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Abstract Neurocutaneous melanosis (NCM) is a rare, non-hereditary neurocutaneous disorder characterized by excessive melanocytic proliferation in the skin and central nervous system. As no major studies have covered the incidence of NCM among Japanese patients with congenital melanocytic nevi (CMN), we prospectively investigated the incidence of NCM among Japanese patients who underwent initial treatment for CMN. The relationship of CMN and NCM was also investigated. Japanese pediatric patients with CMN under 1 year of age were included between January 2020 and November 2022, and all patients underwent brain MRI to check for NCM in this study. NCM lesions were most frequently seen in the amygdala, followed by the cerebellum, brainstem, and cerebral hemispheres. NCM was diagnosed on brain MRI in 31.6% of the 38 patients with CMN and in 25.0% of patients with no prior examination or treatment. Distribution and size of CMN, number of satellite nevi, rugosity and nodules were strongly associated with the existence of NCM, and these findings may guide a future registry study with a large cohort of CMN patients.

Abstract Carbonyl stress is a condition featuring increased rich reactive carbonyl compounds, which facilitate the formation of advanced glycation end products including pentosidine. We previously reported the relationship between enhanced carbonyl stress and disrupted white matter integrity in schizophrenia, although which microstructural component is disrupted remained unclear. In this study, 32 patients with schizophrenia (SCZ) and 45 age- and gender-matched healthy volunteers (HC) were recruited. We obtained blood samples for carbonyl stress markers (plasma pentosidine and serum pyridoxal) and multi-modal magnetic resonance imaging measures of white matter microstructures including apparent axonal density (intra-cellular volume fraction (ICVF)) and orientation (orientation dispersion index (ODI)), and inflammation (free water (FW)). In SCZ, the plasma pentosidine level was significantly increased. Group comparison revealed that mean white matter values were decreased for ICVF, and increased for FW. We found a significant negative correlation between the plasma pentosidine level and mean ICVF values in SCZ, and a significant negative correlation between the serum pyridoxal level and mean ODI value in HC, regardless of age. Our results suggest an association between enhanced carbonyl stress and axonal abnormality in SCZ.

Abstract This study aimed to develop a versatile automatic segmentation model of bladder cancer (BC) on MRI using a convolutional neural network and investigate the robustness of radiomics features automatically extracted from apparent diffusion coefficient (ADC) maps. This two-center retrospective study used multi-vendor MR units and included 170 patients with BC, of whom 140 were assigned to training datasets for the modified U-net model with five-fold cross-validation and 30 to test datasets for assessment of segmentation performance and reproducibility of automatically extracted radiomics features. For model input data, diffusion-weighted images with b = 0 and 1000 s/mm2, ADC maps, and multi-sequence images (b0-b1000-ADC maps) were used. Segmentation accuracy was compared between ours and existing models. The reproducibility of radiomics features on ADC maps was evaluated using intraclass correlation coefficient. The model with multi-sequence images achieved the highest Dice similarity coefficient (DSC) with five-fold cross-validation (mean DSC = 0.83 and 0.79 for the training and validation datasets, respectively). The median (interquartile range) DSC of the test dataset model was 0.81 (0.70–0.88). Radiomics features extracted from manually and automatically segmented BC exhibited good reproducibility. Thus, our U-net model performed highly accurate segmentation of BC, and radiomics features extracted from the automatic segmentation results exhibited high reproducibility.

Abstract Diffusion-weighted magnetic resonance imaging is prone to have susceptibility artifacts in an inhomogeneous magnetic field. We compared distortion and artifacts among three diffusion acquisition techniques (single-shot echo-planar imaging [SS-EPI DWI], readout-segmented EPI [RESOLVE DWI], and 2D turbo gradient- and spin-echo diffusion-weighted imaging with non-Cartesian BLADE trajectory [TGSE-BLADE DWI]) in healthy volunteers and in patients with a cerebral aneurysm clip. Seventeen healthy volunteers and 20 patients who had undergone surgical cerebral aneurysm clipping were prospectively enrolled. SS-EPI DWI, RESOLVE DWI, and TGSE-BLADE DWI of the brain were performed using 3 T scanners. Distortion was the least in TGSE-BLADE DWI, and lower in RESOLVE DWI than SS-EPI DWI near air–bone interfaces in healthy volunteers (P

Abstract The purpose of this study is to evaluate whether thin-slice high-resolution 2D fat-suppressed proton density-weighted image of the knee joint using denoising approach with deep learning-based reconstruction (dDLR) with MPR is more useful than 3D FS-PD multi planar voxel image. Twelve patients who underwent MRI of the knee at 3T and 13 knees were enrolled. Denoising effect was quantitatively evaluated by comparing the coefficient of variation (CV) before and after dDLR. For the qualitative assessment, two radiologists evaluated image quality, artifacts, anatomical structures, and abnormal findings using a 5-point Likert scale between 2D and 3D. All of them were statistically analyzed. Gwet’s agreement coefficients were also calculated. For the scores of abnormal findings, we calculated the percentages of the cases with agreement with high confidence. The CV after dDLR was significantly lower than the one before dDLR (p

Abstract The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively (P = 0.045). The cumulative incidence of recurrence (CIR) was significantly higher in the STAS high-risk group when using the radiomics model than that in the low-risk group (44% vs. 4% at 5 years; P = 0.002) in patients who underwent limited resection in the testing cohort. In contrast, the 5-year CIR was not significantly different among patients who underwent lobectomy (17% vs. 11%; P = 0.469). In conclusion, the machine learning model for STAS prediction based on peritumoral radiomics features performed better than the C/T ratio model.

Abstract We investigated the associations of time-dependent DWI, non-Gaussian DWI, and CEST parameters with histological biomarkers in a breast cancer xenograft model. 22 xenograft mice (7 MCF-7 and 15 MDA-MB-231) were scanned at 4 diffusion times [Td = 2.5/5 ms with 11 b-values (0–600 s/mm2) and Td = 9/27.6 ms with 17 b-values (0–3000 s/mm2), respectively]. The apparent diffusion coefficient (ADC) was estimated using 2 b-values in different combinations (ADC0–600 using b = 0 and 600 s/mm2 and shifted ADC [sADC200–1500] using b = 200 and 1500 s/mm2) at each of those diffusion times. Then the change (Δ) in ADC/sADC between diffusion times was evaluated. Non-Gaussian diffusion and intravoxel incoherent motion (IVIM) parameters (ADC0, the virtual ADC at b = 0; K, Kurtosis from non-Gaussian diffusion; f, the IVIM perfusion fraction) were estimated. CEST images were acquired and the amide proton transfer signal intensity (APT SI) were measured. The ΔsADC9–27.6 (between $${\text{sADC}}_{{9\,{\text{ms}}}}^{200{-}1500}$$ sADC 9 ms 200 - 1500 and $${\text{sADC}}_{{27.6\,{\text{ms}}}}^{200{-}1500}$$ sADC 27.6 ms 200 - 1500 and ΔADC2.5_sADC27.6 (between $${\text{ADC}}_{{2.5\, {\text{ms}}}}^{0{-}600}$$ ADC 2.5 ms 0 - 600 and $${\text{sADC}}_{{27.6\,{\text{ms}}}}^{200{-}1500}$$ sADC 27.6 ms 200 - 1500 ) was significantly larger for MCF-7 groups, and ΔADC2.5_sADC27.6 was positively correlated with Ki67max and APT SI. ADC0 decreased significantly in MDA-MB-231 group and K increased significantly with Td in MCF-7 group. APT SI and cellular area had a moderately strong positive correlation in MDA-MB-231 and MCF-7 tumors combined, and there was a positive correlation in MDA-MB-231 tumors. There was a significant negative correlation between APT SI and the Ki-67-positive ratio in MDA-MB-231 tumors and when combined with MCF-7 tumors. The associations of ΔADC2.5_sADC27.6 and API SI with Ki-67 parameters indicate that the Td-dependent DW and CEST parameters are useful to predict the histological markers of breast cancers.

Abstract Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperative risk stratification is essential for personalized medicine. There have been several radiomics studies for noninvasive risk stratification of EC using MRI. Although tumor segmentation is usually necessary for these studies, manual segmentation is not only labor-intensive but may also be subjective. Therefore, our study aimed to perform the automatic segmentation of EC on MRI with a convolutional neural network. The effect of the input image sequence and batch size on the segmentation performance was also investigated. Of 200 patients with EC, 180 patients were used for training the modified U-net model; 20 patients for testing the segmentation performance and the robustness of automatically extracted radiomics features. Using multi-sequence images and larger batch size was effective for improving segmentation accuracy. The mean Dice similarity coefficient, sensitivity, and positive predictive value of our model for the test set were 0.806, 0.816, and 0.834, respectively. The robustness of automatically extracted first-order and shape-based features was high (median ICC = 0.86 and 0.96, respectively). Other high-order features presented moderate-high robustness (median ICC = 0.57–0.93). Our model could automatically segment EC on MRI and extract radiomics features with high reliability.

Abstract Specifying the exact localization of insulinoma remains challenging due to the lack of insulinoma-specific imaging methods. Recently, glucagon-like peptide-1 receptor (GLP-1R)-targeted imaging, especially positron emission tomography (PET), has emerged. Although various radiolabeled GLP-1R agonist exendin-4-based probes with chemical modifications for PET imaging have been investigated, an optimal candidate probe and its scanning protocol remain a necessity. Thus, we investigated the utility of a novel exendin-4-based probe conjugated with polyethylene glycol (PEG) for [18F]FB(ePEG12)12-exendin-4 PET imaging for insulinoma detection. We utilized [18F]FB(ePEG12)12-exendin-4 PET/CT to visualize mouse tumor models, which were generated using rat insulinoma cell xenografts. The probe demonstrated high uptake value on the tumor as 37.1 ± 0.4%ID/g, with rapid kidney clearance. Additionally, we used Pdx1-Cre;Trp53 R172H ;Rb f/f mice, which developed endogenous insulinoma and glucagonoma, since they enabled differential imaging evaluation of our probe in functional pancreatic neuroendocrine neoplasms. In this model, our [18F]FB(ePEG12)12-exendin-4 PET/CT yielded favorable sensitivity and specificity for insulinoma detection. Sensitivity: 30-min post-injection 66.7%, 60-min post-injection 83.3%, combined 100% and specificity: 30-min post-injection 100%, 60-min post-injection 100%, combined 100%, which was corroborated by the results of in vitro time-based analysis of internalized probe accumulation. Accordingly, [18F]FB(ePEG12)12-exendin-4 is a promising PET imaging probe for visualizing insulinoma.

Abstract This study aimed to evaluate the predictions of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for prognosis of triple-negative breast cancer (TNBC), especially with residual disease (RD) after preoperative chemotherapy. This retrospective analysis included 74 TNBC patients who received preoperative chemotherapy. DCE-MRI findings from three timepoints were examined: at diagnosis (MRIpre), at midpoint (MRImid) and after chemotherapy (MRIpost). These findings included cancer lesion size, washout index (WI) as a kinetic parameter using the difference in signal intensity between early and delayed phases, and time-signal intensity curve types. Distant disease-free survival was analysed using the log-rank test to compare RD group with and without a fast-washout curve. The diagnostic performance of DCE-MRI findings, including positive predictive value (PPV) for pathological responses, was also calculated. RD without fast washout curve was a significantly better prognostic factor, both at MRImid and MRIpost (hazard ratio = 0.092, 0.098, p