Abstract
BACKGROUND: At present, there is a lack of studies in invasive mucinous adenocarcinoma (IMA) that combine clinicopathological and imaging features to stratify risk and select optimal treatment regimen. We aimed to develop and validate a nomogram for predicting recurrence-free survival (RFS) and identifying adjuvant chemotherapy (ACT) beneficiaries for completely resected stage I primary IMA. METHODS: This retrospective study included 750 patients from three hospitals. Patients from two hospitals were divided into training (n=424) and validating cohort (n=185), and patients from the remaining other one hospital constituted external test cohort (n=141) and preoperative computed tomography (CT) image features of each patient were consecutively evaluated. The nomogram was developed by integrating significant prognostic factors of RFS identified in the multivariate analysis. The risk score (RS) based on nomogram was calculated in the entire cohort and the optimal cut-off point for risk stratification was obtained by X-tile software. The Kaplan-Meier method, log-rank test and interaction were used to evaluate the difference in RFS and overall survival (OS) between different risk and treatment groups. RESULTS: Visceral pleural invasion (VPI, P<0.001), lymph-vascular invasion (LVI, P<0.001), tumor size (P<0.001), smoking history (P<0.001), lobulation (P<0.001) were identified as independent prognostic factors for RFS. The concordance index (C-index) of the nomogram was higher than that of tumor-node-metastasis (TNM) staging system (validation cohort: 0.73±0.09 vs. 0.62±0.08, P<0.001; external test cohort: 0.74±0.10 vs. 0.70±0.09, P=0.035). The patients with higher RS were associated with worse RFS [hazard ratios (HRs) ≥4.76] and OS (HRs ≥2.55) in all included cohorts. Chemotherapy benefits in terms of RFS and OS were observed for patients in higher RS group in both stage IB (interaction P=0.012 for RFS and P=0.037 for OS) and stage I IMA (interaction P<0.001 for both RFS and OS). CONCLUSIONS: The nomogram based on CT image and clinicopathologic features showed superior performance in predicting RFS for stage I IMA and might identify ACT candidates for personalized patient treatment.