Objective:To investigate the relationship between spread through air spaces(STAS) of peripheral stage ⅠA small adenocarcinoma of the lung(≤2 cm) and related factors such as clinical and CT morphological features, and to construct a nomogram model.Methods:Relevant clinical, pathological and imaging data of patients who underwent lung surgery and were diagnosed as peripheral stage ⅠA small lung adenocarcinoma by postoperative pathology in the Affiliated Hospital of Nantong University from 2017 to 2022 were collected, of which cases that met the inclusion criteria from 2017 to 2021 served as the training group, and those that met the inclusion criteria in 2022 served as the validation group. The independent risk factors for the occurrence of STAS in peripheral stage ⅠA lung small adenocarcinoma were investigated by using univariate analysis and multifactorial logistic regression analysis, based on which a nomogram prediction model was constructed, and the subjects were analyzed by using the receiver operating characteristic curve( ROC), correction model, etc. were used to evaluate the model. Results:A total of 430 patients who met the criteria were included, including 351 patients in the training group(109 STAS-positive and 242 STAS-negative) and 79 patients in the validation group(23 STAS-positive and 56 STAS-negative). Univariate analysis showed that the patients in the two groups showed a significant difference in age(>58 years old), gender, smoking history, tumor location(subpleural, non-subpleural), pleural pull, nodule type, nodule maximal diameter, solid component maximal diameter, consolidation tumor ratio(CTR), lobulation sign, burr sign, bronchial truncation sign, vascular sign(includes thickening and distortion of blood vessels in/around the nodes), satellite lesions, and ground-glass band sign were statistically significant( P<0.05). The results of multifactorial logistic regression analysis showed that CTR( OR=4.98, P<0.001), lobulation sign( OR=4.07, P=0.013), burr sign( OR=3.66, P<0.001), and satellite lesions( OR=3.56, P=0.009) were the independent risk factors for the occurrence of STAS. Applying the above factors to construct the nomogram model and validate the model, the results showed that the ROC curve was plotted by the nomogram prediction model, and the area under the ROC curve( AUC) of the training set was 0.840(sensitivity 0.835, specificity 0.734), and the validation set had an AUC value of 0.852(sensitivity 0.786, specificity 0.783), and the training set and validation set calibration curves have good overlap with the ideal curve. Conclusion:CTR, lobular sign, burr sign, and satellite lesions are independent risk factors for STAS, and the nomogram model constructed in this study has good predictive value.

Objective:To analyze independent factors for treatment-requiring retinopathy of prematurity (TR-ROP) and establish a predictive nomogram model for TR-ROP.Method:A retrospective cohort study. A total of 6 998 preterm infants who were born at Guangdong Women's and Children's Hospital between January 1, 2012 and March 31, 2022 and were screened for retinopathy of prematurity (ROP) were included in the study. TR-ROP was defined as type 1 ROP and aggressive ROP; 22 independent factors including general information, maternal perinatal conditions, interventions and neonatal diseases related to ROP were collected. The infants were divided at the level at an 8:2 ratio according to clinical experience, with 5 598 in the training cohort and 1 400 in the validation cohort. t test was used for comparison of quantitative data and χ 2 test was used for comparison of counting data between groups. Multivariate logistic regression analysis was carried out for the indicators with differences in the univariate analysis. The visualized regression analysis results of R software were used to obtain the histogram. The accuracy of the nomogram was verified by C-index and receiver operating characteristic curve (ROC curve). Results:Among the 6 998 children tested, 4 069 were males and 2 920 were females. Gestational age was (33.69±3.19) weeks; birth weight was (2 090±660) g. There were 376 cases of TR-ROP (5.4%, 376/6 998). The results of multivariate logistic regression analysis showed that gestational age [odds ratio ( OR) =0.63, 95% confidence interval ( CI) 0.47-0.85, P=0.002], intrauterine distress ( OR=0.30, 95% CI 0.10-0.99, P=0.048), bronchopulmonary dysplasia ( OR=0.23, 95% CI 0.09-0.60, P=0.003), hypoxic-ischemic encephalopathy ( OR=5.40, 95% CI 1.45-20.10, P=0.012), blood transfusion history ( OR=4.05, 95% CI 1.50-10.95, P=0.006) were the independent influencing factors of TR-ROP. Based on this and combined with birth weight, a nomogram prediction model was established. The C-index of the training set and validation set were 0.940 and 0.885, respectively, and the area under ROC curve were 0.945 (95% CI 0.930-0.961) and 0.931 (95% CI 0.876-0.986), respectively. The sensitivity and specificity were 86.2%, 94.0% and 83.2%, 93.3%, respectively. Conclusions:Gestational age, intrauterine distress, bronchopulmonary dysplasia, hypoxic-ischemic encephalopathy and blood transfusion history are the independent factors influencing the occurrence of TR-ROP. The TR-ROP nomogram prediction model based on independent influencing factors has high sensitivity and specificity.