Preoperative prediction of lymphovascular space invasion in endometrioid adenocarcinoma: an MRI-based radiomics nomogram with consideration of the peritumoral region

Author:

Yan Bin12ORCID,Jia Yuxia3ORCID,Li Zhihao4ORCID,Ding Caixia5,Lu Jianrong5,Liu Jixin3,Zhang Yuchen6

Affiliation:

1. Department of Medical Imaging, First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, PR China

2. Department of Radiology, Shaanxi Provincial Tumor Hospital, Xi’an Jiaotong University, Xi’an, PR China

3. Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi’an, PR China

4. GE Healthcare China, Xi’an, Shaanxi, PR China

5. Department of Pathology, Shaanxi Provincial Tumor Hospital, Xi’an Jiaotong University, Xi’an, PR China

6. Department of Nuclear Medicine, First Affiliated Hospital of Xi’an Jiaotong University, PR China

Abstract

Background Lymphovascular space invasion (LVSI) of endometrial cancer (EC) is a postoperative histological index, which is associated with lymph node metastases. A preoperative acknowledgement of LVSI status might aid in treatment decision-making. Purpose To explore the utility of multiparameter magnetic resonance imaging (MRI) and radiomic features obtained from intratumoral and peritumoral regions for predicting LVSI in endometrioid adenocarcinoma (EEA). Material and Methods A total of 334 EEA tumors were retrospectively analyzed. Axial T2-weighted (T2W) imaging and apparent diffusion coefficient (ADC) mapping were conducted. Intratumoral and peritumoral regions were manually annotated as the volumes of interest (VOIs). A support vector machine was applied to train the prediction models. Multivariate logistic regression analysis was used to develop a nomogram based on clinical and tumor morphological parameters and the radiomics score (RadScore). The predictive performance of the nomogram was assessed by the area under the receiver operator characteristic curve (AUC) in the training and validation cohorts. Results Among the features obtained from different imaging modalities (T2W imaging and ADC mapping) and VOIs, the RadScore had the best performance in predicting LVSI classification (AUCtrain = 0.919, and AUCvalidation = 0.902). The nomogram based on age, CA125, maximum anteroposterior tumor diameter on sagittal T2W images, tumor area ratio, and RadScore was established to predict LVSI had AUC values in the training and validation cohorts of 0.962 (sensitivity 94.0%, specificity 86.0%) and 0.965 (sensitivity 90.0%, specificity 85.3%), respectively. Conclusion The intratumoral and peritumoral imaging features were complementary, and the MRI-based radiomics nomogram might serve as a non-invasive biomarker to preoperatively predict LVSI in patients with EEA.

Funder

Department of Science and Technology of Shaanxi Province

Department of Science and Technology of Xi'an Government

Publisher

SAGE Publications

Subject

Radiology, Nuclear Medicine and imaging,General Medicine,Radiological and Ultrasound Technology

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