Chih-Ying Huang1,2, Cheng-Chia Lee2,3,4, Huai-Che Yang2,3, Chung-Jung Lin2,5, Hsiu-Mei Wu2,5, Wen-Yuh Chung2,3, Cheng-Ying Shiau2,6, Wan-Yuo Guo2,5, David Hung-Chi Pan3,7, Syu-Jyun Peng8. 1. Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan. 2. School of Medicine, National Yang-Ming University, Taipei, Taiwan. 3. Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan. 4. Brain Research Center, National Yang-Ming University, Taipei, Taiwan. 5. Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan. 6. Cancer Center, Taipei Veterans General Hospital, Taipei, Taiwan. 7. Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan. 8. Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan. sjpeng2019@tmu.edu.tw.
Abstract
PURPOSE: Gamma Knife radiosurgery (GKRS) is a non-invasive procedure for the treatment of brain metastases. This study sought to determine whether radiomic features of brain metastases derived from pre-GKRS magnetic resonance imaging (MRI) could be used in conjunction with clinical variables to predict the effectiveness of GKRS in achieving local tumor control. METHODS: We retrospectively analyzed 161 patients with non-small cell lung cancer (576 brain metastases) who underwent GKRS for brain metastases. The database included clinical data and pre-GKRS MRI. Brain metastases were demarcated by experienced neurosurgeons, and radiomic features of each brain metastasis were extracted. Consensus clustering was used for feature selection. Cox proportional hazards models and cause-specific proportional hazards models were used to correlate clinical variables and radiomic features with local control of brain metastases after GKRS. RESULTS: Multivariate Cox proportional hazards model revealed that higher zone percentage (hazard ratio, HR 0.712; P = .022) was independently associated with superior local tumor control. Similarly, multivariate cause-specific proportional hazards model revealed that higher zone percentage (HR 0.699; P = .014) was independently associated with superior local tumor control. CONCLUSIONS: The zone percentage of brain metastases, a radiomic feature derived from pre-GKRS contrast-enhanced T1-weighted MRIs, was found to be an independent prognostic factor of local tumor control following GKRS in patients with non-small cell lung cancer and brain metastases. Radiomic features indicate the biological basis and characteristics of tumors and could potentially be used as surrogate biomarkers for predicting tumor prognosis following GKRS.
PURPOSE: Gamma Knife radiosurgery (GKRS) is a non-invasive procedure for the treatment of brain metastases. This study sought to determine whether radiomic features of brain metastases derived from pre-GKRS magnetic resonance imaging (MRI) could be used in conjunction with clinical variables to predict the effectiveness of GKRS in achieving local tumor control. METHODS: We retrospectively analyzed 161 patients with non-small cell lung cancer (576 brain metastases) who underwent GKRS for brain metastases. The database included clinical data and pre-GKRS MRI. Brain metastases were demarcated by experienced neurosurgeons, and radiomic features of each brain metastasis were extracted. Consensus clustering was used for feature selection. Cox proportional hazards models and cause-specific proportional hazards models were used to correlate clinical variables and radiomic features with local control of brain metastases after GKRS. RESULTS: Multivariate Cox proportional hazards model revealed that higher zone percentage (hazard ratio, HR 0.712; P = .022) was independently associated with superior local tumor control. Similarly, multivariate cause-specific proportional hazards model revealed that higher zone percentage (HR 0.699; P = .014) was independently associated with superior local tumor control. CONCLUSIONS: The zone percentage of brain metastases, a radiomic feature derived from pre-GKRS contrast-enhanced T1-weighted MRIs, was found to be an independent prognostic factor of local tumor control following GKRS in patients with non-small cell lung cancer and brain metastases. Radiomic features indicate the biological basis and characteristics of tumors and could potentially be used as surrogate biomarkers for predicting tumor prognosis following GKRS.
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