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Objectives. The intraoperative frozen section examination (IFSE) of pulmonary ground-glass density nodules (GGNs) is a great challenge. In the present study, through comparing the correlation between the computed tomography (CT) findings and pathological diagnosis of GGNs, the CT features as independent risk factors affecting the examination were defined, and their value in the rapid intraoperative examination of GGNs was explored. Methods. The relevant clinical data of 90 patients with GGNs on CT were collected, and all CT findings of GGNs, including the maximum transverse diameter, average CT value, spiculation, solid component, vascular sign, air sign, bronchus sign, lobulation, and pleural indentation, were recorded. All the cases received thoracoscopic surgery, and final pathological results were obtained. The cases were divided into three groups on the basis of pathological diagnosis: benign/atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS)/microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC). The CT findings were analyzed statistically, the independent risk factors were identified through the intergroup bivariate logistic regression analysis on variables with statistically significant differences, and a receiver operating curve (ROC) was plotted to establish a logistic regression model for diagnosing GGNs. A retrospective analysis was conducted on the coincidence rate of the rapid intraoperative and routine postoperative pathological examinations of the 90 cases with GGNs. The relevant clinical data of 49 cases with GGNs were collected. Conventional rapid intraoperative examination and CT-assisted rapid intraoperative examination were performed, and their coincidence rates with routine postoperative pathological examinations were compared. Results. No statistical differences in the onset age, gender, smoking history, and family history of malignant tumors were found among cases with GGNs in the identification of benign/AAH, AIS/MIA, and IAC ( P = 0.158 , P = 0.947 , P = 0.746 , P = 0.566 ). No statistically significant difference was found among the three groups in terms of CT findings, such as lobulation, bronchus sign, pleural indentation, spiculation, vascular sign, and solid component ( P > 0.05 ). The air sign, the maximum transverse diameter of GGNs, and average CT value showed statistically significant differences among the groups ( P < 0.001 , P < 0.05 , P < 0.001 ). Bivariate logistic regression analysis was performed on three risk factors, and the predicted probability value was obtained. A ROC curve was plotted by using the maximum transverse diameter as a predictor for analysis between the groups with benign/AAH and AIS/MIA, and the results demonstrated that the area under the curve (AUC) was 0.692. A ROC curve was plotted by using the predicted probability value, maximum transverse diameter, and average CT value as predictors for distinguishing between the groups with AIS/MIA and IAC, and the results showed that the AUC values of the predicted probability value, maximum transverse diameter, and CT value were 0.920, 0.816, and 0.772, respectively. A regression model Logit P = 2.304 − 2.689 X 1 + 0.302 X 2 + 0.011 X 3 was established to identify GGNs as IAC, obtaining AUC values of up to 0.920 for the groups with AIS/MIA and IAC, the sensitivity of 0.821, and the specificity of 0.894. The coincidence rate of rapid intraoperative and routine postoperative pathological examinations taken for modeling was 79.3%, that of conventional IFSE and postoperative pathological examination in prospective studies was 83.7%, and that of CT-assisted rapid intraoperative and postoperative pathological examinations was 98.0%. The former two were statistically different from the last one ( P = 0.003 and P = 0.031 , respectively). Conclusion. The air sign, maximum transverse diameter, and average CT value of the CT findings of GGNs had superior capabilities to enhance the pathologic classification of GGNs. The auxiliary function of the comprehensive multifactor analysis of GGNs was better than that of single-factor analysis. CT-assisted diagnosis can improve the accuracy of rapid intraoperative examination, thereby increasing the accuracy of the selection of operative approaches in clinical practice.

Gallstone ectopia in the lungs is relatively rare, which accounts for its frequent misdiagnosis. This paper reports a case found in a suspicious lung cancer surgery. The patient received intrahepatic duct stone removal surgery and partial hepatectomy five months prior to the report. He started showing symptoms of cough and hemoptysis without any apparent cause one month before this report. Enhanced computed tomography showed a solid mass in the lower lobe of the right lung, which was considered as hamartoma or lung cancer. A wedge-shaped excision was then performed in the lower lobe of the right lung. After the surgery, postoperative findings and pathological examination results showed gallstone ectopia in the lung. This case reminds us that gallstones that overflow into the intraperitoneal section can enter the thoracic cavity through diaphragmatic weakness and travel to the lung, thus forming an inflammatory mass. The case also reminds us of the following points in clinical diagnosis: 1) remove gallstones to the greatest extent during cholelithiasis surgery to prevent the stones from migrating from the intraperitoneal area, which causes intraperitoneal and thoracic cavity complications; 2) conduct routine chest imaging examination after cholelithiasis surgery during the clinical follow-up period to facilitate early detection and timely treatment of intrathoracic complications; 3) inquire whether the patients suffering from a solid mass of the lower lobe of right lung, have cholelithiasis history to facilitate clinical diagnosis and avoid misdiagnosis, mistreatment, and treatment delay.

Feature extraction is the one of the important techniques in pattern recognition. How to extract the steady and reliable feature is the key step to accomplish the recognition. This paper mostly considers the illumination invariant in the feature extraction technique, and gives attention to the displacement, scale, rotation invariability. We put forward an algorithm based on Local Normalization Technique and improved Hu moments. With the LN algorithm and improved Hu moment, we can extract the perceptive invariability, the recognition result is better than directly extracting Hu moments. Experiments also show that our method is effective and fast.