Preoperative assessment of high-grade endometrial cancer using a radiomic signature and clinical indicators

Abstract

Aim: To develop and validate a radiomics-based combined model (Model^RC) to predict the pathological grade of endometrial cancer. Methods: A total of 403 endometrial cancer patients from two independent centers were enrolled as training, internal validation and external validation sets. Radiomic features were extracted from T2-weighted images, apparent diffusion coefficient map and contrast-enhanced 3D volumetric interpolated breath-hold examination images. Results: Compared with the clinical model and radiomics model, Model^RC showed superior performance; the areas under the receiver operating characteristic curves were 0.920 (95% CI: 0.864–0.962), 0.882 (95% CI: 0.779–0.955) and 0.881 (95% CI: 0.815–0.939) for the training, internal validation and external validation sets, respectively. Conclusion: Model^RC, which incorporated clinical and radiomic features, exhibited excellent performance in the prediction of high-grade endometrial cancer.

Plain language summary

Accurate preoperative evaluation of the pathological grade of endometrial carcinoma is very important for the selection of treatment and prognosis. This study tried to develop a simple combined model based on radiomic features from endometrial carcinoma MRI and clinical features of patients. Compared with the clinical model and the radiomic model, the combined model showed superior performance. Therefore, this combined model would help patients and clinicians to make more rational decisions when choosing treatment strategies.

Tweetable abstract

The prognosis and treatment of endometrial cancer are related to pathological grade. A combined model that incorporated clinical and radiomic features exhibited good performance in the prediction of high-grade endometrial cancer.

Keywords::

• endometrial cancer
• MRI
• radiomics
• random forest

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/suppl/10.2217/fon-2022-0631

Author contributions

T Zheng, J Pan, D Du, X Liang and H Yi designed and coordinated the study; J Du, S Wu, L Liu and G Shi carried out experiments and data processing and drafted the manuscript. All authors gave final approval for publication.

Financial & competing interests disclosure

This research was supported by National Natural Science Foundation of China (81871029) and Scientific Research Fund Project of Health Commission of Hebei Province (20200138). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved. Due to the retrospective nature of the study, the Ethics Committee of the hospital waived the need for informed consent.

Additional information

Funding

This research was supported by National Natural Science Foundation of China (81871029) and Scientific Research Fund Project of Health Commission of Hebei Province (20200138). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.