Jin Junjie, Deng Xijia, Long Ling, Liu Meiling, Cao Meimei, Gong Hao, Liu Huan, Lan Xiaosong, Liu Lili, Zhang Jiuquan
Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, 400030, People's Republic of China.
School of Medicine, Chongqing University, Chongqing, People's Republic of China.
Insights Imaging. 2025 Jul 31;16(1):163. doi: 10.1186/s13244-025-02047-w.
To develop an MRI-based radiomics model for ovarian masses categorization and to compare the model performance to Ovarian-Adnexal Reporting and Data System (O-RADS) and radiologists' assessments.
This retrospective multicenter study included 497 patients (249 benign, 248 malignant) allocated to training, internal, and external validation sets (293/124/80 masses, respectively). Radiomics features were extracted from preoperative MRI. Features were selected using minimum redundancy, maximum relevance, and the least absolute shrinkage and selection operator algorithm. Diagnostic performance of the radiomics model, O-RADS, and independent assessments by junior and senior radiologists was evaluated via the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.
In external validation, the radiomics model (AUC = 0.939) outperformed O-RADS (AUC = 0.862; p = 0.047) and the junior radiologist (AUC = 0.802; p = 0.003) and was similar to the senior radiologist (AUC = 0.886; p = 0.231). Subgroup analysis of O-RADS score 4 showed the model (AUC = 0.879) outperformed both radiologists (junior: p = 0.001; senior: p = 0.005). For solid, cystic-solids, and cystic masses, the AUCs of the model were 0.921, 0.975, and 0.848, respectively.
The performance of the radiomics model to categorize ovarian masses was superior to O-RADS and junior radiologists and similar to senior radiologists. As a complementary tool to O-RADS, it allows for refined risk stratification for ovarian masses with an O-RADS score of 4 and different morphological characteristics, providing clinicians with quantitative decision support to improve preoperative diagnosis and guide treatment planning.
Radiomics model provides improved risk stratification and supports precise clinical decision-making for ovarian masses with an O-RADS score of 4 and solid, cystic-solid ovarian masses, thereby improving the management of patients with ovarian masses.
MRI-based radiomics allows for the characterization of ovarian masses with high accuracy. Radiomics helps differentiate between benign and malignant ovarian masses with an O-RADS score of 4. For solid, cystic-solid, and cystic masses, the radiomics model exhibited higher or similar performance to that of the O-RADS and radiologists.
开发一种基于磁共振成像（MRI）的影像组学模型用于卵巢肿块分类，并将该模型的性能与卵巢附件报告和数据系统（O-RADS）以及放射科医生的评估进行比较。
这项回顾性多中心研究纳入了497例患者（249例良性，248例恶性），分为训练集、内部验证集和外部验证集（分别为293/124/80个肿块）。从术前MRI中提取影像组学特征。使用最小冗余、最大相关性和最小绝对收缩与选择算子算法选择特征。通过受试者操作特征曲线下面积（AUC）评估影像组学模型、O-RADS以及初级和高级放射科医生独立评估的诊断性能，并使用德龙检验进行比较。
在外部验证中，影像组学模型（AUC = 0.939）的表现优于O-RADS（AUC = 0.862；p = 0.047）和初级放射科医生（AUC = 0.802；p = 0.003），与高级放射科医生（AUC = 0.886；p = 0.231）相似。O-RADS 4分亚组分析显示，该模型（AUC = 0.879）的表现优于两位放射科医生（初级：p = 0.001；高级：p = 0.005）。对于实性、囊实性和囊性肿块，该模型的AUC分别为0.921、0.975和0.848。
影像组学模型对卵巢肿块分类的性能优于O-RADS和初级放射科医生，与高级放射科医生相似。作为O-RADS的补充工具，它可为O-RADS评分为4分且具有不同形态特征的卵巢肿块进行精细的风险分层，为临床医生提供定量决策支持，以改善术前诊断并指导治疗方案制定。
影像组学模型为O-RADS评分为4分的卵巢肿块以及实性、囊实性卵巢肿块提供了更好的风险分层，并支持精确的临床决策，从而改善卵巢肿块患者的管理。
基于MRI的影像组学能够高精度地表征卵巢肿块。影像组学有助于鉴别O-RADS评分为4分的良性和恶性卵巢肿块。对于实性、囊实性和囊性肿块，影像组学模型的表现高于或类似于O-RADS和放射科医生。
