Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy.

Silin Chen,Ningning Lu,Yongwen Song,Shulian Wang,Jing Hao,Yexiong Li,Ning Li,Yueping Liu,Yuan Tang,Jun Jiang,Shunan Qi,Bo Chen,Liming Jiang,Hui Fang,Jing Jin

doi:10.3389/fonc.2021.784156

Abstract

ObjectivesTo develop a prognostic prediction MRI-based nomogram model for locally advanced rectal cancer (LARC) treated with neoadjuvant therapy.MethodsThis was a retrospective analysis of 233 LARC (MRI-T stage 3-4 (mrT) and/or MRI-N stage 1-2 (mrN), M0) patients who had undergone neoadjuvant radiotherapy and total mesorectal excision (TME) surgery with baseline MRI and operative pathology assessments at our institution from March 2015 to March 2018. The patients were sequentially allocated to training and validation cohorts at a ratio of 4:3 based on the image examination date. A nomogram model was developed based on the univariate logistic regression analysis and multivariable Cox regression analysis results of the training cohort for disease-free survival (DFS). To evaluate the clinical usefulness of the nomogram, Harrell’s concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were conducted in both cohorts.ResultsThe median follow-up times were 43.2 months (13.3–61.3 months) and 32.0 months (12.3–39.5 months) in the training and validation cohorts. Multivariate Cox regression analysis identified MRI-detected extramural vascular invasion (mrEMVI), pathological T stage (ypT) and perineural invasion (PNI) as independent predictors. Lymphovascular invasion (LVI) (which almost reached statistical significance in multivariate regression analysis) and three other independent predictors were included in the nomogram model. The nomogram showed the best predictive ability for DFS (C-index: 0.769 (training cohort) and 0.776 (validation cohort)). It had a good 3-year DFS predictive capacity [area under the curve, AUC=0.843 (training cohort) and 0.771 (validation cohort)]. DCA revealed that the use of the nomogram model was associated with benefits for the prediction of 3-year DFS in both cohorts.ConclusionWe developed and validated a novel nomogram model based on MRI factors and pathological factors for predicting DFS in LARC treated with neoadjuvant therapy. This model has good predictive value for prognosis, which could improve the risk stratification and individual treatment of LARC patients.

Highlights

The current standard treatment for locally advanced rectal cancer (LARC) is neoadjuvant therapy (NAT) followed by total mesorectal excision (TME) and postoperative adjuvant chemotherapy (ACT) [1]
According to the European Society for Medical Oncology (ESMO) guideline, structured Magnetic resonance imaging (MRI) reports should include the tumor location, primary tumor stage (MRI-T stage, mrT), node stage (MRI-N stage, mrN), extramural vascular invasion (EMVI) and mesorectal fascia (MRF), which demonstrates that pretreatment MRI factors are prognostic factors for LARC [1]
No significant differences were found in the pretreatment MRI and pathology factors were observed between the training and validation cohorts except for TRG (Dworak)

Summary

Introduction

The current standard treatment for locally advanced rectal cancer (LARC) is neoadjuvant therapy (NAT) followed by total mesorectal excision (TME) and postoperative adjuvant chemotherapy (ACT) [1]. Because of the heterogeneity that exists in LARC patients, the prognosis of patients in the same treatment model may be considerably different, which shows that TNM staging is not able to accurately predict clinical prognosis for rectal cancer [2]. Considering the importance of risk stratification and prognosis prediction, a stable and computationally simple prognostic model is necessary for clinical applications. Magnetic resonance imaging (MRI) is an effective imaging modality whose assessment has important clinical value and should be considered for inclusion in prognostic models [5,6,7]. Model construction based on factors of pre-neoadjuvant MRI factors and post-treatment pathological findings is expected to provide a more comprehensive evaluation to prognosis. We will build a model based on standardized structural pre-treatment MRI evaluation and pathological results

Objectives

Methods

Results

Conclusion