Whole-Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer

Abstract

Background: In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI-based radiomic tumor profiling may aid in preoperative risk-stratification and support clinical treatment decisions in EC.

Purpose: To develop MRI-based whole-volume tumor radiomic signatures for prediction of aggressive EC disease.

Study type: Retrospective.

Population: A total of 138 women with histologically confirmed EC, divided into training (n_T = 108) and validation cohorts (n_V = 30).

Field strength/sequence: Axial oblique T₁ -weighted gradient echo volumetric interpolated breath-hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection.

Assessment: Primary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically-verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high-grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area.

Statistical tests: Logistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUC_T ) and validation (AUC_V ) cohorts. Progression-free survival was assessed using the Kaplan-Meier and Cox proportional hazard model.

Results: The whole-tumor radiomic signatures yielded AUC_T /AUC_V of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high-grade (E3) tumor. Single-slice radiomics yielded comparable AUC_T but significantly lower AUC_V for LNM and FIGO III + IV (both P < 0.05). Tumor volume yielded comparable AUC_T to the whole-tumor radiomic signatures for prediction of DMI, LNM, FIGO III + IV, and NE, but significantly lower AUC_T for E3 tumors (P < 0.05). All of the whole-tumor radiomic signatures significantly predicted poor progression-free survival with hazard ratios of 4.6-9.8 (P < 0.05 for all).

Data conclusion: MRI-based whole-tumor radiomic signatures yield medium-to-high diagnostic performance for predicting aggressive EC disease. The signatures may aid in preoperative risk assessment and hence guide personalized treatment strategies in EC.

Level of evidence: 4 TECHNICAL EFFICACY STAGE: 2.

Keywords: LASSO regression; MRI; endometrial cancer; prognostic modeling; radiomics.

FIGURE 1

Outline of the project workflow consisting of whole‐volume (whole‐tumor) manual tumor segmentation on axial oblique contrast‐enhanced T₁‐weighed images, radiomic tumor feature extraction, and construction of radiomic signatures for prediction of high‐risk surgicopathological features in 138 EC patients. Radiomic signatures were derived based on the whole‐tumor masks (whole‐tumor radiomics) and separately based on single‐slice masks (single‐slice radiomics) using only the single image slice depicting the largest tumor area. Least absolute shrinkage and selection operator (LASSO) and elastic net (Enet) were applied for prediction modeling.

FIGURE 2

Receiver operating characteristic (ROC) curves in the training cohort (n_T = 108) for prediction of deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), FIGO stage III + IV, nonendometrioid (NE) histology, and high‐grade tumor (E3), based on the whole‐tumor LASSO radiomic signatures, tumor mask volume, and preoperative high‐risk histology (from biopsy). Equality of areas under the ROC curves (AUCs) were assessed by the DeLong test, with significant P values given in italics.

FIGURE 3

Kaplan–Meier survival curves depicting progression‐free survival in the endometrial cancer cohort (n = 138), using the LASSO whole‐tumor signatures derived in the training set (n_T = 108) for prediction of deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), FIGO stage III + IV, nonendometrioid (NE) histology, and high‐grade tumor (E3). The green and red curves represent patients with whole‐tumor radiomic signatures above and below each signature's cutoff, respectively. The cutoffs are identified from receiver operating characteristics curves in the training cohort using the Youden Index.