Su Qiushi, Wang Bingyan, Guo Jia, Nie Pei, Xu Wenjian
Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, China.
Department of Echocardiography, the Affiliated Hospital of Qingdao University, Qingdao, China.
Transl Lung Cancer Res. 2024 Apr 29;13(4):721-732. doi: 10.21037/tlcr-24-38. Epub 2024 Apr 25.
The occurrence of bone metastasis (BM) will seriously shorten the survival time of lung adenocarcinoma patients and aggravate the suffering of patients. Computed tomography (CT)-based clinical radiomics nomogram may help clinicians stratify the risk of BM in lung adenocarcinoma patients, thereby enabling personalized individualized clinical decision making.
A total of 501 patients with lung adenocarcinoma from March 2017 to March 2019 were enrolled in the study. Based on plain chest CT images, 1130 radiomics features were extracted from each lesion. One-way analysis of variance (ANOVA) and least absolute shrinkage selection operator (LASSO) algorithm were used for radiomics features selection. Univariate and multivariate analyses were used to screen for clinical characteristics and identify independent predictors of BM. Three models (radiomics model, clinical model and combined model) were constructed to predict BM in lung adenocarcinoma patients. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance of the three models. The DeLong test was used to compare the performance of the models.
Finally, the clinical model for predicting BM in lung adenocarcinoma patients was constructed based on 5 independent predictors: cytokeratin 19-fragments (CYFRA21-1), stage, Ki-67, edge, and lobulation. The radiomics model was constructed based on 5 radiomics features. The combined model incorporating clinical independent predictors and radiomics was constructed. In the validation cohort, the area under the curve (AUC) of the clinical model, radiomics model and combined model was 0.824, 0.842 and 0.866, respectively. Delong test showed that in the training cohort, the AUC values of the radiomics model and the combined model were statistically different (P=0.03), and the AUC values of the other models were not statistically different. DCA showed that the nomogram had a highest net clinical benefit.
The CT-based clinical radiomics nomogram can be used as a non-invasive and quantitative method to help clinicians stratify the risk of BM in patients with lung adenocarcinoma, thereby enabling personalized clinical decision making.
骨转移（BM）的发生会严重缩短肺腺癌患者的生存时间，加重患者痛苦。基于计算机断层扫描（CT）的临床影像组学列线图可能有助于临床医生对肺腺癌患者的BM风险进行分层，从而实现个性化的临床决策。
本研究纳入了2017年3月至2019年3月期间共501例肺腺癌患者。基于胸部平扫CT图像，从每个病灶中提取1130个影像组学特征。采用单因素方差分析（ANOVA）和最小绝对收缩选择算子（LASSO）算法进行影像组学特征选择。采用单因素和多因素分析筛选临床特征并确定BM的独立预测因素。构建了三个模型（影像组学模型、临床模型和联合模型）来预测肺腺癌患者的BM。采用受试者操作特征（ROC）曲线和决策曲线分析（DCA）评估三个模型的性能。采用DeLong检验比较模型的性能。
最终，基于5个独立预测因素构建了预测肺腺癌患者BM的临床模型，包括细胞角蛋白19片段（CYFRA21-1）、分期、Ki-67、边缘和分叶情况。基于5个影像组学特征构建了影像组学模型。构建了纳入临床独立预测因素和影像组学的联合模型。在验证队列中，临床模型、影像组学模型和联合模型的曲线下面积（AUC）分别为0.824、0.842和0.866。DeLong检验显示，在训练队列中，影像组学模型和联合模型的AUC值存在统计学差异（P=0.03），其他模型的AUC值无统计学差异。DCA显示列线图具有最高的净临床获益。
基于CT的临床影像组学列线图可作为一种非侵入性的定量方法，帮助临床医生对肺腺癌患者的BM风险进行分层，从而实现个性化的临床决策。
