Radiomics nomogram for prediction disease-free survival and adjuvant chemotherapy benefits in patients with resected stage I lung adenocarcinoma
Radiomics nomogram for prediction disease-free survival and adjuvant chemotherapy benefits in patients with resected stage I lung adenocarcinoma
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DOI：
10.21037/tlcr-19-577
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发表时间：
2020-08-01
影响因子：
4
通讯作者：
Chen, Chang
中科院分区：
文献类型：
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作者：
Xie, Dong;Wang, Ting-Ting;Chen, Chang
背景：I 期肺腺癌患者需要稳健的成像生物标志物进行风险分层，以便选择最佳治疗方案。我们的目的是构建和验证放射组学列线图，用于预测 I 期肺腺癌切除患者的无病生存期 (DFS)，并进一步确定受益于辅助化疗 (ACT) 的候选者。方法：使用放射组学方法，我们分析了来自三个多中心队列的 554 名患者的计算机断层扫描 (CT) 图像。从计算机断层扫描 (CT) 图像中提取预后放射组学特征，并使用最小绝对收缩和选择算子 (LASSO) Cox 回归模型进行选择，以构建 DFS 分层的放射组学特征。通过基因集富集分析 (GSEA) 在放射基因组学数据集 (n=79) 中探索了放射组学的生物学基础。然后，在训练队列 (n=238) 中构建了将多变量分析中的特征与这些重要临床病理因素相结合的列线图，并在验证队列 (n=237) 中评估其预后准确性。最后，评估了列线图对 ACT 益处的预测价值。结果：在训练和验证队列中，得分较高的放射组学特征与较差的 DFS 显着相关（P
Background: Robust imaging biomarkers are needed for risk stratification in stage I lung adenocarcinoma patients in order to select optimal treatment regimen. We aimed to construct and validate a radiomics nomogram for predicting the disease-free survival (DFS) of patients with resected stage I lung adenocarcinoma, and further identifying candidates benefit from adjuvant chemotherapy (ACT).Methods: Using radiomics approach, we analyzed 554 patients' computed tomography (CT) images from three multicenter cohorts. Prognostic radiomics features were extracted from computed tomography (CT) images and selected using least absolute shrinkage and selection operator (LASSO) Cox regression model to build a radiomics signature for DFS stratification. The biological basis of radiomics was explored in the Radiogenomics dataset (n=79) by gene set enrichment analysis (GSEA). Then a nomogram that integrated the signature with these significant clinicopathologic factors in the multivariate analysis were constructed in the training cohort (n=238), and its prognostic accuracy was evaluated in the validation cohort (n=237). Finally, the predictive value of nomogram for ACT benefits was assessed.Results: The radiomics signature with higher score was significantly associated with worse DFS in both the training and validation cohorts (P