Pancreatic cancer is one of the deadliest human malignancies and lack of effective diagnostic and therapeutic methods. Accumulating evidence suggests that the neurotensin (NT) and neurotensin receptors (NTRs) play key roles in pancreatic adenocarcinoma growth and survival. In this study, we not only evaluate the NTR1 expression in pancreatic cancer patient samples, but also explore the PET and fluorescence imaging of NTR1 expression in pancreatic cancer animal models. The NTR1 expression was evaluated by immunohistochemistry staining in clinical patient tissue samples with pancreatic ductal adenocarcinoma, insulinoma, and pancreatitis. The results showed 79.4% positive rate of NRT1 expression in pancreatic ductal adenocarcinoma, compared with 33.3% and 22.7% in insulinoma and pancreatitis samples, respectively. High NTR1 gene expression was also found in Panc-1 cells and confirmed by cell immunofluorescence. 64Cu-AmBaSar-NT and IRDye800-NT were synthesized as imaging probes and maintained the majority of NTR1 binding affinity. In vivo imaging demonstrated that 64Cu-AmBaSar-NT has prominent tumor uptake (3.76 ± 1.45 and 2.29 ± 0.10 %ID/g at 1 and 4 h post injection). NIR fluorescent imaging with IRDye800-NT demonstrated good tumor to background contrast (8.09 ± 0.38 ×108 and 6.67 ± 0.43 ×108 (p/s/cm2/sr)/(μW/cm2) at 30 and 60 min post injection). Fluorescence guided surgery was also performed as a proof of principle experiment. In summary, our results indicated that NTR1 is a promising target for pancreatic ductal adenocarcinoma imaging and therapy. The imaging probes reported here may not only be considered for improved diagnosis of pancreatic ductal adenocarcinoma, but also has the potential to be fully integrated into patient screening and treatment monitoring of future NTR1 targeted therapies.

Purpose: We investigated whether a fluorine-18-fluorodeoxy glucose positron emission tomography/computed tomography (18F-FDG PET/CT)-based radiomics model (RM) could predict the pathological mediastinal lymph node staging (pN staging) in patients with non-small cell lung cancer (NSCLC) undergoing surgery.Methods: A total of 716 patients with a clinicopathological diagnosis of NSCLC were included in this retrospective study. The prediction model was developed in a training cohort that consisted of 501 patients. Radiomics features were extracted from the 18F-FDG PET/CT of the primary tumor. Support vector machine and extremely randomized trees were used to build the RM. Internal validation was assessed. An independent testing cohort contained the remaining 215 patients. The performances of the RM and clinical node staging (cN staging) in predicting pN staging (pN0 vs. pN1 and N2) were compared for each cohort. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess the model's performance.Results: The AUC of the RM [0.81 (95% CI, 0.771–0.848); sensitivity: 0.794; specificity: 0.704] for the predictive performance of pN1 and N2 was significantly better than that of cN in the training cohort [0.685 (95% CI, 0.644–0.728); sensitivity: 0.804; specificity: 0.568], (P-value = 8.29e-07, as assessed by the Delong test). In the testing cohort, the AUC of the RM [0.766 (95% CI, 0.702–0.830); sensitivity: 0.688; specificity: 0.704] was also significantly higher than that of cN [0.685 (95% CI, 0.619–0.747); sensitivity: 0.799; specificity: 0.568], (P = 0.0371, Delong test).Conclusions: The RM based on 18F-FDG PET/CT has a potential for the pN staging in patients with NSCLC, suggesting that therapeutic planning could be tailored according to the predictions.

Objective: We aimed to use an individual metabolic connectome method, the Jensen-Shannon Divergence Similarity Estimation (JSSE), to characterize the aberrant connectivity patterns and topological alterations of the individual-level brain metabolic connectome and predict the long-term surgical outcomes in temporal lobe epilepsy (TLE).Methods: A total of 128 patients with TLE (63 females, 65 males; 25.07 ± 12.01 years) who underwent Positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) imaging were enrolled. Patients were classified either as experiencing seizure recurrence (SZR) or seizure free (SZF) at least 1 year after surgery. Each individual’s metabolic brain network was ascertained using the proposed JSSE method. We compared the similarity and difference in the JSSE network and its topological measurements between the two groups. The two groups were then classified by combining the information from connection and topological metrics, which was conducted by the multiple kernel support vector machine. The validation was performed using the nested leave-one-out cross-validation strategy to confirm the performance of the methods.Results: With a median follow-up of 33 months, 50% of patients achieved SZF. No relevant differences in clinical features were found between the two groups except age at onset. The proposed JSSE method showed marked degree reductions in IFGoperc.R, ROL. R, IPL. R, and SMG. R; and betweenness reductions in ORBsup.R and IOG. R; meanwhile, it found increases in the degree analysis of CAL. L and PCL. L, and in the betweenness analysis of PreCG.R, IOG. R, PoCG.R, PCL. L and PCL.R. Exploring consensus significant metabolic connections, we observed that the most involved metabolic motor networks were the INS-TPOmid.L, MTG. R-SMG. R, and MTG. R-IPL.R pathways between the two groups, and yielded another detailed individual pathological connectivity in the PHG. R-CAU.L, PHG. R-HIP.L, TPOmid.L-LING.R, TPOmid.L-DCG.R, MOG. R-MTG.R, MOG. R-ANG.R, and IPL. R-IFGoperc.L pathways. These aberrant functional network measures exhibited ideal classification performance in predicting SZF individuals from SZR ones at a sensitivity of 75.00%, a specificity of 92.79%, and an accuracy of 83.59%.Conclusion: The JSSE method indicator can identify abnormal brain networks in predicting an individual’s long-term surgical outcome of TLE, thus potentially constituting a clinically applicable imaging biomarker. The results highlight the biological meaning of the estimated individual brain metabolic connectome.

Every year, more than 20,000,000 patients were plagued by brain diseases worldwide. Brain diseases include stroke, brain tumors, alzheimer's disease，parkinson's disease and many other diseases. Common diagnostic methods include computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography, etc, and among these methods, MRI is the diagnostic method with the best safety, the highest detection rate and the best reproducibility. As an advanced imaging technique, MRI has the advantages of non-invasive, non-radioactive, multi-parameter imaging, which means MRI has an indelible role in diagnosing brain diseases. MRI can be divided into perfusion weighted imaging (PWI), diffusion weighted imaging (DWI) and functional magnetic resonance imaging (fMRI). This paper focuses on the technical principles, characteristics and clinical applications of these three methods, for the purpose of promoting the development of magnetic resonance technology and the advancement of clinical diagnosis and treatment of brain diseases.