Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.
INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France.
Sci Rep. 2018 Aug 22;8(1):12611. doi: 10.1038/s41598-018-30657-6.
Treatment of locally advanced rectal cancer involves chemoradiation, followed by total mesorectum excision. Complete response after chemoradiation is an accurate surrogate for long-term local control. Predicting complete response from pre-treatment features could represent a major step towards conservative treatment. Patients with a T2-4 N0-1 rectal adenocarcinoma treated between June 2010 and October 2016 with neo-adjuvant chemoradiation from three academic institutions were included. All clinical and treatment data was integrated in our clinical data warehouse, from which we extracted the features. Radiomics features were extracted from the tumor volume from the treatment planning CT Scan. A Deep Neural Network (DNN) was created to predict complete response, as a methodological proof-of-principle. The results were compared to a baseline Linear Regression model using only the TNM stage as a predictor and a second model created with Support Vector Machine on the same features used in the DNN. Ninety-five patients were included in the final analysis. There were 49 males (52%) and 46 females (48%). Median tumour size was 48 mm (15-130). Twenty-two patients (23%) had pathologic complete response after chemoradiation. One thousand six hundred eighty-three radiomics features were extracted. The DNN predicted complete response with an 80% accuracy, which was better than the Linear Regression model (69.5%) and the SVM model (71.58%). Our model correctly predicted complete response after neo-adjuvant rectal chemoradiotherapy in 80% of the patients of this multicenter cohort. Our results may help to identify patients who would benefit from a conservative treatment, rather than a radical resection.
局部晚期直肠癌的治疗包括放化疗，然后进行全直肠系膜切除术。放化疗后完全缓解是长期局部控制的准确替代指标。从治疗前的特征预测完全缓解可能是向保守治疗迈出的重要一步。
纳入了 2010 年 6 月至 2016 年 10 月期间在三个学术机构接受新辅助放化疗的 T2-4 N0-1 直肠腺癌患者。从我们的临床数据仓库中提取了所有临床和治疗数据，从中提取了特征。从治疗计划 CT 扫描中的肿瘤体积中提取了放射组学特征。创建了一个深度神经网络 (DNN) 来预测完全缓解，作为方法学原理验证。将结果与仅使用 TNM 分期作为预测因子的基线线性回归模型和使用 DNN 中相同特征创建的支持向量机模型进行比较。
最终分析纳入了 95 例患者。其中 49 例男性（52%），46 例女性（48%）。肿瘤大小中位数为 48mm（15-130）。22 例（23%）患者放化疗后病理完全缓解。提取了 1683 个放射组学特征。DNN 预测完全缓解的准确率为 80%，优于线性回归模型（69.5%）和支持向量机模型（71.58%）。
我们的模型正确预测了多中心队列中 80%的患者接受新辅助直肠放化疗后的完全缓解。我们的结果可能有助于识别那些将从保守治疗而不是激进切除中获益的患者。
