AUTHOR=Yu Xinping , Zou Yuwei , Wang Lei , Yang Hongjuan , Jiao Jinwen , Yu Haiyang , Zhang Shuai 

TITLE=Radiomics nomogram for preoperative differentiation of early-stage serous borderline ovarian tumors and serous malignant ovarian tumors

JOURNAL=Frontiers in Oncology

VOLUME=13

YEAR=2024

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1269589

DOI=10.3389/fonc.2023.1269589

ISSN=2234-943X

ABSTRACT=<sec><title>Objectives</title><p>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).</p></sec><sec><title>Methods</title><p>Data were collected from 80 patients with early-stage SBOTs and 102 with early-stage SMOTs (training set: <italic>n</italic> = 127; validation set: <italic>n</italic> = 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.</p></sec><sec><title>Results</title><p>In terms of clinicoradiological characteristics, age (<italic>p</italic> = 0.001), the diameter of the solid component (<italic>p</italic> = 0.009), and human epididymis protein 4 level (<italic>p</italic> &lt; 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.</p></sec><sec><title>Conclusions</title><p>The nomogram model can be employed as an individualized preoperative non-invasive tool for differentiating early-stage SBOTs from SMOTs.</p></sec>