September 2021, VOLUME 217
NUMBER 3

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September 2021, Volume 217, Number 3

Genitourinary Imaging

Original Research

Development of MRI-Based Radiomics Model to Predict the Risk of Recurrence in Patients With Advanced High-Grade Serous Ovarian Carcinoma

+ Affiliations:
1Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Rd, Shanghai 200032, China

2Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

3Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA

4Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China

Citation: American Journal of Roentgenology. 2021;217: 664-675. 10.2214/AJR.20.23195

ABSTRACT :

OBJECTIVE. The purpose of our study was to develop a radiomics model based on preoperative MRI and clinical information for predicting recurrence-free survival (RFS) in patients with advanced high-grade serous ovarian carcinoma (HGSOC).

MATERIALS AND METHODS. This retrospective study enrolled 117 patients with HGSOC, including 90 patients with recurrence and 27 without recurrence; 1046 radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images using a manual segmentation method. L1 regularization–based least absolute shrinkage and selection operator (LASSO) regression was performed to select features, and the synthetic minority oversampling technique (SMOTE) was used to balance our dataset. A support vector machine (SVM) classifier was used to build the classification model. To validate the performance of the proposed models, we applied a leave-one-out cross-validation method to train and test the classifier. Cox proportional hazards regression, Harrell concordance index (C-index), and Kaplan-Meier plots analysis were used to evaluate the associations between radiomics signatures and RFS.

RESULTS. The fusion radiomics-based model yielded a significantly higher AUC value of 0.85 in evaluating RFS than the model using contrast-enhanced T1-weighted imaging features alone or T2-weighted imaging features alone (AUC = 0.79 and 0.74 and p = .02 and .01, respectively). Kaplan-Meier survival curves showed significant differences between high and low recurrence risk in patients with HGSOC by different models. The fusion model combining radiomics features and clinical information showed higher performance than the clinical model (C-index = 0.62 and 0.60, respectively).

CONCLUSION. The proposed MRI-based radiomics signatures may provide a potential way to develop a prediction model and can help identify patients with advanced HGSOC who have a high risk of recurrence.

Keywords: machine learning, MRI, ovarian neoplasms, radiomics, recurrence

Supported by the National Natural Science Foundation of China (81901704 and 81971579), Shanghai Municipal Commission of Health and Family Planning (Youth Fund Project 20194Y0489), Shanghai Municipal Commission of Science and Technology (19411972000), and Imaging Foundation of Fudan University Shanghai Cancer Center (YX201803)

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Address correspondence to Y. J. Gu ().

H. M. Li and J. Gong contributed equally to this work.

The authors declare that they have no disclosures relevant to the subject matter of this article.

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