Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China.
Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Clin Radiol. 2023 Jan;78(1):8-17. doi: 10.1016/j.crad.2022.08.140. Epub 2022 Oct 1.
To establish and verify a 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)/computed tomography (CT)-based radiomics nomogram to predict mediastinal lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) patients preoperatively.
This retrospective study enrolled 155 NSCLC patients (primary cohort, n=93; validation cohort, n=62). For each patient, 2,704 radiomic features were extracted from the primary lung cancer regions. Four procedures including the Mann-Whitney U-test, Spearman's correlation analysis, minimum redundancy-maximum relevance (mRMR), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were utilised for determining essential features and establishing a radiomics signature. After that, a nomogram was established. The nomogram's potential was assessed based on its discrimination, calibration, and clinical usefulness. The radiomics signature and nomogram predictive performances were evaluated with respect to the area under the receiver operating characteristic curve (AUC), specificity, accuracy, and sensitivity.
The radiomics signature composed of eight selected features had good discriminatory performance of LNM versus non-LNM groups an AUC of 0.851 and 0.826 in primary and validation cohorts, respectively. The nomogram also indicated good discrimination with an AUC of 0.869 and 0.847 in the primary and validation cohorts, respectively. Furthermore, good calibration was demonstrated utilising the nomogram.
An F-FDG PET/CT-based radiomics nomogram that integrates the radiomics signature and age was promoted to predict mediastinal LNM within NSCLC patients, which could potentially facilitate individualised therapy for mediastinal LNM before treatment. The nomogram was beneficial in clinical practice, as illustrated by decision curve analysis.
建立并验证一种基于 2-[F]-氟-2-脱氧-D-葡萄糖（FDG）正电子发射断层扫描（PET）/计算机断层扫描（CT）的放射组学列线图，以预测非小细胞肺癌（NSCLC）患者术前纵隔淋巴结转移（LNM）。
本回顾性研究纳入了 155 例 NSCLC 患者（主要队列，n=93；验证队列，n=62）。对每位患者，从原发性肺癌区域提取了 2704 个放射组学特征。采用 Mann-Whitney U 检验、Spearman 相关分析、最小冗余最大相关性（mRMR）和最小绝对收缩和选择算子（LASSO）二项逻辑回归等四种方法确定特征并建立放射组学特征。然后建立列线图。根据其鉴别力、校准度和临床实用性来评估该列线图的潜力。利用受试者工作特征曲线（ROC）下面积（AUC）、特异性、准确性和敏感性来评估放射组学特征和列线图的预测性能。
该放射组学特征由 8 个特征组成，对 LNM 与非 LNM 组具有良好的鉴别能力，在主要和验证队列中的 AUC 分别为 0.851 和 0.826。该列线图的 AUC 分别为 0.869 和 0.847，也表现出良好的鉴别能力。此外，该列线图具有良好的校准能力。
该 F-FDG PET/CT 基于放射组学的列线图结合了放射组学特征和年龄，可预测 NSCLC 患者的纵隔 LNM，这可能有助于在治疗前针对纵隔 LNM 进行个体化治疗。列线图在决策曲线分析中也显示出了其在临床实践中的优势。
