背景:孤立性肺结节的诊断一直是临床研究的难点和重点。尤其是肉芽肿结节(GNs)与分叶和针状征象,容易误诊为恶性肿瘤。因此,在这项研究中,我们利用CT深度学习(DL)模型来区分具有分叶和针状征象的GNs与实性肺腺癌(LADCs),提高术前诊断的准确性。
方法:对来自3家医疗机构的420例经病理证实的GNs和LADCs患者进行回顾性研究。识别并标记非增强CT(NECT)和静脉对比增强CT(VECT)中的感兴趣区域,并构建了自我监督标签。来自机构1的病例被随机分为训练集(TS)和内部验证集(IVS),来自机构2和3的病例被视为外部验证集(EVS)。使用自监督迁移学习进行训练和验证,并将结果与放射科医生的诊断结果进行比较。
结果:DL模型在区分GNs和LADC方面取得了良好的性能,IVS中的曲线下面积(AUC)值为0.917、0.876和0.896,NECT中的EVS为0.889、0.879和0.881,VECT,静脉造影增强CT(NEVECT)图像未增强,分别。放射科医生1、2、3和4的AUC为,分别,(IVS)中的0.739、0.783、0.883和0.901,以及EVS中的0.760、0.760、0.841和0.844。
结论:CTDL模型对术前从实体LADC中分化出具有分叶和毛刺征象的GNs具有重要价值,其预测性能高于放射科医师。
BACKGROUND: The diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tumors. Therefore, in this study, we utilised a CT deep learning (DL) model to distinguish GNs with lobulation and spiculation signs from solid lung adenocarcinomas (LADCs), to improve the diagnostic accuracy of preoperative diagnosis.
METHODS: 420 patients with pathologically confirmed GNs and LADCs from three medical institutions were retrospectively enrolled. The regions of interest in non-enhanced CT (NECT) and venous contrast-enhanced CT (VECT) were identified and labeled, and self-supervised labels were constructed. Cases from institution 1 were randomly divided into a training set (TS) and an internal validation set (IVS), and cases from institutions 2 and 3 were treated as an external validation set (EVS). Training and validation were performed using self-supervised transfer learning, and the results were compared with the radiologists\' diagnoses.
RESULTS: The DL model achieved good performance in distinguishing GNs and LADCs, with area under curve (AUC) values of 0.917, 0.876, and 0.896 in the IVS and 0.889, 0.879, and 0.881 in the EVS for NECT, VECT, and non-enhanced with venous contrast-enhanced CT (NEVECT) images, respectively. The AUCs of radiologists 1, 2, 3, and 4 were, respectively, 0.739, 0.783, 0.883, and 0.901 in the (IVS) and 0.760, 0.760, 0.841, and 0.844 in the EVS.
CONCLUSIONS: A CT DL model showed great value for preoperative differentiation of GNs with lobulation and spiculation signs from solid LADCs, and its predictive performance was higher than that of radiologists.