RESUMO
Objective:To evaluate the clinical radiological features combined with circulating tumor cells in the diagnosis of benign and malignant pulmonary solid nodules.Methods:Clinical data of 437 patients from Shanghai Pulmonary Hospital(SPH cohort) from January to April 2021 and 82 patients from Lanzhou University First Hospital (LZH cohort) from August 2019 to May 2022 were retrospectively included. Patients in Shanghai pulmonary hospital were randomly divided into training set and internal validation set in a ratio of 4∶1 by random number table method and patients in Lanzhou University First Hospital were as external validation set. Independent risk factors were selected by regression analysis of training set constructed a Nomogram prediction model. The performance of the Nomogram prediction model was estimated by applying receiver operating curve( ROC) analysis, tested in different nodules size and intermediate risk IPSNs and tested by calibration curve. Results:Independent risk factors selected by regression analysis for solid pulmonary nodules were age, the level of CTC, pleural Indentation, lobulation, spiculation. The Nomogram prediction mode provided an area under ROC( AUC) of 0.888, 0.833 in internal validation set and external validation set, outperforming radiological features model(0.835, P=0.007; 0.804, P=0.043) Mayo clinical model(0.781, P=0.019; 0.726, P=0.033) and CTCs(0.699, P=0.002; 0.648, P=0.012) in both two validation sets, C-index of 0.888, 0.871 and corrected C-index of 0.853, 0.842 in both two validation sets . The AUC of the prediction model with internal validation set was 0.905 and 0.871 for nodule diameter of 5-20 mm and intermediate risk probability. Conclusion:The prediction model in this study has better diagnostic value and practicability, and is more effective in clinical diagnosis of diseases.
RESUMO
@#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.
