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Purpose: To evaluate the utility of model-based deep learning reconstruction in prostate diffusion-weighted imaging (DWI). Methods: This retrospective study evaluated two prostate diffusion-weighted imaging (DWI) methods: deep learning reconstruction (DL-DWI) and traditional parallel imaging (PI-DWI). We examined 32 patients with radiologically diagnosed and histologically confirmed prostate cancer (PCa) lesions ≥10 mm. Image quality was evaluated both qualitatively (for overall quality, prostate conspicuity, and lesion conspicuity) and quantitatively, using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) for prostate tissue. Results: In the qualitative evaluation, DL-DWI scored significantly higher than PI-DWI for all three parameters (p

Abstract Background The differences in pre- and early post-procedural blood flow dynamics between the two major types of bioprosthetic valves, the balloon-expandable valve (BEV) and self-expandable valve (SEV), in patients with aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR), have not been investigated. We aimed to investigate the differences in blood flow dynamics between the BEV and SEV using four-dimensional flow cardiovascular magnetic resonance (4D flow CMR). Methods We prospectively examined 98 consecutive patients with severe AS who underwent TAVR between May 2018 and November 2021 (58 BEV and 40 SEV) after excluding those without CMR because of a contraindication, inadequate imaging from the analyses, or patients’ refusal. CMR was performed in all participants before (median interval, 22 [interquartile range (IQR) 4–39] days) and after (median interval, 6 [IQR 3–6] days) TAVR. We compared the changes in blood flow patterns, wall shear stress (WSS), and energy loss (EL) in the ascending aorta (AAo) between the BEV and SEV using 4D flow CMR. Results The absolute reductions in helical flow and flow eccentricity were significantly higher in the SEV group compared in the BEV group after TAVR (BEV: − 0.22 ± 0.86 vs. SEV: − 0.85 ± 0.80, P

Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for automating this identification process. This study explored the impact of semi-supervised learning on identifying mammographic calcifications by including 712 mammographic images from 252 patients in public datasets. Initially, 212 mammogram images were segmented into patches and classified visually for calcification presence. A subset of these patches, derived from 169 mammogram images, was used to train a ResNet50-based classifier. The classifier was evaluated using patches generated from 43 mammograms as a test data set. Additionally, 500 more mammogram images were processed into patches and analyzed using the trained ResNet50 model, with semi-supervised learning applied to patches exceeding certain classification probabilities. This process aimed to enhance the classifier’s accuracy and achieve improvements over the initial model. The findings indicated that semi-supervised learning significantly benefits the accuracy of calcification detection in mammography, underscoring its utility in enhancing diagnostic methodologies.

Whether diurnal variation exists in quantitative MRI indices such as the T1rho relaxation time (T1ρ) of the intervertebral disc (IVD) is yet to be explored. This prospective study aimed to evaluate the diurnal variation in T1ρ, apparent diffusion coefficient (ADC), and electrical conductivity (σ) of lumbar IVD and its relationship with other MRI or clinical indices. Lumbar spine MRI, including T1ρ imaging, diffusion-weighted imaging (DWI), and electric properties tomography (EPT), was conducted on 17 sedentary workers twice (morning and evening) on the same day. The T1ρ, ADC, and σ of IVD were compared between the time points. Their diurnal variation, if any, was tested for correlation with age, body mass index (BMI), IVD level, Pfirrmann grade, scan interval, and diurnal variation in IVD height index. The results showed a significant decrease in T1ρ and ADC and a significant increase in the σ of IVD in the evening. T1ρ variation had a weak correlation with age and scan interval, and ADC variation with scan interval. Diurnal variation exists for the T1ρ, ADC, and σ of lumbar IVD, which should be accounted for in image interpretation. This variation is thought to be due to diurnal variations in intradiscal water, proteoglycan, and sodium ion concentration.

The development of three-dimensional printers has facilitated the creation of patient-specific hollow vessel models. Preoperative simulations using these types of models have improved our ability to select appropriate devices and embolic materials before performing complex endovascular procedures. This report describes 2 cases of high-flow renal arteriovenous fistulas (r-AVFs) that were successfully treated via short-segment embolization using the preloading coil-in-plug (p-CIP) technique. To our knowledge, this is the first report of r-AVF being treated using the p-CIP technique. Our findings demonstrate that preoperative simulation has the potential to improve the safety and reliability of complex vascular embolization procedures.

Abstract Background In nasal or sinonasal tumors, orbital invasion beyond periorbita by the tumor is one of the important criteria in the selection of the surgical procedure. We investigated the usefulness of the convolutional neural network (CNN)-based deep learning technique for the diagnosis of orbital invasion, using computed tomography (CT) images. Methods A total of 168 lesions with malignant nasal or sinonasal tumors were divided into a training dataset (n = 119) and a test dataset (n = 49). The final diagnosis (invasion-positive or -negative) was determined by experienced radiologists who carefully reviewed all of the CT images. In a CNN-based deep learning analysis, a slice of the square target region that included the orbital bone wall was extracted and fed into a deep-learning training session to create a diagnostic model using transfer learning with the Visual Geometry Group 16 (VGG16) model. The test dataset was subsequently tested in CNN-based diagnostic models and by two other radiologists who were not specialized in head and neck radiology. At approx. 2 months after the first reading session, two radiologists again reviewed all of the images in the test dataset, referring to the diagnoses provided by the trained CNN-based diagnostic model. Results The diagnostic accuracy was 0.92 by the CNN-based diagnostic models, whereas the diagnostic accuracies by the two radiologists at the first reading session were 0.49 and 0.45, respectively. In the second reading session by two radiologists (diagnosing with the assistance by the CNN-based diagnostic model), marked elevations of the diagnostic accuracy were observed (0.94 and 1.00, respectively). Conclusion The CNN-based deep learning technique can be a useful support tool in assessing the presence of orbital invasion on CT images, especially for non-specialized radiologists.

A 62-year-old woman presented with a tumor in the right kidney. A right partial nephrectomy was performed, and the tumor was diagnosed as clear cell renal cell carcinoma (RCC) on histopathological examination. A right ovarian tumor was detected on follow-up computed tomography (CT) 5 years after partial nephrectomy and pathology proved RCC metastasis. RCC rarely metastasizes to the ovaries. There is limited information on the radiological features of ovarian metastasis in RCC. In this case report, we presented the CT and magnetic resonance images of ovarian metastasis of RCC. In addition, we also presented a literature review with special emphasis on the imaging features of ovarian metastasis of RCC.

In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study investigated whether the combination of PU-MRI and super-resolution (SR) deep learning models improves the visibility of the prostatic urinary tract. We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy by insertion of fiducial markers. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo imaging sequence. Four different SR deep learning models were used: the enhanced deep SR network (EDSR), widely activated SR network (WDSR), SR generative adversarial network (SRGAN), and residual dense network (RDN). The complex wavelet structural similarity index measure (CW-SSIM) was used to quantitatively assess the performance of the proposed SR images compared to PU-MRI. Two radiation oncologists used a 1-to-5 scale to subjectively evaluate the visibility of the prostatic urinary tract. Cohen's weighted kappa (k) was used as a measure of agreement of inter-operator reliability. The mean CW-SSIM in EDSR, WDSR, SRGAN, and RDN was 99.86%, 99.89%, 99.30%, and 99.67%, respectively. The mean prostatic urinary tract visibility scores of the radiation oncologists were 3.70 and 3.53 for PU-MRI (k = 0.93), 3.67 and 2.70 for EDSR (k = 0.89), 3.70 and 2.73 for WDSR (k = 0.88), 3.67 and 2.73 for SRGAN (k = 0.88), and 4.37 and 3.73 for RDN (k = 0.93), respectively. The results suggest that SR images using RDN are similar to the original images, and the SR deep learning models subjectively improve the visibility of the prostatic urinary tract.

Abstract Background We investigated the correlation between texture features extracted from apparent diffusion coefficient (ADC) maps or diffusion-weighted images (DWIs), and grade group (GG) in the prostate peripheral zone (PZ) and transition zone (TZ), and assessed reliability in repeated examinations. Methods Patients underwent 3-T pelvic magnetic resonance imaging (MRI) before radical prostatectomy with repeated DWI using b-values of 0, 100, 1,000, and 1,500 s/mm2. Region of interest (ROI) for cancer was assigned to the first and second DWI acquisition separately. Texture features of ROIs were extracted from comma-separated values (CSV) data of ADC maps generated from several sets of two b-value combinations and DWIs, and correlation with GG, discrimination ability between GG of 1–2 versus 3–5, and data repeatability were evaluated in PZ and TZ. Results Forty-four patients with 49 prostate cancers met the eligibility criteria. In PZ, ADC 10% and 25% based on ADC map of two b-value combinations of 100 and 1,500 s/mm2 and 10% based on ADC map with b-value of 0 and 1,500 s/mm2 showed significant correlation with GG, acceptable discrimination ability, and good repeatability. In TZ, higher-order texture feature of busyness extracted from ADC map of 100 and 1,500 s/mm2, and high gray-level run emphasis, short-run high gray-level emphasis, and high gray-level zone emphasis from DWI with b-value of 100 s/mm2 demonstrated significant correlation, excellent discrimination ability, but moderate repeatability. Conclusions Some DWI-related features showed significant correlation with GG, acceptable to excellent discrimination ability, and moderate to good data repeatability in prostate cancer, and differed between PZ and TZ.

Abstract Aims Although soluble interleukin 2 receptor (sIL‐2R) is a potentially useful biomarker in the diagnosis and evaluation of disease severity in patients with sarcoidosis, its prognostic implication in patients with cardiac sarcoidosis (CS) is unclear. We sought to investigate whether sIL‐2R was associated with clinical outcomes and to clarify the relationship between sIL‐2R levels and disease activity in patients with CS. Methods and results We examined 83 consecutive patients with CS in our hospital who had available serum sIL‐2R data between May 2003 and February 2020. The primary outcome was a composite of advanced atrioventricular block, ventricular tachycardia or ventricular fibrillation, heart failure hospitalization, and all‐cause death. Inflammatory activity in the myocardium and lymph nodes was assessed by 18F‐fluorideoxyglucose positron emission tomography/computed tomography. During a median follow‐up period of 2.96 (IQR 2.24–4.27) years, the primary outcome occurred in 24 patients (29%). Higher serum sIL‐2R levels (>538 U/mL, the median) were significantly related to increased incidence of primary outcome (P = 0.037). Multivariable Cox regression analysis showed that a higher sIL‐2R was independently associated with an increased subsequent risk of adverse events (HR 3.71, 95% CI 1.63–8.44, P = 0.002), even after adjustment for significant covariates. sIL‐2R levels were significantly correlated to inflammatory activity in lymph nodes (r = 0.346, P = 0.003) but not the myocardium (r = 0.131, P = 0.27). Conclusions Increased sIL‐2R is associated with worse long‐term clinical outcomes accompanied by increased systemic inflammatory activity in CS patients.

BackgroundDue to the development of medical data, a large amount of clinical data has been generated. These unstructured data contain substantial information. Extracting useful knowledge from this data and making scientific decisions for diagnosing and treating diseases have become increasingly necessary. Unstructured data, such as in the Marketplace for Medical Information in Intensive Care III (MIMIC-III) data set, contain several ambiguous words that demonstrate the subjectivity of doctors, such as descriptions of patient symptoms. These data could be used to further improve the accuracy of medical diagnostic system assessments. To the best of our knowledge, there is currently no method for extracting subjective words that express the extent of these symptoms (hereinafter, “degree words”). ObjectiveTherefore, we propose using the fuzzy c-means (FCM) method and Gaussian membership to quantify the degree words in the clinical medical data set MIMIC-III. MethodsFirst, we preprocessed the 381,091 radiology reports collected in MIMIC-III, and then we used the FCM method to extract degree words from unstructured text. Thereafter, we used the Gaussian membership method to quantify the extracted degree words, which transform the fuzzy words extracted from the medical text into computer-recognizable numbers. ResultsThe results showed that the digitization of ambiguous words in medical texts is feasible. The words representing each degree of each disease had a range of corresponding values. Examples of membership medians were 2.971 (atelectasis), 3.121 (pneumonia), 2.899 (pneumothorax), 3.051 (pulmonary edema), and 2.435 (pulmonary embolus). Additionally, all extracted words contained the same subjective words (low, high, etc), which allows for an objective evaluation method. Furthermore, we will verify the specific impact of the quantification results of ambiguous words such as symptom words and degree words on the use of medical texts in subsequent studies. These same ambiguous words may be used as a new set of feature values to represent the disorders. ConclusionsThis study proposes an innovative method for handling subjective words. We used the FCM method to extract the subjective degree words in the English-interpreted report of the MIMIC-III and then used the Gaussian functions to quantify the subjective degree words. In this method, words containing subjectivity in unstructured texts can be automatically processed and transformed into numerical ranges by digital processing. It was concluded that the digitization of ambiguous words in medical texts is feasible.

A 40-year-old female (gravida 3 para 1) presented with menstrual, urinary, and anal pain. Computed tomography revealed type II acquired uterine arteriovenous malformation, a common dilated venous sac with bilateral uterine arteries, and multiple branches of iliac arteries draining to the bilateral ovarian veins. Venous sac transvenous embolization via the left ovarian vein of dominant outflow was planned, since complete arteriovenous malformation occlusion was difficult with super-selective transarterial embolization of multiple feeders. Therefore, transarterial embolization of the minor feeder was performed before completing transvenous embolization using coils and 50% glue under left iliac artery flow control. Immediately thereafter, angiography confirmed the complete disappearance of the uterine arteriovenous malformation, and all pain symptoms remitted. In conclusion, transvenous embolization combined with adjunctive transarterial embolization can be an effective and radical treatment for type II uterine arteriovenous malformations.

Abstract Background This study aimed to assess the utility of deep learning analysis using pretreatment FDG-PET images to predict local treatment outcome in oropharyngeal squamous cell carcinoma (OPSCC) patients. Methods One hundred fifty-four OPSCC patients who received pretreatment FDG-PET were included and divided into training (n = 102) and test (n = 52) sets. The diagnosis of local failure and local progression-free survival (PFS) rates were obtained from patient medical records. In deep learning analyses, axial and coronal images were assessed by three different architectures (AlexNet, GoogLeNET, and ResNet). In the training set, FDG-PET images were analyzed after the data augmentation process for the diagnostic model creation. A multivariate clinical model was also created using a binomial logistic regression model from a patient’s clinical characteristics. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. Assessment of local PFS rates was also performed. Results Training sessions were successfully performed with an accuracy of 74–89%. ROC curve analyses revealed an AUC of 0.61–0.85 by the deep learning model in the test set, whereas it was 0.62 by T-stage, 0.59 by clinical stage, and 0.74 by a multivariate clinical model. The highest AUC (0.85) was obtained with deep learning analysis of ResNet architecture. Cox proportional hazards regression analysis revealed deep learning-based classification by a multivariate clinical model (P

Although 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) is an established method for the staging of malignancies, benign lesions (e.g, active inflammatory lesions) often show increased metabolic activity. Herpes zoster is the clinical manifestation of the activation and replication of dormant varicella-zoster virus (VZV) in individuals with decreased cell-mediated immunity. Although the diagnosis of herpes zoster is clinical, it is sometimes observed incidentally during imaging for another disease. We describe the case of a 67-year-old Japanese female patient diagnosed with cervical cancer in whom FDG-PET/CT revealed herpes zoster manifestations: hypermetabolic cutaneous lesions in the buttock and pelvic lymph node involvement. The resected lymph nodes showed no malignant lesions but revealed lymphoid follicle formation, probably related to viral infection. There has been no report comparing FDG-PET findings of lymph nodes with histologic findings; the present findings are compatible with a clinically VZV-induced inflammatory reaction in regional lymph nodes, which increased FDG accumulation. Active infection with VZV displays increased FDG uptake in regional lymph nodes and may lead to incorrect malignant disease management in oncology. Misdiagnoses can be avoided by a careful interpretation by experienced nuclear medicine physicians as well as proper clinical evaluation.

Abstract Background This study aimed to investigate the ability of whole-body bone scintigraphy (WB-BS) in the detection of multifocal osteonecrosis (ON) compared to whole-body magnetic resonance imaging (WB-MRI) and to clarify the characteristics of patients with multifocal ON among those with ON of the femoral head (ONFH) using WB-MRI. Methods Forty-six patients who had symptomatic ONFH and underwent surgery in our hospital from April 2019 to October 2020 were included in the study. Data on patient demographics, including age, sex, body mass index (BMI), history of corticosteroid intake, alcohol abuse, smoking, and symptomatic joints, were collected from their medical records. All patients underwent WB-MRI and WB-BS before surgery. Results The agreement in the detection of ON by WB-MRI vs the uptake lesions by WB-BS in the hip joints was moderate (κ = 0.584), while that in other joints was low (κ

Abstract Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Materials and methods Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[18F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. Results Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. Conclusions It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[18F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy.

Abstract Background Halo artifacts from urinary catheters can occur due to inaccurate scatter correction, and the artifacts affect the tumor visibility in 18F-FDG PET/CT images. We investigated the incidence rate and the mechanisms of halo-artifact generation and explored several scatter correction techniques to prevent artifacts. Methods We conducted patient and phantom studies. (1) We retrospectively reviewed the cases of patients who had undergone 18F-FDG PET/CT scans. To determine the frequency of halo-artifact generation, we used the patients’ PET images with a standard scatter correction based on a tail-fitted single-scatter simulation (TF-SSS) using 4-mm voxel μ-maps (TFS 4-mm). (2) We performed phantom studies to evaluate the effects of a urine catheter and two scatter correction techniques, i.e., TF-SSS with 2-mm voxel μ-maps (TFS 2-mm) and a Monte Carlo-based single-scatter simulation (MC-SSS) using 4-mm voxel μ-maps (MCS 4-mm). The average standardized uptake values (SUVs) were measured for axial PET images. (3) Using the patients’ data, we investigated whether TFS 2-mm and MCS 4-mm can eliminate the artifacts in the clinical images. Results (1) There were 61 patients with urinary catheters; in five (8.2%), halo artifacts were observed in the TFS 4-mm PET images. (2) The phantom study clearly reproduced the halo artifacts in the TFS 4-mm PET images. The halo artifacts were generated when urine moved in the interval between the CT and PET imaging, and when the urinary catheter was placed in a circular shape. The SUVs for the TFS 4-mm and TFS-2mm PET images were underestimated at the halo-artifact regions, whereas the SUVs for the MCS 4-mm PET images were close to the true values. (3) The halo artifacts disappeared in the TFS 2-mm PET images in 4/5 patients but not 1/5 patient, whereas the halo artifacts were completely absent in the MCS 4-mm PET images in 5/5 patients. Conclusions These data suggest that halo artifacts are caused if the PET images do not correspond to the physical material in the μ-maps, which induces the scatter correction error. With the MC-SSS, it was possible to accurately estimate the scatter without generating halo artifacts.

Background The aim of this study was to determine the associations of cerebral small vessel disease (SVD) burden with renal dysfunction and albuminuria in patients taking oral antithrombotic agents. Methods and Results Patients who newly started or continued taking oral antiplatelets or anticoagulants were enrolled in a prospective, multicenter, observational study. Obligatorily acquired multimodal magnetic resonance imaging at registration with prespecified imaging conditions was assessed for cerebral microbleeds, white matter hyperintensities, enlarged basal ganglia perivascular spaces, or lacunes, and an ordinal SVD score was calculated (range, 0–4). Multivariable adjusting covariates were age, sex, hypertension, diabetes, dyslipidemia, current smoking, drinking, and estimated glomerular filtration rate (eGFR). Of 5324 patients (1762 women; median age, 73 years), 4797 (90.1%) patients were taking oral antithrombotic agents for secondary stroke prevention. Cerebral microbleeds were present in 32.7%, confluent white matter hyperintensities in 51.8%, extensive basal ganglia perivascular spaces in 38.9%, and lacunes in 59.4%. Median SVD score was 2. Compared with eGFR category G1 (eGFR ≥90 mL/min per 1.73 m2), adjusted odds ratios for SVD score increment were 1.63 (95% CI, 1.11–2.39) at category G4 (eGFR 15–

BackgroundThe oxygen extraction fraction (OEF) has been applied to identify ischemic penumbral tissue, but is difficult to use in an urgent care setting. This study aimed to investigate whether an OEF map generated via magnetic resonance quantitative susceptibility mapping (QSM) could help identify the ischemic penumbra in patients with acute ischemic stroke.Materials and MethodsThis prospective imaging study included 21 patients with large anterior circulation vessel occlusion who were admitted 6 s during dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) and calculated the perfusion-core mismatch ratio between the ischemic penumbra and infarct core volumes. The OEF values were measured based on magnetic susceptibility differences between the venous structures and brain tissues using rapid QSM acquisition. Volumes with increased OEF values were compared to the ischemic penumbra volumes using an anatomical template.ResultsEleven patients had a perfusion-core mismatch ratio of ≥1.8, and reperfusion therapy was recommended. In these patients, the volumes with increased OEF values of >51.5%, which was defined using the anterior circulation territory OEF values from the 21 healthy controls, were positively correlated with the ischemic penumbra volumes (r = 0.636, 95% CI: 0.059 to 0.895, P = 0.035) and inversely correlated with the 30-day change in the National Institutes of Health Stroke Scale scores (r = −0.624, 95% CI: −0.891 to −0.039, P = 0.041).ConclusionTissue volumes with increased OEF values could predict ischemic penumbra volumes based on DSC-MRI, highlighting the potential of the QSM-derived OEF map as a penumbra biomarker to guide treatment selection in patients with acute ischemic stroke.

Abstract Background Vascular abnormalities in neurofibromatosis type 1 (NF1) are rare, but are the second leading cause of death in persons with NF1. In NF1 vasculopathy (NF-V), fatal bleeding due to a spontaneous arterial rupture sometimes occurs. Ruptured extracranial arteries in patients with NF1 often involve thoracic vessels, such as the intercostal and subclavian arteries; very few reports exist regarding the abdominal region. Herein, we present the first case of intraperitoneal bleeding due to spontaneous pancreatic arcade artery (PAA) rupture associated with NF1, successfully treated by transcatheter arterial embolization (TAE) combined with stent-graft placement and partial intra-aortic balloon occlusion (IABO). Case presentation A 40-year-old woman complained of back and abdominal pain. Upon admission, her blood pressure was 85/41 mmHg and heart rate was 129 beats/min. Computed tomography (CT) showed large intraperitoneal bleeding due to PAA rupture. After CT scanning, her systolic blood pressure decreased to 50 mmHg. Therefore, we performed emergency TAE with partial IABO. She was treated by TAE of the anterior superior pancreaticoduodenal artery, anterior inferior pancreaticoduodenal artery, and inferior pancreaticoduodenal artery. However, even after TAE, minor extravasation around the superior mesenteric artery continued, and her vital signs remained unstable. Stent-graft placement was selected to stop the haemorrhage, preserving normal blood flow of the superior mesenteric artery trunk. Excellent patency of the stent graft was confirmed on follow-up CT, and she was discharged on postoperative day 56. Conclusion PAA rupture associated with NF1 can be successfully treated by TAE combined with partial intra-aortic balloon occlusion, and stent-graft placement.

Background: Diagnostic reports contribute not only to the particular patient, but also to constructing massive training dataset in the era of artificial intelligence (AI). The maximum standardized uptake value (SUVmax) is often described in daily diagnostic reports of [18F] fluorodeoxyglucose (FDG) positron emission tomography (PET) – computed tomography (CT). If SUVmax can be used as an identifier of lesion, that would greatly help AI interpret diagnostic reports. We aimed to clarify whether the lesion can be localized using SUVmax strings.Methods: The institutional review board approved this retrospective study. We investigated a total of 112 lesions from 30 FDG PET-CT images acquired with 3 different scanners. SUVmax was calculated from DICOM files based on the latest Quantitative Imaging Biomarkers Alliance (QIBA) publication. The voxels showing the given SUVmax were exhaustively searched in the whole-body images and counted. SUVmax was provided with 5 different degrees of precision: integer (e.g., 3), 1st decimal places (DP) (3.1), 2nd DP (3.14), 3rd DP (3.142), and 4th DP (3.1416). For instance, when SUVmax = 3.14 was given, the voxels with 3.135 ≤ SUVmax < 3.145 were extracted. We also evaluated whether local maximum restriction could improve the identifying performance, where only the voxels showing the highest intensity within some neighborhood were considered. We defined that “identical detection” was achieved when only single voxel satisfied the criterion.Results: A total of 112 lesions from 30 FDG PET-CT images were investigated. SUVmax ranged from 1.3 to 49.1 (median = 5.6). Generally, when larger and more precise SUVmax values were given, fewer voxels satisfied the criterion. The local maximum restriction was very effective. When SUVmax was determined to 4 decimal places (e.g., 3.1416) and the local maximum restriction was applied, identical detection was achieved in 33.3% (lesions with SUVmax < 2), 79.5% (2 ≤ SUVmax < 5), and 97.8% (5 ≤ SUVmax) of lesions.Conclusion: In this preliminary study, SUVmax of FDG PET-CT could be used as an identifier to localize the lesion if precise SUVmax is provided and local maximum restriction was applied, although the lesions showing SUVmax < 2 were difficult to identify. The proposed method may have potential to make use of diagnostic reports retrospectively for constructing training datasets for AI.

Abstract Background To assess the utility of histogram and texture analysis of magnetic resonance (MR) fat-suppressed T2-weighted imaging (Fs-T2WI) for the prediction of histological diagnosis of head and neck squamous cell carcinoma (SCC) and malignant lymphoma (ML). Methods The cases of 57 patients with SCC (45 well/moderately and 12 poorly differentiated SCC) and 10 patients with ML were retrospectively analyzed. Quantitative parameters with histogram features (relative mean signal, coefficient of variation, kurtosis and skewness) and gray-level co-occurrence matrix (GLCM) features (contrast, correlation, energy and homogeneity) were calculated using Fs-T2WI data with a manual tumor region of interest (ROI). Results The following significantly different values were obtained for the total SCC versus ML groups: relative mean signal (3.65 ± 0.86 vs. 2.61 ± 0.49), contrast (72.9 ± 16.2 vs. 49.3 ± 8.7) and homogeneity (2.22 ± 0.25 × 10− 1 vs. 2.53 ± 0.12 × 10− 1). In the comparison of the SCC histological grades, the relative mean signal and contrast were significantly lower in the poorly differentiated SCC (2.89 ± 0.63, 56.2 ± 12.9) compared to the well/moderately SCC (3.85 ± 0.81, 77.5 ± 13.9). The homogeneity in poorly differentiated SCC (2.56 ± 0.15 × 10− 1) was higher than that of the well/moderately SCC (2.1 ± 0.18 × 10− 1). Conclusions Parameters obtained by histogram and texture analysis of Fs-T2WI may be useful for noninvasive prediction of histological type and grade in head and neck malignancy.

Background: Positron emission tomography with 11C-methionine (MET) is well established in the diagnostic work-up of malignant brain tumors. Texture analysis is a novel technique for extracting information regarding relationships among surrounding voxels, in order to quantify their inhomogeneity. This study evaluated whether the texture analysis of MET uptake has prognostic value for patients with glioma. Methods: We retrospectively analyzed adults with glioma who had undergone preoperative metabolic imaging at a single center. Tumors were delineated using a threshold of 1.3-fold of the mean standardized uptake value for the contralateral cortex, and then processed to calculate the texture features in glioma. Results: The study included 42 patients (median age: 56 years). The World Health Organization classifications were grade II (7 patients), grade III (17 patients), and grade IV (18 patients). Sixteen (16.1%) all-cause deaths were recorded during the median follow-up of 18.8 months. The univariate analyses revealed that overall survival (OS) was associated with age (hazard ratio (HR) 1.04, 95% confidence interval (CI) 1.01–1.08, p = 0.0093), tumor grade (HR 3.64, 95% CI 1.63–9.63, p = 0.0010), genetic status (p < 0.0001), low gray-level run emphasis (LGRE, calculated from the gray-level run-length matrix) (HR 2.30 × 1011, 95% CI 737.11–4.23 × 1019, p = 0.0096), and correlation (calculated from the gray-level co-occurrence matrix) (HR 5.17, 95% CI 1.07–20.93, p = 0.041). The multivariate analyses revealed OS was independently associated with LGRE and correlation. The survival curves were also significantly different (both log-rank p < 0.05). Conclusion: Textural features obtained using preoperative MET positron emission tomography may compliment the semi-quantitative assessment for prognostication in glioma cases.

The purpose of this study was to determine the predictive power for treatment outcome of a machine-learning algorithm combining magnetic resonance imaging (MRI)-derived data in patients with sinonasal squamous cell carcinomas (SCCs). Thirty-six primary lesions in 36 patients were evaluated. Quantitative morphological parameters and intratumoral characteristics from T2-weighted images, tumor perfusion parameters from arterial spin labeling (ASL) and tumor diffusion parameters of five diffusion models from multi-b-value diffusion-weighted imaging (DWI) were obtained. Machine learning by a non-linear support vector machine (SVM) was used to construct the best diagnostic algorithm for the prediction of local control and failure. The diagnostic accuracy was evaluated using a 9-fold cross-validation scheme, dividing patients into training and validation sets. Classification criteria for the division of local control and failure in nine training sets could be constructed with a mean sensitivity of 0.98, specificity of 0.91, positive predictive value (PPV) of 0.94, negative predictive value (NPV) of 0.97, and accuracy of 0.96. The nine validation data sets showed a mean sensitivity of 1.0, specificity of 0.82, PPV of 0.86, NPV of 1.0, and accuracy of 0.92. In conclusion, a machine-learning algorithm using various MR imaging-derived data can be helpful for the prediction of treatment outcomes in patients with sinonasal SCCs.

Currently, there is increasing interest in human bone marrow stromal cells (hBMSCs) as regeneration therapy against cerebral stroke. The aim of the present study was to evaluate the feasibility and validity of hBMSC cultures with allogeneic platelet lysates (PLs). Platelet concentrates (PC) were harvested from healthy volunteers and made into single donor-derived PL (sPL). The PL mixtures (mPL) were made from three different sPL. Some growth factors and platelet cell surface antigens were detected by enzyme-linked immunosorbent assay (ELISA). The hBMSCs cultured with 10% PL were analyzed for their proliferative potential, surface markers, and karyotypes. The cells were incubated with superparamagnetic iron oxide (SPIO) agents and injected into a pig brain. MRI and histological analysis were performed. Consequently, nine lots of sPL and three mPL were prepared. ELISA analysis showed that PL contained adequate growth factors and a particle of platelet surface antigens. Cell proliferation capacity of PLs was equivalent to or higher than that of fetal calf serum (FCS). No contradiction in cell surface markers and no chromosomal aberrations were found. The MRI detected the distribution of SPIO-labeled hBMSCs in the pig brain. In summary, the hBMSCs cultured with allogeneic PL are suitable for cell therapy against stroke.

OBJECTIVE:To determine the relationship between tumor glucose metabolism and tumor blood flow (TBF) in head and neck squamous cell carcinoma (HNSCC). METHODS:We retrospectively analyzed 57 HNSCC patients. Tumor glucose metabolism was assessed by maximum and mean standardized uptake values (SUVmax and SUVmean) obtained by 18F-fluorodeoxyglucose positron-emission tomography. TBF values were obtained by arterial spin labeling with 3-tesla MRI. The correlations between both SUVs and TBF were assessed in the total series and among patients divided by T-stage (T1-T3 and T4 groups) and tumor location (pharynx/oral cavity and sinonasal cavity groups). Pearson's correlation coefficients were calculated for significant correlations. RESULTS:Significant correlations were detected: a negative correlation in the advanced T-stage group (TBF and SUV max: r, -0.61, SUVmean: r, -0.62), a positive correlation in the non-advanced T-stage pharynx/oral cavity group (TBF and SUVmax: r, 0.70, SUVmean: r, 0.73), a negative correlation in the advanced T-stage pharynx/oral cavity group (TBF and SUVmax: r, -0.62, SUVmean: r, -0.65), and a negative correlation in the advanced T-stage sinonasal cavity group (TBF and SUVmax: r, -0.61, SUVmean: r, -0.65). CONCLUSION:Significant correlations between glucose uptake and TBF in HNSCC were revealed by the division of T-stage and tumor location.

To determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying "triple-negative" breast cancers.In this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other subtypes, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation.Imaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p