Multi-parametric MRI-based radiomics for preoperative prediction of multiple biological characteristics in endometrial cancer

Changjun Ma; Ying Zhao; Qingling Song; Xing Meng; Qihao Xu; Shifeng Tian; Lihua Chen; Nan Wang; Qingwei Song; Liangjie Lin; Jiazheng Wang; Ailian Liu

doi:10.3389/fonc.2023.1280022

Multi-parametric MRI-based radiomics for preoperative prediction of multiple biological characteristics in endometrial cancer

Front Oncol. 2023 Dec 15:13:1280022. doi: 10.3389/fonc.2023.1280022. eCollection 2023.

Authors

Changjun Ma^#^{1

2}, Ying Zhao^#^{1

2}, Qingling Song^{1

2}, Xing Meng³, Qihao Xu^{1

2}, Shifeng Tian^{1

2}, Lihua Chen^{1

2}, Nan Wang^{1

2}, Qingwei Song^{1

2}, Liangjie Lin⁴, Jiazheng Wang⁴, Ailian Liu^{1

2}

Affiliations

¹ Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China.
² Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China.
³ Dalian Women and Children's Medical Group, Dalian, China.
⁴ Clinical & Technical Support, Philips Healthcare, Beijing, China.

^# Contributed equally.

Abstract

Purpose: To develop and validate multi-parametric MRI (MP-MRI)-based radiomics models for the prediction of biological characteristics in endometrial cancer (EC).

Methods: A total of 292 patients with EC were divided into LVSI (n = 208), DMI (n = 292), MSI (n = 95), and Her-2 (n = 198) subsets. Total 2316 radiomics features were extracted from MP-MRI (T₂WI, DWI, and ADC) images, and clinical factors (age, FIGO stage, differentiation degree, pathological type, menopausal state, and irregular vaginal bleeding) were included. Intra-class correlation coefficient (ICC), spearman's rank correlation test, univariate logistic regression, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features; univariate and multivariate logistic regression were used to identify clinical independent risk factors. Five classifiers were applied (logistic regression, random forest, decision tree, K-nearest neighbor, and Bayes) to construct radiomics models for predicting biological characteristics. The clinical model was built based on the clinical independent risk factors. The combined model incorporating the radiomics score (radscore) and the clinical independent risk factors was constructed. The model was evaluated by ROC curve, calibration curve (H-L test), and decision curve analysis (DCA).

Results: In the training cohort, the RF radiomics model performed best among the five classifiers for the three subsets (MSI, LVSI, and DMI) according to AUC values (AUC_MSI: 0.844; AUC_LVSI: 0.952; AUC_DMI: 0.840) except for Her-2 subset (Decision tree: AUC=0.714), and the combined model had higher AUC than the clinical model in each subset (MSI: AUC_combined =0.907, AUC_clinical =0.755; LVSI: AUC_combined =0.959, AUC_clinical =0.835; DMI: AUC_combined = 0.883, AUC_clinical =0.796; Her-2: AUC_combined =0.812, AUC_clinical =0.717; all P<0.05). Nevertheless, in the validation cohort, significant differences between the two models (combined vs. clinical model) were found only in the DMI and LVSI subsets (DMI: AUC_combined =0.803, AUC_clinical =0.698; LVSI: AUC_combined =0.926, AUC_clinical =0.796; all P<0.05).

Conclusion: The radiomics analysis based on MP-MRI and clinical independent risk factors can potentially predict multiple biological features of EC, including DMI, LVSI, MSI, and Her-2, and provide valuable guidance for clinical decision-making.

Keywords: deep myometrium invasion; endometrial cancer; human epidermal growth factor receptor-2; lympho-vascular space invasion; microsatellite instability; radiomics.

Grants and funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.