%0 Journal Article
%T Predicting the response to neoadjuvant chemoradiation for rectal cancer using nomograms based on MRI tumour regression grade.
%A Qin S
%A Chen Y
%A Liu K
%A Li Y
%A Zhou Y
%A Zhao W
%A Xin P
%A Wang Q
%A Lu S
%A Wang H
%A Lang N
%J Cancer Radiother
%V 28
%N 4
%D 2024 Aug 8
%M 38981746
%F 1.217
%R 10.1016/j.canrad.2024.01.004
%X <B>OBJECTIVE: </B>This study aimed to develop nomograms that combine clinical factors and MRI tumour regression grade to predict the pathological response of mid-low locally advanced rectal cancer to neoadjuvant chemoradiotherapy.<BR><B>METHODS: </B>The retrospective study included 204 patients who underwent neoadjuvant chemoradiotherapy and surgery between January 2013 and December 2021. Based on pathological tumour regression grade, patients were categorized into four groups: complete pathological response (pCR, n=45), non-complete pathological response (non-pCR; n=159), good pathological response (pGR, n=119), and non-good pathological response (non-pGR, n=85). The patients were divided into a training set and a validation set in a 7:3 ratio. Based on the results of univariate and multivariate analyses in the training set, two nomograms were respectively constructed to predict complete and good pathological responses. Subsequently, these predictive models underwent validation in the independent validation set. The prognostic performances of the models were evaluated using the area under the curve (AUC).<BR><B>RESULTS: </B>The nomogram predicting complete pathological response incorporates tumour length, post-treatment mesorectal fascia involvement, white blood cell count, and MRI tumour regression grade. It yielded an AUC of 0.787 in the training set and 0.716 in the validation set, surpassing the performance of the model relying solely on MRI tumour regression grade (AUCs of 0.649 and 0.530, respectively). Similarly, the nomogram predicting good pathological response includes the distance of the tumour's lower border from the anal verge, post-treatment mesorectal fascia involvement, platelet/lymphocyte ratio, and MRI tumour regression grade. It achieved an AUC of 0.754 in the training set and 0.719 in the validation set, outperforming the model using MRI tumour regression grade alone (AUCs of 0.629 and 0.638, respectively).<BR><B>CONCLUSIONS: </B>Nomograms combining MRI tumour regression grade with clinical factors may be useful for predicting pathological response of mid-low locally advanced rectal cancer to neoadjuvant chemoradiotherapy. The proposed models could be applied in clinical practice after validation in large samples.