An Integrated Clinical‐MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer

Abstract

Background Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning. Purpose To identify clinical features and imaging signatures on T2‐weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects. Study Type Retrospective. Population Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years). Field Strength/Sequence 1.5‐T and 3‐T scanners with sagittal T2‐weighted spin echo sequence. Assessment Tumor regions were manually segmented on T2‐weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets. Statistical Tests A likelihood‐ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model. Results Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively. Data Conclusion The proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer. Evidence Level 4 Technical Efficacy Stage 2

Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features

“…Initially, 611 cases were recruited as training datasets; the inclusion criteria regarding MRI were replicated from a previous study [ 25 ]. For this classification study, the specific inclusion criteria regarding clinical data were: (1) availability of DMI (cancer stage IA vs. IB) information, information on LVSI, histological risk score, and histological type, and (2) no other type of co-existing cancer.…”

“…Four subjects were scanned with 3.0 T scanners, while the remaining majority of the 406 subjects were scanned with a 1.5 T scanner. The independent holdout dataset included 102 patients collected from 2017 to 2019, and 82 cases were included in the final analysis once the aforementioned inclusion criteria were applied [ 25 ]. Table 1 shows the clinical target/response variable and the number of cases for the classification studies.…”

“…The image and data analysis pipeline is displayed in Figure 1 . A medical image segmentation software (ITK-snap, version 3.6.0, , 5 April 2023) was employed to delineate each tumor region slice by slice from the T2-weighted MR image by four radiologists (JR, AS, NB, and AR) with a minimum of four years’ experience reading uterine MRI [ 25 , 26 ]. The segmented images were saved as a mask image for analysis in the later steps.…”

“…For the MRI image, the cubic spine was adopted for the interpolation, while for the associated binary mask image, nearest neighbor interpolation was applied. To avoid different scanner effects, T2-weighted images were normalized using a Z-score method [ 25 ]. Image feature extraction was performed using TexLAB tool (version 2.0) on MATLAB (version R2022b; The MathWorks Inc., Natick, MA, USA; , accessed on 5 April 2023), pyRadiomics (version 3.0.1, , accessed on 5 April 2023), and Scikit-image (version 0.19.3, , accessed on 5 April 2023) in Python (Python3.8, , accessed on 5 April 2023).…”

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study

“…In terms of DFS in EC, few studies have been published. Nakajo et al created a [ 18 F]-FDG PET-based radiomic ML model and obtained an AUC of 0.89 for the prediction of PFS [ 59 ], while other studies using MRI-based models alone or combined with clinical data obtained mean AUCs varying between 0.62 and 0.85 [ 67 , 68 , 69 ]. To complete this knowledge gap, our study aimed to evaluate the efficacy of CT-based radiomics in predicting recurrences in EC.…”