Yu Xinping, Zou Yuwei, Wang Lei, Yang Hongjuan, Jiao Jinwen, Yu Haiyang, Zhang Shuai
Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Front Oncol. 2024 Jan 15;13:1269589. doi: 10.3389/fonc.2023.1269589. eCollection 2023.
This study aimed to construct a radiomics nomogram and validate its performance in the preoperative differentiation between early-stage (I and II) serous borderline ovarian tumors (SBOTs) and serous malignant ovarian tumors (SMOTs).
Data were collected from 80 patients with early-stage SBOTs and 102 with early-stage SMOTs (training set: = 127; validation set: = 55). Univariate and multivariate analyses were performed to identify the independent clinicoradiological factors. A radiomics signature model was constructed using radiomics features extracted from multidetector computed tomography images of the venous phase, in which the least absolute shrinkage and selection operator regression was employed to lessen the dimensionality of the data and choose the radiomics features. A nomogram model was established by combining independent clinicoradiological factors with the radiomics signature. The performance of nomogram calibration, discrimination, and clinical usefulness was evaluated using training and validation sets.
In terms of clinicoradiological characteristics, age ( = 0.001), the diameter of the solid component ( = 0.009), and human epididymis protein 4 level ( < 0.001) were identified as the independent risk factors of SMOT, for which the area under the curves (AUCs) were calculated to be 0.850 and 0.836 in the training and validation sets, respectively. Nine features were finally selected to construct the radiomics signature model, which exhibited AUCs of 0.879 and 0.826 for the training and validation sets, respectively. The nomogram model demonstrated considerable calibration and discrimination with AUCs of 0.940 and 0.909 for the training and validation sets, respectively. The nomogram model displayed more prominent clinical usefulness than the clinicoradiological and radiomics signature models according to the decision curve analysis.
The nomogram model can be employed as an individualized preoperative non-invasive tool for differentiating early-stage SBOTs from SMOTs.
本研究旨在构建一个影像组学列线图，并验证其在术前鉴别早期（I期和II期）浆液性交界性卵巢肿瘤（SBOT）和浆液性恶性卵巢肿瘤（SMOT）中的性能。
收集了80例早期SBOT患者和102例早期SMOT患者的数据（训练集：n = 127；验证集：n = 55）。进行单因素和多因素分析以确定独立的临床放射学因素。使用从静脉期多排螺旋计算机断层扫描图像中提取的影像组学特征构建影像组学特征模型，其中采用最小绝对收缩和选择算子回归来降低数据维度并选择影像组学特征。通过将独立的临床放射学因素与影像组学特征相结合建立列线图模型。使用训练集和验证集评估列线图校准、鉴别和临床实用性的性能。
在临床放射学特征方面，年龄（P = 0.001）、实性成分直径（P = 0.009）和人附睾蛋白4水平（P < 0.001）被确定为SMOT的独立危险因素，其在训练集和验证集中的曲线下面积（AUC）分别计算为0.850和0.836。最终选择9个特征构建影像组学特征模型，其在训练集和验证集中的AUC分别为0.879和0.826。列线图模型显示出良好的校准和鉴别能力，训练集和验证集的AUC分别为0.940和0.909。根据决策曲线分析，列线图模型比临床放射学和影像组学特征模型显示出更突出的临床实用性。
列线图模型可作为术前个体化的非侵入性工具，用于鉴别早期SBOT和SMOT。
