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PurposeImperfect clinical reference standards can preclude accurately estimating the diagnostic accuracy of DAT-SPECT and MIBG myocardial scintigraphy for diagnosing DLB. To investigate the validity of unadjusted accuracy, we updated our previous meta-analysis.MethodsLiterature search was updated to March 18, 2018. We also examined published systematic review reports. Two investigators extracted data and rated study validity using the QUADAS-2 tool. We performed a Bayesian latent class model meta-analysis accounting for imperfect reference standards.ResultsWe evaluated 27 studies including 2236 patients. With the exception of two DAT-SPECT studies that involved postmortem neuropathological verification, studies were susceptible to bias from imperfect reference standards. Compared with the unadjusted accuracy estimates, the adjusted sensitivity values were similar, whereas the adjusted specificity values were generally lower for detecting α-synuclein pathology in the brain. The adjusted summary sensitivity and specificity were 0.86 (95% credible interval [CrI], 0.76–0.95) and 0.81 (CrI, 0.70–0.92), and 0.93 (CrI, 0.74–1.00) and 0.75 (CI, 0.47–0.94) for visual and semi-quantitative assessments of DAT-SPECT, respectively; 0.92 (CrI, 0.81–0.99) and 0.80 (CrI, 0.67–0.93), and 0.87 (CrI, 0.74–0.98) and 0.80 (CrI, 0.69–0.93), for delayed- and early-phase scans of MIBG scintigraphy, respectively. When diagnosing the typical clinical syndrome, the adjusted accuracy values were similar to the unadjusted estimates. The adjusted sensitivity and specificity were 0.89 (CrI, 0.75–0.98) and 0.87 (CrI, 0.72–0.97), and 0.97 (CrI, 0.78–1.0) and 0.70 (CrI, 0.43–0.92) for visual and semi-quantitative assessments of DAT-SPECT, respectively; and 0.93 (CrI, 0.81–0.98) and 0.90 (CrI, 0.73–0.97), and 0.85 (CrI, 0.66–0.96) and 0.96 (95% CI, 0.83–1.0) for delayed- and early-phase scans of MIBG scintigraphy, respectively.ConclusionsIn our adjusted analyses, both imaging biomarkers had high diagnostic accuracy for detecting the hallmark pathology in the brain and for diagnosing the typical clinical syndrome.

Abstract The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity-modulated radiation therapy (IMRT) dose distribution using only contours in prostate cancer. In this study, which included 95 IMRT-treated prostate cancer patients with available dose distributions and contours for planning target volume (PTVs) and organs at risk (OARs), a supervised-learning approach was used for training, where the dose for a voxel set in the dataset was defined as the label. The adaptive moment estimation algorithm was employed for optimizing a 3D U-net similar network. Eighty cases were used for the training and validation set in 5-fold cross-validation, and the remaining 15 cases were used as the test set. The predicted dose distributions were compared with the clinical dose distributions, and the model performance was evaluated by comparison with RapidPlan™. Dose–volume histogram (DVH) parameters were calculated for each contour as evaluation indexes. The mean absolute errors (MAE) with one standard deviation (1SD) between the clinical and CNN-predicted doses were 1.10% ± 0.64%, 2.50% ± 1.17%, 2.04% ± 1.40%, and 2.08% ± 1.99% for D2, D98 in PTV-1 and V65 in rectum and V65 in bladder, respectively, whereas the MAEs with 1SD between the clinical and the RapidPlan™-generated doses were 1.01% ± 0.66%, 2.15% ± 1.25%, 5.34% ± 2.13% and 3.04% ± 1.79%, respectively. Our CNN model could predict dose distributions that were superior or comparable with that generated by RapidPlan™, suggesting the potential of CNN in dose distribution prediction.

To examine the effect of multicomponent exercise program on memory function in older adults with mild cognitive impairment (MCI), and identify biomarkers associated with improvement of cognitive functions. Subjects were 100 older adults (mean age, 75 years) with MCI. The subjects were classified to an amnestic MCI group (n = 50) with neuroimaging measures, and other MCI group (n = 50) before the randomization. Subjects in each group were randomized to either a multicomponent exercise or an education control group using a ratio of 1∶1. The exercise group exercised for 90 min/d, 2 d/wk, 40 times for 6 months. The exercise program was conducted under multitask conditions to stimulate attention and memory. The control group attended two education classes. A repeated-measures ANOVA revealed that no group × time interactions on the cognitive tests and brain atrophy in MCI patients. A sub-analysis of amnestic MCI patients for group × time interactions revealed that the exercise group exhibited significantly better Mini-Mental State Examination (p = .04) and logical memory scores (p = .04), and reducing whole brain cortical atrophy (p<.05) compared to the control group. Low total cholesterol levels before the intervention were associated with an improvement of logical memory scores (p<.05), and a higher level of brain-derived neurotrophic factor was significantly related to improved ADAS-cog scores (p<.05). The results suggested that an exercise intervention is beneficial for improving logical memory and maintaining general cognitive function and reducing whole brain cortical atrophy in older adults with amnestic MCI. Low total cholesterol and higher brain-derived neurotrophic factor may predict improvement of cognitive functions in older adults with MCI. Further studies are required to determine the positive effects of exercise on cognitive function in older adults with MCI. UMIN-CTR UMIN000003662 ctr.cgi?function = brows&action = brows&type = summary&recptno = R000004436&language = J.

Functional diversity rather than species richness is critical for the understanding of ecological patterns and processes. This study aimed to develop novel integrated analytical strategies for the functional characterization of fish diversity based on the quantification, prediction and integration of the chemical and physical features in fish muscles. Machine learning models with an improved random forest algorithm applied on 1867 muscle nuclear magnetic resonance spectra belonging to 249 fish species successfully predicted the mobility patterns of fishes into four categories (migratory, territorial, rockfish, and demersal) with accuracies of 90.3–95.4%. Markov blanket-based feature selection method with an ecological–chemical–physical integrated network based on the Bayesian network inference algorithm highlighted the importance of nitrogen metabolism, which is critical for environmental adaptability of fishes in nutrient-rich environments, in the functional characterization of fish biodiversity. Our study provides valuable information and analytical strategies for fish home-range assessment on the basis of the chemical and physical characterization of fish muscle, which can serve as an ecological indicator for fish ecotyping and human impact monitoring.

Early regression—the regression in tumor volume during the initial phase of radiotherapy (approximately 2 weeks after treatment initiation)—is a common occurrence during radiotherapy. This rapid radiation-induced tumor regression may alter target coordinates, necessitating adaptive radiotherapy (ART). We developed a deep learning-based radiomics (DLR) approach to predict early head and neck tumor regression and thereby facilitate ART. Primary gross tumor volume (GTVp) was monitored in 96 patients and nodal GTV (GTVn) in 79 patients during treatment. All patients underwent two computed tomography (CT) scans: one before the start of radiotherapy for initial planning and one during radiotherapy for boost planning. Patients were assigned to regression and nonregression groups according to their median tumor regression rate (ΔGTV/treatment day from initial to boost CT scan). We input a GTV image into the convolutional neural network model, which was pretrained using natural image datasets, via transfer learning. The deep features were extracted from the last fully connected layer. To clarify the prognostic power of the deep features, machine learning models were trained. The models then predicted the regression and nonregression of GTVp and GTVn and evaluated the predictive performance by 0.632 + bootstrap area under the curve (AUC). Predictive performance for GTVp regression was highest using the InceptionResNetv2 model (mean AUC = 0.75) and that for GTVn was highest using NASNetLarge (mean AUC = 0.73). Both models outperformed the handcrafted radiomics features (mean AUC = 0.63 for GTVp and 0.61 for GTVn) or clinical factors (0.64 and 0.67, respectively). DLR may facilitate ART for improved radiation side-effects and target coverage.

Nuclear magnetic resonance (NMR) spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proof-of-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures. NMR-TS is available at https://github.com/tsudalab/NMR-TS .

Background This study aimed to identify the appropriate computer-based cognitive tests and cut-off values for estimating amyloid burden in preclinical Alzheimer’s disease drug trials. Methods Data from 103 older individuals, who underwent 18 F-florbetapir positron emission tomography and cognitive testing, were analyzed. Cognitive tests evaluated word list memory (immediate recognition and delayed recall), attention (Trail Making Test-part A), executive function (Trail Making Test-Part B), and processing speed (Digit Symbol Substitution Test [DSST]). Results The Aβ burden was significantly associated with word list memory (odds ratio [OR] = 0.42, 95% confidence interval [CI], 0.19–0.91) and DSST (OR = 0.35; 95% CI, 0.14–0.85). Positive predictive value and number needed to screen at a cut-off of 1.5 SD were better for word list memory and DSST among predictive values. Conclusions The computer-based memory and processing speed tests have the potential to reduce failure rates while screening individuals with Aβ accumulation in community settings.

Amyloid-β (Aβ) deposition is known to starts decades before the onset of clinical symptoms of Alzheimer’s disease (AD), however, the detailed pathophysiological processes underlying this preclinical period are not well understood. This study aimed to investigate functional network alterations in cognitively intact elderly individuals at risk for AD, and assessed the association between these network alterations and changes in Aβ deposition, glucose metabolism, and brain structure. Forty-five cognitively normal elderly subjects, who were classified into Aβ-positive (CN+) and Aβ-negative (CN−) groups using 11 C-Pittsburgh compound B PET, underwent resting state magnetoencephalography measurements, 18 F-fluorodeoxyglucose PET (FDG-PET) and structural MRI. Results demonstrated that in the CN+ group, functional connectivity (FC) within the precuneus was significantly decreased, whereas it was significantly enhanced between the precuneus and the bilateral inferior parietal lobules in the low-frequency bands (theta and delta). These changes were suggested to be associated with local cerebral Aβ deposition. Most of Aβ+ individuals in this study did not show any metabolic or anatomical changes, and there were no significant correlations between FC values and FDG-PET or MRI volumetry data. These results demonstrate that functional network alterations, which occur in association with Aβ deposition, are detectable using magnetoencephalography before metabolic and anatomical changes are seen.

Background: Dosimetric parameters of the planning target volume (PTV) and organs at risk (OARs) were compared among 3 different radiotherapy (RT) modalities in left breast cancer patients after breast-conserving surgery (BCS). Methods: Eleven patients with left breast cancer after BCS were enrolled and underwent CT simulation in the free breathing (FB) and deep inspiration breath-hold (DIBH) position. Three-dimensional conformal RT (3DCRT) and volumetric modulated arc therapy (VMAT) plans were generated for each patient in the DIBH positions. A 3DCRT plan was also created in the FB position. A dose-volume histogram (DVH) was used to analyze each evaluation index of PTV and OARs. The principal outcomes were PTV dose, heart dose, right breast dose, left anterior descending coronary artery (LADCA) dose, and left lung dose. Results: For 3DCRT plans, significant dose reductions were demonstrated in all evaluation parameters of the heart, LADCA, and left lung doses in the DIBH position compared with those in the FB position (P < 0.05). In the DIBH position, significant dose reductions were found in the heart and LADCA in VMAT plans compared to those in 3DCRT plans (P < 0.05). For the right breast, VMAT reduced Dmean significantly (0.32 Gy vs 0.08  Gy, P < 0.01). There were no significant differences between 3DCRT and VMAT plans for the left lung dose in the DIBH position. The indicators of PTV had no significant difference between the 3 plans. Conclusion: DIBH and VMAT could reduce dosimetric parameters of the OARs in left breast cancer patients after BCS. RT plans for left breast cancer after BCS can be optimized by DIBH and VMAT techniques to minimize radiation-induced toxicity.

Tumor invasion of the iliacus muscle is rare, and its clinical implications remain unclear. While malignant psoas syndrome (MPS) is characterized by severe pain and hip flexion contracture due to psoas muscle involvement, isolated iliacus muscle infiltration presents differently. In this report, we propose tumor-induced iliacus syndrome as a distinct clinical entity characterized by gait initiation impairment and slowed walking speed rather than fixed contracture or complete hip flexion failure. A woman in her sixties was diagnosed with stage IVB ovarian endometrioid carcinoma after routine lung cancer screening revealed abnormal diaphragmatic shadows. Imaging studies identified peritoneal nodules, a left ovarian mass, and a lytic bone lesion in the right iliac bone. A biopsy confirmed ovarian endometrioid carcinoma. She underwent chemotherapy with carboplatin and paclitaxel but developed sudden gait impairment without neurological abnormalities on brain and spinal imaging. Despite being able to stand and walk slowly with assistance, she experienced delays initiating each step, requiring a conscious effort to move her leg forward. Follow-up imaging showed iliacus muscle thickening and infiltration adjacent to the enlarging iliac bone lesion. Given her progressive gait impairment and worsening pain, chemotherapy was discontinued, and palliative radiotherapy was initiated. She received a total dose of 40 Gy in 15 fractions using 10 MV and 6 MV X-ray beams, targeting the iliac bone lesion and iliacus muscle involvement. Initially, she required a wheelchair due to difficulty initiating movement. After radiotherapy, her ambulatory function improved, allowing her to walk independently with mild residual gait disturbances. This case introduces tumor-induced iliacus syndrome as a newly recognized clinical entity distinct from MPS. Unlike MPS, which presents with severe pain and hip flexion contracture, iliacus muscle invasion predominantly affects gait initiation and walking speed. Recognizing this syndrome may improve diagnostic accuracy and optimize treatment strategies. Further research and case accumulation are needed to define its clinical significance and establish appropriate management approaches.