Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Purchasing Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ann Med. 2024 Dec;56(1):2405075. doi: 10.1080/07853890.2024.2405075. Epub 2024 Sep 19.
Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters for invasiveness prediction.
Patients with stage 0-IB NSCLC after surgical resection were retrospectively analysed. Preoperative CTs were evaluated with specialized software for nodule segmentation and CT quantification. Pathology was the reference for invasiveness. Univariate and multivariate logistic regression assessed predictors of high-risk SPN.
Three hundred and fifty-five SPN were included. On multivariate analysis, CT value mean and nodule type (ground glass opacity vs. solid) were independent predictors of high-risk SPN. The area under the curve (AUC) was 0.811 for identifying high-risk nodules.
Quantitative CT measures and nodule type correlated with invasiveness. Software-based CT assessment shows potential for noninvasive prediction to guide extent of resection. Further prospective validation is needed, including comparison with benign nodules.
人工智能（AI）在评估计算机断层扫描（CT）上的孤立性肺结节（SPN）方面显示出了潜力。准确判断癌症侵袭性可以指导治疗。我们旨在研究用于侵袭性预测的定量 CT 参数。
回顾性分析了手术后 0-IB 期非小细胞肺癌患者。使用专门的软件对术前 CT 进行结节分割和 CT 量化评估。病理检查是侵袭性的参考标准。单变量和多变量逻辑回归评估了高危 SPN 的预测因子。
共纳入 355 个 SPN。多变量分析中，CT 值均值和结节类型（磨玻璃密度影与实性）是高危 SPN 的独立预测因子。识别高危结节的曲线下面积（AUC）为 0.811。
定量 CT 测量和结节类型与侵袭性相关。基于软件的 CT 评估显示出了用于指导切除范围的非侵入性预测的潜力。需要进一步的前瞻性验证，包括与良性结节的比较。
