MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer

Abstract

Purpose: To establish and evaluate multiregional T2-weighted imaging (T2WI)-based clinical-radiomics model for predicting lymph node metastasis (LNM) and prognosis in patients with resectable rectal cancer.

Methods: A total of 346 patients with pathologically confirmed rectal cancer from two hospitals between January 2019 and December 2021 were prospectively enrolled. Intra- and peritumoral features were extracted separately, and least absolute shrinkage and selection operator regression was applied for feature selection. Radiomics signatures were built using the selected features from different regions. The clinical-radiomic nomogram was developed by combining the intratumoral and peritumoral radiomics signatures score (radscore) and the most predictive clinical parameters. The diagnostic performances of the nomogram and clinical model were evaluated using the area under the receiver operating characteristic curve (AUC). The prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis.

Results: The intratumoral radscore (radscore 1) included four features, the peritumoral radscore (radscore 2) included five features, and the combined intratumoral and peritumoural radscore (radscore 3) included ten features. The AUCs for radscore 3 were higher than that of radscore 1 in training cohort (0.77 vs. 0.71, P=0.182) and internal validation cohort (0.76 vs. 0.64, P=0.041). The AUCs for radscore 3 were higher than that of radscore 2 in training cohort (0.77 vs. 0.74, P=0.215) and internal validation cohort (0.76 vs. 0.68, P=0.083). A clinical-radiomic nomogram showed a higher AUC compared with the clinical model in training cohort (0.84 vs. 0.67, P<0.001) and internal validation cohort (0.78 vs. 0.64, P=0.038) but not in external validation (0.72 vs. 0.76, P=0.164). Multivariate Cox analysis showed MRI-reported extramural vascular invasion (EMVI) (HR=1.099, 95%CI: 0.462-2.616; P=0.031) and clinical-radiomic nomogram-based LNM (HR=2.232, 95%CI:1.238-7.439; P=0.017) were independent risk factors for assessing 3-year RFS. Combined clinical-radiomic nomogram based LNM and MRI-reported EMVI showed good performance in training cohort (AUC=0.748), internal validation cohort (AUC=0.706) and external validation (AUC=0.688) for predicting 3-year RFS.

Conclusion: A clinical-radiomics nomogram exhibits good performance for predicting preoperative LNM. Combined clinical-radiomic nomogram based LNM and MRI-reported EMVI showed clinical potential for assessing 3-year RFS.

Keywords: lymph node; magnetic resonance imaging; prognosis; radiomics; rectal neoplasms.

Figure 1

Flowchart of patient selection.

Figure 2

The workflow of a typical radiomics process in our study included tumor segmentation, feature selection, and model construction and evaluation.

Figure 3

Receiver operating characteristic curves of three radiomics models for predicting lymph node metastasis in training cohort (A) and internal validation cohort (B).

Figure 4

The performance and validation of the final selected model to predict lymph node metastasis (LNM). (A), The predictive nomogram of LNM in training cohort. (B), Receiver operating characteristic curves (ROC) of clinical model and nomogram to predict LNM with rectal cancer in training cohort. (C), ROC of clinical model and nomogram to predict LNM with rectal cancer in internal validation cohort. (D) ROC of clinical model and nomogram to predict LNM with rectal cancer in external validation cohort.

Figure 5

The calibration curves for the nomogram in training cohort (A), internal validation cohort (B) and external validation cohort (C). The diagonal gray line represents a perfect prediction by an ideal model. The pink dotted line represents the performance of the nomogram, of which a closer fit to the diagonal gray line represents a better prediction (Hosmer-Lemeshow test all p-values >0.05). Decision curve analysis of nomogram to investigate the clinical usefulness in predicting lymph node metastasis (D). It indicates the nomogram model obtains more benefit than “treat all”, “treat none”, and the clinical model when the threshold probability is >10% in training cohort.

Figure 6

Receiver operating characteristic (ROC) curves of the nomogram and clinical model for differentiating extranodal extension (ENE) positive from lymph node metastasis (LNM) negative (A), lateral lymph node positive from LNM negative (B), and N2 stage from LNM negative (C). At T1-T2 stage subgroup analysis, ROC curves of nomogram and clinical model for predicting LNM (D). At T3 stage subgroup analysis, ROC curves of a clinical-radiomics nomogram and clinical model for predicting LNM (E).

Figure 7

Kaplan-Meier estimates of the nomogram for predicting 3-year recurrence-free survival in patients with rectal cancer in training cohort (A), internal validation cohort (B), external validation cohort (C), at T1-T2 subgroup (D), and at T3-T4 subgroup (E).