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A nomogram for preoperative prediction of invasiveness in solitary pulmonary adenocarcinoma: a multicenter study
Xiaocui Liu1,2,3, Dan Wang4, Xiuying Yang5
1Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background:
The invasive pulmonary adenocarcinoma and preinvasive-minimally invasive lesions usually need different surgical operation methods. This study aims to develop a clinical prediction model for preoperatively assessing invasiveness in solitary pulmonary adenocarcinoma.
Methods:
From January 2020 to December 2024, patients with solitary pulmonary nodules who underwent preoperative computed tomography (CT) scans in four centers were included. The patients were divided into a training dataset and an external testing dataset. Based on postoperative histopathology, patients were categorized into group A (atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma) and group B (invasive adenocarcinoma). Logistic regression analyses were performed to establish a clinical prediction model and of which its performance was validated. Receiver operating characteristic curve was used to quantify discriminative ability, and a nomogram was generated to visualize the individualized risk probability.
Results:
Four hundred and forty-eight patients were included, with 335 patients in the training dataset (Group A: 156; Group B: 179) and 113 patients in the external testing dataset (Group A: 53; Group B: 60). Four independent predictors were identified in the training dataset: cytokeratin 19 fragment (CYFRA 21-1) [odds ratio (OR): 4.175; 95% confidence interval (CI): 1.253-13.904; P=0.02], maximum diameter (OR: 1.247; 95% CI: 1.143-1.361; P<0.001), density type (OR: 6.604; 95% CI: 3.519-12.393; P<0.001), and air bronchogram (OR: 3.149; 95% CI: 1.406-7.051; P=0.005). The nomogram model demonstrated a robust diagnostic performance with area under the curves (AUCs) of 0.925 (95% CI: 0.897-0.953) in the training cohort and 0.895 (95% CI: 0.829-0.961) in the testing cohort.
Conclusions:
The integration of tumor markers with CT imaging features enables preoperative noninvasive prediction of invasiveness in pulmonary adenocarcinoma.
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