Prediction of Lymphovascular Space Invision in Endometrial Cancer based on Multi-parameter MRI Radiomics Model

Jin Jun Wang; Xiao Hong Zhang; Xing Hua Guo; Yang Ying; Xiang Wang; Zhong Hua Luan; Wei Qin Lv; Peng Fei Wang

doi:10.2174/0115734056266366231219111246

Prediction of Lymphovascular Space Invision in Endometrial Cancer based on Multi-parameter MRI Radiomics Model

Curr Med Imaging. 2024 Mar 19. doi: 10.2174/0115734056266366231219111246. Online ahead of print.

Authors

Jin Jun Wang¹, Xiao Hong Zhang¹, Xing Hua Guo¹, Yang Ying¹, Xiang Wang¹, Zhong Hua Luan¹, Wei Qin Lv¹, Peng Fei Wang¹

Affiliation

¹ Department of Radiology, Yuncheng Central Hospital of Shanxi Province, Yuncheng Hospital Affiliated to Shanxi Medical University, No. 3690 He Dong East Road, YanHu Distract, Shanxi, P.R. China.

PMID: 38529651
DOI: 10.2174/0115734056266366231219111246

Abstract

<p>Objective: To explore the application value of a combined model based on multi-parameter MRI radiomics and clinical features in preoperative prediction of lymphatic vascular space invasion (LVSI) in endometrial carcinoma (EC).

Methods: This retrospective study collected the clinicopathological and imaging data of 218 patients with EC in Yuncheng Central Hospital from March 2018 to May 2022. The patients were randomly divided into training group (n=152) and validation group (n= 66) according to the ratio of 7: 3. Based on the ADC, CE-sag, CE-tra, DWI, T2WI-sag-fs, T2WI-tra sequence images of each patient, the region of interest was manually segmented and the features were extracted. The four-step dimensionality reduction method based on max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and radiomics model construction. Independent predictors of clinicopathological features were screened by multivariate logistic regression analysis. The imaging model based on ADC, CE-sag, CE-tra, DWI, T2WI-sag-fs, T2WI-tra single sequence and combined sequence and the fusion model with clinicopathological features were constructed, and the nomogram was made. ROC curve, correction curve and decision analysis curve were used to evaluate the efficacy and clinical benefits of the nomogram.

Results: There was no significant difference in general clinical data between the training and validation groups (P > 0.05). After screening the extracted features, 16 radiomics features were obtained, which were all related to LVSI in EC patients (P < 0.05). The area under the ROC curve (AUC) of the six independent sequence radiomics models in the training group was 0.807, 0.794, 0.826, 0.794, 0.828, 0.824, respectively. The AUC corresponding to the radiomics model constructed by the combined sequence was 0.884, and the diagnostic efficiency was the best, which was verified in the validation group. The AUC of the nomogram constructed by the combined radiomics model and age maximum tumor diameter(MTD), lymph node enlargement (LNE) in the training group and the validation group were 0.914 and 0.912, respectively. The correction curve shows that the nomogram has good correction performance. The decision curve suggests that taking radiomics nomogram to predict LVSI net benefit when the risk threshold is > 10% is better than considering all patients as LVSI+ or LVSI-.

Conclusion: The combined model based on multi-parametric MRI radiomics features and clinical features has good predictive value for LVSI status in EC patients.</p>.

Keywords: Breast cancer; Endometrial carcinoma; Lung cancer.; Lymphovascular space invision; Magnetic resonance imaging; Radiomics.