[Clinical Multi-features Analysis of Cystic Lung Adenocarcinoma 
and Construction of Invasive Risk Prediction Model].

BACKGROUND: Cystic lung cancer, a special type of lung cancer, has been paid more and more attention. The most common pathological type of cystic lung cancer is adenocarcinoma. The invasiveness of cystic lung adenocarcinoma is vital for the selection of clinical treatment and prognosis. The aim of this study is to analyze the multiple clinical features of cystic lung adenocarcinoma, explore the independent risk factors of its invasiveness, and establish a risk prediction model. METHODS: A total of 129 cases of cystic lung adenocarcinoma admitted to the Department of Thoracic Surgery of the First Affiliated Hospital of Nanjing Medical University from January 2021 to July 2022 were retrospectively analyzed and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA)] and invasive group [invasive adenocarcinoma (IAC)] according to pathological findings. There were 47 cases in the pre-invasive group, including 19 males and 28 females, with an average age of (51.23±14.96) years. There were 82 cases in the invasive group, including 60 males and 22 females, with an average age of (61.27±11.74) years. Multiple clinical features of the two groups were collected, including baseline data, imaging data and tumor markers. Univariate analysis, LASSO regression and multivariate Logistic regression analysis were used to screen out the independent risk factors of the invasiveness of cystic lung adenocarcinoma, and the risk prediction model was established. RESULTS: In univariate analysis, age, gender, smoking history, history of emphysema, neuron-specific enolase (NSE), number of cystic airspaces, lesion diameter, cystic cavity diameter, nodule diameter, solid components diameter, cyst wall nodule, smoothness of cyst wall, shape of cystic airspace, lobulation, short burr sign, pleural retraction, vascular penetration and bronchial penetration were statistically different between the pre-invasive group and invasive groups (P<0.05). The above variables were processed by LASSO regression dimensionality reduction and screened as follows: age, gender, smoking history, NSE, number of cystic airspaces, lesion diameter, cystic cavity diameter, cyst wall nodule, smoothness of cyst wall and lobulation. Then the above variables were included in multivariate Logistic regression analysis. Cyst wall nodule (P=0.035) and lobulation (P=0.001) were found to be independent risk factors for the invasiveness of cystic lung adenocarcinoma (P<0.05). The prediction model was established as follows: P=e^x/(1+e^x), x=-7.927+1.476* cyst wall nodule+2.407* lobulation, and area under the curve (AUC) was 0.950. CONCLUSIONS: Cyst wall nodule and lobulation are independent risk factors for the invasiveness of cystic lung adenocarcinoma, which have certain guiding significance for the prediction of the invasiveness of cystic lung adenocarcinoma.

CT imaging features of lung ground-glass nodule patients with upgraded intraoperative frozen pathology.

PURPOSE: Intraoperative frozen section pathology (FS) is widely used to guide surgical strategies while the accuracy is relatively low. Underestimating the pathological condition may result in inadequate surgical margins. This study aims to identify CT imaging features related to upgraded FS and develop a predictive model. METHODS: Collected data from 860 patients who underwent lung surgery from January to December 2019. We analyzed the consistency rate of FS and categorized the patients into three groups: Group 1 (n = 360) had both FS and Formalin-fixed Paraffin-embedded section (FP) as non-invasive adenocarcinoma (IAC); Group 2 (n = 128) had FS as non-IAC but FP as IAC; Group 3 (n = 372) had both FS and FP as IAC. Clinical baseline characteristics were compared and propensity score adjustment was used to mitigate the effects of these characteristics. Univariate analyses identified imaging features with inter-group differences. A multivariate analysis was conducted to screen independent risk factors for FS upgrade, after which a logistic regression prediction model was established and a receiver operating characteristic (ROC) curve was plotted. RESULTS: The consistency rate of FS with FP was 84.19%. 26.67% of the patients with non-IAC FS diagnosis were upgraded to IAC. The predictive model's Area Under Curve (AUC) is 0.785. Consolidation tumor ratio (CTR) ≤ 0.5 and smaller nodule diameter are associated with the underestimation of IAC in FS. CONCLUSION: CT imaging has the capacity to effectively detect patients at risk of upstaging during FS.