Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learning

• URL: http://arxiv.org/abs/2302.08802v1
• Date: Fri, 17 Feb 2023 10:55:18 GMT
• Title: Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learning
• Authors: Philipp Sommer, Yixing Huang, Christoph Bert, Andreas Maier, Manuel Schmidt, Arnd D\"orfler, Rainer Fietkau and Florian Putz
• Abstract summary: We hypothesized that using radiomics and machine learning (ML), metastases at high risk for subsequent progression could be identified during follow-up prior to the onset of significant tumor growth. The classification is realized via the maximum-relevance minimal-redundancy (MRMR) technique and support vector machines (SVM) The results indicate that risk stratification of BM based on radiomics and machine learning during post-SRT follow-up is possible with good accuracy and should be further pursued to personalize and improve post-SRT follow-up.
• Score: 7.165205048529115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stereotactic radiotherapy (SRT) is one of the most important treatment for patients with brain metastases (BM). Conventionally, following SRT patients are monitored by serial imaging and receive salvage treatments in case of significant tumor growth. We hypothesized that using radiomics and machine learning (ML), metastases at high risk for subsequent progression could be identified during follow-up prior to the onset of significant tumor growth, enabling personalized follow-up intervals and early selection for salvage treatment. All experiments are performed on a dataset from clinical routine of the Radiation Oncology department of the University Hospital Erlangen (UKER). The classification is realized via the maximum-relevance minimal-redundancy (MRMR) technique and support vector machines (SVM). The pipeline leads to a classification with a mean area under the curve (AUC) score of 0.83 in internal cross-validation and allows a division of the cohort into two subcohorts that differ significantly in their median time to progression (low-risk metastasis (LRM): 17.3 months, high-risk metastasis (HRM): 9.6 months, p < 0.01). The classification performance is especially enhanced by the analysis of medical images from different points in time (AUC 0.53 -> AUC 0.74). The results indicate that risk stratification of BM based on radiomics and machine learning during post-SRT follow-up is possible with good accuracy and should be further pursued to personalize and improve post-SRT follow-up.

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