Department of Radiation Oncology (MAASTRO), Maastricht University Medical Centre, The Netherlands.
Radiother Oncol. 2011 Jan;98(1):126-33. doi: 10.1016/j.radonc.2010.12.002. Epub 2010 Dec 20.
To develop and validate an accurate predictive model and a nomogram for pathologic complete response (pCR) after chemoradiotherapy (CRT) for rectal cancer based on clinical and sequential PET-CT data. Accurate prediction could enable more individualised surgical approaches, including less extensive resection or even a wait-and-see policy.
Population based databases from 953 patients were collected from four different institutes and divided into three groups: clinical factors (training: 677 patients, validation: 85 patients), pre-CRT PET-CT (training: 114 patients, validation: 37 patients) and post-CRT PET-CT (training: 107 patients, validation: 55 patients). A pCR was defined as ypT0N0 reported by pathology after surgery. The data were analysed using a linear multivariate classification model (support vector machine), and the model's performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.
The occurrence rate of pCR in the datasets was between 15% and 31%. The model based on clinical variables (AUC(train)=0.61±0.03, AUC(validation)=0.69±0.08) resulted in the following predictors: cT- and cN-stage and tumour length. Addition of pre-CRT PET data did not result in a significantly higher performance (AUC(train)=0.68±0.08, AUC(validation)=0.68±0.10) and revealed maximal radioactive isotope uptake (SUV(max)) and tumour location as extra predictors. The best model achieved was based on the addition of post-CRT PET-data (AUC(train)=0.83±0.05, AUC(validation)=0.86±0.05) and included the following predictors: tumour length, post-CRT SUV(max) and relative change of SUV(max). This model performed significantly better than the clinical model (p(train)<0.001, p(validation)=0.056).
The model and the nomogram developed based on clinical and sequential PET-CT data can accurately predict pCR, and can be used as a decision support tool for surgery after prospective validation.
基于临床和连续 PET-CT 数据，开发和验证用于预测直肠癌放化疗后病理完全缓解（pCR）的准确预测模型和列线图。准确的预测可以使个体化的手术方法更具针对性，包括更广泛的切除甚至观察等待策略。
从四个不同的机构收集了 953 名患者的基于人群的数据库，并将其分为三组：临床因素（训练：677 例，验证：85 例）、放化疗前 PET-CT（训练：114 例，验证：37 例）和放化疗后 PET-CT（训练：107 例，验证：55 例）。pCR 定义为术后病理报告的 ypT0N0。使用线性多变量分类模型（支持向量机）对数据进行分析，并使用接收器操作特征（ROC）曲线的曲线下面积（AUC）评估模型的性能。
数据集的 pCR 发生率在 15%至 31%之间。基于临床变量的模型（AUC(train)=0.61±0.03，AUC(validation)=0.69±0.08）得出的预测因素包括 cT 和 cN 期以及肿瘤长度。添加放化疗前的 PET 数据并未显著提高性能（AUC(train)=0.68±0.08，AUC(validation)=0.68±0.10），并揭示了最大放射性同位素摄取（SUV(max)）和肿瘤位置作为额外的预测因素。基于添加放化疗后 PET 数据的最佳模型（AUC(train)=0.83±0.05，AUC(validation)=0.86±0.05）包括以下预测因素：肿瘤长度、放化疗后 SUV(max)和 SUV(max)的相对变化。该模型的性能明显优于临床模型（p(train)<0.001，p(validation)=0.056）。
基于临床和连续 PET-CT 数据开发的模型和列线图可以准确预测 pCR，并可作为前瞻性验证后手术的决策支持工具。
