Clinical features combined with ultrasound-based radiomics nomogram for discrimination between benign and malignant lesions in ultrasound suspected supraclavicular lymphadenectasis.

IF 3.5 3区 医学 Q2 ONCOLOGY Frontiers in Oncology Pub Date : 2023-01-01 DOI:10.3389/fonc.2023.1048205
Jieli Luo, Peile Jin, Jifan Chen, Yajun Chen, Fuqiang Qiu, Tingting Wang, Ying Zhang, Huili Pan, Yurong Hong, Pintong Huang
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Abstract

Background: 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.

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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超声疑似锁骨上淋巴结肿大的临床特征结合超声放射组学影像学鉴别良恶性病变。
背景:常规超声(CUS)是鉴别锁骨上淋巴结(SCLNs)良恶性淋巴结肿大的首选方法,对进一步治疗具有重要意义。放射组学提供的信息比人眼难以察觉的放射影像更全面、更丰富。目的:探讨基于CUS的放射组学分析在疑似scns淋巴结肿大术前良恶性鉴别中的临床价值。方法:回顾性分析189例scns的影像学特征,其中病理证实的良性scns 139例,恶性scns 50例。数据按7:3的比例随机分为训练集(n=131)和验证集(n=58)。从CUS图像中提取744个放射组学特征,采用最小绝对收缩和选择算子(LASSO)逻辑回归建立放射组学评分(Rad-score)。采用logistic回归方法建立Rad-score模型、CUS模型、放射组学-CUS (Rad-score + CUS)模型、临床-放射组学(clinin + Rad-score)模型、临床-放射组学(clinin + CUS + Rad-score)联合模型。采用受试者工作特征(ROC)曲线分析评估诊断准确性。结果:从744个放射组学特征中选取20个放射组学特征,计算构建Rad-score。Rad-score模型、CUS模型、Clin + Rad-score模型、Rad-score + CUS模型和Clin + CUS + Rad-score模型的auc在训练集中分别为0.80、0.72、0.85、0.83、0.86,在验证集中分别为0.77、0.80、0.82、0.81、0.85。训练集和验证集各模型的AUC差异无统计学意义。标定曲线也表明所提出的模态图具有良好的预测性能。结论:基于锁骨上超声图像的rad评分模型对疑似锁骨上淋巴结肿大的良恶性鉴别有较好的预测作用。
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来源期刊
Frontiers in Oncology
Frontiers in Oncology Biochemistry, Genetics and Molecular Biology-Cancer Research
CiteScore
6.20
自引率
10.60%
发文量
6641
审稿时长
14 weeks
期刊介绍: Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.
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