Mbanu P, Osorio E Vasquez, Mistry H, Malcomson L, Yousif S, Aznar M, Kochhar R, Van Herk M, Renehan A G, Saunders M P
Department of Clinical Oncology, Christie NHS Foundation Trust, Manchester, United Kingdom.
Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.
Cancer Treat Res Commun. 2022;31:100540. doi: 10.1016/j.ctarc.2022.100540. Epub 2022 Feb 24.
Prediction of clinical complete response in rectal cancer before neoadjuvant chemo-radiotherapy treatment enables treatment selection. Patients predicted to have complete response could have chemo-radiotherapy, and others could have additional doublet chemotherapy at this stage of their treatment to improve their overall outcome. This work investigates the role of clinical variables in predicting clinical complete response.
Using the UK-based OnCoRe database (2008 to 2019), we performed a propensity-score matched study of 322 patients who received neoadjuvant chemoradiotherapy. We collected pre-treatment clinic-pathological, inflammatory and radiotherapy-related characteristics. We determined the odds for the occurrence of cCR using conditional logistic regression models. We derived the post-model Area under the Curve (AUC) as an indicator of discrimination performance and stated a priori that an AUC of 0.75 or greater was required for potential clinical utility.
Pre-treatment tumour diameter, mrT-stage, haemoglobin, alkaline phosphate and total radiotherapy depths were associated with cCR on univariable and multivariable analysis. Additionally, neutrophil to lymphocyte ratio (NLR), neutrophil-monocyte to lymphocyte ratio (NMLR), lymphocyte count and albumin were all significantly associated with cCR on multivariable analysis. A nomogram using the above parameters was developed with a resulting ROC AUC of 0.75.
We identified routine clinic-pathological, inflammatory and radiotherapy-related variables which are independently associated with cCR. A nomogram was developed to predict cCR. The performance characteristics from this model were on the prior clinical utility threshold. Additional research is required to develop more associated variables to better select patients with rectal cancer undergoing chemoradiotherapy who may benefit from pursuing a W&W strategy.
在新辅助放化疗治疗前预测直肠癌的临床完全缓解有助于治疗方案的选择。预计能达到完全缓解的患者可接受放化疗，而其他患者在此治疗阶段可接受额外的双联化疗以改善其总体预后。本研究探讨临床变量在预测临床完全缓解中的作用。
利用英国的OnCoRe数据库（2008年至2019年），我们对322例接受新辅助放化疗的患者进行了倾向评分匹配研究。我们收集了治疗前的临床病理、炎症和放疗相关特征。我们使用条件逻辑回归模型确定cCR发生的几率。我们得出模型后的曲线下面积（AUC）作为区分性能的指标，并事先声明潜在临床应用需要AUC为0.75或更高。
在单变量和多变量分析中，治疗前肿瘤直径、mrT分期、血红蛋白、碱性磷酸酶和总放疗深度与cCR相关。此外，在多变量分析中，中性粒细胞与淋巴细胞比值（NLR）、中性粒细胞 - 单核细胞与淋巴细胞比值（NMLR）、淋巴细胞计数和白蛋白均与cCR显著相关。使用上述参数开发了一个列线图，其ROC AUC为0.75。
我们确定了与cCR独立相关的常规临床病理、炎症和放疗相关变量。开发了一个列线图来预测cCR。该模型的性能特征达到了先前的临床应用阈值。需要进一步研究以开发更多相关变量，以便更好地选择可能从等待观察（W&W）策略中获益的接受放化疗的直肠癌患者。
