髙野 一輝

Sarcopenia, characterised by a decline in muscle mass and strength, affects the health of the elderly, leading to increased falls, hospitalisation, and mortality rates. Muscle quality, reflecting microscopic and macroscopic muscle changes, is a critical determinant of physical function. To utilise radiomic features extracted from magnetic resonance (MR) images to assess age-related changes in muscle quality, a dataset of 24 adults, divided into older (male/female: 6/6, 66-79 years) and younger (male/female: 6/6, 21-31 years) groups, was used to investigate the radiomics features of the dorsiflexor and plantar flexor muscles of the lower leg that are critical for mobility. MR images were processed using MaZda software for feature extraction. Dimensionality reduction was performed using principal component analysis and recursive feature elimination, followed by classification using machine learning models, such as support vector machine, extreme gradient boosting, and naïve Bayes. A leave-one-out validation test was used to train and test the classifiers, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the classification performance. The analysis revealed that significant differences in radiomic feature distributions were found between age groups, with older adults showing higher complexity and variability in muscle texture. The plantar flexors showed similar or higher AUC than the dorsiflexors in all models. When the dorsiflexor muscles were combined with the plantar flexor muscles, they tended to have a higher AUC than when they were used alone. Radiomic features in lower-leg MR images reflect ageing, especially in the plantar flexor muscles. Radiomic analysis can offer a deeper understanding of age-related muscle quality than traditional muscle mass assessments.

OBJECTIVES: To clarify the association between a radiomics score (Rad-score) derived from T1-weighted signal intensity to T2-weighted signal intensity (T1-w/T2-w) ratio images and the progression of motor symptoms in Parkinson's disease (PD). MATERIALS AND METHODS: This retrospective study included patients with PD enrolled in the Parkinson's Progression Markers Initiative. The Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III score ≥ 33 and/or Hoehn and Yahr stage ≥ 3 indicated motor function decline. The Rad-score was constructed using radiomics features extracted from T1-w/T2-w ratio images. The Kaplan-Meier analysis and Cox regression analyses were used to assess the time differences in motor function decline between the high and low Rad-score groups. RESULTS: A total of 171 patients with PD were divided into training (n = 101, mean age at baseline, 61.6 ± 9.3 years) and testing (n = 70, mean age at baseline, 61.6 ± 10 years). The patients in the high Rad-score group had a shorter time to motor function decline than those in the low Rad-score group in the training dataset (log-rank test, p < 0.001) and testing dataset (log-rank test, p < 0.001). The multivariate Cox regression using the Rad-score and clinical factors revealed a significant association between the Rad-score and motor function decline in the training dataset (HR = 2.368, 95%CI:1.423-3.943, p < 0.001) and testing dataset (HR = 2.931, 95%CI:1.472-5.837, p = 0.002). CONCLUSION: Rad-scores based on radiomics features derived from T1-w/T2-w ratio images were associated with the progression of motor symptoms in PD. CLINICAL RELEVANCE STATEMENT: The radiomics score derived from the T1-weighted/T2-weighted ratio images offers a predictive tool for assessing the progression of motor symptom in patients with PD. KEY POINTS: Radiomics score derived from T1-weighted/T2-weighted ratio images is correlated with the motor symptoms of Parkinson's disease. A high radiomics score correlated with faster motor function decline in patients with Parkinson's disease. The proposed radiomics score offers predictive insight into the progression of motor symptoms of Parkinson's disease.

PURPOSE: As we are exposed to stress on a daily basis, it is important to detect and treat stress during the subclinical period. However, methods to quantify and confirm stress are currently unavailable, and the detection of subclinical stressors is difficult. This study aimed to determine whether manganese-enhanced magnetic resonance imaging (MEMRI) could be used to assess stress in rat brains. METHODS: We exposed male Wistar/ST rats bred in a specific pathogen-free environment to ultrasound stimuli (22 kHz and 55 kHz) for 10 days and then assessed brain activities using MEMRI, the light/dark box test, and ΔFosB immunohistochemical staining. RESULTS: In the MEMRI assessments, exposure at 22 kHz activated the periaqueductal gray, while exposure at 55 kHz specifically enhanced activity in the nucleus accumbens core and the orbitofrontal cortex. The exploratory behavior of the 55-kHz group increased sharply, while that of the 22-kHz group showed a lower exploratory value. ΔFosB expression increased in the orbitofrontal cortex, nucleus accumbens, periaqueductal gray, and amygdaloid nucleus in the 22-kHz group. CONCLUSION: Ultrasound stimuli at 22 kHz suppressed weight gain in rats and excessive ΔFosB induction in the nucleus accumbens caused excessive sensitization of the neural circuit, thereby contributing to pathological behavior. We thus demonstrated that MEMRI can be useful to objectively assess the pathophysiology of stress-related disorders.

Abstract Background We hypothesised that the radiomics signature, which includes texture information of dopamine transporter single-photon emission computed tomography (DAT-SPECT) images for Parkinson’s disease (PD), may assist semi-quantitative indices. Herein, we constructed a radiomics signature using DAT-SPECT-derived radiomics features that effectively discriminated PD from healthy individuals and evaluated its classification performance. Results We analysed 413 cases of both normal control (NC, n = 101) and PD (n = 312) groups from the Parkinson’s Progression Markers Initiative database. Data were divided into the training and two test datasets with different SPECT manufacturers. DAT-SPECT images were spatially normalised to the Montreal Neurologic Institute space. We calculated 930 radiomics features, including intensity- and texture-based features in the caudate, putamen, and pallidum volumes of interest. The striatum uptake ratios (SURs) of the caudate, putamen, and pallidum were also calculated as conventional semi-quantification indices. The least absolute shrinkage and selection operator was used for feature selection and construction of the radiomics signature. The four classification models were constructed using a radiomics signature and/or semi-quantitative indicator. Furthermore, we compared the classification performance of the semi-quantitative indicator alone and the combination with the radiomics signature for the classification models. The receiver operating characteristics (ROC) analysis was used to evaluate the classification performance. The classification performance of SUR_putamen was higher than that of other semi-quantitative indicators. The radiomics signature resulted in a slightly increased area under the ROC curve (AUC) compared to SUR_putamen in each test dataset. When combined with SUR_putamen and radiomics signature, all classification models showed slightly higher AUCs than that of SUR_putamen alone. Conclusion We constructed a DAT-SPECT image-derived radiomics signature. Performance analysis showed that the current radiomics signature would be helpful for the diagnosis of PD and has the potential to provide robust diagnostic performance.

The utility of three-dimensional (3D) imaging with cone-beam computed tomography (CBCT) during interventional radiology (IVR) in colonic diverticular bleeding was compared to that of contrast-enhanced computed tomography (CECT). Additionally, to identify the responsible vessels in the absence of extravascular leakage using digital subtraction angiography, we examined the detection rate using software conventionally applied to transcatheter arterial chemoembolization (TACE). The 3D images obtained by CECT before IVR did not clearly show the destroyed vessels, whereas the 3D images obtained by CBCT during IVR clearly depicted the peripheral vessels. The TACE-assisted software identified the responsible vessels with a high probability, even in cases without extravascular leakage. CBCT could delineate vascular positions more accurately than CECT. Moreover, 80% of the responsible vessels could be delineated using the software; however, caution should be exercised as results may differ depending on the positioning of the region of interest.