Pathological changes in a diseased site are often accompanied by abnormal activities of various biomolecules in and around the involved cells. Identifying the location and expression levels of these biomolecules could enable early-stage diagnosis of the related disease, the design of an appropriate treatment strategy, and the accurate assessment of the treatment outcomes. Over the past two decades, a great diversity of peptide-based nanoprobes (PBNs) have been developed, aiming to improve the in vitro and in vivo performances of water-soluble molecular probes through engineering of their primary chemical structures as well as the physicochemical properties of their resultant assemblies. In this review, we introduce strategies and approaches adopted for the identification of functional peptides in the context of molecular imaging and disease diagnostics, and then focus our discussion on the design and construction of PBNs capable of navigating through physiological barriers for targeted delivery and improved specificity and sensitivity in recognizing target biomolecules. We highlight the biological and structural roles that low-molecular-weight peptides play in PBN design and provide our perspectives on the future development of PBNs for clinical translation.

The strongest genetic risk factor for Alzheimer's disease (AD) is the Apolipoprotein E type 4 allele (ApoE ε4). The interaction between sex and ApoE ε4 carrier status on AD risk remains an area of intense investigation. We hypothesized that sex modulates the relationship between ApoE ε4 carrier status and brain tau deposition (a quantitative endophenotype in AD) in individuals with mild cognitive impairment (MCI). Methods: Preprocessed 18F-AV-1451 tau and 18F-AV-45 amyloid PET images, T1-weighted structural magnetic resonance imaging (MRI) scans, demographic information, and cerebrospinal fluid (CSF) total tau (t-tau) and phosphorylated tau (p-tau) measurements from 108 MCI subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were included. After downloading pre-processed images from ADNI, an iterative reblurred Van Cittertiteration partial volume correction (PVC) method was applied to all PET images. MRIs were used for PET spatial normalization. Regions of interest (ROIs) were defined in standard space, and standardized uptake value ratio (SUVR) images relative to cerebellum were computed. ApoE ε4 by sex interaction analyses on 18F-AV-1451 and CSF tau (t-tau, p-tau) were assessed using generalized linear models. The association between 18F-AV-1451 SUVR and CSF tau (t-tau, p-tau) was assessed. Results: After applying PVC and controlling for age, education level and global cortical 18F-AV-45 SUVR, we found that the entorhinal cortex, amygdala, parahippocampal gyrus, posterior cingulate, and occipital ROIs exhibited a significant ApoE ε4 by sex interaction effect (false discovery rate P < 0.1) among MCI individuals. We also found a significant ApoE ε4 by sex interaction effect on CSF t-tau and p-tau. 18F-AV-1451 SUVR in the 5 ROIs with ApoE ε4 by sex interaction was significantly correlated with CSF p-tau and t-tau. Conclusions: Our findings suggest that women are more susceptible to ApoE ε4-associated accumulation of neurofibrillary tangles in MCI compared to males. Both CSF tau (p-tau, t-tau) and brain tau PET are robust quantitative biomarkers for studying ApoE ε4 by sex effects on brain tau deposition in MCI participants.

To evaluate the potential clinical value of quantitative functional FDG PET and pathological amyloid-β PET with cerebrospinal fluid (CSF) biomarkers and clinical assessments in the prediction of Alzheimer’s disease (AD) progression. We studied 82 subjects for up to 96 months (median = 84 months) in a longitudinal Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. All preprocessed PET images were spatially normalized to standard Montreal Neurologic Institute space. Regions of interest (ROI) were defined on MRI template, and standard uptake values ratios (SUVRs) to the cerebellum for FDG and amyloid-β PET were calculated. Predictive values of single and multiparametric PET biomarkers with and without clinical assessments and CSF biomarkers for AD progression were evaluated using receiver operating characteristic (ROC) analysis and logistic regression model. The posterior precuneus and cingulate SUVRs were identified for both FDG and amyloid-β PET in predicating progression in normal controls (NCs) and subjects with mild cognitive impairment (MCI). FDG parietal and lateral temporal SUVRs were suggested for monitoring NCs and MCI group progression, respectively. ¹⁸F-AV45 global cortex attained (78.6%, 74.5%, 75.4%) (sensitivity, specificity, accuracy) in predicting NC progression, which is comparable to the ¹¹C-PiB global cortex SUVR’s in predicting MCI to AD. A logistic regression model to combine FDG parietal and posterior precuneus SUVR and Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-Cog) Total Mod was identified in predicating NC progression with (80.0%, 94.9%, 93.9%) (sensitivity, specificity, accuracy). The selected model including FDG posterior cingulate SUVR, ADAS-Cog Total Mod, and Mini-Mental State Exam (MMSE) scores for predicating MCI to AD attained (96.4%, 81.2%, 83.6%) (sensitivity, specificity, accuracy). ¹¹C-PiB medial temporal SUVR with MMSE significantly increased ¹¹C-PiB PET AUC to 0.915 (p<0.05) in predicating MCI to AD with (77.8%, 90.4%, 88.5%) (sensitivity, specificity, accuracy). Quantitative FDG and ¹¹C-PiB PET with clinical cognitive assessments significantly improved accuracy in the predication of AD progression.

Bone metastasis has a significant influence on the prognosis of prostate cancer(PCa) patients. In this review, we discussed the current application of PCa bone metastasis diagnosis with single-photon emission computed tomography (SPECT) and positron emission tomography/computed tomography (PET/CT) computer-aided diagnosis(CAD) systems. A literature search identified articles concentrated on PCa bone metastasis and PET/CT or SPECT CAD systems using the PubMed database. We summarized the previous studies focused on CAD systems and manual quantitative markers calculation, and the coincidence rate was acceptable. We also analyzed the quantification methods, advantages, and disadvantages of CAD systems. CAD systems can detect abnormal lesions of PCa patients’ ^99mTc-MDP-SPECT, ¹⁸F-FDG-PET/CT, ¹⁸F-NaF-PET/CT, and ⁶⁸ Ga-PSMA PET/CT images automated or semi-automated. CAD systems can also calculate the quantitative markers, which can quantify PCa patients’ whole-body bone metastasis tumor burden accurately and quickly and give a standardized and objective result. SPECT and PET/CT CAD systems are potential tools to monitor and quantify bone metastasis lesions of PCa patients simply and accurately, the future clinical application of CAD systems in diagnosing PCa bone metastasis lesions is necessary and feasible.

Purpose: To explore valuable predictors for mediastinal lymph node metastasis in non-small cell lung cancer (NSCLC) patients, we analyzed the potential roles of standardized uptake value (SUV)-derived parameters from preoperative ¹⁸F-FDG PET/CT combined with clinical characteristics. Methods: Data from 224 NSCLC patients who underwent preoperative ¹⁸F-FDG PET/CT scans in our hospital were collected. Then, a series of clinical parameters including SUV-derived features [SUVmax of mediastinal lymph node and primary-tumor SUVmax, SUVpeak, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG)] were evaluated. The best possible cutoff points for all measuring parameters were calculated using receiver operating characteristic curve (ROC) analysis. Predictive analyses were performed using a Logistic regression model to determine the predictive factors for mediastinal lymph node metastasis in NSCLC and lung adenocarcinoma patients. After multivariate model construction, data of another 100 NSCLC patients were recorded. Then, 224 patients and 100 patients were enrolled to validate the predictive model by the area under the receiver operating characteristic curve (AUC). Results: The mediastinal lymph node metastasis rates in 224 patients for model construction and 100 patients for model validation were 24.1% (54/224) and 25% (25/100), respectively. It was found that SUVmax of mediastinal lymph node ≥ 2.49, primary-tumor SUVmax ≥ 4.11, primary-tumor SUVpeak ≥ 2.92, primary-tumor SUVmean ≥ 2.39, primary-tumor MTV ≥ 30.88 cm³, and primary-tumor TLG ≥ 83.53 were more prone to mediastinal lymph node metastasis through univariate logistic regression analyses. The multivariate logistic regression analyses showed that the SUVmax of mediastinal lymph nodes (≥ 2.49: OR 7.215, 95% CI 3.326–15.649), primary-tumor SUVpeak (≥ 2.92: OR 5.717, 95% CI 2.094–15.605), CEA (≥ 3.94 ng/ml: OR 2.467, 95% CI 1.182–5.149), and SCC (< 1.15 ng/ml: OR 4.795, 95% CI 2.019–11.388) were independent predictive factors for lymph node metastasis in the mediastinum. It was found that SUVmax of the mediastinal lymph node (≥ 2.49: OR 8.067, 95% CI 3.193–20.383), primary-tumor SUVpeak (≥ 2.92: OR 9.219, 95% CI 3.096–27.452), and CA19-9 (≥ 16.6 U/ml: OR 3.750, 95% CI 1.485–9.470) were significant predictive factors for mediastinal lymph node metastasis in lung adenocarcinoma patients. The AUCs for the predictive value of the NSCLC multivariate model through internal and external validation were 0.833 (95% CI 0.769- 0.896) and 0.811 (95% CI 0.712–0.911), respectively. Conclusion: High SUV-derived parameters (SUVmax of mediastinal lymph node and primary-tumor SUVmax, SUVpeak, SUVmean, MTV and TLG) might provide varying degrees of predictive value for mediastinal lymph node metastasis in NSCLC patients. In particular, the SUVmax of mediastinal lymph nodes and primary-tumor SUVpeak could be independently and significantly associated with mediastinal lymph node metastasis in NSCLC and lung adenocarcinoma patients. Internal and external validation confirmed that the pretherapeutic SUVmax of the mediastinal lymph node and primary-tumor SUVpeak combined with serum CEA and SCC can effectively predict mediastinal lymph node metastasis of NSCLC patients.