To analyze the CT features of solid pseudopapillary tumor of pancreas (SPTP), and correlation with the pathological findings of the disease so as to improve the diagnostic abilities, the CT images and the clinical manifestations, we retrospectively analyzed the pathological materials of 23 cases with surgery and pathology proved SPTP. In the 23 patients, 21 cases were female (91.3%) and 2 were male (8.7%). The most common symptom was abdominal discomfort with dull pain in 12 patients (52.2%). Others included the pancreatic mass that was detected incidentally during physical examination in 9 patients (39.1%), nausea/vomiting in 2 patients (8.7%). And 1 case of female patients had 2 lesions. In the 24 tumors, 6 cases were located at the head (25.0%), 3 were at neck (12.5%), 8 cases were at body (33.3%), and 7 cases were at tail of pancreas respectively (29.2%). The long-axis diameter ranged from 2.1cm to 20.1cm (mean 6.4cm). 9 tumors were mostly solid component (37.5%), 10 tumors were contained similar proportion of solid and cystic part (41.7%), and mainly cystic components in 5 tumors (20.8%). In 9 of the 23 patients, calcification was found in the tumor (39.1%). In 2 of the 23 patients, bleeding was seen in the mass (8.7%). The dilation of intrahepatic bile duct was found in 1 patient (8.7%). Liver metastasis was showed in one patient (8.7%). On post-contrast CT scan, solid parts demonstrated mild enhancement at the arterial phase. At the portal phase, solid parts were enhanced continuously in all cases, and the enhancement degrees were lower than normal pancreatic tissue. The cystic parts of all lesions showed no enhancement. Pseudo papillary structure, hemorrhage, necrosis, or cystic degeneration were found in all patients by histological study. In a word, SPTP has comparatively characteristic CT imaging features consistent with histological features, when combined with clinical manifestations, could be correctly diagnosed and differentially diagnosed.
Objective To systematically review the CT characteristic differences between pulmonary cryptococcosis (PC) and lung cancer presenting as solitary nodules. Methods PubMed, Embase, Web of Science, Cochrane Library, Wanfang, China National Knowledge Infrastructure and SinoMed were searched to collect case-control studies of the patients with PC and lung cancer presenting as solitary nodules on CT. The search period was from databases establishment to December 2023. We conducted a meta-analysis on the included studies. Results A total of 7 studies were included, 342 PC patients, and 370 lung cancer patients. The meta-analysis results showed that compared with lung cancer, PC patients were more likely to have the following characteristics in CT (P<0.05): nodules located in the lower lobe of the lung [odds ratio (OR)=1.91, 95% confidence interval (CI) (1.39, 2.62)], presence of bronchial inflation sign [OR=5.79, 95%CI (1.45, 23.21)], and halo sign [OR=6.64, 95%CI (2.87, 15.38)]. Compared with PC, lung cancer patients were more likely to have the following characteristics in CT (P<0.05): nodules located in the upper lobe of the lung [OR=0.52, 95%CI (0.35, 0.78)], presence of lobulation sign [OR=0.15, 95%CI (0.08, 0.27)], spiculation sign [OR=0.48, 95%CI (0.35, 0.65)], pleural indentation sign [OR=0.15, 95%CI (0.04, 0.50)], and vascular bundle sign [OR=0.20, 95%CI (0.05, 0.77)]. There was no statistically significant difference in CT between PC patients and lung cancer patients in terms of lesion size, relationship with pleura, vacuolar sign, cavity, and whether the broad base was connected to pleura (P>0.05). Conclusions For CT showing solitary nodules, PC nodules are more likely to be distributed in the lower lobe of the lungs, with bronchial inflation sign and halo sign. Lung cancer nodules are more likely to be distributed in the upper lobe of the lungs, with lobulation sign, spiculation sign, pleural indentation sign and vascular bundle sign.
Objective To explore the application value of artificial intelligence (AI) auxiliary diagnosis system in chest CT diagnosis of pulmonary nodules in plateau area. MethodsNon-calcified pulmonary nodules found in chest CT of the People’s Hospital of Xizang Autonomous Region were retrospectively collected between January 2022 and May 2024. All pulmonary nodules were diagnosed based on pathological results as the gold standard using an AI assisted diagnostic system, with physicians manually reviewing the images and analyzing relevant parameters. The diagnostic efficacy of three diagnostic methods for pulmonary nodules was compared. Results A total of 154 patients were included. Among them, there were 79 males and 75 females. The average age was (54.2±13.9) years old. There were 172 non-calcified pulmonary nodules. The combined diagnostic method of physician+AI [area under the curve (AUC)=0.848, 95% confidence interval (CI) (0.697, 0.835), P=0.003] was superior to physician [AUC=0.739, 95%CI (0.663, 0.806), P=0.031] or AI [AUC=0.771, 95%CI (0.697, 0.835), P=0.012]. There was no significant difference between AI and physician’s ability to distinguish (P>0.05). There was no significant difference in the benign and malignant rates of the three density types of pulmonary nodules identified by AI (P=0.386). The distribution of malignant pulmonary nodules in different lung segments in the plateau area was different, and the distribution was mainly in the apical segment of the right upper lobe and the posterior segment of the left upper lobe. There was no systematic deviation between physicians and AI in measuring the maximum cross-sectional area of pulmonary nodules (t=?0.687, P=0.493). Conclusions In order to improve the efficiency and avoid the risk of misjudgment, the judgment of benign and malignant pulmonary nodules in clinical practice should be completed by doctors and AI. In the plateau area, there was no significant difference in the distribution of benign and malignant pulmonary nodules in solid, partial solid and ground glass density. Different from low altitude areas, they should be regarded as equally important identification objects when reading films. AI can effectively replace doctors to measure the cross-sectional area of nodules and improve efficiency. There are significant differences in the distribution of malignant pulmonary nodules in the plateau area, mainly in the apical segment of the upper lobe of the right lung and the posterior segment of the upper lobe of the left lung.