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Randomized Controlled Trial
. 2022 Dec 1;5(12):e2245141.
doi: 10.1001/jamanetworkopen.2022.45141.

Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma

Mandi Wang  1   2 , Jose A U Perucho  3 , Yangling Hu  4 , Moon Hyung Choi  5 , Lujun Han  6 , Esther M F Wong  7 , Grace Ho  8 , Xiaoling Zhang  4 , Philip Ip  9 , Elaine Y P Lee  2
Affiliations
Randomized Controlled Trial

Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma

Mandi Wang et al. JAMA Netw Open. .

Abstract

Importance: Epithelial ovarian carcinoma is heterogeneous and classified according to the World Health Organization Tumour Classification, which is based on histologic features and molecular alterations. Preoperative prediction of the histologic subtypes could aid in clinical management and disease prognostication.

Objective: To assess the value of radiomics based on contrast-enhanced computed tomography (CT) in differentiating histologic subtypes of epithelial ovarian carcinoma in multicenter data sets.

Design, setting, and participants: In this diagnostic study, 665 patients with histologically confirmed epithelial ovarian carcinoma were retrospectively recruited from 4 centers (Hong Kong, Guangdong Province of China, and Seoul, South Korea) between January 1, 2012, and February 28, 2022. The patients were randomly divided into a training cohort (n = 532) and a testing cohort (n = 133) with a ratio of 8:2. This process was repeated 100 times. Tumor segmentation was manually delineated on each section of contrast-enhanced CT images to encompass the entire tumor. The Mann-Whitney U test and voted least absolute shrinkage and selection operator were performed for feature reduction and selection. Selected features were used to build the logistic regression model for differentiating high-grade serous carcinoma and non-high-grade serous carcinoma.

Exposures: Contrast-enhanced CT-based radiomics.

Main outcomes and measures: Intraobserver and interobserver reproducibility of tumor segmentation were measured by Dice similarity coefficients. The diagnostic efficiency of the model was assessed by receiver operating characteristic curve and area under the curve.

Results: In this study, 665 female patients (mean [SD] age, 53.6 [10.9] years) with epithelial ovarian carcinoma were enrolled and analyzed. The Dice similarity coefficients of intraobserver and interobserver were all greater than 0.80. Twenty radiomic features were selected for modeling. The areas under the curve of the logistic regression model in differentiating high-grade serous carcinoma and non-high-grade serous carcinoma were 0.837 (95% CI, 0.835-0.838) for the training cohort and 0.836 (95% CI, 0.833-0.840) for the testing cohort.

Conclusions and relevance: In this diagnostic study, radiomic features extracted from contrast-enhanced CT were useful in the classification of histologic subtypes in epithelial ovarian carcinoma. Intraobserver and interobserver reproducibility of tumor segmentation was excellent. The proposed logistic regression model offered excellent discriminative ability among histologic subtypes.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Choi reported receiving grants from Siemens Healthineers and Guerbet outside the submitted work. Dr Lee reported receiving grants from the Health and Medical Research Fund during the conduct of the study. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Diagram of Patient Inclusion
ceCT indicates contrast-enhanced computed tomography; EOC, epithelial ovarian carcinoma.
Figure 2.
Figure 2.. Workflow Diagram of the Voted Least Absolute Shrinkage and Selection Operator (LASSO) Method
Figure 3.
Figure 3.. Top 10% of Radiomic Features Selected by Voted Least Absolute Shrinkage and Selection Operator Method
Frequency denotes the number of resamples that selected that particular feature. Descriptive definition of each of the selected radiomics features can be found in the eAppendix in the Supplement.
Figure 4.
Figure 4.. Receiver Operating Characteristic Curves and Metrics for Training and Testing Cohorts of Logistic Regression Model
AUC indicates area under the curve.
See this image and copyright information in PMC

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