Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Background: In patients with metastatic renal cell carcinoma (mRCC), sites of metastatic involvement have been reported to be associated with a difference in survival. However, the frequency and survival according to different sites of metastases in Korean patients with mRCC remain unclear. Therefore, this study aimed to assess the frequency of metastatic site involvement and the association between sites of metastatic involvement and survival in Korean patients with mRCC. Methods: This retrospective study used the multicenter cohort of the Korean Renal Cancer Study Group mRCC database to identify patients who started targeted therapy between December 2005 and March 2018. Data on the frequency of metastatic organ involvement at the time of mRCC diagnosis and oncologic outcomes according to different sites of metastasis were analyzed. Results: A total of 1,761 patients were eligible for analysis. Of the 1,761 patients, 1,564 (88.8%) had clear cell RCC, and 1,040 (59.1%) had synchronous metastasis. The median number of metastasis sites was 2 (interquartile range [IQR], 1–6). The median age at the initiation of systemic therapy was 60 years (IQR, 29–88), 1,380 (78.4%) were men, and 1,341 (76.1%) underwent nephrectomy. Based on the International Metastatic Renal Cell Carcinoma Database Consortium model, patients were stratified into favorable-, intermediate-, and poor-risk groups with 359 (20.4%), 1,092 (62.0%), and 310 (17.6%) patients, respectively. The lung (70.9%), lymph nodes (37.9%), bone (30.7%), liver (12.7%), adrenal gland (9.8%), and brain (8.2%) were the most common sites of metastasis, followed by the pancreas, pleura, peritoneum, spleen, thyroid, and bowel. Among the most common sites of metastasis (> 5%), the median cancer-specific survival (CSS) ranged from 13.9 (liver) to 29.1 months (lung). An association was observed between liver, bone, and pleural metastases and the shortest median CSS (< 19 months). Conclusion In Korean patients with mRCC, metastases to the lung, lymph nodes, bone, liver, adrenal gland, and brain were more frequent than those to other organs. Metastases to the liver, bone, and pleura were associated with poor CSS. The findings of this study may be valuable for patient counseling and guiding future study designs.

Background: In patients with metastatic renal cell carcinoma (mRCC), sites of metastatic involvement have been reported to be associated with a difference in survival. However, the frequency and survival according to different sites of metastases in Korean patients with mRCC remain unclear. Therefore, this study aimed to assess the frequency of metastatic site involvement and the association between sites of metastatic involvement and survival in Korean patients with mRCC. Methods: This retrospective study used the multicenter cohort of the Korean Renal Cancer Study Group mRCC database to identify patients who started targeted therapy between December 2005 and March 2018. Data on the frequency of metastatic organ involvement at the time of mRCC diagnosis and oncologic outcomes according to different sites of metastasis were analyzed. Results: A total of 1,761 patients were eligible for analysis. Of the 1,761 patients, 1,564 (88.8%) had clear cell RCC, and 1,040 (59.1%) had synchronous metastasis. The median number of metastasis sites was 2 (interquartile range [IQR], 1–6). The median age at the initiation of systemic therapy was 60 years (IQR, 29–88), 1,380 (78.4%) were men, and 1,341 (76.1%) underwent nephrectomy. Based on the International Metastatic Renal Cell Carcinoma Database Consortium model, patients were stratified into favorable-, intermediate-, and poor-risk groups with 359 (20.4%), 1,092 (62.0%), and 310 (17.6%) patients, respectively. The lung (70.9%), lymph nodes (37.9%), bone (30.7%), liver (12.7%), adrenal gland (9.8%), and brain (8.2%) were the most common sites of metastasis, followed by the pancreas, pleura, peritoneum, spleen, thyroid, and bowel. Among the most common sites of metastasis (> 5%), the median cancer-specific survival (CSS) ranged from 13.9 (liver) to 29.1 months (lung). An association was observed between liver, bone, and pleural metastases and the shortest median CSS (< 19 months). Conclusion In Korean patients with mRCC, metastases to the lung, lymph nodes, bone, liver, adrenal gland, and brain were more frequent than those to other organs. Metastases to the liver, bone, and pleura were associated with poor CSS. The findings of this study may be valuable for patient counseling and guiding future study designs.

Background: In patients with metastatic renal cell carcinoma (mRCC), sites of metastatic involvement have been reported to be associated with a difference in survival. However, the frequency and survival according to different sites of metastases in Korean patients with mRCC remain unclear. Therefore, this study aimed to assess the frequency of metastatic site involvement and the association between sites of metastatic involvement and survival in Korean patients with mRCC. Methods: This retrospective study used the multicenter cohort of the Korean Renal Cancer Study Group mRCC database to identify patients who started targeted therapy between December 2005 and March 2018. Data on the frequency of metastatic organ involvement at the time of mRCC diagnosis and oncologic outcomes according to different sites of metastasis were analyzed. Results: A total of 1,761 patients were eligible for analysis. Of the 1,761 patients, 1,564 (88.8%) had clear cell RCC, and 1,040 (59.1%) had synchronous metastasis. The median number of metastasis sites was 2 (interquartile range [IQR], 1–6). The median age at the initiation of systemic therapy was 60 years (IQR, 29–88), 1,380 (78.4%) were men, and 1,341 (76.1%) underwent nephrectomy. Based on the International Metastatic Renal Cell Carcinoma Database Consortium model, patients were stratified into favorable-, intermediate-, and poor-risk groups with 359 (20.4%), 1,092 (62.0%), and 310 (17.6%) patients, respectively. The lung (70.9%), lymph nodes (37.9%), bone (30.7%), liver (12.7%), adrenal gland (9.8%), and brain (8.2%) were the most common sites of metastasis, followed by the pancreas, pleura, peritoneum, spleen, thyroid, and bowel. Among the most common sites of metastasis (> 5%), the median cancer-specific survival (CSS) ranged from 13.9 (liver) to 29.1 months (lung). An association was observed between liver, bone, and pleural metastases and the shortest median CSS (< 19 months). Conclusion In Korean patients with mRCC, metastases to the lung, lymph nodes, bone, liver, adrenal gland, and brain were more frequent than those to other organs. Metastases to the liver, bone, and pleura were associated with poor CSS. The findings of this study may be valuable for patient counseling and guiding future study designs.