Chen Shuxiang, Zhang Huijuan, Chen Yifan, Chen Shuo, Cao Wenfu, Tong Yongxiu
Department of Radiology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China.
Front Oncol. 2025 May 19;15:1573735. doi: 10.3389/fonc.2025.1573735. eCollection 2025.
Differentiating between benign and malignant pure ground-glass nodule (pGGN) is of great clinical significance. The aim of our study was to evaluate whether AI-derived quantitative parameters could predict benignity versus early-stage tumors manifesting as pGGN.
A total of 1,538 patients with pGGN detected by chest CT at different campuses of our hospital from May 2013 to December 2023 were retrospectively analyzed. This included CT and clinical data, as well as AI-derived quantitative parameters. All patients were randomly divided into a training group (n=893), an internal validation group (n=382), and an external validation group (n=263). Hazard factors for early-stage tumors were identified using univariate analysis and multivariate logistic regression analysis. Independent risk factors were then screened, and a prediction nomogram was constructed to maximize predictive efficacy and clinical application value. The performance of the nomogram was evaluated using ROC curves and calibration curves, while decision curve analysis (DCA) was used to assess the net benefit prediction threshold.
The final logistic model included nine independent predictors (age, location, minimum CT value, standard deviation, kurtosis, compactness, energy, costopleural distance, and volume) and was developed into a user-friendly nomogram. The AUCs of the ROC curves in the training, internal validation, and external validation cohorts were 0.696 (95% CI: 0.638-0.754), 0.627 (95% CI: 0.533-0.722), and 0.672 (95% CI: 0.543-0.801), respectively. The calibration plot demonstrated a good correlation between observed and predicted values, and the nomogram remained valid in the validation cohort. DCA showed that the model's predictive performance was acceptable, providing substantial net benefit for clinical application.
The clinical prediction nomogram, based on AI-derived quantitative parameters, visually displays an overall score to differentiate benign lesions from early-stage tumors manifesting as pGGN. This nomogram may serve as a convenient screening tool for clinical use and provides a reference for formulating individualized follow-up and treatment plans for patients with pGGN.
鉴别纯磨玻璃结节（pGGN）的良恶性具有重要的临床意义。本研究旨在评估人工智能衍生的定量参数能否预测表现为pGGN的良性病变与早期肿瘤。
回顾性分析2013年5月至2023年12月在我院不同院区经胸部CT检查发现pGGN的1538例患者。这包括CT和临床数据，以及人工智能衍生的定量参数。所有患者随机分为训练组（n = 893）、内部验证组（n = 382）和外部验证组（n = 263）。采用单因素分析和多因素逻辑回归分析确定早期肿瘤的危险因素。然后筛选独立危险因素，构建预测列线图以最大化预测效能和临床应用价值。使用ROC曲线和校准曲线评估列线图的性能，同时采用决策曲线分析（DCA）评估净效益预测阈值。
最终的逻辑模型包括九个独立预测因子（年龄、位置、最小CT值、标准差、峰度、致密性、能量、肋胸膜距离和体积），并开发成一个用户友好的列线图。训练组、内部验证组和外部验证组的ROC曲线下面积（AUC）分别为0.696（95%CI：0.638 - 0.754）、0.627（95%CI：0.533 - 0.722）和0.672（95%CI：0.543 - 0.801）。校准图显示观察值与预测值之间具有良好的相关性，列线图在验证队列中仍然有效。DCA表明该模型的预测性能可接受，为临床应用提供了显著的净效益。
基于人工智能衍生定量参数的临床预测列线图直观地显示总分，以区分表现为pGGN的良性病变与早期肿瘤。该列线图可作为临床方便的筛查工具，为制定pGGN患者的个体化随访和治疗方案提供参考。
