Department of Nuclear Medicine, the First Affiliated Hospital of Soochow University, Suzhou, China.
Department of Nuclear Medicine, the Affiliated Suzhou Science & Technology Town Hospital of Nanjing Medical University, Suzhou, China.
Technol Cancer Res Treat. 2023 Jan-Dec;22:15330338231175735. doi: 10.1177/15330338231175735.
Differential diagnosis of single-nodule pulmonary metastasis (SNPM) and second primary lung cancer (SPLC) in patients with colorectal cancer (CRC) prior to lung surgery is relatively complex. Radiomics is an emerging technique for image information analysis, while it has not yet been applied to construct a differential diagnostic model between SNPM and SPLC in patients with CRC. In the present study, we aimed to extract radiomics signatures from thin-section computed tomography (CT) images of the chest. These radiomics signatures were combined with clinical features to construct a composite differential diagnostic model.
A total of 91 patients with CRC, including 66 patients with SNPM and 25 patients with SPLC, were enrolled in this study. Patients were randomly assigned to the training cohort (n = 63) and validation cohort (n = 28) at a ratio of 7 to 3. Moreover, 107 radiomics features were extracted from the chest thin-section CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to filter these features, and clinical features were screened by univariate analysis. The screened radiomics and clinical features were combined to construct a multifactorial logistic regression composite model. The receiver operating characteristic (ROC) curves were adopted to evaluate the models, and the corresponding nomograms were created.
A series of 6 radiomics characteristics was screened by LASSO. After univariate logistic regression analysis, the composite model finally included 4 radiomics features and 4 clinical features. In the training cohort, the area under the curve scores of ROC curves were 0.912 (95% confidence interval [CI]: 0.813-0.969), 0.884 (95% CI: 0.778-0.951), and 0.939 (95% CI: 0.848-0.984) for models derived from radiomics, clinical, and combined features, respectively. Similarly, these values were 0.756 (95% CI: 0.558-0.897), 0.888 (95% CI: 0.711-0.975), and 0.950 (95% CI: 0.795-0.997) in the validation cohort, respectively.
We constructed a model for differential diagnosis of SNPM and SPLC in patients with CRC using radiomics and clinical features. Moreover, our findings provided a new assessment tool for patients with CRC in the future.
在进行肺癌手术前，对结直肠癌（CRC）患者的单个肺转移结节（SNPM）和第二原发性肺癌（SPLC）进行鉴别诊断较为复杂。放射组学是一种新兴的图像信息分析技术，但尚未应用于构建 CRC 患者 SNPM 和 SPLC 之间的鉴别诊断模型。本研究旨在从胸部薄层 CT 图像中提取放射组学特征，并结合临床特征构建综合鉴别诊断模型。
本研究共纳入 91 例 CRC 患者，其中 66 例为 SNPM，25 例为 SPLC。患者按 7:3 的比例随机分配到训练队列（n=63）和验证队列（n=28）。此外，从胸部薄层 CT 图像中提取了 107 个放射组学特征。采用最小绝对收缩和选择算子（LASSO）回归筛选这些特征，并进行单因素分析筛选临床特征。将筛选出的放射组学和临床特征相结合，构建多因素逻辑回归综合模型。采用受试者工作特征（ROC）曲线评估模型，并绘制相应的列线图。
通过 LASSO 筛选出一系列 6 个放射组学特征。经过单因素逻辑回归分析，综合模型最终纳入 4 个放射组学特征和 4 个临床特征。在训练队列中，ROC 曲线下面积评分分别为 0.912（95%置信区间[CI]：0.813-0.969）、0.884（95% CI：0.778-0.951）和 0.939（95% CI：0.848-0.984）。同样，这些值在验证队列中分别为 0.756（95% CI：0.558-0.897）、0.888（95% CI：0.711-0.975）和 0.950（95% CI：0.795-0.997）。
本研究构建了一种基于放射组学和临床特征的 CRC 患者 SNPM 和 SPLC 鉴别诊断模型。该研究结果为未来 CRC 患者的评估提供了一种新的工具。
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