Background Clinical management decisions on prostate cancer (PCa) are often based on a determination of risk. 68Ga-prostate-specific membrane antigen (PSMA)-11-positron emission tomography (PET)/computer tomography (CT) is an attractive modality to assess biochemical recurrence of PCa, detect metastatic disease and stage of primary PCa, making it a promising strategy for risk stratification. However, due to some limitation of 68Ga-PSMA-11 the development of alternative tracers is of high interest. In this study, we aimed to investigate the value of 18F-PSMA-1007 in identifying non-metastatic high-risk PCa. Methods A total of 101 patients with primary non-metastatic PCa who underwent 18F-PSMA-1007 PET/CT were retrospectively analyzed. According to the European Association of Urology guidelines on PCa, patients were classified into intermediate-risk (IR) group or high-risk (HR) group. The maximum standardized uptake values (SUVmax) of the primary prostate tumor were measured on PET/CT images. The diagnostic performance of PET/CT for IR and HR PCa was calculated, and the relationship between the SUVmax of primary prostate tumor, prostate-specific antigen (PSA) level and Gleason score (GS) was analyzed. Results Of all 101 patients, 49 patients were classified into IR group and 52 patients were classified into HR group. There was a significant positive correlation between PSA level/GS and SUVmax (r = 0.561, r = 0.496, P < 0.001, respectively). Tumors with GS 6 and 7a showed significantly lower 18F-PSMA-1007 uptake compared to patients with GS 8 and 9 (P < 0.01). SUVmax in patients of HR was significantly higher than those of IR (median SUVmax: 16.85 vs 7.80; P < 0.001). In receiver operating characteristic curve analysis, the optimal cutoff value of the SUVmax for identifying high-risk PCa was set as 9.05 (area under the curve: 0.829; sensitivity: 90.4%; specificity: 65.3%). Conclusion 18F-PSMA-1007 PET/CT showed the powerful diagnosis efficacy for high-risk PCa, which can be used as an objective imaging reference index for clinical reference.

Intercellular adhesions are vital hubs for signaling pathways during multicellular development and animal morphogenesis. In eukaryotes, under aberrant intracellular conditions, cadherins are abnormally regulated, which can result in cellular pathologies such as carcinoma, kidney disease, and autoimmune diseases. As a member of the Ca2+-dependent adhesion super-family, Fat proteins were first described in the 1920s as an inheritable lethal mutant phenotype in Drosophila, consisting of four member proteins, FAT1, FAT2, FAT3, and FAT4, all of which are highly conserved in structure. Functionally, FAT1 was found to regulate cell migration and growth control through specific protein–protein interactions of its cytoplasmic tail. FAT2 and FAT3 are relatively less studied and are thought to participate in the development of human cancer through a pathway similar to that of the Ena/VASP proteins. In contrast, FAT4 has been widely studied in the context of biological functions and tumor mechanisms and has been shown to regulate the planar cell polarity pathway, the Hippo signaling pathway, the canonical Wnt signaling cascade, and the expression of YAP1. Overall, Fat cadherins may be useful as emerging disease biomarkers and as novel therapeutic targets.

Background: This study aimed to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting occult lymph node metastasis (OLM) in clinical N0 (cN0) solid lung adenocarcinoma. Methods: The preoperative fluorine-18-fludeoxyglucose ( 18 F-FDG) PET images of 370 patients with cN0 lung adenocarcinoma confirmed by histopathological examination were retrospectively reviewed. Patients were divided into training and validation sets. Radiomics features and relevant data were extracted from PET images. A nomogram was developed in a training set via univariate and multivariate logistic analyses, and its performance was assessed by concordance-index (C-index), calibration curves, and decision curve analysis (DCA) in the training and validation sets. Results: The multivariate logistic regression analysis showed that only carcinoembryonic antigen (CEA), metabolic tumor volume (MTV), and the radiomics signature had statistically significant differences between patients with and without OLM (P<0.05). A nomogram was developed based on the logistic analyses, and its C-index was 0.769 in the training set and 0.768 in the validation set. The calibration curve demonstrated good consistency between the nomogram-predicted probability of OLM and the actual rate. The DCA also confirmed the clinical utility of the nomogram. Conclusions: A PET/computed tomography (CT)-based radiomics model including CEA, MTV, and the radiomics signature was developed and demonstrated adequate predictive accuracy and clinical net benefit in the present study, and was conveniently used to facilitate the individualized preoperative prediction of OLM.