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Mismatch Repair Status and Clinico-radiological Feature-Based Model for Pre-treatment Evaluation of Perineural
Xiangchun Liu1, Lijuan Wan1, Rui Zhao1
1Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, #17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
Rationale And Objectives:
To develop and validate a predictive model for the pre-treatment evaluation of perineural invasion (PNI) status and to examine its prognostic stratification effectiveness in patients with stage I-III rectal cancer (RC) based on mismatch repair (MMR) status, clinical data, and magnetic resonance imaging (MRI) evaluated features.
Materials And Methods:
This retrospective study included 815 patients with stage I-III RC who underwent MRI scans from January 2016 to November 2023 and were randomly assigned to the training and validation cohorts. MMR status, clinical data, and MRI-evaluated features associated with PNI status were identified as independent predictors for developing a predictive model by univariable and multivariable logistic regression analyses in the training cohort. The receiver operating characteristic curves and the area under the curves (AUCs) were utilized to evaluate the diagnostic performance of the prediction model in both the training and validation cohorts. The Kaplan-Meier survival curves and Cox proportional hazards regression analysis were utilized to evaluate the prognostic stratification value of the model in both the training and validation cohorts.
Results:
The predictive model developed with independent predictors, including deficient MMR (odds ratio [OR]=0.434, P=0.021), male gender (OR=1.578, P=0.013), MRI-evaluated tumor morphology (partly annular, OR=3.257, P<0.001; annular, OR=5.184, P<0.001), tumor stage (T3, OR=1.953, P=0.004; T4, OR=2.627, P=0.013), extramural vascular invasion (OR=1.736, P=0.041), tumor deposit (OR=3.902, P<0.001) and mesorectal fascia involvement (OR=2.679, P=0.023), achieved AUCs of 0.748 (95% confidence interval [CI]: 0.711-0.785, P<0.001) and 0.719 (95% CI: 0.640-0.798, P<0.001) in the training and validation cohorts, respectively. The Kaplan-Meier survival curves show effectively prognostic stratification for disease-free survival (DFS), distant metastasis-free survival (DMFS), and recurrence-free survival (RFS) between predicted PNI-positive and PNI-negative patients (both P<0.05). Cox regression analysis indicated that predicted PNI-positive status was a significant risk factor associated with inferior DFS and DMFS in both training and validation cohorts (both P<0.05). The predicted PNI-positive status was a significant risk factor associated with inferior RFS in the training cohort (P=0.002); however, no significant association was observed in the validation cohort (P=0.104).
Conclusion:
The developed prediction model for evaluating the PNI status of RC prior to treatment showing acceptable performance and helping with prognostic stratification, which may assist in personalized treatment decisions.
