Background Collecting (Bellini) duct carcinoma (CDC) is a highly malignant and rare kidney tumor. We report our 12-year experience with CDC and the results of a retrospective analysis of patients and tumor characteristics, clinical manifestations, and imaging features by computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)/CT. Methods Retrospective examination of tumors between January 2007 and December 2019 identified 13 cases of CDC from three medical centers in northern China. All 13 patients underwent CT scan, among which eight underwent dynamic enhanced CT scan, two underwent PET/CT scan, and one underwent magnetic resonance cholangiopancreatography (MRCP) examination. The lesions were divided into nephritis type and mass type according to the morphology of the tumors. Results The study group included ten men and three women with an average age of 64.23 ± 10.74 years. The clinical manifestations were gross hematuria, flank pain, and waist discomfort. The mean tumor size was 8.48 ± 2.48 cm. Of the 13 cases, six (46.2%) were cortical-medullary involved type and seven (53.8%) were cortex–medullary–pelvis involved type. Eleven (84.6%) cases were nephritis type and two (15.4%) were mass type. The lesions appeared solid or complex solid and cystic on CT and MRI. The parenchymal area of the tumors showed isodensity or slightly higher density on unenhanced CT scan in the 13 cases. PET/CT in two cases showed increased radioactivity intake. Evidence of intra-abdominal metastatic disease was present on CT in nine (69.2%) cases. Conclusions The imaging characteristics of CDC differ from those of other renal cell carcinomas. In renal tumors located in the junction zone of the renal cortex and medulla that show unclear borders, slight enhancement, and metastases in the early stage, a diagnosis of CDC needs to be considered. PET/CT provides crucial information for the diagnosis of CDC, as well as for designing treatment strategies including surgery.
PurposeMachine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features.MethodsOne hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters—four clinical factors, four laboratory indicators, and 91 [18F]F-FDG PET/CT radiomic features—were used for data analysis and model construction. The Boruta algorithm was used to screen the features. The retained minimum optimal feature subset was input into ten machine learning to construct a classifier for distinguishing between LUAD and LUSC. Univariate and multivariate analyses were used to identify the independent risk factors of the NSCLC subtype and constructed the Clinical model. Finally, the area under the receiver operating characteristic curve (AUC) values, sensitivity, specificity, and accuracy (ACC) was used to validate the machine learning model with the best performance effect and Clinical model in the validation group, and the DeLong test was used to compare the model performance.ResultsBoruta algorithm selected the optimal subset consisting of 13 features, including two clinical features, two laboratory indicators, and nine PEF/CT radiomic features. The Random Forest (RF) model and Support Vector Machine (SVM) model in the training group showed the best performance. Gender (P=0.018) and smoking status (P=0.011) construct the Clinical model. In the validation group, the SVM model (AUC: 0.876, ACC: 0.800) and RF model (AUC: 0.863, ACC: 0.800) performed well, while Clinical model (AUC:0.712, ACC: 0.686) performed moderately. There was no significant difference between the RF and Clinical models, but the SVM model was significantly better than the Clinical model. ConclusionsThe proposed SVM and RF models successfully identified LUAD and LUSC. The results indicate that the proposed model is an accurate and noninvasive predictive tool that can assist clinical decision-making, especially for patients who cannot have biopsies or where a biopsy fails.
ObjectiveFAP plays a vital role in myocardial injury and fibrosis. Although initially used to study imaging of primary and metastatic tumors, the use of FAPI tracers has recently been studied in cardiac remodeling after myocardial infarction. The study aimed to investigate the application of FAPI PET/CT imaging in human myocardial fibrosis and its relationship with clinical factors.Materials and methodsRetrospective analysis of FAPI PET/CT scans of twenty-one oncological patients from 05/2021 to 03/2022 with visual uptake of FAPI in the myocardium were applying the American Heart Association 17-segment model of the left ventricle. The patients’ general data, echocardiography, and laboratory examination results were collected, and the correlation between PET imaging data and the above data was analyzed. Linear regression models, Kendall’s TaU-B test, the Spearman test, and the Mann–Whitney U test were used for the statistical analysis.Results21 patients (60.1 ± 9.4 years; 17 men) were evaluated with an overall mean LVEF of 59.3 ± 5.4%. The calcific plaque burden of LAD, LCX, and RCA are 14 (66.7%), 12 (57.1%), and 9 (42.9%). High left ventricular SUVmax correlated with BMI (P < 0.05) and blood glucose level (P < 0.05), and TBR correlated with age (P < 0.05). A strong correlation was demonstrated between SUVmean and CTnImax (r = 0.711, P < 0.01). Negative correlation of SUVmean and LVEF (r = −0.61, P < 0.01), SUVmax and LVEF (r = −0.65, P < 0.01) were found. ROC curve for predicting calcified plaques by myocardial FAPI uptake (SUVmean) in LAD, LCX, and RCA territory showed AUCs were 0.786, 0.759, and 0.769.ConclusionFAPI PET/CT scans might be used as a new potential method to evaluate cardiac fibrosis to help patients’ management further. FAPI PET imaging can reflect the process of myocardial fibrosis. High FAPI uptakes correlate with cardiovascular risk factors and the distribution of coronary plaques.
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