Department of Bone and Soft Tissue Tumor, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
The Sino-Russian Joint Research Center for Bone Metastasis in Malignant Tumor, Tianjin, China.
BMC Med Imaging. 2024 Aug 5;24(1):203. doi: 10.1186/s12880-024-01383-5.
Radiomics provided opportunities to quantify the tumor phenotype non-invasively. This study extracted contrast-enhanced computed tomography (CECT) radiomic signatures and evaluated clinical features of bone metastasis in non-small-cell lung cancer (NSCLC). With the combination of the revealed radiomics and clinical features, the predictive modeling on bone metastasis in NSCLC was established.
A total of 318 patients with NSCLC at the Tianjin Medical University Cancer Institute & Hospital was enrolled between January 2009 and December 2019, which included a feature-learning cohort (n = 223) and a validation cohort (n = 95). We trained a radiomics model in 318 CECT images from feature-learning cohort to extract the radiomics features of bone metastasis in NSCLC. The Kruskal-Wallis and the least absolute shrinkage and selection operator regression (LASSO) were used to select bone metastasis-related features and construct the CT radiomics score (Rad-score). Multivariate logistic regression was performed with the combination of the Rad-score and clinical data. A predictive nomogram was subsequently developed.
Radiomics models using CECT scans were significant on bone metastasis prediction in NSCLC. Model performance was enhanced with each information into the model. The radiomics nomogram achieved an AUC of 0.745 (95% confidence interval [CI]: 0.68,0.80) on predicting bone metastasis in the training set and an AUC of 0.808(95% confidence interval [CI]: 0.71,0.88) in the validation set.
The revealed invisible image features were of significance on guiding bone metastasis prediction in NSCLC. Based on the combination of the image features and clinical characteristics, the predictive nomogram was established. Such nomogram can be used for the auxiliary screening of bone metastasis in NSCLC.
放射组学提供了无创量化肿瘤表型的机会。本研究提取了增强 CT（CECT）放射组学特征，并评估了非小细胞肺癌（NSCLC）骨转移的临床特征。通过揭示的放射组学和临床特征相结合，建立了 NSCLC 骨转移的预测模型。
共纳入 2009 年 1 月至 2019 年 12 月在天津医科大学肿瘤医院的 318 例 NSCLC 患者，包括特征学习队列（n=223）和验证队列（n=95）。我们在特征学习队列的 318 例 CECT 图像中训练放射组学模型，以提取 NSCLC 骨转移的放射组学特征。Kruskal-Wallis 和最小绝对收缩和选择算子回归（LASSO）用于选择与骨转移相关的特征并构建 CT 放射组学评分（Rad-score）。随后，将 Rad-score 与临床数据进行多变量逻辑回归。建立预测列线图。
使用 CECT 扫描的放射组学模型对 NSCLC 骨转移预测具有显著意义。随着信息的不断加入，模型性能得到了增强。放射组学列线图在训练集中预测骨转移的 AUC 为 0.745（95%置信区间 [CI]：0.68，0.80），在验证集中 AUC 为 0.808（95%置信区间 [CI]：0.71，0.88）。
所揭示的不可见图像特征对指导 NSCLC 骨转移预测具有重要意义。基于图像特征和临床特征的组合，建立了预测列线图。该列线图可用于 NSCLC 骨转移的辅助筛查。
