基于影像组学分类器术前预测直肠非黏液性腺癌淋巴结转移

目的：探讨影像组学方法在术前预测直肠非黏液性腺癌淋巴结转移中的价值。方法：回顾性分析91例手术病理切片证实为直肠非黏液性腺癌患者的影像学资料，其中61例为训练样本，30例为验证样本。基于全瘤体积，从每个原发病灶术前高分辨T2加权成像(T2-weighted imaging，T2WI)图像中提取影像组学特征1 301个。基于训练样本，利用最小绝对收缩和选择算子(the least absolute shrinkage and selection operator，LASSO)逻辑回归方法筛选关键特征并构建影像组学分类器。采用受试者工作特征(receiver operating characteristic，ROC)曲线评价影像组学分类器的辨别效能，并将其与形态学标准进行比较。在验证样本中验证影像组学分类器的价值。结果：由5个影像组学特征构建的分类器与淋巴结转移状态有关(P<0.001)。在训练样本和验证样本中，影像组学分类器诊断淋巴结转移的曲线下面积分别为0.874(95% CI：0.787~0.960)和0.878(95% CI：0.727~1.000)，形态学标准诊断淋巴结转移的曲线下面积分别为0.619(95% CI：0.487~0.752)和0.556(95% CI：0.355~0.756)。无论是训练样本还是验证样本，影像组学分类器的诊断效能均高于形态学标准(均P<0.05)。结论：影像组学分类器可术前个体化预测直肠非黏液性腺癌淋巴结转移，而且其诊断效能高于形态学标准。

Preoperative prediction for lymph node metastasis of rectal nonmucinous adenocarcinoma based on radiomics classifier

Objective: To determine the value of radiomics in identifying lymph node (LN) metastasis in patients with rectal nonmucinous adenocarcinoma.Methods: Imaging data of 91 patients were retrospectively analyzed (61 in the training set and 30 in the test set). A total of 1 301 radiomics features were extracted from high-resolution T2-weighted images of the whole primary tumor. The least absolute shrinkage and selection operator (LASSO) logistic regression was performed to choose the optimal features and construct a radiomics classifier in the training set. Its discrimination performance was compared with that of morphological criteria by receiver operating characteristic (ROC) curve analysis, which was validated in the test set.Results: The radiomics classifier combined with five key features was significantly associated with LN metastasis, which distinguished LN metastasis with an area under curve (AUC) at 0.874 (95% CI 0.787 to 0.960) in the training set, and the performance was similar in the test set (AUC 0.878, 95% CI 0.727 to 1.000). The AUCs according to the morphological criteria in the training set and test set were 0.619 (95% CI 0.487 to 0.752) and 0.556 (95% CI 0.355 to 0.756), respectively. Discrimination of the radiomics classifier was superior to that of morphological criteria in both the two datasets (both P <0.05).Conclusion: The radiomics classifier provides individualized risk estimation for LN metastasis in rectal nonmucinous adenocarcinoma patients and it has the advantage over the morphological criteria.