A radiomics-based model for predicting local control of resected brain metastases receiving adjuvant SRS
| Autor: | Christopher Wilke, Chuyu Chen, Yoichi Watanabe, Matthew A. Hunt, Clark C. Chen, Jianling Yuan, Paul W. Sperduto, Kathryn E. Dusenbery, Kellen Mulford |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
medicine.medical_treatment R895-920 Gamma knife radiosurgery Predictive capability Article Radiosurgery 030218 nuclear medicine & medical imaging Medical physics. Medical radiology. Nuclear medicine 03 medical and health sciences 0302 clinical medicine Radiomics Chart review parasitic diseases medicine Radiology Nuclear Medicine and imaging Cognitive decline Radiation treatment planning RC254-282 Gamma Knife business.industry Neoplasms. Tumors. Oncology. Including cancer and carcinogens Brain metastases Oncology Local control 030220 oncology & carcinogenesis Radiology business Adjuvant |
| Zdroj: | Clinical and Translational Radiation Oncology Clinical and Translational Radiation Oncology, Vol 29, Iss, Pp 27-32 (2021) |
| ISSN: | 2405-6308 |
| DOI: | 10.1016/j.ctro.2021.05.001 |
| Popis: | Highlights • SRS has emerged as an alternative to WBRT for surgically resected brain metastases. • Pre-therapy radiomic features are predictive of local control after cavity SRS. • Radiomic features were superior to clinical features for predicting local control. Purpose Adjuvant radiosurgery to the cavities of surgically resected brain metastases provides excellent local tumor control while reducing the risk of deleterious cognitive decline associated with whole brain radiotherapy. A subset of these patients, however, will develop disease recurrence following radiosurgery. In this study, we sought to assess the predictive capability of radiomic-based models, as compared with standard clinical features, in predicting local tumor control. Methods We performed a retrospective chart review of patients treated with adjuvant radiosurgery for resected brain metastases at the “Institution” from 2009 to 2019. Shape, intensity and texture based radiomics features of the cavities were extracted from the pre-radiosurgery treatment planning MRI scans and trained using a gradient boosting technique with K-fold cross validation. Results In total, 71 cavities from 67 treated patients were included for analysis. The 6 and 12 month local control estimates were 86% and 76%, respectively. The 6 and 12 month overall survival was 78% and 55%, respectively. Thirty-six patients developed intracranial failures outside of the surgical cavity. The predictive model for local control trained on imaging features from the whole cavity achieved an area-under-the-curve (AUC) of 0.73 on the validation set versus an AUC of 0.40 for the clinical features. Conclusions Here we report a single institutional experience using radiomic-based predictive modeling of local tumor control following adjuvant Gamma Knife radiosurgery for resected brain metastases. We found the radiomics features to provide more robust predictive models of local control rates versus clinical features alone. Such techniques could potentially prove useful in the clinical setting and warrant further investigation. |
| Databáze: | OpenAIRE |
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