Journal Article

Predicting stereotactic radiosurgery outcomes with multi-observer qualitative appearance labelling versus MRI radiomics

Tang, Terence, Hajdok, George, Albweady, Ali, Johnson, Carol, Laba, Joanna, DeVries, David A., Zindler, Jaap, Leung, Andrew, Ward, Aaron D., Lagerwaard, Frank

2023

Overview

Qualitative observer-based and quantitative radiomics-based analyses of T1w contrast-enhanced magnetic resonance imaging (T1w-CE MRI) have both been shown to predict the outcomes of brain metastasis (BM) stereotactic radiosurgery (SRS). Comparison of these methods and interpretation of radiomics-based machine learning (ML) models remains limited. To address this need, we collected a dataset of n = 123 BMs from 99 patients including 12 clinical features, 107 pre-treatment T1w-CE MRI radiomic features, and BM post-SRS progression scores. A previously published outcome model using SRS dose prescription and five-way BM qualitative appearance scoring was evaluated. We found high qualitative scoring interobserver variability across five observers that negatively impacted the model’s risk stratification. Radiomics-based ML models trained to replicate the qualitative scoring did so with high accuracy (bootstrap-corrected AUC = 0.84–0.94), but risk stratification using these replicated qualitative scores remained poor. Radiomics-based ML models trained to directly predict post-SRS progression offered enhanced risk stratification (Kaplan–Meier rank-sum p  = 0.0003) compared to using qualitative appearance. The qualitative appearance scoring enabled interpretation of the progression radiomics-based ML model, with necrotic BMs and a subset of heterogeneous BMs predicted as being at high-risk of post-SRS progression, in agreement with current radiobiological understanding. Our study’s results show that while radiomics-based SRS outcome models out-perform qualitative appearance analysis, qualitative appearance still provides critical insight into ML model operation.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/166/985 / 639/705/794 / 692/4028/67/1059/485 / 692/4028/67/1922 / 692/4028/67/2321 / Brain cancer / Brain Neoplasms - diagnostic imaging / Brain Neoplasms - radiotherapy / Brain Neoplasms - secondary / Humanities and Social Sciences / Humans / Labeling / Machine Learning / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Metastases / multidisciplinary / Neuroimaging / Observer Variation / Radiomics / Radiosurgery / Radiosurgery - methods / Retrospective Studies / Science / Science (multidisciplinary)