Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules

Abstract

Background There is a lack of clinical-radiological predictive models for the small (≤ 20 mm) solitary pulmonary nodules (SPNs). We aim to establish a clinical-radiological predictive model for differentiating malignant and benign small SPNs. Materials and methods Between January 2013 and December 2018, a retrospective cohort of 250 patients with small SPNs was used to construct the predictive model. A second retrospective cohort of 101 patients treated between January 2019 and December 2020 was used to independently test the model. The model was also compared to two other models that had previously been identified. Results In the training group, 250 patients with small SPNs including 156 (62.4%) malignant SPNs and 94 (37.6%) benign SPNs patients were included. Multivariate logistic regression analysis indicated that older age, pleural retraction sign, CT bronchus sign, and higher CEA level were the risk factors of malignant small SPNs. The predictive model was established as: X = − 10.111 + [0.129 × age (y)] + [1.214 × pleural retraction sign (present = 1; no present = 0)] + [0.985 × CT bronchus sign (present = 1; no present = 0)] + [0.21 × CEA level (ug/L)]. Our model had a significantly higher region under the receiver operating characteristic (ROC) curve (0.870; 50% CI: 0.828–0.913) than the other two models. Conclusions We established and validated a predictive model for estimating the pre-test probability of malignant small SPNs, that can help physicians to choose and interpret the outcomes of subsequent diagnostic tests.

Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis

“…These 20 studies (Table 1 ) included 5171 total SPNs (malignant: 3662; benign: 1509). PET/CT results were included in 7 of these studies [ 14 , 15 , 18 – 20 , 22 , 24 ], while 6 included tumor marker results [ 11 , 16 , 23 , 24 , 27 , 29 ]. Moreover, 10 studies had predictive models consisting of > 4 factors [ 14 – 16 , 20 , 21 , 23 – 27 , 30 ].…”

Development and external validation of a multimodal integrated feature neural network (MIFNN) for the diagnosis of malignancy in small pulmonary nodules (≤10 mm)

“…Chae et al [14] achieved an AUC of 0.85 using deep learning for nodules 20 mm, while Zhang et al [15] reported an internal validation AUC of 0.85 for nodules ranging from 5-20 mm. In contrast, both Chen et al [11] and Zhao et al [12] externally validated their models, achieving AUC of 0.847 and 0.870 for nodule size 8-20 mm and 20 mm, respectively. Notably, these models target nodules of varying sizes, complicating direct performance comparisons with our MIFNN model.…”

“…Recent studies focusing on the diagnosis of small pulmonary nodules, including the nomograms proposed by Chen et al [11] and Zhao et al [12], as well as the machine learning models introduced by Zhang et al [13] and Chae et al [14], primarily address nodules smaller than 20 mm in diameter. Mao et al's work [15], in particular, targets nodules ranging from 6 to 15 mm.…”

18F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis

“…In cases where these nodules are > 6 mm in size, computed tomography (CT)-based routine follow-up is warranted [ 4 ], with a 1.1-fold increase in the risk of PN malignancy with each 1 mm increase in diameter [ 5 ]. Analyses of patient clinical data and CT imaging findings are the most commonly used approach to PN diagnosis [ 6 – 8 ].…”

“…CT features often indicative of PN malignancy include CT bronchus sign, vascular convergence sign, pleural retraction, lobulation, and spiculated sign [ 6 – 8 ]. Clinical risk factors for PN malignancy include more advanced age, elevated serum levels of tumor marker proteins, and a history of smoking [ 6 , 9 ].…”