Iyoshi Shohei, Emoto Ryo, Yoshikawa Nobuhisa, Yoshida Kosuke, Yoshihara Masato, Tamauchi Satoshi, Yokoi Akira, Shimizu Yusuke, Ikeda Yoshiki, Niimi Kaoru, Kawai Michiyasu, Matsui Shigeyuki, Kajiyama Hiroaki
Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Institute for Advanced Research, Nagoya University, Nagoya, Japan.
Cancer Med. 2025 Aug;14(15):e71118. doi: 10.1002/cam4.71118.
The precision management of ovarian clear cell carcinoma (OCCC) faces limitations due to the absence of personalized prognostic tools. This study aimed to establish predictive risk scores to enable effective stratification and tailored treatment of OCCC.
Retrospective data from 206 OCCC patients treated between 2004 and 2019 at two hospitals were analyzed. Penalized regression models were utilized to develop three risk scores based on preoperative laboratory results and intraoperative findings. These scores underwent internal and external validation.
The median follow-up periods were 65.7 and 44.0 months for the derivation and validation cohorts, respectively. In internal validation with the derivation cohort, all three risk scores effectively identified the high-risk group for tumor recurrence. Upon validation with the external cohort, Risk Score 3, which incorporated variables selected in most cross-validations by the penalized regression (Elastic Net), distinctly differentiated the high- and low-risk groups (p = 0.03). Risk Score 2, consisting solely of preoperatively available variables, also demonstrated marginal significance (p = 0.08).
Our findings underscore the significance and utility of the developed risk scores in tailoring personalized treatments for patients with OCCC.
由于缺乏个性化的预后工具，卵巢透明细胞癌（OCCC）的精准管理面临局限。本研究旨在建立预测风险评分，以实现对OCCC患者的有效分层和个体化治疗。
分析了2004年至2019年间在两家医院接受治疗的206例OCCC患者的回顾性数据。利用惩罚回归模型，根据术前实验室检查结果和术中发现制定了三个风险评分。这些评分进行了内部和外部验证。
推导队列和验证队列的中位随访期分别为65.7个月和44.0个月。在推导队列的内部验证中，所有三个风险评分均有效地识别出肿瘤复发的高危组。在外部队列验证时，风险评分3纳入了惩罚回归（弹性网络）在大多数交叉验证中选择的变量，能明显区分高危组和低危组（p = 0.03）。仅由术前可用变量组成的风险评分2也显示出边缘显著性（p = 0.08）。
我们的研究结果强调了所制定的风险评分在为OCCC患者量身定制个性化治疗中的重要性和实用性。
