Abstract：
OBJECTIVE: 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.
METHODS: 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).
RESULTS: 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).
CONCLUSIONS: 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.
METHODS: 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).
RESULTS: 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).
CONCLUSIONS: 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.
摘要：
目的：本研究旨在建立结合临床因素和MRI肿瘤回归分级的列线图，以预测中低位局部晚期直肠癌对新辅助放化疗的病理反应。
方法：回顾性研究包括2013年1月至2021年12月期间接受新辅助放化疗和手术的204例患者。根据病理肿瘤回归分级，患者分为四组：完全病理反应(pCR，n=45),非完全病理反应（非pCR；n=159），良好的病理反应（pGR，n=119),和不良病理反应(非pGR，n=85）。以7:3的比例将患者分成训练集和验证集。根据训练集中的单变量和多变量分析的结果，分别构建了两个列线图来预测完整和良好的病理反应。随后，这些预测模型在独立验证集中进行了验证.使用曲线下面积(AUC)评价模型的预后性能。
结果：预测完全病理反应的列线图包含肿瘤长度，治疗后，直肠系膜筋膜受累，白细胞计数,和MRI肿瘤回归分级。它在训练集中产生0.787的AUC，在验证集中产生0.716的AUC，超越仅依赖于MRI肿瘤回归分级的模型的性能（AUC分别为0.649和0.530）。同样，预测良好病理反应的列线图包括肿瘤下边界与肛门边缘的距离，治疗后，直肠系膜筋膜受累，血小板/淋巴细胞比率，和MRI肿瘤回归分级。它在训练集中实现了0.754的AUC，在验证集中实现了0.719的AUC，仅使用MRI肿瘤回归分级（AUC分别为0.629和0.638）优于模型。
结论：列线图结合MRI肿瘤消退分级与临床因素可能有助于预测中低位局部晚期直肠癌对新辅助放化疗的病理反应。所提出的模型可以在大样本验证后应用于临床实践。
方法：回顾性研究包括2013年1月至2021年12月期间接受新辅助放化疗和手术的204例患者。根据病理肿瘤回归分级，患者分为四组：完全病理反应(pCR，n=45),非完全病理反应（非pCR；n=159），良好的病理反应（pGR，n=119),和不良病理反应(非pGR，n=85）。以7:3的比例将患者分成训练集和验证集。根据训练集中的单变量和多变量分析的结果，分别构建了两个列线图来预测完整和良好的病理反应。随后，这些预测模型在独立验证集中进行了验证.使用曲线下面积(AUC)评价模型的预后性能。
结果：预测完全病理反应的列线图包含肿瘤长度，治疗后，直肠系膜筋膜受累，白细胞计数,和MRI肿瘤回归分级。它在训练集中产生0.787的AUC，在验证集中产生0.716的AUC，超越仅依赖于MRI肿瘤回归分级的模型的性能（AUC分别为0.649和0.530）。同样，预测良好病理反应的列线图包括肿瘤下边界与肛门边缘的距离，治疗后，直肠系膜筋膜受累，血小板/淋巴细胞比率，和MRI肿瘤回归分级。它在训练集中实现了0.754的AUC，在验证集中实现了0.719的AUC，仅使用MRI肿瘤回归分级（AUC分别为0.629和0.638）优于模型。
结论：列线图结合MRI肿瘤消退分级与临床因素可能有助于预测中低位局部晚期直肠癌对新辅助放化疗的病理反应。所提出的模型可以在大样本验证后应用于临床实践。
