A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics.
A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics.
复制标题
基于机器学习的放射组学预测浸润性肺腺癌的微乳头/固体生长模式
DOI：
10.21037/tlcr-21-44
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发表时间：
2021-03
影响因子：
4
通讯作者：
Chen C
中科院分区：
文献类型：
--
作者：
He B;Song Y;Wang L;Wang T;She Y;Hou L;Zhang L;Wu C;Babu BA;Bagci U;Waseem T;Yang M;Xie D;Chen C
肺腺癌的微乳头状/实性（MP/S）生长模式对于外科干预的临床决策至关重要。本研究旨在使用放射组学分析预测肺腺癌中MP/S组分的存在。纳入了2011年1月至2013年12月期间接受根治性浸润性肺腺癌切除术的患者。使用“PyRadiomics”软件包，我们从术前计算机断层扫描（CT）图像中提取了90个放射组学特征。随后，利用适合放射组学分析的传统机器学习方法建立了四个预测模型：广义线性模型（GLM），朴素贝叶斯，支持向量机（SVM）和随机森林分类器。使用受试者工作曲线（ROC）分析评估模型的准确性，并在内部和外部验证模型的稳定性。共纳入268例患者作为主要队列，其中36.6%（98/268）的肺腺癌伴MP/S组分。有MP/S成分的患者淋巴结转移率较高（18.4%对5.3%），无复发生存率和总生存率较差。选择五个放射组学特征进行模型构建，在内部验证中，四个模型在曲线下面积（AUC）方面实现了MP/S预测的可比性能：GLM，0.74 [95%置信区间（CI）：0.65-0.83];朴素贝叶斯，0.75（95% CI：0.65-0.85）; SVM，0.73（95% CI：0.61-0.83）;和随机森林，0.72（95% CI：0.63-0.81）。使用包含193例患者的测试队列进行外部验证，朴素贝叶斯、SVM、随机森林和GLM的AUC值分别为0.70、0.72、0.73和0.69。基于放射组学的机器学习方法是术前预测肺腺癌MP/S生长模式的一种非常强大的工具，可以帮助定制治疗和监测策略。
Micropapillary/solid (MP/S) growth patterns of lung adenocarcinoma are vital for making clinical decisions regarding surgical intervention. This study aimed to predict the presence of a MP/S component in lung adenocarcinoma using radiomics analysis. Between January 2011 and December 2013, patients undergoing curative invasive lung adenocarcinoma resection were included. Using the “PyRadiomics” package, we extracted 90 radiomics features from the preoperative computed tomography (CT) images. Subsequently, four prediction models were built by utilizing conventional machine learning approaches fitting into radiomics analysis: a generalized linear model (GLM), Naïve Bayes, support vector machine (SVM), and random forest classifiers. The models’ accuracy was assessed using a receiver operating curve (ROC) analysis, and the models’ stability was validated both internally and externally. A total of 268 patients were included as a primary cohort, and 36.6% (98/268) of them had lung adenocarcinoma with an MP/S component. Patients with an MP/S component had a higher rate of lymph node metastasis (18.4% versus 5.3%) and worse recurrence-free and overall survival. Five radiomics features were selected for model building, and in the internal validation, the four models achieved comparable performance of MP/S prediction in terms of area under the curve (AUC): GLM, 0.74 [95% confidence interval (CI): 0.65–0.83]; Naïve Bayes, 0.75 (95% CI: 0.65–0.85); SVM, 0.73 (95% CI: 0.61–0.83); and random forest, 0.72 (95% CI: 0.63–0.81). External validation was performed using a test cohort with 193 patients, and the AUC values were 0.70, 0.72, 0.73, and 0.69 for Naïve Bayes, SVM, random forest, and GLM, respectively. Radiomics-based machine learning approach is a very strong tool for preoperatively predicting the presence of MP/S growth patterns in lung adenocarcinoma, and can help customize treatment and surveillance strategies.