Radiomics outperforms semantic features for prediction of response to stereotactic radiosurgery in brain metastases. (January 2022)

• Record Type: Journal Article
• Authors: Gutsche, Robin; Lohmann, Philipp; Hoevels, Mauritius; Ruess, Daniel; Galldiks, Norbert; Visser-Vandewalle, Veerle; Treuer, Harald; Ruge, Maximilian; Kocher, Martin
• Is Part Of: Radiotherapy and oncology. Volume 166(2022)
• Page Start: 37
• Page End: 43
• Publication Date: 2022-01
• Subjects: AUC area under the receiver operating characteristic curve -- FLAIR fluid-attenuated inversion recovery -- FFP freedom from progression -- GLCM gray level co-occurrence matrix -- GLDM gray level dependence matrix -- GLRLM gray level run length matrix -- GLSZM gray level size zone matrix -- GTV gross tumor volume -- ICC intraclass correlation coefficient -- LoG Laplacian of Gaussian -- NGTDM neighboring gray tone difference matrix -- NSCLC non-small cell lung cancer -- PTV planning target volume -- ROC receiver operating characteristic -- VOI volume of interest; Brain tumor -- Machine learning -- Artificial intelligence (AI) -- Response assessment -- MRI; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2021.11.010
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: MRI enhancement patterns are related to radiosurgery response in brain metastases. Visual classification of MRI patterns has a low prediction performance. Radiomics features are partially correlated with semantic features. Radiomics and combined models achieve highest performance in response prediction. Abstract: Background: 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. Patients and methods: 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). Results: 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. Conclusion: 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.