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Predicting myometrial invasion in endometrial cancer based on whole-uterine magnetic resonance radiomics
Yuqing Han1, Han Xu2, Ying Ming3, Qingwei Liu2, Chencui Huang4, Jingxu Xu4, Jie Zhang2, Yan Li5
1 Department of Radiology, Cheeloo College of Medicine, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
2 Department of Radiology, Cheeloo College of Medicine, Shandong Provincial Hospital, Shandong University; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
3 Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
4 Department of Research Collaboration, R&D Center, Beijing Deepwise and League of PHD Technology Co., Ltd., Beijing, China
5 Department of Medical Imaging Center, Cheeloo College of Medicine, The Second Hospital, Shandong University, Jinan, Shandong, China
Correspondence Address:
Jie Zhang
Department of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, 324 Jingwu Weiqi Road; Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong
China
Yan Li
Department of Medical Imaging Center, The Second Hospital, Cheeloo College of Medicine, Shandong University, 247#Beiyuan Road, Jinan 250033, Shandong
China
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jcrt.JCRT_1393_20
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Objective: The objective of this study was to evaluate whether whole-uterine magnetic resonance imaging (MRI) radiomic features can predict myometrial invasion (MI) depth in endometrial cancer (EC).
Materials and Methods: The preoperative 3.0 T magnetic resonance examinations of EC patients were retrospectively reviewed. Whole-uterus segmentation was performed, and features were extracted based on sagittal T2-weighted imaging (T2WI) and axial diffusion-weighted imaging (DWI). The logistic regression (LR) classifier algorithm was used to establish the radiomic model, which was verified by ten times five-fold cross-validation. The areas under the receiver operating characteristic (ROC) curves (AUCs) were assessed by the DeLong test to compare differences among the models based on different sequences. The LR model was compared with the subjective diagnosis results by the Chi-square test.
Results: Of the 163 EC patients included, 44 had deep myometrial invasion (DMI). The feature consistency of the whole uterus was higher than that of the lesion (P < 0.05). The sagittal T2WI, axial DWI, and combined models had AUCs of 0.76, 0.80, and 0.85 in the validation set, respectively. The DeLong test showed that there were no significant differences in AUCs among the models (P > 0.05). The single-sequence LR models had lower specificity and accuracy than the corresponding subjective diagnostic results (P < 0.05), while the sensitivity was higher (P > 0.05). The combined model included 24 radiomic features, and the accuracy, sensitivity, and specificity were 0.83, 0.77, and 0.85 for DMI, respectively. There was no significant difference compared with subjective diagnosis (P > 0.05).
Conclusion: Whole-uterine MRI radiomic features based on sagittal T2WI and axial DWI show potential in predicting MI in EC.
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