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Recently, new disease phenotyping has been proposed based on the origin site of α-synuclein pathology in Parkinson's disease (PD). In addition, a great deal of evidences suggested of parallel degeneration in the central nervous system and peripheral nervous system in PD. The myocardial uptake pattern ofsup123/supI-meta-iodobenzylguanidine can be a surrogate imaging biomarker for the peripheral nervous system involvement in PD. This study aimed to compare the clinical progression between brain-predominant PD (br-PD) and PD with body-involvement (bo-PD) phenotypes according to the onset of cardiac sympathetic denervation (CSD); the bo-PD group was defined as having the early onset of CSD and the br-PD phenotype was defined as those without initial CSD but later developed CSD in subsequent scans (the delayed onset of CSD). Clinical chracteristics, dopamine transporter activity, and non-motor manifestations were compared between the groups. Motor symptoms and cognitive functions at the initial and follow-up tests [3.1 (±1.4) years interval] were compared between the groups. This study included 29 br-PD and 103 bo-PD patients. Symptoms of rapid-eye-movement sleep behavior disorder, excessive daytime sleepiness, constipation, and orthostatic hypotension were more frequent in the bo-PD than in the br-PD group. The Unified Parkinson's Disease Rating Scale part III score was higher at the initial and increased more steeply during the follow-up period in the bo-PD patients than in the br-PD patients. Although the general cognitive status was not much different between the groups at initial and follow-up, each group showed statistically different cognitive domain profiles and progression patterns. The results demonstrated that the bo-PD group had more severe initial symptoms and steeper motor deterioration than the br-PD group, which indicated that there may be the more pathological involvements of central and peripheral nervous systems in the bo-PD group.

Background Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and real-world data. We aimed to develop a model trained only by normal brain PET data with an unsupervised manner to identify an abnormality in various disorders as imaging data of the clinical routine. Methods Using variational autoencoder, a type of unsupervised learning, Abnormality Score was defined as how far a given brain image is from the normal data. The model was applied to FDG PET data of Alzheimer's disease (AD) and mild cognitive impairment (MCI) and clinical routine FDG PET data for assessing behavioral abnormality and seizures. Accuracy was measured by the area under curve (AUC) of receiver-operating-characteristic (ROC) curve. We investigated whether deep learning has additional benefits with experts' visual interpretation to identify abnormal patterns. Findings The AUC of the ROC curve for differentiating AD was 0.90. The changes in cognitive scores from baseline to 2-year follow-up were significantly correlated with Abnormality Score at baseline. The AUC of the ROC curve for discriminating patients with various disorders from controls was 0.74. Experts' visual interpretation was helped by the deep learning model to identify abnormal patterns in 60% of cases initially not identified without the model. Interpretation We suggest that deep learning model trained only by normal data was applicable for identifying wide-range of abnormalities in brain diseases, even uncommon ones, proposing its possible use for interpreting real-world clinical data.

Purpose To evaluate the impact of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) on changes in treatment plan and target definition for preoperative radiotherapy in patients with rectal cancer. Methods Embase, PubMed, and Cochrane Library were searched up to November 2020 for all studies investigating the role of preoperative FDG PET in patients who underwent neoadjuvant radiotherapy before curative-intent surgery. The proportion of patients whose treatment plan (curative vs. palliative intent) or target definition was changed after FDG PET was analyzed. A random-effects model was used for pooled analysis. The change in target definition was compared between conventional radiological imaging-based target volume [gross tumor volume (GTV) or planning target volume (PTV)] and PET-based target volume (GTV or PTV) using the standardized mean difference (SMD) and 95% confidence interval (CI). Results A total of 336 patients from twelve studies were included. In eight studies, PET changed either the treatment intent or target definition in 24.8% of patients (95% CI 15.1% to 37.9%, I2 = 69%). In ten studies, the PET-based GTV was lower than the conventional imaging-based target volume (SMD -7.0, 95% CI -1.39 to -0.01). However, there was no significant difference between conventional imaging-based and PET-based PTV (SMD -0.07, 95% CI -0.75 to 0.62). In six studies evaluating the initial staging based on PET, the initial staging (nodal or metastasis status) was changed in 53 of 229 patients (23.1%). Newly detected or additional distant metastases were identified in 22 patients (9.6%) after FDG PET. Conclusion The use of FDG PET influences radiotherapy planning in a fourth of patients with rectal cancer. FDG PET can provide additive information for accurate tumor delineation, although PET-based PTV did not significantly change. These findings suggest that FDG PET may be beneficial to patients with rectal cancer before establishing a radiotherapy plan.

Development of c-Met targeting probes based on specifically designed peptides with high affinity and stability could help enhance diagnostic efficacy and therapeutic effects in c-Met positive cancers. The Random non-standard Peptides Integrated Discovery (RaPID) system for synthesizing natural product-like macrocyclic peptides via in vitro translation-based selection has recently emerged to overcome the shortcomings of traditional peptide synthesis. Here, we developed non-standard macrocyclic peptides specific to c-Met, and examined the cancer-targeting efficiency of fluorescein-labeled (FL) anti-c-Met peptides, referred to as aML5-FL and aMD4-FL, both in vitro and in vivo. The aML5-FL effectively targeted SNU-638 gastric cancer cells with high c-Met expression, compared to the aMD4-FL due to its high affinity. After intravenous administration of aML5-FL in a tumor xenograft mouse model, FL signal intensity in the extracted SNU-638 tumors was higher than that in SNU-216 tumors. This study provides preclinical data for the usefulness of novel non-standard macrocyclic peptides developed by the RaPID system for specific biomarker imaging.

Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver variability and result in inconsistent diagnosis. In this study, we developed a deep learning-based FP-CIT SPECT interpretation system to refine the imaging diagnosis of Parkinson's disease. This system trained by SPECT images of PD patients and normal controls shows high classification accuracy comparable with the experts' evaluation referring quantification results. Its high accuracy was validated in an independent cohort composed of patients with PD and nonparkinsonian tremor. In addition, we showed that some patients clinically diagnosed as PD who have scans without evidence of dopaminergic deficit (SWEDD), an atypical subgroup of PD, could be reclassified by our automated system. Our results suggested that the deep learning-based model could accurately interpret FP-CIT SPECT and overcome variability of human evaluation. It could help imaging diagnosis of patients with uncertain Parkinsonism and provide objective patient group classification, particularly for SWEDD, in further clinical studies., Highlights • Deep learning-based FP-CIT SPECT interpretation model was developed. • Deep learning-based model could overcome interobserver variability. • Its accuracy for discriminating PD from normal was comparable to the clinical standard. • It also showed high accuracy for differentiating PD from nonparkinsonian tremor. • Clinical follow-up results showed SWEDD could be reclassified to PD by our model.