用于确定肿瘤性磨玻璃结节侵袭性的人工智能测量结节质量
Artificial intelligence-measured nodule mass for determining the invasiveness of neoplastic ground glass nodules.
作者信息
Xiong Ting-Wei, Gan Hui, Lv Fa-Jin, Zhang Xiao-Chuan, Fu Bin-Jie, Chu Zhi-Gang
机构信息
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Radiology, The Second Affiliated Hospital of Army Medical University, Chongqing, China.
出版信息
Quant Imaging Med Surg. 2024 Sep 1;14(9):6698-6710. doi: 10.21037/qims-24-665. Epub 2024 Aug 28.
BACKGROUND
The nodule mass is an important indicator for evaluating the invasiveness of neoplastic ground-glass nodules (GGNs); however, the efficacy of nodule mass acquired by artificial intelligence (AI) has not been validated. This study thus aimed to determine the efficacy of nodule mass measured by AI in predicting the invasiveness of neoplastic GGNs.
METHODS
From May 2019 to September 2023, a retrospective study was conducted on 755 consecutive patients comprising 788 pathologically confirmed neoplastic GGNs, among which 259 were adenocarcinoma in situ (AIS), 282 minimally invasive adenocarcinoma (MIA), and 247 invasive adenocarcinoma (IAC). Nodule mass was quantified using AI software, and other computed tomography (CT) features were concurrently evaluated. Clinical data and CT features were compared using the Kruskal-Wallis test or Pearson chi-square test. The predictive efficacy of mass and CT features for evaluating invasive lesions (ILs) (MIAs and IACs) and IACs was analyzed and compared via receiver operating characteristic (ROC) analysis and the Delong test.
RESULTS
ROC curve analysis revealed that the optimal cutoff value of mass for distinguishing ILs and AISs was 225.25 mg [area under the curve (AUC) 0.821; 95% confidence interval 0.792-0.847; sensitivity 64.27%; specificity 89.19%; P<0.001], and for differentiating IACs from AISs and MIAs, it was 390.4 mg (AUC 0.883; 95% confidence interval 0.858-0.904; sensitivity 80.57%; specificity 86.32%; P<0.001). The efficacy of nodule mass in distinguishing ILs and AISs was comparable to that of size (P=0.2162) and significantly superior to other CT features (each P value <0.001). Additionally, the ability of nodule mass to differentiate IACs from AISs and MIAs was significantly better than that of CT features (each P value <0.001).
CONCLUSIONS
AI-based nodule mass analysis is an effective indicator for determining the invasiveness of neoplastic GGNs.
背景
结节质量是评估肿瘤性磨玻璃结节（GGN）侵袭性的重要指标；然而，人工智能（AI）获取的结节质量的有效性尚未得到验证。因此，本研究旨在确定AI测量的结节质量在预测肿瘤性GGN侵袭性方面的有效性。
方法
2019年5月至2023年9月，对755例连续患者进行回顾性研究，这些患者包括788个经病理证实的肿瘤性GGN，其中259个为原位腺癌（AIS），282个为微浸润腺癌（MIA），247个为浸润性腺癌（IAC）。使用AI软件对结节质量进行量化，并同时评估其他计算机断层扫描（CT）特征。使用Kruskal-Wallis检验或Pearson卡方检验比较临床数据和CT特征。通过受试者操作特征（ROC）分析和Delong检验分析并比较质量和CT特征对评估侵袭性病变（ILs）（MIA和IAC）和IAC的预测效能。
结果
ROC曲线分析显示，区分ILs和AISs的质量最佳截断值为225.25mg[曲线下面积（AUC）0.821；95%置信区间0.792-0.847；灵敏度64.27%；特异性89.19%；P<0.001]，区分IACs与AISs和MIAs的质量最佳截断值为390.4mg（AUC 0.883；95%置信区间0.858-0.904；灵敏度80.57%；特异性86.32%；P<0.001）。结节质量区分ILs和AISs的效能与大小相当（P=0.2162），且显著优于其他CT特征（各P值<0.001）。此外，结节质量区分IACs与AISs和MIAs的能力显著优于CT特征（各P值<0.001）。
结论
基于AI的结节质量分析是确定肿瘤性GGN侵袭性的有效指标。
Zotero 插件