Luo Jieli, Jin Peile, Chen Jifan, Chen Yajun, Qiu Fuqiang, Wang Tingting, Zhang Ying, Pan Huili, Hong Yurong, Huang Pintong
Department of Ultrasound in Medicine, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China.
Research Center of Ultrasound in Medicine and Biomedical Engineering, Zhejiang University School of Medicine Second Affiliated Hospital, Zhejiang University, Hangzhou, China.
Front Oncol. 2023 Mar 9;13:1048205. doi: 10.3389/fonc.2023.1048205. eCollection 2023.
Conventional ultrasound (CUS) is the first choice for discrimination benign and malignant lymphadenectasis in supraclavicular lymph nodes (SCLNs), which is important for the further treatment. Radiomics provide more comprehensive and richer information than radiographic images, which are imperceptible to human eyes.
This study aimed to explore the clinical value of CUS-based radiomics analysis in preoperative differentiation of malignant from benign lymphadenectasis in CUS suspected SCLNs.
The characteristics of CUS images of 189 SCLNs were retrospectively analyzed, including 139 pathologically confirmed benign SCLNs and 50 malignant SCLNs. The data were randomly divided (7:3) into a training set (n=131) and a validation set (n=58). A total of 744 radiomics features were extracted from CUS images, radiomics score (Rad-score) built were using least absolute shrinkage and selection operator (LASSO) logistic regression. Rad-score model, CUS model, radiomics-CUS (Rad-score + CUS) model, clinic-radiomics (Clin + Rad-score) model, and combined CUS-clinic-radiomics (Clin + CUS + Rad-score) model were built using logistic regression. Diagnostic accuracy was assessed by receiver operating characteristic (ROC) curve analysis.
A total of 20 radiomics features were selected from 744 radiomics features and calculated to construct Rad-score. The AUCs of Rad-score model, CUS model, Clin + Rad-score model, Rad-score + CUS model, and Clin + CUS + Rad-score model were 0.80, 0.72, 0.85, 0.83, 0.86 in the training set and 0.77, 0.80, 0.82, 0.81, 0.85 in the validation set. There was no statistical significance among the AUC of all models in the training and validation set. The calibration curve also indicated the good predictive performance of the proposed nomogram.
The Rad-score model, derived from supraclavicular ultrasound images, showed good predictive effect in differentiating benign from malignant lesions in patients with suspected supraclavicular lymphadenectasis.
传统超声(CUS)是鉴别锁骨上淋巴结(SCLNs)良恶性淋巴结肿大的首选方法,这对进一步治疗很重要。放射组学比人类肉眼无法察觉的影像学图像提供更全面、更丰富的信息。
本研究旨在探讨基于CUS的放射组学分析在术前鉴别CUS怀疑的SCLNs中恶性与良性淋巴结肿大的临床价值。
回顾性分析189个SCLNs的CUS图像特征,包括139个经病理证实的良性SCLNs和50个恶性SCLNs。数据随机分为(7:3)训练集(n = 131)和验证集(n = 58)。从CUS图像中提取744个放射组学特征,使用最小绝对收缩和选择算子(LASSO)逻辑回归构建放射组学评分(Rad-score)。使用逻辑回归构建Rad-score模型、CUS模型、放射组学-CUS(Rad-score + CUS)模型、临床-放射组学(Clin + Rad-score)模型和联合CUS-临床-放射组学(Clin + CUS + Rad-score)模型。通过受试者操作特征(ROC)曲线分析评估诊断准确性。
从744个放射组学特征中选择20个放射组学特征并计算以构建Rad-score。Rad-score模型、CUS模型、Clin + Rad-score模型、Rad-score + CUS模型和Clin + CUS + Rad-score模型在训练集中的AUC分别为0.80、0.72、0.85、0.83、0.86,在验证集中的AUC分别为0.77、0.80、0.82、0.81、0.85。训练集和验证集中所有模型的AUC之间无统计学意义。校准曲线也表明所提出的列线图具有良好的预测性能。
源自锁骨上超声图像的Rad-score模型在鉴别疑似锁骨上淋巴结肿大患者的良恶性病变方面显示出良好的预测效果。