Palmeri Federica, Zerunian Marta, Polici Michela, Nardacci Stefano, De Dominicis Chiara, Allegra Bianca, Monterubbiano Andrea, Mancini Massimiliano, Ferrari Riccardo, Paolantonio Pasquale, De Santis Domenico, Laghi Andrea, Caruso Damiano
Department of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, Sapienza - University of Rome, Sant'Andrea University Hospital, Rome, Italy.
PhD School in Translational Medicine and Oncology, Department of Medical and Surgical Sciences and Translational Medicine, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy.
Radiol Med. 2025 May 22. doi: 10.1007/s11547-025-02022-x.
This study evaluated the performance of CT radiomics in distinguishing between lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) at baseline imaging, exploring its potential as a noninvasive virtual biopsy.
A retrospective analysis was conducted, enrolling 330 patients between September 2015 and January 2023. Inclusion criteria were histologically proven ADC or SCC and baseline contrast-enhanced chest CT. Exclusion criteria included significant motion artifacts and nodules < 6 mm. Radiological features, including lung lobe affected, peripheral/central location, presence of emphysema, and T/N radiological stage, were assessed for each patient. Volumetric segmentation of lung cancers was performed on baseline CT scans at the portal-venous phase using 3DSlicer software (v5.2.2). A total of 107 radiomic features were extracted and selected using the least absolute shrinkage and selection operator (LASSO) and tenfold cross-validation. Multivariable logistic regression analysis was employed to develop three predictive models: radiological features-only, radiomics-only, and a combined model, with statistical significance set at p < 0.05. Additionally, an independent external validation cohort of 16 patients, meeting the same inclusion and exclusion criteria, was identified.
The final cohort comprised 200 ADC and 100 SCC patients (mean age 68 ± 10 years, 184 men). Two radiological and 21 radiomic features were selected (p < 0.001). The Radiological model achieved AUC 0.73 (95% CI 0.68-0.78, p < 0.001), 72.3% accuracy. The radiomics model achieved AUC 0.80 (95% CI 0.75-0.85, p < 0.001), 75.6% accuracy. The combined model achieved AUC 0.84 (95% CI 0.80-0.88, p < 0.001), 75.3% accuracy. External validation (n = 15) yielded AUC 0.78 (p = 0.05).
The combined radiologic-radiomics model showed the best performance in differentiating ADC from SCC.
本研究评估了CT放射组学在基线成像时区分肺腺癌（ADC）和鳞状细胞癌（SCC）的性能，探讨其作为无创虚拟活检的潜力。
进行回顾性分析，纳入2015年9月至2023年1月期间的330例患者。纳入标准为组织学证实的ADC或SCC以及基线对比增强胸部CT。排除标准包括明显的运动伪影和直径小于6mm的结节。评估每位患者的放射学特征，包括受影响的肺叶、外周/中央位置、肺气肿的存在以及T/N放射学分期。使用3DSlicer软件（v5.2.2）在门静脉期对基线CT扫描进行肺癌的体积分割。使用最小绝对收缩和选择算子（LASSO）和十折交叉验证提取并选择了总共107个放射组学特征。采用多变量逻辑回归分析建立了三个预测模型：仅放射学特征模型、仅放射组学模型和联合模型，设定统计学显著性为p<0.05。此外，确定了一个由16名患者组成的独立外部验证队列，这些患者符合相同的纳入和排除标准。
最终队列包括200例ADC患者和100例SCC患者（平均年龄68±10岁，男性184例）。选择了两个放射学特征和21个放射组学特征（p<0.001）。放射学模型的AUC为0.73（95%CI 0.68-0.78，p<0.001），准确率为72.3%。放射组学模型的AUC为0.80（95%CI 0.75-0.85，p<0.001），准确率为75.6%。联合模型的AUC为0.84（95%CI 0.80-0.88，p<0.001），准确率为75.3%。外部验证（n=15）的AUC为0.78（p=0.05）。
放射学-放射组学联合模型在区分ADC和SCC方面表现最佳。
