiotherapy outcome prediction in BM. The findings of this study demonstrate that while the effect of tumour delineation accuracy is substantial for segmentation models with lower dice scores (dice score ≤ 0.85), radiomic features and prediction models are rather resilient to imperfections in the produced tumour masks. Specifically, the selected radiomic features (six shared features out of seven) and performance of the prediction model (accuracy of 80% versus 80%, AUC of 0.81 versus 0.78) were fairly similar for the ground-truth and automatically generated segmentation masks, with dice scores close to 0.90. The positive outcome of this work paves the way for adopting high-throughput automatically generated tumour masks for discovering diagnostic and prognostic imaging biomarkers in BM without sacrificing accuracy.