ArXiv Preprint
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.
Philipp Sommer, Yixing Huang, Christoph Bert, Andreas Maier, Manuel Schmidt, Arnd Dörfler, Rainer Fietkau, Florian Putz
2023-02-17
