Zhao Yunqing, Ye Zhaoxiang, Yan Qingna, Sun Haoran, Zhao Fengnian
Department of Radiology, Chinese Academy of Medical Sciences Institute of Hematology and Blood Diseases Hospital, Tianjin, China.
Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
J Thorac Dis. 2024 Oct 31;16(10):6713-6726. doi: 10.21037/jtd-24-775. Epub 2024 Oct 12.
Limited surgery is deemed advantageous due to its potential to minimize damage and preserve a greater extent of functional lung tissue, contingent upon the invasiveness of lung adenocarcinoma (ADC). The aim of this study was to non-invasively predict the invasiveness of ground-glass opacity (GGO) predominant nodules presented on preoperative computed tomography (CT) of ADC patients with clinical stage Ia.
We constructed a primary cohort comprising 437 clinical stage Ia ADC patients from the Tianjin Medical University Cancer Institute and Hospital and utilized data from 135 patients from the Tianjin Medical University General Hospital for validation. Radiomics features were extracted by the PyRadiomics software and screened by spearman correlation analysis, minimum redundancy maximum relevance and the least absolute shrinkage and selection operator (LASSO) regression analysis. The radiomics score (Rad-score) formula was then created by linearly combining the selected features, using their regression coefficients as weights. Univariate analysis followed by multivariable logistic regression were performed to estimate the independent predictors. An initial univariate analysis was followed by a multivariable logistic regression to estimate independent predictors. Area under the curve (AUC) was calculated after the model established through visual nomogram and external validation.
Three hundred and seventy-four patients were pathologically confirmed as invasive ADC (65.4%), and three independent predictors were identified: maximum consolidation diameter (P=0.02), texture (P=0.042) and Rad-score (P<0.001). The combined model showed good calibration with an AUC of 0.911 [95% confidence interval (CI): 0.872, 0.951], compared with 0.883 (95% CI: 0.849, 0.932; DeLong's test P=0.16) and 0.842 (95% CI: 0.801, 0.896; DeLong's test P<0.001) when radiomics or CT semantic features were used alone. Combined prediction model accuracy for validation group was 0.865 (95% CI: 0.816, 0.908), which is reasonable.
Our study has provided a non-invasive prediction tool based on radiomics and CT semantic characteristics that can accurately assess the quantitative risk associated with the invasiveness of GGO predominant ADC in clinical stage Ia.
鉴于有限手术有可能将损害降至最低并保留更大范围的功能性肺组织，其优势取决于肺腺癌（ADC）的侵袭性。本研究的目的是对临床Ia期ADC患者术前计算机断层扫描（CT）上以磨玻璃影（GGO）为主的结节的侵袭性进行无创预测。
我们构建了一个包含来自天津医科大学肿瘤医院的437例临床Ia期ADC患者的原始队列，并利用来自天津医科大学总医院的135例患者的数据进行验证。通过PyRadiomics软件提取影像组学特征，并通过斯皮尔曼相关性分析、最小冗余最大相关性分析和最小绝对收缩和选择算子（LASSO）回归分析进行筛选。然后，通过将选定特征线性组合，并以其回归系数作为权重，创建影像组学评分（Rad-score）公式。进行单因素分析，随后进行多因素逻辑回归分析以估计独立预测因子。在通过视觉列线图和外部验证建立模型后计算曲线下面积（AUC）。
374例患者病理确诊为浸润性ADC（65.4%），并确定了三个独立预测因子：最大实性直径（P=0.02）、纹理（P=0.042）和Rad-score（P<0.001）。联合模型显示出良好的校准，AUC为0.911[95%置信区间（CI）：0.872，0.951]，而单独使用影像组学或CT语义特征时，AUC分别为0.883（95%CI：0.849，0.932；德龙检验P=0.16）和0.842（95%CI：0.801，0.896；德龙检验P<0.001）。验证组的联合预测模型准确率为0.865（95%CI：0.816，0.908），这是合理的。
我们的研究提供了一种基于影像组学和CT语义特征的无创预测工具，可准确评估临床Ia期以GGO为主的ADC侵袭性相关的定量风险。
