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. 2022 Nov 24:13:963559.
doi: 10.3389/fendo.2022.963559. eCollection 2022.

A nomogram model based on clinical markers for predicting malignancy of ovarian tumors

Bingsi Gao  1   2 , Xingping Zhao  1 , Pan Gu  1 , Dan Sun  1 , Xinyi Liu  3 , Waixing Li  1 , Aiqian Zhang  1 , Enuo Peng  1 , Dabao Xu  1
Affiliations

A nomogram model based on clinical markers for predicting malignancy of ovarian tumors

Bingsi Gao et al. Front Endocrinol (Lausanne). .

Abstract

Objective: The aim of this study was to build a nomogram based on clinical markers for predicting the malignancy of ovarian tumors (OTs).

Method: A total of 1,268 patients diagnosed with OTs that were surgically removed between October 2017 and May 2019 were enrolled. Clinical markers such as post-menopausal status, body mass index (BMI), serum human epididymis protein 4 (HE4) value, cancer antigen 125 (CA125) value, Risk of Ovarian Malignancy Algorithm (ROMA) index, course of disease, patient-generated subjective global assessment (PG-SGA) score, ascites, and locations and features of masses were recorded and analyzed (p 0.05). Significant variables were further selected using multivariate logistic regression analysis and were included in the decision curve analysis (DCA) used to assess the value of the nomogram model for predicting OT malignancy.

Result: The significant variables included post-menopausal status, BMI, HE4 value, CA125 value, ROMA index, course of disease, PG-SGA score, ascites, and features and locations of masses (p 0.05). The ROMA index, BMI (≥ 26), unclear/blurred mass boundary (on magnetic resonance imaging [MRI]/computed tomography [CT]), mass detection (on MRI/CT), and mass size and features (on type B ultrasound [BUS]) were screened out for multivariate logistic regression analysis to assess the value of the nomogram model for predicting OT malignant risk (p 0.05). The DCA revealed that the net benefit of the nomogram's calculation model was superior to that of the CA125 value, HE4 value, and ROMA index for predicting OT malignancy.

Conclusion: We successfully tailored a nomogram model based on selected clinical markers which showed superior prognostic predictive accuracy compared with the use of the CA125, HE4, or ROMA index (that combines both HE and CA125 values) for predicting the malignancy of OT patients.

Keywords: clinical markers; malignant; ovarian cancer; ovarian tumors; prognostic nomogram model.

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Conflict of interest statement

The reviewer WZ declared a shared affiliation, with no collaboration, with several of the authors, XZ, PG, DS, WL, AZ, EP, DX, to the handling editor at the time of the review. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The nomogram for predicting malignant risk of ovarian tumors.The nomogram was developed in the primary cohort, with the variables of ROMA score, BMI ≥26, blurred and unclear boundary (MR/CT), mass detection (MR/CT), solid and mixed mass (BUS), mass detection (BUS), and mass size (BUS, diameter, mm) incorporated. ROMA, the Risk of Ovarian Malignancy Algorithm; BMI, body mass index; BUS, ultrasound type B; MR, magnetic resonance; CT, computed tomography.
Figure 2
Figure 2
Decision curve analysis (DCA) for detection of malignant ovarian tumors. The x-axis represents the threshold probability. The y-axis measures the net benefit. The threshold probability is where the expected benefit of treatment balances the expected benefit of avoiding treatment. ROMA, the Risk of Ovarian Malignancy Algorithm; CA125, human carbohydrate antigen 125/mucin-16; HE4, human epididymis protein 4 The ROMA scores were calculated following: premenopausal women, PI=−12.0+2.38×LN [HE4]+0.0626×LN [CA125]; postmenopausal women, PI=−8.09+1.04×LN [HE4]+0.732×LN [CA125]; and ROMA (%) = exp(PI)/[1+exp(PI)]×100.
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