Yu Xinping, Zou Yuwei, Wang Chang, Wang Lei, Jiao Jinwen, Yu Haiyang, Zhang Shuai
Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Front Oncol. 2025 May 20;15:1559398. doi: 10.3389/fonc.2025.1559398. eCollection 2025.
To evaluate the effectiveness of habitat-based radiomics in differentiating early-stage serous borderline ovarian tumors (SBOTs) from serous malignant ovarian tumors (SMOTs), thereby enhancing diagnostic precision and treatment strategies.
We conducted a retrospective analysis of 210 patients with histopathologically confirmed SBOTs (n=95) and SMOTs (n=115) between December 2017 and February 2021. Multi-detector computed tomography images were obtained and analyzed using habitat-based radiomics, which segments tumors into distinct microenvironments based on Hounsfield Unit values. Clinical characteristics and imaging features were assessed, and predictive models were developed using logistic regression. Model performance was evaluated through receiver operating characteristic analysis, calibration curves, and decision curve analysis (DCA).
The habitat-based models, the Habitat2 and the combined model, demonstrated high area under the curve values of 0.960 and 0.957 in the training set, with similar performance observed in the validation set. Solid components of tumors were identified as key differentiators, with only one radiomics feature from cystic regions retained in the final model. DCA indicated that habitat-based models provided significant clinical utility.
Habitat-based radiomics model was developed and validated for accurately preoperative differentiation between SBOTs and SMOTs, emphasizing the importance of solid tumor regions for accurate diagnosis.
评估基于瘤内微环境的影像组学在鉴别早期浆液性交界性卵巢肿瘤（SBOT）与浆液性恶性卵巢肿瘤（SMOT）中的有效性，从而提高诊断准确性和治疗策略。
我们对2017年12月至2021年2月期间210例经组织病理学确诊的SBOT（n = 95）和SMOT（n = 115）患者进行了回顾性分析。获取多排螺旋CT图像并使用基于瘤内微环境的影像组学进行分析，该方法根据Hounsfield单位值将肿瘤分割为不同的微环境。评估临床特征和影像特征，并使用逻辑回归建立预测模型。通过受试者工作特征分析、校准曲线和决策曲线分析（DCA）评估模型性能。
基于瘤内微环境的模型，即Habitat2模型和联合模型，在训练集中的曲线下面积值分别为0.960和0.957，在验证集中观察到类似的性能。肿瘤的实性成分被确定为关键鉴别因素，最终模型中仅保留了来自囊性区域的一个影像组学特征。DCA表明基于瘤内微环境的模型具有显著的临床实用性。
开发并验证了基于瘤内微环境的影像组学模型，用于术前准确鉴别SBOT和SMOT，强调了实体瘤区域对准确诊断的重要性。
