Evaluation of Ovarian Tumors with Multidetector Computed Tomography and Tumor Markers: Differentiation of Stage I Serous Borderline Tumors and Stage I Serous Malignant Tumors Presenting as Solid-Cystic Mass

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

Objectives

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).

Methods

Data were collected from 80 patients with early-stage SBOTs and 102 with early-stage SMOTs (training set: n = 127; validation set: n = 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.

Results

In terms of clinicoradiological characteristics, age (p = 0.001), the diameter of the solid component (p = 0.009), and human epididymis protein 4 level (p < 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.

Conclusions

The nomogram model can be employed as an individualized preoperative non-invasive tool for differentiating early-stage SBOTs from SMOTs.

A Clinical Diagnostic Value Analysis of Serum CA125, CA199, and HE4 in Women with Early Ovarian Cancer: Systematic Review and Meta-Analysis

Objective

To evaluate the value of combined detection of serum CA125, CA199, and HE4 in the diagnosis of ovarian cancer.

Methods

Relevant articles retrieved from PubMed, Elsevier Science, Springer, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP databases were screened strictly according to inclusion and exclusion criteria. Included literature published from January 2005 to December 2021. (2) Serum HE4, CA125, CA199, and their combination for ovarian cancer diagnostic tests were studied, and healthy subjects or patients with the benign disease were taken as a control group. (3) Pathological tissue diagnosis as the gold standard. (4) Complete original data can be obtained. (5) The sample size was ≥20. (6) Language is limited to Chinese and English. Data features and QUADAS table were extracted from the included literature, and QUADAS evaluation tool detail table was used for the included study. Conduct quality evaluation. Statistical analysis was carried out using meta-disc software version 1.4. Appropriate effect model was selected to merge the effect size, and the forest maps of merge sensitivity, merge specificity, and merge likelihood ratio were obtained.

Results

The results of meta-analysis showed that there was a statistical difference in diagnostic specificity analysis of CA125 (OR = 1.91, 95% CI (1.58, 2.32), P < 0.00001, I² = 67%, Z = 6.58); diagnostic sensitivity analysis of CA125 (OR = 2.50, 95% CI (1.73, 3.62), P < 0.00001, I² = 0%, Z = 4.90); diagnostic specificity analysis of CA199 (OR = 1.98, 95% CI (1.60, 2.44), P < 0.00001, I² = 89%, Z = 6.35); diagnostic sensitivity analysis of CA199 (OR = 1.92, 95% CI (1.46, 2.52), P < 0.00001, I² = 73%, Z = 4.70); diagnostic specificity analysis of HE4 (OR = 2.08, 95% CI (1.65, 2.63), P < 0.00001, I² = 73%, Z = 6.19); diagnostic sensitivity analysis of HE4 (OR = 2.37, 95% CI (1.87, 3.00), P < 0.00001, I² = 83%, Z = 7.19).

Conclusion

In the clinical assisted diagnosis of ovarian cancer, combined detection of CA125, CA199, and HE4 has the stronger discriminant ability and higher accuracy than single detection of CA125, which can improve the diagnostic efficiency.

MDCT-Based Radiomics Features for the Differentiation of Serous Borderline Ovarian Tumors and Serous Malignant Ovarian Tumors

Objective

To investigate whether multidetector computed tomography (MDCT)-based radiomics features can discriminate between serous borderline ovarian tumors (SBOTs) and serous malignant ovarian tumors (SMOTs).

Patients and Methods

Eighty patients with SBOTs and 102 patients with SMOTs, confirmed by pathology (training set: n = 127; validation set: n = 55) from December 2017 to June 2020, were enrolled in this study. The interclass correlation coefficient (ICC) and least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics parameters derived from MDCT images on the arterial phase (AP), venous phase (VP), and equilibrium phase (EP). Receiver operating characteristic (ROC) analysis of each selected parameter was carried out. Heat maps were created to illustrate the distribution of the radiomics parameters. Three models incorporating selected radiomics parameters generated by support vector machine (SVM) classifiers in each phase were analyzed by ROC and compared using the DeLong test.

Results

The most predictive features selected by ICC and LASSO regression between SBOTs and SMOTs included 9 radiomics parameters on AP, VP, and EP each. Three models on AP, VP, and EP incorporating the selected features generated by SVM classifiers produced AUCs of 0.80 (accuracy, 0.75; sensitivity, 0.74; specificity, 0.75), 0.86 (accuracy, 0.78; sensitivity, 0.80; specificity, 0.75), and 0.73 (accuracy, 0.69; sensitivity, 0.71; specificity, 0.67), respectively. There were no significant differences in the AUCs among the three models (AP vs. VP, P = 0.199; AP vs. EP, P = 0.260; VP vs. EP, P = 0.793).

Conclusion

MDCT-based radiomics features could be used as biomarkers for the differentiation of SBOTs and SMOTs.