Cui Xiaonan, Heuvelmans Marjolein A, Sidorenkov Grigory, Zhao Yingru, Fan Shuxuan, Groen Harry J M, Dorrius Monique D, Oudkerk Matthijs, de Bock Geertruida H, Vliegenthart Rozemarijn, Ye Zhaoxiang
Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Centre of Cancer, Tianjin, China.
Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
J Thorac Dis. 2021 Jul;13(7):4407-4417. doi: 10.21037/jtd-21-588.
To develop and validate a contrast-enhanced CT based classification tree model for classifying solid lung tumors in clinical patients into malignant or benign.
Between January 2015 and October 2017, 827 pathologically confirmed solid lung tumors (487 malignant, 340 benign; median size, 27.0 mm, IQR 18.0-39.0 mm) from 827 patients from a dedicated Chinese cancer hospital were identified. Nodules were divided randomly into two groups, a training group (575 cases) and a testing group (252 cases). CT characteristics were collected by two radiologists, and analyzed using a classification and regression tree (CART) model. For validation, we used the decision analysis threshold to evaluate the classification performance of the CART model and radiologist's diagnosis (benign; malignant) in the testing group.
Three out of 19 characteristics [margin (smooth; slightly lobulated/lobulated/spiculated), and shape (round/oval; irregular), subjective enhancement (no/uniform enhancement; heterogeneous enhancement)] were automatically generated by the CART model for classifying solid lung tumors. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the CART model is 98.5%, 58.1%, 80.6%, 98.6%, 79.8%, and 90.4%, 54.7%, 82.4% 98.5%, 74.2% for the radiologist's diagnosis by using three-threshold decision analysis.
Tumor margin and shape, and subjective tumor enhancement were the most important CT characteristics in the CART model for classifying solid lung tumors as malignant. The CART model had higher discriminatory power than radiologist's diagnosis. The CART model could help radiologists making recommendations regarding follow-up or surgery in clinical patients with a solid lung tumor.
开发并验证一种基于对比增强CT的分类树模型，用于将临床患者的实性肺肿瘤分为恶性或良性。
2015年1月至2017年10月期间，从一家专门的中国癌症医院的827例患者中确定了827个经病理证实的实性肺肿瘤（487个恶性，340个良性；中位大小27.0mm，IQR 18.0 - 39.0mm）。结节被随机分为两组，训练组（575例）和测试组（252例）。由两名放射科医生收集CT特征，并使用分类回归树（CART）模型进行分析。为进行验证，我们使用决策分析阈值来评估CART模型和放射科医生在测试组中的诊断（良性；恶性）分类性能。
CART模型自动生成了19个特征中的3个[边缘（光滑；轻度分叶/分叶状/毛刺状）、形状（圆形/椭圆形；不规则）、主观强化（无/均匀强化；不均匀强化）]用于实性肺肿瘤的分类。使用三阈值决策分析时，CART模型的敏感性、特异性、阳性预测值、阴性预测值和诊断准确性分别为98.5%、58.1%、80.6%、98.6%、79.8%，放射科医生诊断的分别为90.4%、54.7%、82.4%、98.5%、74.2%。
肿瘤边缘和形状以及主观肿瘤强化是CART模型中将实性肺肿瘤分类为恶性的最重要CT特征。CART模型比放射科医生的诊断具有更高的鉴别力。CART模型可以帮助放射科医生对患有实性肺肿瘤的临床患者提出随访或手术建议。
