ABSTRACT
@#Objective To evaluate the clinical radiological features combined with circulating tumor cells (CTCs) in the diagnosis of invasiveness evaluation of subsolid nodules in lung cancers. Methods Clinical data of 296 patients from the First Hospital of Lanzhou University between February 2019 and February 2021 were retrospectively included. There were 130 males and 166 females with a median age of 62.00 years. Patients were randomly divided into a training set and an internal validation set with a ratio of 3 : 1 by random number table method. The patients were divided into two groups: a preinvasive lesion group (atypical adenomatoid hyperplasia and adenocarcinoma in situ) and an invasive lesion group (microinvasive adenocarcinoma and invasive adenocarcinoma). Independent risk factors were selected by regression analysis of training set and a Nomogram prediction model was constructed. The accuracy and consistency of the model were verified by the receiver operating characteristic curve and calibration curve respectively. Subgroup analysis was conducted on nodules with different diameters to further verify the performance of the model. Specific performance metrics, including sensitivity, specificity, positive predictive value, negative predictive value and accuracy at the threshold were calculated. Results Independent risk factors selected by regression analysis for subsolid nodules were age, CTCs level, nodular nature, lobulation and spiculation. The Nomogram prediction mode provided an area under the curve (AUC) of 0.914 (0.872, 0.956), outperforming clinical radiological features model AUC [0.856 (0.794, 0.917), P=0.003] and CTCs AUC [0.750 (0.675, 0.825), P=0.001] in training set. C-index was 0.914, 0.894 and corrected C-index was 0.902, 0.843 in training set and internal validation set, respectively. The AUC of the prediction model in training set was 0.902 (0.848, 0.955), 0.913 (0.860, 0.966) and 0.873 (0.730, 1.000) for nodule diameter of 5-20 mm, 10-20 mm and 21-30 mm, respectively. Conclusion The prediction model in this study has better diagnostic value, and is more effective in clinical diagnosis of diseases.
ABSTRACT
@#Objective To explore the independent risk factors for benign and malignant subsolid pulmonary nodules and establish a malignant probability prediction model. Methods A retrospective analysis was performed in 443 patients with subsolid pulmonary nodules admitted to Subei People's Hospital of Jiangsu Province from 2014 to 2018 with definite pathological findings. The patients were randomly divided into a modeling group and a validation group. There were 296 patients in the modeling group, including 125 males and 171 females, with an average age of 55.9±11.1 years. There were 147 patients in the verification group, including 68 males and 79 females, with an average age of 56.9±11.6 years. Univariate and multivariate analysis was used to screen the independent risk factors for benign and malignant lesions of subsolid pulmonary nodules, and then a prediction model was established. Based on the validation data, the model of this study was compared and validated with Mayo, VA, Brock and PKUPH models. Results Univariate and multivariate analysis showed that gender, consolidation/tumor ratio (CTR), boundary, spiculation, lobulation and carcinoembryonic antigen (CEA) were independent risk factors for the diagnosis of benign and malignant subsolid pulmonary nodules. The prediction model formula for malignant probability was: P=ex/(1+ex). X=0.018+(1.436× gender)+(2.068×CTR)+(−1.976×boundary)+ (2.082×spiculation)+(1.277×lobulation)+(2.296×CEA). In this study, the area under the curve was 0.856, the sensitivity was 81.6%, the specificity was 75.6%, the positive predictive value was 95.4%, and the negative predictive value was 39.8%. Compared with the traditional model, the predictive value of this model was significantly better than that of Mayo, VA, Brock and PKUPH models. Conclusion Compared with Mayo, VA, Brock and PKUPH models, the predictive value of the model is more ideal and has greater clinical application value, which can be used for early screening of subsolid nodules.
ABSTRACT
BACKGROUND@#Subsolid pulmonary nodules are common computed tomography (CT) findings of primary lung adenocarcinoma. It is of clinical value to determine the clinical treatment strategies based on CT features. The aim of this study is to find the valuable CT characteristics on differential diagnosis and the degree of invasion prediction by a retrospectively analysis of three groups subsolid nodules, including benign, and invasive adenocarcinoma.@*METHODS@#The CT findings of 106 cases of resected sub-solid nodules were retrospectively analyzed. The nodules were firstly divided into benign and malignant groups and the malignant group was further divided into non/micro-invasive group (atypical adenomatous hyperplasia/adenocarcinoma in situ/minimally invasive adenocarcinoma) and invasive adenocarcinoma group. The nodule size, proportion of solid components, tumor-lung interface, shape, margin, pleural traction, air bronchus sign, vascular abnormalities inside the nodule were evaluated. The univariate analysis (χ2 test, non-parametric test Mann-Whitney U test) was performed to screen statistically significant variables and then enrolled in further multivariate Logistic regression analysis.@*RESULTS@#Multivariate logistic regression analysis showed that a clear tumor-lung interface, air bronchus sign, and pulmonary vascular abnormalities were important indicators of malignant nodules with hazard ratios of 38.1 (95%CI: 5.0-287.7; P<0.01), 7.9 (95%CI: 1.3-49.3; P=0.03), 7.2 (95%CI: 1.4-37.0; P=0.02), respectively. The proportion of solid components was the only significant indicator for identifying invasive adenocarcinoma from AAH/AIS/MIA , with a risk ratio of 1.04 (95%CI: 1.01-1.06, P=0.01).@*CONCLUSIONS@#SSNs with clear tumor-lung interface, air bronchus sign, and pulmonary vascular abnormality inside nodule are more likely to be malignant. A higher percentage of solid components indicates a higher likelihood to be an invasive lesion in malignant SPNs.