[Clinical value of a differentiation prediction model for invasive lung adenocarcinoma].

Objective: To investigate the value of predicting the degree of differentiation of pulmonary invasive adenocarcinoma (IAC) based on CT image radiomics model and the expression difference of immunohistochemical factors between different degrees of differentiation of lesions. Methods: The clinicopathological data of patients with pulmonary IAC confirmed by surgical pathology in the Affiliated Huai'an First People's Hospital to Nanjing Medical University from December 2017 to September 2018 were collected. High-throughput feature acquisition was performed for all outlined regions of interest, and prediction models were constructed after dimensionality reduction by the minimum absolute shrinkage operator. Receiver operating characteristic curve was used to assess the predictive efficacy of clinical characteristic model, radiomics model and individualized prediction model combined with both to identify the degree of pulmonary IAC differentiation, and immunohistochemical expressions of Ki-67, NapsinA and TTF-1 were compared between groups with different degrees of IAC differentiation using rank sum test. Results: A total of 396 high-throughput features were extracted from all IAC lesions, and 10 features with high generalization ability and correlation with the degree of IAC differentiation were screened. The mean radiomics score of poorly differentiated IAC in the training group (1.206) was higher than that of patients with high and medium differentiation (0.969, P=0.001), and the mean radiomics score of poorly differentiated IAC in the test group (1.545) was higher than that of patients with high and medium differentiation (-0.815, P<0.001). The differences in gender (P<0.001), pleural stretch sign (P=0.005), and burr sign (P=0.033) were statistically significant between patients in the well and poorly differentiated IAC groups. Multifactorial logistic regression analysis showed that gender and pleural stretch sign were related to the degree of IAC differentiation (P<0.05). The clinical feature model consisted of age, gender, pleural stretch sign, burr sign, tumor vessel sign, and vacuolar sign, and the individualized prediction model consisted of gender, pleural stretch sign, and radiomic score, and was represented by a nomogram. The Akaike information standard values of the radiomics model, clinical feature model and individualized prediction model were 54.756, 82.214 and 53.282, respectively. The individualized prediction model was most effective in identifying the degree of differentiation of pulmonary IAC, and the area under the curves (AUC) of the individualized prediction model in the training group and the test group were 0.92 (95% CI: 0.86-0.99) and 0.88 (95% CI: 0.74-1.00, respectively). The AUCs of the radiomics group model for predicting the degree of differentiation of pulmonary IAC in the training group and the test group were 0.91 (95% CI: 0.83-0.98) and 0.87 (95% CI: 0.72-1.00), respectively. The AUCs of the clinical characteristics model for predicting the degree of differentiation of pulmonary IACs in the training and test groups were 0.75 (95% CI: 0.63-0.86) and 0.76 (95% CI: 0.59-0.94), respectively. The expression level of Ki-67 in poorly differentiated IAC was higher than that in well-differentiated IAC (P<0.001). The expression levels of NapsinA, TTF-1 in poorly differentiated IAC were higher than those in well-differentiated IAC (P<0.05). Conclusions: Individualized prediction model consisted of gender, pleural stretch sign and radiomics score can discriminate the differentiation degree of IAC with the best performance in comparison with clinical feature model and radiomics model. Ki-67, NapsinA and TTF-1 express differently in different degrees of differentiation of IAC.目的： 探讨基于CT图像影像组学列线图模型预测肺浸润性腺癌(IAC)分化程度的价值及免疫组化因子在肿瘤不同分化程度间的表达差异。 方法： 收集2017年12月至2018年9月南京医科大学附属淮安第一医院经手术病理证实为肺IAC患者的临床病理资料。对所有勾画感兴趣区进行高通量特征采集，经最小绝对收缩算子降维处理后构建预测模型。采用受试者工作特征曲线评估临床特征模型、影像组学模型及两者联合的个体化预测模型鉴别肺IAC分化程度的预测效能，免疫组化Ki-67、NapsinA、甲状腺转录因子1(TTF-1)在IAC不同分化程度的组间比较采用秩和检验。 结果： 全组IAC病灶中共提取出396个高通量特征，筛选出10个泛化能力较高、与IAC分化程度相关的特征。训练组低分化IAC的影像组学评分平均值(1.206)高于中高分化患者(0.969，P＝0.001)，测试组低分化IAC的影像组学评分平均值(1.545)高于中高分化患者(-0.815，P<0.001)。中高分化IAC组和低分化IAC组患者的性别(P<0.001)、胸膜牵拉征(P＝0.005)、毛刺征(P＝0.033)差异均有统计学意义。多因素logistic回归分析显示，性别、胸膜牵拉征与IAC分化程度有关(均P<0.05)。临床特征模型由年龄、性别、胸膜牵拉征、毛刺征、肿瘤血管征、空泡征组成，个体化预测模型由性别、胸膜牵拉征及影像组学评分构成，并由列线图表示。影像组学模型、临床特征模型和个体化预测模型的Akaike信息标准值分别为54.756、82.214和53.282。个体化预测模型对鉴别肺IAC分化程度的效能最高，个体化预测模型在训练组和测试组中的曲线下面积(AUC)分别为0.92(95% CI：0.86~0.99)和0.88(95% CI：0.74~1.00)；影像组模型在训练组和测试组中预测肺IAC分化程度的AUC分别为0.91(95% CI：0.83~0.98)和0.87(95% CI：0.72~1.00)；临床特征模型在训练组和测试组中预测肺IAC分化程度的AUC分别为0.75(95% CI：0.63~0.86)和0.76(95% CI：0.59~0.94)。Ki-67在低分化IAC中的表达水平高于中高分化IAC(P<0.001)，NapsinA、TTF-1在中高分化IAC中的表达高于低分化IAC(均P<0.05)。 结论： 由性别、胸膜牵拉征及影像组学评分构建的个体化预测模型对浸润性肺腺癌的分化程度具有较高的鉴别效能。Ki-67、NapsinA、TTF-1在浸润性肺腺癌不同分化程度间的表达不同。.