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. 2023 Dec 19;28(1):609.
doi: 10.1186/s40001-023-01561-1.

Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors

Jia Chen #  1   2   3   4 , Fei Yang #  5 , Chanzhen Liu  6 , Xinwei Pan  6 , Ziying He  6 , Danhui Fu  1   2   3   4 , Guanqiao Jin  7   8   9   10 , Danke Su  11   12   13   14
Affiliations

Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors

Jia Chen et al. Eur J Med Res. .

Abstract

Background: This study aimed to identify the diagnostic value of models constructed using computed tomography-based radiomics features for discrimination of benign and early stage malignant ovarian tumors.

Methods: The imaging and clinicopathological data of 197 cases of benign and early stage malignant ovarian tumors (FIGO stage I/II), were retrospectively analyzed. The patients were randomly assigned into training data set and validation data set. Radiomics features were extracted from images of plain computed tomography scan and contrast-enhanced computed tomography scan, were then screened in the training data set, and a radiomics model was constructed. Multivariate logistic regression analysis was used to construct a radiomic nomogram, containing the traditional diagnostic model and the radiomics model. Moreover, the decision curve analysis was used to assess the clinical application value of the radiomics nomogram.

Results: Six textural features with the greatest diagnostic efficiency were finally screened. The value of the area under the receiver operating characteristic curve showed that the radiomics nomogram was superior to the traditional diagnostic model and the radiomics model (P < 0.05) in the training data set. In the validation data set, the radiomics nomogram was superior to the traditional diagnostic model (P < 0.05), but there was no statistically significant difference compared to the radiomics model (P > 0.05). The calibration curve and the Hosmer-Lemeshow test revealed that the three models all had a great degree of fit (All P > 0.05). The results of decision curve analysis indicated that utilization of the radiomics nomogram to distinguish benign and early stage malignant ovarian tumors had a greater clinical application value when the risk threshold was 0.4-1.0.

Conclusions: The computed tomography-based radiomics nomogram could be a non-invasive and reliable imaging method to discriminate benign and early stage malignant ovarian tumors.

Keywords: Computed tomography; Malignant ovarian tumor; Radiomics nomogram.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Study flowchart. The study flowchart includes 4 main steps: the first step was the acquisition of CT images and the drawing of the ROI of the primary lesion. The second step was the feature extraction from images of plain CT scan and contrast-enhanced CT scan. The third step was data analysis, including multivariate logistic regression analysis of traditional diagnostic factors and dimensionality reduction analysis of radiomics features; the fourth step was model construction and analysis of diagnostic efficacy
Fig. 2
Fig. 2
Feature dimensionality reduction analysis. A Features were ranked according to their mRMR (maximum correlation and minimum redundancy) scores. The top 20 features were selected using the mRMR algorithm. B Selection of the tuning parameter (Lambda) in the LASSO model using tenfold cross-validation. Binomial deviances from the LASSO regression cross-validation model were plotted as a function of log (Lambda). The dotted vertical line at the right was drawn at the optimal value based on the minimum criteria and the 1-standard error rule (the 1-SE criteria). An optimal Lambda value of 0.067 with log (Lambda) = − 1.174 and 6 non-zero coefficients were selected. C Profiles of the LASSO coefficients for the 6 texture features. The vertical line was drawn at a value selected from the log (λ) sequence using tenfold cross-validation. Six features of non-zero coefficients are shown. D Selected radiomic features and corresponding coefficients
Fig. 3
Fig. 3
Comparison of the Radscore for benign and early stage malignant ovarian tumors on the training and validation sets
Fig. 4
Fig. 4
Radiomics nomogram for predicting benign and early stage malignant ovarian tumors
Fig. 5
Fig. 5
A, B ROC analysis of the traditional, radiomics algorithm and radiomic nomogram in predicting benign and early stage malignant ovarian tumors. C, D Calibration curve of the radiomics nomogram. E, F Decision curve of the radiomics nomogram
See this image and copyright information in PMC

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