Zhao Wen, Zhao Ziqian, Wang Yingxia, Yang Haiyan, Zhang Weiyuan, Chen Jianyou, Yang Xinhui, Duan Zhijie, Li Fengyi, Han Zhiquan, Zhang Xin, Li Zhilin, Han Dan, Ke Tengfei
Department of Medical imaging, the First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Department of Pathology, the First Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
PLoS One. 2025 Sep 8;20(9):e0331336. doi: 10.1371/journal.pone.0331336. eCollection 2025.
Bronchiolar adenoma (BA) is a rare benign pulmonary neoplasm originating from the bronchial mucosal epithelium and mimics lung adenocarcinoma (LAC) both radiographically and microscopically. This study aimed to develop a nomogram for distinguishing BA from LAC by integrating clinical characteristics and artificial intelligence (AI)-derived histogram parameters across two medical centers.
This retrospective study included 215 patients with diagnoses confirmed by postoperative pathology from two medical centers. Medical center 1 provided 151 patients (68 BA and 83 LAC nodules) as the training cohort, while medical center 2 contributed 64 patients (28 BA and 36 LAC nodules) as the external validation cohort. Risk predictors and the nomogram were developed using clinical characteristics and AI-derived histogram parameters.
Nodule density (solid, ground glass, and subsolid) exhibited a statistically significant difference between the BA and LAC groups (p < 0.01). The following parameters were significantly higher in the LAC group compared to the BA group (all p < 0.05): 2D long diameter, 2D short diameter, 2D average diameter, 2D maximum surface area, 3D long diameter, 3D surface area, 3D volume, and entropy. In contrast, CT value variance was significantly lower in the LAC group than in the BA group (p < 0.01). A nomogram was constructed incorporating density, 2D short diameter, and CT value variance. The area under the curve (AUC) of the nomogram in the training and validation cohorts were 0.821, 0.811.
The AI-based nomogram, as a non-invasive preoperative tool, had the potential to enhance diagnostic accuracy for distinguishing BA from LAC.
细支气管腺瘤（BA）是一种罕见的起源于支气管黏膜上皮的良性肺肿瘤，在影像学和显微镜下均与肺腺癌（LAC）相似。本研究旨在通过整合两个医学中心的临床特征和人工智能（AI）衍生的直方图参数，开发一种用于区分BA和LAC的列线图。
这项回顾性研究纳入了来自两个医学中心的215例经术后病理确诊的患者。医学中心1提供了151例患者（68例BA和83例LAC结节）作为训练队列，而医学中心2贡献了64例患者（28例BA和36例LAC结节）作为外部验证队列。使用临床特征和AI衍生的直方图参数开发风险预测指标和列线图。
BA组和LAC组的结节密度（实性、磨玻璃和亚实性）存在统计学显著差异（p < 0.01）。与BA组相比，LAC组的以下参数显著更高（均p < 0.05）：二维长径、二维短径、二维平均直径、二维最大表面积、三维长径、三维表面积、三维体积和熵。相比之下，LAC组的CT值方差显著低于BA组（p < 0.01）。构建了一个包含密度、二维短径和CT值方差的列线图。训练队列和验证队列中列线图的曲线下面积（AUC）分别为0.821、0.811。
基于AI的列线图作为一种非侵入性术前工具，有可能提高区分BA和LAC的诊断准确性。
