A Novel Scale for Diagnosis of Pulmonary Ground-Glass Nodules: A Multicenter and Ambispective Cohort Study

IF 1.9 4区医学 Q3 RESPIRATORY SYSTEM Clinical Respiratory Journal Pub Date : 2024-11-09 DOI:10.1111/crj.70027

Minhao Yu, Yalin Cheng, Tao Wen, Liming Zhang, Xiubo Wei, Yonghong Wang, Jiang Du, GuangKe Xie, Lei Bi

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Abstract

Background

A screening tool was devised to aid the diagnosis and treatment of ground-glass nodules (GGNs).

Methods

The current ambispective cohort study included retrospective collation of 20 variables synthesizing a patient's clinical characteristics, serum tumor markers, and CT results, which allowed division into noninvasive (benign, atypical adenomatous hyperplasia, and adenocarcinoma in situ) and invasive (minimally invasive and invasive adenocarcinomas) tumors to build a prediction nomogram and GGN screening scale. The model was verified internally. A prospective cohort of patients was randomly divided by envelope method into those assessed by the GGN screening scale and those assessed via CT values. The diagnostic efficiencies were compared to allow external verification of the model.

Result

A total of 223 patients with 225 GGNs were recruited into the retrospective cohort between January 2021 and December 2022. Multivariable analysis showed sex, diameter, air bronchogram, and vessel convergence sign to be independent factors for prediction of noninvasive and invasive GGNs. Internal verification showed the model had a sensitivity of 70.7% and specificity of 75.0% with the Youden index at 0.457 and area under the curve (AUC) of 0.793 (95% CI: 0.734–0.852). Calibration curves indicated good internal stability (p = 0.357). Between January 2023 and March 2023, 147 patients with 148 GGNs were recruited into the prospective cohort. External verification showed the model had a sensitivity of 92.4% and specificity of 40.0% with the Youden index at 0.324 and AUC of 0.678 (95% CI: 0.509–0.847). Calibration curves indicated good external stability (p = 0.088). The scale was shown to have a sensitivity of 75.00%, specificity of 37.50%, positive predictive value of 91.53%, negative predictive value of 14.29%, and accuracy of 71.25%.

Conclusion

The GGN screening scale has high sensitivity and accuracy, making it suitable for diagnosis of GGNs.

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诊断肺磨玻璃结节的新型量表：一项多中心、前瞻性队列研究

背景：为帮助诊断和治疗磨玻璃结节（GGN），设计了一种筛查工具：我们设计了一种筛查工具来帮助诊断和治疗磨玻璃结节（GGNs）：目前的前瞻性队列研究包括对患者的临床特征、血清肿瘤标志物和 CT 结果等 20 个变量进行回顾性整理，从而将肿瘤分为非侵袭性肿瘤（良性、非典型腺瘤性增生和原位腺癌）和侵袭性肿瘤（微侵袭性和侵袭性腺癌），以建立预测提名图和 GGN 筛查量表。该模型已经过内部验证。通过包络法将一组前瞻性患者随机分为通过 GGN 筛查量表评估的患者和通过 CT 值评估的患者。对诊断效率进行比较，以便对模型进行外部验证：结果：2021年1月至2022年12月期间，共有223名225个GGN的患者被纳入回顾性队列。多变量分析显示，性别、直径、气管造影和血管汇聚征是预测无创和有创 GGN 的独立因素。内部验证显示，该模型的灵敏度为 70.7%，特异度为 75.0%，尤登指数为 0.457，曲线下面积（AUC）为 0.793（95% CI：0.734-0.852）。校准曲线显示出良好的内部稳定性（p = 0.357）。2023 年 1 月至 2023 年 3 月期间，前瞻性队列招募了 147 名患者，其中有 148 名 GGN。外部验证显示，该模型的灵敏度为 92.4%，特异性为 40.0%，尤登指数为 0.324，AUC 为 0.678（95% CI：0.509-0.847）。校准曲线显示出良好的外部稳定性（p = 0.088）。该量表的灵敏度为 75.00%，特异度为 37.50%，阳性预测值为 91.53%，阴性预测值为 14.29%，准确度为 71.25%：GGN筛查量表具有较高的灵敏度和准确性，适用于GGN的诊断。

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来源期刊

Clinical Respiratory Journal 医学-呼吸系统

CiteScore

3.70

自引率

0.00%

发文量

104

审稿时长

>12 weeks

期刊介绍： Overview Effective with the 2016 volume, this journal will be published in an online-only format. Aims and Scope The Clinical Respiratory Journal (CRJ) provides a forum for clinical research in all areas of respiratory medicine from clinical lung disease to basic research relevant to the clinic. We publish original research, review articles, case studies, editorials and book reviews in all areas of clinical lung disease including: Asthma Allergy COPD Non-invasive ventilation Sleep related breathing disorders Interstitial lung diseases Lung cancer Clinical genetics Rhinitis Airway and lung infection Epidemiology Pediatrics CRJ provides a fast-track service for selected Phase II and Phase III trial studies. Keywords Clinical Respiratory Journal, respiratory, pulmonary, medicine, clinical, lung disease, Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Embase (Elsevier) Health & Medical Collection (ProQuest) Health Research Premium Collection (ProQuest) HEED: Health Economic Evaluations Database (Wiley-Blackwell) Hospital Premium Collection (ProQuest) Journal Citation Reports/Science Edition (Clarivate Analytics) MEDLINE/PubMed (NLM) ProQuest Central (ProQuest) Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier)