Koki Shimizu1, Shunsuke Hamada2, Tomohisa Sakai3, Shinji Ito4, Hiroshi Urakawa3,5, Eisuke Arai3, Kunihiro Ikuta3, Hiroshi Koike3, Naoki Ishiguro3, Yoshihiro Nishida3,6. 1. Department of Orthopedic Surgery, Tonokosei Hospital, Mizunami, Gifu, Japan. 2. Department of Orthopedic Surgery, Aichi Cancer Center Hospital, Nagoya, Japan. 3. Department of Orthopedic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan. 4. Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan. 5. Department of Clinical Oncology and Chemotherapy, Nagoya University Hospital, Nagoya, Japan. 6. Department of Rehabilitation Medicine, Nagoya University Hospital, Nagoya, Japan.
Abstract
INTRODUCTION: This study aimed to determine the clinical significance of MRI characteristics as a possible predictor of responsiveness to meloxicam treatment in patients with desmoid-type fibromatosis (DF). Additionally, it analysed the correlation between CTNNB1 mutation status and signal intensity of MRI. METHODS: Forty-six patients consecutively treated with meloxicam composed this study. The low-intensity area (LIA) on T2-weighted MRI was determined. We divided patients into two groups based on the efficacy of meloxicam: a clinical benefit group (CB group, including CR: complete response; PR: partial response; and SD: stable disease) and non-clinical benefit group (NB group, including PD: progressive disease). Correlations of the efficacy with LIA and CTNNB1 mutation status with LIA were investigated. RESULTS: In total, 11, 17 and 18 patients showed PR, SD and PD, respectively. The mean LIA ratio before treatment was significantly higher (P < 0.001) in the CB group than in the NB group. For predicting the efficacy, sensitivity was 68%, and specificity was 89% when setting the cut-off value as 20% for LIA. Mean changes in the LIA ratio before and after treatment were significantly higher (P = 0.01) in the CB group than in the NB group. Mean LIA ratio before treatment was significantly lower (P < 0.001) in the S45F mutation group than in the other mutation group. In multivariate analysis, the LIA ratio before treatment was a significant predictor of responsiveness (P = 0.02). CONCLUSIONS: MRI characteristics were a useful predictor of the efficacy of meloxicam in DF patients. It may be possible to predict the clinical outcome more accurately when combined with other factors, such as CTNNB1 mutantion status.
INTRODUCTION: This study aimed to determine the clinical significance of MRI characteristics as a possible predictor of responsiveness to meloxicam treatment in patients with desmoid-type fibromatosis (DF). Additionally, it analysed the correlation between CTNNB1 mutation status and signal intensity of MRI. METHODS: Forty-six patients consecutively treated with meloxicam composed this study. The low-intensity area (LIA) on T2-weighted MRI was determined. We divided patients into two groups based on the efficacy of meloxicam: a clinical benefit group (CB group, including CR: complete response; PR: partial response; and SD: stable disease) and non-clinical benefit group (NB group, including PD: progressive disease). Correlations of the efficacy with LIA and CTNNB1 mutation status with LIA were investigated. RESULTS: In total, 11, 17 and 18 patients showed PR, SD and PD, respectively. The mean LIA ratio before treatment was significantly higher (P < 0.001) in the CB group than in the NB group. For predicting the efficacy, sensitivity was 68%, and specificity was 89% when setting the cut-off value as 20% for LIA. Mean changes in the LIA ratio before and after treatment were significantly higher (P = 0.01) in the CB group than in the NB group. Mean LIA ratio before treatment was significantly lower (P < 0.001) in the S45F mutation group than in the other mutation group. In multivariate analysis, the LIA ratio before treatment was a significant predictor of responsiveness (P = 0.02). CONCLUSIONS: MRI characteristics were a useful predictor of the efficacy of meloxicam in DF patients. It may be possible to predict the clinical outcome more accurately when combined with other factors, such as CTNNB1 mutantion status.