Substantia nigral dopamine transporter uptake in dementia with Lewy bodies

Abstract Nigrostriatal dopaminergic degeneration is a pathological hallmark of dementia with Lewy bodies (DLB). To identify the subregional dopamine transporter (DAT) uptake patterns that improve the diagnostic accuracy of DLB, we analyzed N-(3-[18F] fluoropropyl)-2β-carbomethoxy-3β-(4-iodophenyl)-nortropane (FP-CIT) PET in 51 patients with DLB, in 36 patients with mild cognitive impairment with Lewy body (MCI-LB), and in 40 healthy controls (HCs). In addition to a high affinity for DAT, FP-CIT show a modest affinity to serotonin or norepinephrine transporters. Specific binding ratios (SBRs) of the nigrostriatal subregions were transformed to age-adjusted z-scores (zSBR) based on HCs. The diagnostic accuracy of subregional zSBRs were tested using receiver operating characteristic (ROC) curve analyses separately for MCI-LB and DLB versus HCs. Then, the effect of subregional zSBRs on the presence of clinical features and gray matter (GM) density were evaluated in all patients with MCI-LB or DLB as a group. ROC curve analyses showed that the diagnostic accuracy of DLB based on the zSBR of substantia nigra (area under the curve [AUC], 0.90) or those for MCI-LB (AUC, 0.87) were significantly higher than that based on the zSBR of posterior putamen for DLB (AUC, 0.72) or MCI-LB (AUC, 0.65). Lower zSBRs in nigrostriatal regions were associated with visual hallucination, severe parkinsonism, and cognitive dysfunction, while lower zSBR of substantia nigra was associated with widespread GM atrophy in DLB and MCI-LB patients. Taken together, our results suggest that evaluation of nigral DAT uptake may increase the diagnostic accuracy of DLB and MCI-LB than other striatal regions

Sandwich spatial saturation for neuromelanin-sensitive MRI: Development and multi-center trial

Neuromelanin (NM)-sensitive MRI using a magnetization transfer (MT)-prepared T1-weighted sequence has been suggested as a tool to visualize NM contents in the brain. In this study, a new NM-sensitive imaging method, sandwichNM, is proposed by utilizing the incidental MT effects of spatial saturation RF pulses in order to generate consistent high-quality NM images using product sequences. The spatial saturation pulses are located both superior and inferior to the imaging volume, increasing MT weighting while avoiding asymmetric MT effects. When the parameters of the spatial saturation were optimized, sandwichNM reported a higher NM contrast ratio than those of conventional NM-sensitive imaging methods with matched parameters for comparability with sandwichNM (SandwichNM: 23.6 +/- 5.4%; MT-prepared TSE: 20.6 +/- 7.4%; MT-prepared GRE: 17.4 +/- 6.0%). In a multi-vendor experiment, the sandwichNM images displayed higher means and lower standard deviations of the NM contrast ratio across subjects in all three vendors (SandwichNM vs. MT-prepared GRE; Vendor A: 28.4 +/- 1.5% vs. 24.4 +/- 2.8%; Vendor B: 27.2 +/- 1.0% vs. 13.3 +/- 1.3%; Vendor C: 27.3 +/- 0.7% vs. 20.1 +/- 0.9%). For each subject, the standard deviations of the NM contrast ratio across the vendors were substantially lower in SandwichNM (SandwichNM vs. MT-prepared GRE; subject 1: 1.5% vs. 8.1%, subject 2: 1.1 % vs. 5.1%, subject 3: 0.9% vs. 4.0%, subject 4: 1.1% vs. 5.3%), demonstrating consistent contrasts across the vendors. The proposed method utilizes product sequences, requiring no alteration of a sequence and, therefore, may have a wide practical utility in exploring the NM imaging.Y

Deep-Learning for the Diagnosis of Esophageal Cancers and Precursor Lesions in Endoscopic Images: A Model Establishment and Nationwide Multicenter Performance Verification Study

Background: Suspicion of lesions and prediction of the histology of esophageal cancers or premalignant lesions in endoscopic images are not yet accurate. The local feature selection and optimization functions of the model enabled an accurate analysis of images in deep learning. Objectives: To establish a deep-learning model to diagnose esophageal cancers, precursor lesions, and non-neoplasms using endoscopic images. Additionally, a nationwide prospective multicenter performance verification was conducted to confirm the possibility of real-clinic application. Methods: A total of 5162 white-light endoscopic images were used for the training and internal test of the model classifying esophageal cancers, dysplasias, and non-neoplasms. A no-code deep-learning tool was used for the establishment of the deep-learning model. Prospective multicenter external tests using 836 novel images from five hospitals were conducted. The primary performance metric was the external-test accuracy. An attention map was generated and analyzed to gain the explainability. Results: The established model reached 95.6% (95% confidence interval: 94.2–97.0%) internal-test accuracy (precision: 78.0%, recall: 93.9%, F1 score: 85.2%). Regarding the external tests, the accuracy ranged from 90.0% to 95.8% (overall accuracy: 93.9%). There was no statistical difference in the number of correctly identified the region of interest for the external tests between the expert endoscopist and the established model using attention map analysis (P = 0.11). In terms of the dysplasia subgroup, the number of correctly identified regions of interest was higher in the deep-learning model than in the endoscopist group, although statistically insignificant (P = 0.48). Conclusions: We established a deep-learning model that accurately classifies esophageal cancers, precursor lesions, and non-neoplasms. This model confirmed the potential for generalizability through multicenter external tests and explainability through the attention map analysis