Kurokawa Mariko, Gonoi Wataru, Hanaoka Shouhei, Kurokawa Ryo, Uehara Shunichi, Kato Masayoshi, Suzuki Mizuka, Toyohara Yusuke, Takaki Yasunobu, Kusakabe Misako, Kino Nao, Tsukazaki Takehiro, Unno Toshiyuki, Sone Kenbun, Abe Osamu
Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Department of Radiology, Showa General Hospital, Tokyo, Japan.
Jpn J Radiol. 2025 Jun 12. doi: 10.1007/s11604-025-01812-7.
Uterine sarcoma is a rare disease whose association with body composition parameters is poorly understood. This study explored the impact of body composition parameters on overall survival with uterine sarcoma.
This multicenter study included 52 patients with uterine sarcomas treated at three Japanese hospitals between 2007 and 2023. A semi-automatic segmentation program based on deep learning analyzed transaxial CT images at the L3 vertebral level, calculating body composition parameters as follows: area indices (areas divided by height squared) of skeletal muscle, visceral and subcutaneous adipose tissue (SMI, VATI, and SATI, respectively); skeletal muscle density; and the visceral-to-subcutaneous fat area ratio (VSR). The optimal cutoff values for each parameter were calculated using maximally selected rank statistics with several p value approximations. The effects of body composition parameters and clinical data on overall survival (OS) and cancer-specific survival (CSS) were analyzed.
Univariate Cox proportional hazards regression analysis revealed that advanced stage (III-IV) and high VSR were unfavorable prognostic factors for both OS and CSS. Multivariate Cox proportional hazard regression analysis revealed that advanced stage (III-IV) (hazard ratios (HRs), 4.67 for OS and 4.36 for CSS, p < 0.01), and high VSR (HRs, 9.36 for OS and 8.22 for CSS, p < 0.001) were poor prognostic factors for both OS and CSS. Added values were observed when the VSR was incorporated into the OS and the CSS prediction models.
Increased VSR and tumor stage are significant predictors of poor overall survival in patients with uterine sarcoma.
子宫肉瘤是一种罕见疾病，其与身体成分参数之间的关联尚不清楚。本研究探讨了身体成分参数对子宫肉瘤总生存期的影响。
这项多中心研究纳入了2007年至2023年间在日本三家医院接受治疗的52例子宫肉瘤患者。基于深度学习的半自动分割程序分析了L3椎体水平的横断面CT图像，计算身体成分参数如下：骨骼肌、内脏和皮下脂肪组织的面积指数（面积除以身高的平方）（分别为SMI、VATI和SATI）；骨骼肌密度；以及内脏与皮下脂肪面积比（VSR）。使用具有多个p值近似值的最大选择秩统计量计算每个参数的最佳截断值。分析了身体成分参数和临床数据对总生存期（OS）和癌症特异性生存期（CSS）的影响。
单因素Cox比例风险回归分析显示，晚期（III-IV期）和高VSR是OS和CSS的不良预后因素。多因素Cox比例风险回归分析显示，晚期（III-IV期）（OS的风险比（HRs）为4.67，CSS的风险比为4.36，p<0.01）和高VSR（OS的HRs为9.36，CSS的HRs为8.22，p<0.001）是OS和CSS的不良预后因素。当将VSR纳入OS和CSS预测模型时，观察到了附加值。
VSR升高和肿瘤分期是子宫肉瘤患者总生存期较差的重要预测因素。
