Zhang Chengzhou, Zhou Huihui, Li Mengfei, Yang Xinyu, Liu Jinling, Dai Zhengjun, Ma Heng, Wang Ping
Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China.
Department of Pathology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, China.
Front Oncol. 2024 Jul 2;14:1427404. doi: 10.3389/fonc.2024.1427404. eCollection 2024.
This study aimed to explore the value of radiomics nomogram based on computed tomography (CT) on the diagnosis of benign and malignant solitary indeterminate smoothly marginated solid pulmonary nodules (SMSPNs).
This study retrospectively reviewed 205 cases with solitary indeterminate SMSPNs on CT, including 112 cases of benign nodules and 93 cases of malignant nodules. They were divided into training (n=143) and validation (n=62) cohorts based on different CT scanners. Radiomics features of the nodules were extracted from the lung window CT images. The variance threshold method, SelectKBest, and least absolute shrinkage and selection operator were used to select the key radiomics features to construct the rad-score. Through multivariate logistic regression analysis, a nomogram was built by combining rad-score, clinical factors, and CT features. The nomogram performance was evaluated by the area under the receiver operating characteristic curve (AUC).
A total of 19 radiomics features were selected to construct the rad-score, and the nomogram was constructed by the rad-score, one clinical factor (history of malignant tumor), and three CT features (including calcification, pleural retraction, and lobulation). The nomogram performed better than the radiomics model, clinical model, and experienced radiologists who specialized in thoracic radiology for nodule diagnosis. The AUC values of the nomogram were 0.942 in the training cohort and 0.933 in the validation cohort. The calibration curve and decision curve showed that the nomogram demonstrated good consistency and clinical applicability.
The CT-based radiomics nomogram achieved high efficiency in the preoperative diagnosis of solitary indeterminate SMSPNs, and it is of great significance in guiding clinical decision-making.
本研究旨在探讨基于计算机断层扫描（CT）的影像组学列线图在诊断边界光滑的孤立性实性肺结节（SMSPNs）良恶性方面的价值。
本研究回顾性分析了205例CT显示的孤立性不确定SMSPNs患者，其中良性结节112例，恶性结节93例。根据不同的CT扫描仪将其分为训练组（n = 143）和验证组（n = 62）。从肺窗CT图像中提取结节的影像组学特征。采用方差阈值法、SelectKBest法和最小绝对收缩和选择算子法选择关键影像组学特征以构建影像组学评分（rad-score）。通过多因素逻辑回归分析，将rad-score、临床因素和CT特征相结合构建列线图。采用受试者操作特征曲线（ROC）下面积（AUC）评估列线图的性能。
共选择19个影像组学特征构建rad-score，并由rad-score、一个临床因素（恶性肿瘤病史）和三个CT特征（包括钙化、胸膜凹陷和分叶）构建列线图。该列线图在结节诊断方面的表现优于影像组学模型、临床模型以及专门从事胸部放射学的经验丰富的放射科医生。训练组列线图的AUC值为0.942，验证组为0.933。校准曲线和决策曲线表明列线图具有良好的一致性和临床适用性。
基于CT的影像组学列线图在术前诊断孤立性不确定SMSPNs方面具有较高效率，对指导临床决策具有重要意义。
