Medicine Institute, Cleveland Clinic, Cleveland, OH.
Respiratory Institute, Cleveland Clinic, Cleveland, OH.
Chest. 2019 Aug;156(2):367-375. doi: 10.1016/j.chest.2019.01.038. Epub 2019 Mar 30.
Malignancy probability models for pulmonary nodules (PN) are most accurate when used within populations similar to those in which they were developed. Our goal was to develop a malignancy probability model that estimates the probability of malignancy for PNs considered high enough risk to recommend biopsy.
This retrospective analysis included training and validation datasets of patients with PNs who had a histopathologic diagnosis of malignant or benign. Radiographic and clinical characteristics associated with lung cancer were collected. Univariate logistic regression was used to identify potential predictors. Stepdown selection and multivariate logistic regression were used to build several models, each differing according to available data.
Two hundred malignant nodules and 101 benign nodules were used to generate and internally validate eight models. Predictors of lung cancer used in the final models included age, smoking history, upper lobe location, solid and irregular/spiculated nodule edges, emphysema, fluorodeoxyglucose-PET avidity, and history of cancer other than lung. The concordance index (C-index) of the models ranged from 0.75 to 0.81. They were more accurate than the Mayo Clinic model (P < .05 for four of the models), and each had fair to excellent calibration. In an independent sample used for validation, the C-index for our model was 0.67 compared with 0.63 for the Mayo Clinic model. The ratio of malignant to benign nodules within each probability decile showed a greater potential to influence clinical decisions than the Mayo Clinic model.
We developed eight models to help characterize PNs considered high enough risk by a clinician to recommend biopsy. These models may help to guide clinicians' decision-making and be used as a resource for patient communication.
用于肺部结节（PN）的恶性概率模型在与开发模型时所采用的人群相似时最为准确。我们的目标是开发一种恶性概率模型，该模型用于估计需要进行活检的高危肺部结节的恶性概率。
本回顾性分析纳入了经组织病理学诊断为恶性或良性的肺部结节患者的训练数据集和验证数据集。收集了与肺癌相关的影像学和临床特征。采用单变量逻辑回归确定潜在预测因子。采用逐步选择和多变量逻辑回归建立了几种模型，每种模型根据可用数据而有所不同。
使用 200 个恶性结节和 101 个良性结节生成并内部验证了 8 个模型。最终模型中用于预测肺癌的预测因子包括年龄、吸烟史、上叶位置、实性和不规则/分叶状结节边缘、肺气肿、氟脱氧葡萄糖-PET 摄取率以及非肺癌的癌症病史。模型的一致性指数（C 指数）范围为 0.75 至 0.81。它们比 Mayo 诊所模型更准确（四个模型的 P 值均<.05），且均具有良好至优秀的校准度。在用于验证的独立样本中，我们的模型的 C 指数为 0.67，而 Mayo 诊所模型为 0.63。在每个概率十分位数中，恶性结节与良性结节的比值更能影响临床决策，优于 Mayo 诊所模型。
我们开发了 8 种模型来帮助确定临床医生认为有足够高风险需要进行活检的肺部结节。这些模型可能有助于指导临床医生的决策，并可作为患者沟通的资源。
