Quantitative imaging in medicine and surgery · 2026
Differential diagnosis of benign and malignant pulmonary nodules attached to the interlobar pleura.
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Yang Xin-Yi, Liao Ji-Chun, Li Xian, Wang Yi, Li Qi
Аннотация
BACKGROUND: Pulmonary nodules attached to the interlobar pleura (PNs-AIP) represent a distinct subset of pulmonary nodules (PNs), for which accurate differentiation between benign and malignant status is critical for guiding clinical decisions. Chest computed tomography (CT) has become an effective method for evaluating PNs. However, the correlation between specific CT characteristics of PNs-AIP-particularly their spatial relationship with the interlobar pleura (IP)-and their pathological nature remains inadequately understood. Therefore, this study aimed to investigate the clinical and CT features of PNs-AIP, with a particular focus on their spatial relationship with the IP, and to identify features that may help to distinguish benign from malignant PNs-AIP, thereby predicting malignant PNs-AIP.
METHODS: A retrospective review was conducted of the clinical and CT imaging data from 464 patients diagnosed with PNs-AIP. We assigned the patients to benign and malignant cohorts based on either surgical pathology or the complete resolution observed in follow-up CT scans. To identify discriminative features, we utilized univariate analysis to compare clinical and CT characteristics across the two groups. Based on these findings, we successively constructed two binary logistic regression models to identify predictors associated with malignant PNs-AIP. In addition to the clinical and morphological nodular features utilized in Model 1, Model 2 further incorporated CT features related to the IP. The performance of both models was subsequently compared to identify which one had superior efficacy. Additionally, 116 patients from a different medical center were enrolled and utilized for external validation.
RESULTS: The univariate analysis identified two clinical features and 10 CT features that exhibited significant differences between benign and malignant PNs-AIP (all P<0.05). Model 1 indicated that age ≥56 years, female sex, nodule size ≥11.80 mm, regular shape, lobulation, spiculation, and subsolid density were independent predictors of malignant PNs-AIP, obtaining an area under the curve (AUC) of 0.863. In contrast, Model 2 revealed that alongside predictors from Model 1, nodule-IP angle type I and nodule-IP positional relationship type IV were also independent predictors of malignant PNs-AIP, achieving an improved AUC of 0.886. In the external validation cohort, Model 1 and Model 2 achieved AUCs of 0.859 and 0.903 in the external validation cohort, respectively. Model 2 significantly outperformed Model 1 in both cohorts, as evidenced by the DeLong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) (P<0.05).
CONCLUSIONS: Benign and malignant PNs-AIP exhibit distinct clinical and imaging characteristics. In addition to the morphological features of nodules, a careful assessment of the relationship between the nodules and the IP will enhance the diagnostic accuracy of PNs-AIP.
METHODS: A retrospective review was conducted of the clinical and CT imaging data from 464 patients diagnosed with PNs-AIP. We assigned the patients to benign and malignant cohorts based on either surgical pathology or the complete resolution observed in follow-up CT scans. To identify discriminative features, we utilized univariate analysis to compare clinical and CT characteristics across the two groups. Based on these findings, we successively constructed two binary logistic regression models to identify predictors associated with malignant PNs-AIP. In addition to the clinical and morphological nodular features utilized in Model 1, Model 2 further incorporated CT features related to the IP. The performance of both models was subsequently compared to identify which one had superior efficacy. Additionally, 116 patients from a different medical center were enrolled and utilized for external validation.
RESULTS: The univariate analysis identified two clinical features and 10 CT features that exhibited significant differences between benign and malignant PNs-AIP (all P<0.05). Model 1 indicated that age ≥56 years, female sex, nodule size ≥11.80 mm, regular shape, lobulation, spiculation, and subsolid density were independent predictors of malignant PNs-AIP, obtaining an area under the curve (AUC) of 0.863. In contrast, Model 2 revealed that alongside predictors from Model 1, nodule-IP angle type I and nodule-IP positional relationship type IV were also independent predictors of malignant PNs-AIP, achieving an improved AUC of 0.886. In the external validation cohort, Model 1 and Model 2 achieved AUCs of 0.859 and 0.903 in the external validation cohort, respectively. Model 2 significantly outperformed Model 1 in both cohorts, as evidenced by the DeLong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) (P<0.05).
CONCLUSIONS: Benign and malignant PNs-AIP exhibit distinct clinical and imaging characteristics. In addition to the morphological features of nodules, a careful assessment of the relationship between the nodules and the IP will enhance the diagnostic accuracy of PNs-AIP.