Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Clin Respir J. 2023 Apr;17(4):320-328. doi: 10.1111/crj.13597. Epub 2023 Feb 5.
The potential of artificial intelligence (AI) to predict the nature of part-solid nodules based on chest computed tomography (CT) is still under exploration.
To determine the potential of AI to predict the nature of part-solid nodules.
Two hundred twenty-three patients diagnosed with part-solid nodules (241) by chest CT were retrospectively collected that were divided into benign group (104) and malignant group (137). Intraclass correlation coefficient (ICC) was used to assess the agreement in predicting malignancy, and the predictive effectiveness was compared between AI and senior radiologists. The parameters measured by AI and the size of solid components measured by senior radiologists were compared between two groups. Receiver operating characteristic (ROC) curve was chosen for calculating the Youden index of each quantitative parameter, which has statistical significance between two groups. Binary logistic regression performed on the significant indicators to suggest predictors of malignancy.
AI was in moderate agreement with senior radiologists (ICC = 0.686). The sensitivity, specificity and accuracy of two groups were close (p > 0.05). The longest diameter, volume and mean CT attenuation value and the largest diameter of solid components between benign and malignant groups were different significantly (p < 0.001). Logistic regression analysis showed that the longest diameter and mean CT attenuation value and the largest diameter of solid components were indicators for malignant part-solid nodules, the threshold of which were 9.45 mm, 425.0 HU and 3.45 mm, respectively.
Potential of quantitative parameter measured by AI to predict malignant part-solid nodules can provide a certain value for the clinical management.
人工智能（AI）在基于胸部计算机断层扫描（CT）预测部分实性结节性质方面的潜力仍在探索中。
确定 AI 预测部分实性结节性质的潜力。
回顾性收集了 223 例经胸部 CT 诊断为部分实性结节（241 个）的患者，分为良性组（104 个）和恶性组（137 个）。采用组内相关系数（ICC）评估预测恶性肿瘤的一致性，并比较 AI 和高级放射科医生的预测效果。比较两组 AI 测量的参数和高级放射科医生测量的实性成分大小。选择受试者工作特征（ROC）曲线计算每个定量参数的约登指数，该指数在两组间有统计学意义。对有统计学意义的指标进行二元逻辑回归，提示恶性肿瘤的预测指标。
AI 与高级放射科医生具有中度一致性（ICC=0.686）。两组的敏感性、特异性和准确性均接近（p>0.05）。良性组和恶性组之间最长直径、体积和平均 CT 衰减值以及实性成分的最大直径差异均有统计学意义（p<0.001）。Logistic 回归分析显示，最长直径、平均 CT 衰减值和实性成分的最大直径是恶性部分实性结节的预测指标，其阈值分别为 9.45mm、425.0HU 和 3.45mm。
AI 测量的定量参数预测恶性部分实性结节的潜力可为临床管理提供一定的价值。
