Yang Zhang1, Jeon-Hor Chen1,2, Tai-Yuan Chen3,4, Sher-Wei Lim5,6, Te-Chang Wu3,4,7, Yu-Ting Kuo3,8, Ching-Chung Ko9,10, Min-Ying Su1. 1. Department of Radiological Sciences, University of California, Irvine, CA, USA. 2. Department of Radiology, E-DA Hospital, E-DA Cancer Hospital, I-Shou University, Kaohsiung, Taiwan. 3. Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan. 4. Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan, Taiwan. 5. Department of Neurosurgery, Chi-Mei Medical Center, Chiali, Tainan, Taiwan. 6. Department of Nursing, Min-Hwei College of Health Care, Management, Tainan, Taiwan. 7. Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan. 8. Department of of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan. 9. Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan. crazyboy0729@gmail.com. 10. Center of General Education, Chia Nan University of Pharmacy and Science, Tainan, Taiwan. crazyboy0729@gmail.com.
Abstract
PURPOSE: A subset of skull base meningiomas (SBM) may show early progression/recurrence (P/R) as a result of incomplete resection. The purpose of this study is the implementation of MR radiomics to predict P/R in SBM. METHODS: From October 2006 to December 2017, 60 patients diagnosed with pathologically confirmed SBM (WHO grade I, 56; grade II, 3; grade III, 1) were included in this study. Preoperative MRI including T2WI, diffusion-weighted imaging (DWI), and contrast-enhanced T1WI were analyzed. On each imaging modality, 13 histogram parameters and 20 textural gray level co-occurrence matrix (GLCM) features were extracted. Random forest algorithms were utilized to evaluate the importance of these parameters, and the most significant three parameters were selected to build a decision tree for prediction of P/R in SBM. Furthermore, ADC values obtained from manually placed ROI in tumor were also used to predict P/R in SBM for comparison. RESULTS: Gross-total resection (Simpson Grades I-III) was performed in 33 (33/60, 55%) patients, and 27 patients received subtotal resection. Twenty-one patients had P/R (21/60, 35%) after a postoperative follow-up period of at least 12 months. The three most significant parameters included in the final radiomics model were T1 max probability, T1 cluster shade, and ADC correlation. In the radiomics model, the accuracy for prediction of P/R was 90%; by comparison, the accuracy was 83% using ADC values measured from manually placed tumor ROI. CONCLUSIONS: The results show that the radiomics approach in preoperative MRI offer objective and valuable clinical information for treatment planning in SBM.
PURPOSE: A subset of skull base meningiomas (SBM) may show early progression/recurrence (P/R) as a result of incomplete resection. The purpose of this study is the implementation of MR radiomics to predict P/R in SBM. METHODS: From October 2006 to December 2017, 60 patients diagnosed with pathologically confirmed SBM (WHO grade I, 56; grade II, 3; grade III, 1) were included in this study. Preoperative MRI including T2WI, diffusion-weighted imaging (DWI), and contrast-enhanced T1WI were analyzed. On each imaging modality, 13 histogram parameters and 20 textural gray level co-occurrence matrix (GLCM) features were extracted. Random forest algorithms were utilized to evaluate the importance of these parameters, and the most significant three parameters were selected to build a decision tree for prediction of P/R in SBM. Furthermore, ADC values obtained from manually placed ROI in tumor were also used to predict P/R in SBM for comparison. RESULTS: Gross-total resection (Simpson Grades I-III) was performed in 33 (33/60, 55%) patients, and 27 patients received subtotal resection. Twenty-one patients had P/R (21/60, 35%) after a postoperative follow-up period of at least 12 months. The three most significant parameters included in the final radiomics model were T1 max probability, T1 cluster shade, and ADC correlation. In the radiomics model, the accuracy for prediction of P/R was 90%; by comparison, the accuracy was 83% using ADC values measured from manually placed tumor ROI. CONCLUSIONS: The results show that the radiomics approach in preoperative MRI offer objective and valuable clinical information for treatment planning in SBM.
Entities:
Keywords:
MRI; Meningioma; Radiomics; Recurrence; Skull base
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