Abstract

PurposeThe overall survival (OS) of resected locally advanced rectal cancer patients who underwent neoadjuvant chemoradiotherapy (nCRT) was significantly different, even among patients with the same tumor stage. The nomogram was designed to predict OS of rectal cancer with nCRT and divide the patients into different risk groups.Materials and MethodsBased on materials from 911 rectal cancer patients with nCRT, the multivariable Cox regression model was carried out to select the significant prognostic factors for overall survival. And then, the nomogram was formulated using these independent prognostic factors. The discrimination of the nomogram was assessed by concordance index (C-index), calibration curves and time-dependent area under curve (AUC). The patients respective risk scores were calculated through the nomogram. The best cut-off risk score was calculated to stratify the patients. The survival curves of the two different risk cohorts were performed, which assessed the predictive ability of the nomogram.ResultsAge, cT stage, pretreatment CEA, pretreatment CA19-9, surgery, posttreatment CEA, posttreatment CA19-9, pT stage, pN stage and adjuvant chemotherapy were selected for the construction of the nomogram. And then the nomogram was constructed with independent prognostic factors. The C-index of the nomogram was 0.724, which showed the nomogram provided good discernment. The acceptable agreement between the predictions of nomogram and actual observations was illustrated by calibration plots for 3-, 5- and 10-year OS in the cohort. Time-dependent AUC with 6-fold cross-validation also showed consistent results of the nomogram. Risk group stratification confirmed that the nomogram had great capacity for distinguishing the prognosis.ConclusionThe nomogram was developed and validated to predict overall survival of resected locally advanced rectal cancer patients with nCRT. The proposed nomogram might help clinicians to develop individualized treatment strategies. However, further studies are warranted to optimize the nomogram by finding out other unknown prognostic factors, and more external validation is still required.

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