放射外科
无线电技术
医学
放射科
放射治疗
核医学
作者
Robin Gutsche,Philipp Lohmann,Mauritius Hoevels,Daniel Rueß,Norbert Galldiks,Veerle Visser-Vanderwalle,Harald Treuer,Maximilian I. Ruge,Martin Köcher
标识
DOI:10.1016/j.radonc.2021.11.010
摘要
Brain metastases show different patterns of contrast enhancement, potentially reflecting hypoxic and necrotic tumor regions with reduced radiosensitivity. An objective evaluation of these patterns might allow a prediction of response to radiotherapy. We therefore investigated the potential of MRI radiomics in comparison with the visual assessment of semantic features to predict early response to stereotactic radiosurgery in patients with brain metastases.In this retrospective study, 150 patients with 308 brain metastases from solid tumors (NSCLC in 53% of patients) treated by stereotactic radiosurgery (single dose of 17-20 Gy) were evaluated. The response of each metastasis (partial or complete remission vs. stabilization or progression) was assessed within 180 days after radiosurgery. Patterns of contrast enhancement in the pre-treatment T1-weighted MR images were either visually classified (homogenous, heterogeneous, necrotic ring-like) or subjected to a radiomics analysis. Random forest models were optimized by cross-validation and evaluated in a hold-out test data set (30% of metastases).In total, 221/308 metastases (72%) responded to radiosurgery. The optimal radiomics model comprised 10 features and outperformed the model solely based on semantic features in the test data set (AUC, 0.71 vs. 0.56; accuracy, 69% vs. 54%). The diagnostic performance could be further improved by combining semantic and radiomics features resulting in an AUC of 0.74 and an accuracy of 75% in the test data set.The developed radiomics model allowed prediction of early response to radiosurgery in patients with brain metastases and outperformed the visual assessment of patterns of contrast enhancement.
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