Abstract
BACKGROUND: Preoperative fluoropyrimidine with radiotherapy was regarded as the standard of care for locally advanced rectal cancer (LARC). The model for predicting pCR in LARC patients was based on standard treatment only. This study aimed to establish a nomogram with pretherapeutic parameters and different neoadjuvant regimens for predicting pathologic complete response (pCR) and tumor downstaging or good response (ypT0-2N0M0) after receiving neoadjuvant treatment in patients with LARC based on a randomized clinical trial. METHODS: Between January 2011 and February 2015, 309 patients with rectal cancer were enrolled from a prospective randomized study (NCT01211210). All pretreatment clinical parameters were collected to build a nomogram for predicting pCR and tumor downstaging. The model was subjected to bootstrap internal validation. The predictive performance of the model was assessed with concordance index (C-index) and calibration plots. RESULTS: Of the 309 patients, 53 (17.2%) achieved pCR and 132 (42.7%) patients were classified as tumor downstaging with ypT0-2N0M0. Based on the logistic-regression analysis and clinical consideration, tumor length (P = 0.005), tumor circumferential extent (P = 0.036), distance from the anal verge (P = 0.019), and neoadjuvant treatment regimen (P < 0.001) showed independent association with pCR following neoadjuvant treatment. The tumor length (P = 0.015), tumor circumferential extent (P = 0.001), distance from the anal verge (P = 0.032), clinical T category (P = 0.012), and neoadjuvant treatment regimen (P = 0.001) were significantly associated with good tumor downstaging (ypT0-2N0M0). Nomograms were developed to predict the probability of pCR and tumor downstaging with a C-index of 0.802 (95% confidential interval [CI], 0.736-0.867) and 0.730 (95% CI, 0.672-0.784). Internal validation revealed good performance of the calibration plots. CONCLUSIONS: The nomogram provided individual prediction responses to different preoperative treatment for patients with rectal cancer. This model might help physicians in selecting an optimized treatment, but warrants further external validation.