Preclinical Evaluation of Biodistribution and Toxicity of [211At]PSMA-5 in Mice and Primates for the Targeted Alpha Therapy against Prostate Cancer.

Astatine (211At) is a cyclotron-produced alpha emitter with a physical half-life of 7.2 h. In our previous study, the 211At-labeled prostate-specific membrane antigen (PSMA) compound ([211At]PSMA-5) exhibited excellent tumor growth suppression in a xenograft model. We conducted preclinical biodistribution and toxicity studies for the first-in-human clinical trial. [211At]PSMA-5 was administered to both normal male ICR mice (n = 85) and cynomolgus monkeys (n = 2). The mice were divided into four groups for the toxicity study: 5 MBq/kg, 12 MBq/kg, 35 MBq/kg, and vehicle control, with follow-ups at 1 day (n = 10 per group) and 14 days (n = 5 per group). Monkeys were observed 24 h post-administration of [211At]PSMA-5 (9 MBq/kg). Blood tests and histopathological examinations were performed at the end of the observation period. Blood tests in mice indicated no significant myelosuppression or renal dysfunction. However, the monkeys displayed mild leukopenia 24 h post-administration. Despite the high accumulation in the kidneys and thyroid, histological analysis revealed no abnormalities. On day 1, dose-dependent single-cell necrosis/apoptosis was observed in the salivary glands of mice and intestinal tracts of both mice and monkeys. Additionally, tingible body macrophages in the spleen and lymph nodes indicated phagocytosis of apoptotic B lymphocytes. Cortical lymphopenia (2/10) in the thymus and a decrease in the bone marrow cells (9/10) were observed in the 35 MBq/kg group in mice. These changes were transient, with no irreversible toxicity observed in mice 14 days post-administration. This study identified no severe toxicities associated with [211At]PSMA-5, highlighting its potential as a next-generation targeted alpha therapy for prostate cancer. The sustainable production of 211At using a cyclotron supports its applicability for clinical use.

Photon-counting CT: technical features and clinical impact on abdominal imaging.

Photon-counting CT has a completely different detector mechanism than conventional energy-integrating CT. In the photon-counting detector, X-rays are directly converted into electrons and received as electrical signals. Photon-counting CT provides virtual monochromatic images with a high contrast-to-noise ratio for abdominal CT imaging and may improve the ability to visualize small or low-contrast lesions. In addition, photon-counting CT may offer the possibility of reducing radiation dose. This review provides an overview of the actual clinical operation of photon-counting CT and its diagnostic utility in abdominal imaging. We also describe the clinical implications of photon-counting CT including imaging of hepatocellular carcinoma, liver metastases, hepatic steatosis, pancreatic cancer, intraductal mucinous neoplasm of the pancreas, and thrombus.

Production of [211At]NaAt solution under GMP compliance for investigator-initiated clinical trial.

BACKGROUND: The alpha emitter astatine-211 (211At) is garnering attention as a novel targeted alpha therapy for patients with refractory thyroid cancer resistant to conventional therapy using beta emitter radioiodine (131I). Herein, we aimed to establish a robust method for the manufacturing and quality control of [211At]NaAt solution for intravenous administration under the good manufacturing practice guidelines for investigational products to conduct an investigator-initiated clinical trial. RESULTS: 211At was separated and purified via dry distillation using irradiated Bi plates containing 211At obtained by the nuclear reaction of 209Bi(4He, 2n)211At. After purification, the 211At trapped in the cold trap was collected in a reaction vessel using 15 mL recovery solution (1% ascorbic acid and 2.3% sodium hydrogen carbonate). After stirring the 211At solution for 1 h inside a closed system, the reaction solution was passed through a sterile 0.22 µm filter placed in a Grade A controlled area and collected in a product vial to prepare the [211At]NaAt solution. According to the 3-lot tests, decay collected radioactivity and radiochemical yield of [211At]NaAt were 78.8 ± 6.0 MBq and 40 ± 3%, respectively. The radiochemical purity of [211At]At- obtained via ion-pair chromatography at the end of synthesis (EOS) was 97 ± 1%, and remained > 96% 6 h after EOS; it was detected at a retention time (RT) 3.2-3.3 min + RT of I-. LC-MS analysis indicated that this principal peak corresponded with an astatide ion (m/z = 210.988046). In gamma-ray spectrometry, the 211At-related peaks were identified (X-ray: 76.9, 79.3, 89.3, 89.8, and 92.3 keV; γ-ray: 569.7 and 687.0 keV), whereas the peak at 245.31 keV derived from 210At was not detected during the 22 h continuous measurement. The target material, Bi, was below the 9 ng/mL detection limit in all lots of the finished product. The pH of the [211At]NaAt solution was 7.9-8.6; the concentration of ascorbic acid was 9-10 mg/mL. Other quality control tests, including endotoxin and sterility tests, confirmed that the [211At]NaAt solution met all quality standards. CONCLUSIONS: We successfully established a stable method of [211At]NaAt solution that can be administered to humans intravenously as an investigational product.

Vision transformer to differentiate between benign and malignant slices in 18F-FDG PET/CT.

Fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) is widely used for the detection, diagnosis, and clinical decision-making in oncological diseases. However, in daily medical practice, it is often difficult to make clinical decisions because of physiological FDG uptake or cancers with poor FDG uptake. False negative clinical diagnoses of malignant lesions are critical issues that require attention. In this study, Vision Transformer (ViT) was used to automatically classify 18F-FDG PET/CT slices as benign or malignant. This retrospective study included 18F-FDG PET/CT data of 207 (143 malignant and 64 benign) patients from a medical institute to train and test our models. The ViT model achieved an area under the receiver operating characteristic curve (AUC) of 0.90 [95% CI 0.89, 0.91], which was superior to the baseline Convolutional Neural Network (CNN) models (EfficientNet, 0.87 [95% CI 0.86, 0.88], P < 0.001; DenseNet, 0.87 [95% CI 0.86, 0.88], P < 0.001). Even when FDG uptake was low, ViT produced an AUC of 0.81 [95% CI 0.77, 0.85], which was higher than that of the CNN (DenseNet, 0.65 [95% CI 0.59, 0.70], P < 0.001). We demonstrated the clinical value of ViT by showing its sensitive analysis of easy-to-miss cases of oncological diseases.

Association between interstitial lung abnormality and mortality in patients with esophageal cancer.

PURPOSE: To investigate the relationship between interstitial lung abnormalities (ILAs) and mortality in patients with esophageal cancer and the cause of mortality. MATERIALS AND METHODS: This retrospective study investigated patients with esophageal cancer from January 2011 to December 2015. ILAs were visually scored on baseline CT using a 3-point scale (0 = non-ILA, 1 = indeterminate for ILA, and 2 = ILA). ILAs were classified into subcategories of non-subpleural, subpleural non-fibrotic, and subpleural fibrotic. Five-year overall survival (OS) was compared between patients with and without ILAs using the multivariable Cox proportional hazards model. Subgroup analyses were performed based on cancer stage and ILA subcategories. The prevalences of treatment complications and death due to esophageal cancer and pneumonia/respiratory failure were analyzed using Fisher's exact test. RESULTS: A total of 478 patients with esophageal cancer (age, 66.8 years ± 8.6 [standard deviation]; 64 women) were evaluated in this study. Among them, 267 patients showed no ILAs, 125 patients were indeterminate for ILAs, and 86 patients showed ILAs. ILAs were a significant factor for shorter OS (hazard ratio [HR] = 1.68, 95% confidence interval [CI] 1.10-2.55, P = 0.016) in the multivariable Cox proportional hazards model adjusting for age, sex, smoking history, clinical stage, and histology. On subgroup analysis using patients with clinical stage IVB, the presence of ILAs was a significant factor (HR = 3.78, 95% CI 1.67-8.54, P = 0.001). Subpleural fibrotic ILAs were significantly associated with shorter OS (HR = 2.22, 95% CI 1.25-3.93, P = 0.006). There was no significant difference in treatment complications. Patients with ILAs showed a higher prevalence of death due to pneumonia/respiratory failure than those without ILAs (non-ILA, 2/95 [2%]; ILA, 5/39 [13%]; P = 0.022). The prevalence of death due to esophageal cancer was similar in patients with and without ILA (non-ILA, 82/95 [86%]; ILA 32/39 [82%]; P = 0.596). CONCLUSION: ILAs were significantly associated with shorter survival in patients with esophageal cancer.

Manual on the proper use of the 211At-labeled PSMA ligand ([211At]PSMA-5) for clinical trials of targeted alpha therapy (1st edition).

Recently, an astatine-labeled prostate-specific membrane antigen (PSMA) ligand ([211At]PSMA-5) has been developed for the targeted alpha therapy of patients with prostate cancer. This manual delineates its physicochemical characteristics to assist healthcare professionals in understanding the α-ray-emitting drug of [211At]PSMA-5 when administered to patients. The safety considerations regarding the handling and use of this drug in clinical trials are outlined, based on the proper usage manual of previous studies. The dose limits, as defined by the guidelines of the International Commission on Radiological Protection (ICRP) and the International Atomic Energy Agency (IAEA), are assessed for patients' caregivers and the general public. According to the calculations provided in this manual, clinical trials involving [211At]PSMA-5 can be safely conducted for these populations even if patients are released after its administration. Moreover, this manual provides comprehensive guidance on the handling of [211At]PSMA-5 for healthcare facilities, and compiles a list of precautionary measures to be distributed among patients and their caregivers. While this manual was created by a research team supported by Ministry of Health, Labour, and Welfare in Japan and approved by Japanese Society of Nuclear Medicine, its applicability extends to healthcare providers in other countries. This manual aims to facilitate conducting clinical trials using [211At]PSMA-5 in patients with prostate cancer.

Automated entry of paper-based patient-reported outcomes: Applying deep learning to the Japanese orthopaedic association back pain evaluation questionnaire.

BACKGROUND: Health-related patient-reported outcomes (HR-PROs) are crucial for assessing the quality of life among individuals experiencing low back pain. However, manual data entry from paper forms, while convenient for patients, imposes a considerable tallying burden on collectors. In this study, we developed a deep learning (DL) model capable of automatically reading these paper forms. METHODS: We employed the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire, a globally recognized assessment tool for low back pain. The questionnaire comprised 25 low back pain-related multiple-choice questions and three pain-related visual analog scales (VASs). We collected 1305 forms from an academic medical center as the training set, and 483 forms from a community medical center as the test set. The performance of our DL model for multiple-choice questions was evaluated using accuracy as a categorical classification task. The performance for VASs was evaluated using the correlation coefficient and absolute error as regression tasks. RESULT: In external validation, the mean accuracy of the categorical questions was 0.997. When outputs for categorical questions with low probability (threshold: 0.9996) were excluded, the accuracy reached 1.000 for the remaining 65 % of questions. Regarding the VASs, the average of the correlation coefficients was 0.989, with the mean absolute error being 0.25. CONCLUSION: Our DL model demonstrated remarkable accuracy and correlation coefficients when automatic reading paper-based HR-PROs during external validation.

Comparison of natural language processing algorithms in assessing the importance of head computed tomography reports written in Japanese.

PURPOSE: To propose a five-point scale for radiology report importance called Report Importance Category (RIC) and to compare the performance of natural language processing (NLP) algorithms in assessing RIC using head computed tomography (CT) reports written in Japanese. MATERIALS AND METHODS: 3728 Japanese head CT reports performed at Osaka University Hospital in 2020 were included. RIC (category 0: no findings, category 1: minor findings, category 2: routine follow-up, category 3: careful follow-up, and category 4: examination or therapy) was established based not only on patient severity but also on the novelty of the information. The manual assessment of RIC for the reports was performed under the consensus of two out of four neuroradiologists. The performance of four NLP models for classifying RIC was compared using fivefold cross-validation: logistic regression, bidirectional long-short-term memory (BiLSTM), general bidirectional encoder representations of transformers (general BERT), and domain-specific BERT (BERT for medical domain). RESULTS: The proportion of each RIC in the whole data set was 15.0%, 26.7%, 44.2%, 7.7%, and 6.4%, respectively. Domain-specific BERT showed the highest accuracy (0.8434 ± 0.0063) in assessing RIC and significantly higher AUC in categories 1 (0.9813 ± 0.0011), 2 (0.9492 ± 0.0045), 3 (0.9637 ± 0.0050), and 4 (0.9548 ± 0.0074) than the other models (p < .05). Analysis using layer-integrated gradients showed that the domain-specific BERT model could detect important words, such as disease names in reports. CONCLUSIONS: Domain-specific BERT has superiority over the other models in assessing our newly proposed criteria called RIC of head CT radiology reports. The accumulation of similar and further studies of has a potential to contribute to medical safety by preventing missed important findings by clinicians.

Pictorial Review of Pleural Disease: Multimodality Imaging and Differential Diagnosis.

The pleura is a thin, smooth, soft-tissue structure that lines the pleural cavity and separates the lungs from the chest wall, consisting of the visceral and parietal pleurae and physiologic pleural fluid. There is a broad spectrum of normal variations and abnormalities in the pleura, including pneumothorax, pleural effusion, and pleural thickening. Pneumothorax is associated with pulmonary diseases and is caused by iatrogenic or traumatic factors. Chest radiography and US help detect pneumothorax with various signs, and CT can also help assess the causes. Pleural effusion occurs in a wide spectrum of diseases, such as heart failure, cirrhosis, asbestos-related diseases, infections, chylothorax, and malignancies. Chest US allows detection of a small pleural effusion and evaluation of echogenicity or septa in pleural effusion. Pleural thickening may manifest as unilateral or bilateral and as focal, multifocal, or diffuse. Various diseases can demonstrate pleural thickening, such as asbestos-related diseases, neoplasms, and systemic diseases. CT, MRI, and fluorodeoxyglucose (FDG) PET/CT can help differentiate between benign and malignant lesions. Knowledge of these features can aid radiologists in suggesting diagnoses and recommending further examinations with other imaging modalities. The authors provide a comprehensive review of the clinical and multimodality imaging findings of pleural diseases and their differential diagnoses. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix.

PURPOSE: To predict solid and micropapillary components in lung invasive adenocarcinoma using radiomic analyses based on high-spatial-resolution CT (HSR-CT). MATERIALS AND METHODS: For this retrospective study, 64 patients with lung invasive adenocarcinoma were enrolled. All patients were scanned by HSR-CT with 1024 matrix. A pathologist evaluated subtypes (lepidic, acinar, solid, micropapillary, or others). Total 61 radiomic features in the CT images were calculated using our modified texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features for predicting solid and micropapillary components in lung invasive adenocarcinoma. Final data were obtained by repeating tenfold cross-validation 10 times. Two independent radiologists visually predicted solid or micropapillary components on each image of the 64 nodules with and without using the radiomics results. The quantitative values were analyzed with logistic regression models. The receiver operating characteristic curves were generated to predict of solid and micropapillary components. P values < 0.05 were considered significant. RESULTS: Two features (Coefficient Variation and Entropy) were independent indicators associated with solid and micropapillary components (odds ratio, 30.5 and 11.4; 95% confidence interval, 5.1-180.5 and 1.9-66.6; and P = 0.0002 and 0.0071, respectively). The area under the curve for predicting solid and micropapillary components was 0.902 (95% confidence interval, 0.802 to 0.962). The radiomics results significantly improved the accuracy and specificity of the prediction of the two radiologists. CONCLUSION: Two texture features (Coefficient Variation and Entropy) were significant indicators to predict solid and micropapillary components in lung invasive adenocarcinoma.

Fleischner Society: Glossary of Terms for Thoracic Imaging.

Members of the Fleischner Society have compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984, 1996, and 2008, respectively. The impetus to update the previous version arose from multiple considerations. These include an awareness that new terms and concepts have emerged, others have become obsolete, and the usage of some terms has either changed or become inconsistent to a degree that warranted a new definition. This latest glossary is focused on terms of clinical importance and on those whose meaning may be perceived as vague or ambiguous. As with previous versions, the aim of the present glossary is to establish standardization of terminology for thoracic radiology and, thereby, to facilitate communications between radiologists and clinicians. Moreover, the present glossary aims to contribute to a more stringent use of terminology, increasingly required for structured reporting and accurate searches in large databases. Compared with the previous version, the number of images (chest radiography and CT) in the current version has substantially increased. The authors hope that this will enhance its educational and practical value. All definitions and images are hyperlinked throughout the text. Click on each figure callout to view corresponding image. © RSNA, 2024 Supplemental material is available for this article. See also the editorials by Bhalla and Powell in this issue.

Association of circulating tumor HPV16DNA levels and quantitative PET parameters in patients with HPV-positive head and neck squamous cell carcinoma.

Circulating tumor DNA (ctDNA), which circulates in the blood after being shed from cancer cells in the body, has recently gained attention as an excellent tumor marker. The purpose of this study was to evaluate whether ct human papillomavirus (HPV) 16 DNA (ctHPV16DNA) levels were associated with quantitative PET parameters in patients with HPV-positive head and neck (HN) squamous cell carcinoma (SCC). Fifty patients with oropharyngeal SCC (OPSCC) and 5 with SCC of unknown primary (SCCUP) before treatment were included. They all underwent blood sampling to test ctHPV16DNA levels and FDG PET-CT examinations. Quantitative PET parameters included SUVmax, metabolic tumor volume (MTV), MTV of whole-body lesions (wbMTV), and 56 texture features. ctHPV16DNA levels were compared to texture features of primary tumors in OPSCC patients (Group A) or the largest primary or metastatic lymph node lesions in OPSCC and SCCUP patients (Group B) and to other PET parameters. Spearman rank correlation test and multiple regression analysis were used to confirm the associations between ctHPV16DNA levels and PET parameters. ctHPV16DNA levels moderately correlated with wbMTV, but not with SUVmax or MTV in Groups A and B. ctHPV16DNA levels exhibited a weak negative correlation with low gray-level zone emphasis in Groups A and B. Multiple regression analysis revealed that wbMTV and high gray-level zone emphasis were the significant factors for ctHPV16DNA levels in Group B. These results were not observed in Group A. This study demonstrated that ctHPV16DNA levels correlated with the whole-body tumor burden and tumor heterogeneity visualized on FDG PET-CT in patients with HPV-positive HNSCC.

Hepatic and pancreatic extracellular volume fraction analysis using contrast-enhanced CT in patients with diabetes mellitus and pre-diabetes.

PURPOSE: Liver and pancreatic fibrosis is associated with diabetes mellitus (DM), and liver fibrosis is associated with pancreatic fibrosis. This study aimed to investigate the relationship between the hepatic and pancreatic extracellular volume fractions (fECVs), which correlate with tissue fibrosis, and their relationships with DM and pre-DM (pDM). MATERIAL AND METHODS: We included 100 consecutive patients with known or suspected liver and/or pancreatic diseases who underwent contrast-enhanced CT. Patients were classified as nondiabetes, pDM, and DM with hemoglobin A1c (HbA1c) levels of < 5.7%, 5.7%-6.5%, and ≥ 6.5% or fasting plasma glucose (FPG) levels of < 100, 100-125 mg/dL, and ≥ 126 mg/dL, respectively. Subtraction images between unenhanced and equilibrium-phase images were prepared. The liver and the pancreas were automatically extracted using a high-speed, three-dimensional image analysis system, and their respective mean CT values were calculated. The enhancement degree of the aorta (Δaorta) was measured. fECV was calculated using the following equation: fECV = (100 - hematocrit) * Δliver or pancreas/Δaorta. Differences were investigated in hepatic and pancreatic fECVs among the three groups, and the correlation between each two in hepatic fECV, pancreatic fECV, and HbA1c was determined. RESULTS: The pancreatic fECV, which was positively correlated with the hepatic fECV and HbA1c (r = 0.51, P < 0.001, and r = 0.51, P < 0.001, respectively), significantly differed among the three groups (P < 0.001) and was significantly greater in DM than in pDM or nondiabetes and in pDM with nondiabetes (P < 0.001). Hepatic fECV was significantly greater in DM than in nondiabetes (P < 0.05). CONCLUSION: The pancreatic fECV and pDM/DM are closely related.

Imaging Biomarker for Early-Stage Alzheimer Disease: Utility of Hippocampal Histogram Analysis of Diffusion Metrics.

BACKGROUND AND PURPOSE: Biomarkers have been required for diagnosing early Alzheimer disease. We assessed the utility of hippocampal diffusion parameters for diagnosing Alzheimer disease pathology in mild cognitive impairment. MATERIALS AND METHODS: Sixty-nine patients with mild cognitive impairment underwent both CSF measurement and multi-shell diffusion imaging at 3T. Based on the CSF biomarker level, patients were classified according to the presence (Alzheimer disease group, n = 35) or absence (non-Alzheimer disease group, n = 34) of Alzheimer disease pathology. Neurite orientation dispersion and density imaging and diffusion tensor imaging parametric maps were generated. Two observers independently created the hippocampal region of interest for calculating histogram features. Interobserver correlations were calculated. The statistical significance of intergroup differences was tested by using the Mann-Whitney U test. Logistic regression analyses, using both the clinical scale and the image data, were used to predict intergroup differences, after which group discriminations were performed. RESULTS: Most intraclass correlation coefficient values were between 0.59 and 0.91. In the regions of interest of both observers, there were statistically significant intergroup differences for the left-side neurite orientation dispersion and density imaging-derived intracellular volume fraction, right-side diffusion tensor imaging-derived mean diffusivity, left-side diffusion tensor imaging-derived mean diffusivity, axial diffusivity, and radial diffusivity (P < .05). Logistic regression models revealed that diffusion parameters contributed the most to discriminating between the groups. The areas under the receiver operating characteristic curve for the regions of interest of observers A/B were 0.69/0.68, 0.69/0.68, 0.73/0.68, 0.71/0.68, and 0.68/0.68 for the left-side intracellular volume fraction (mean), right-side mean diffusivity (mean), left-side mean diffusivity (10th percentile), axial diffusivity (10th percentile), and radial diffusivity (mean). CONCLUSIONS: Hippocampal diffusion parameters might be useful for the early diagnosis of Alzheimer disease.

The Tumor Growth Inhibitory Effect of a Standardized Extract of Cultured Lentinula edodes Mycelia Using Patient Derived Xenograft Model.

Patient derived xenograft (PDX) is a powerful tool to confirm pharmacological efficacy in non-clinical studies for the development of various drugs including anti-cancer agents and therapeutic research. A standardized extract of cultured Lentinula edodes mycelia, a product name AHCC® is produced by Amino Up Co., Ltd. (Sapporo, Japan). In this study, we investigated the inhibitory effect of AHCC® on the growth of tumor PDX in Super SCID (severe combined immunodeficiency) mice. Effects of AHCC® and BCG administration on the growth of renal cancer PDX implanted in Super SCID mice were evaluated by PDX growth curve. Tendency for the effects on the growth of renal cancer PDX in Super SCID by administration of AHCC® and BCG before implanting the PDX were demonstrated. The effects of the oral administration of AHCC® on the growth of renal, invasive and non-invasive breast cancer PDX in Super SCID mice were studied. In Super SCID mice transplanted with renal cancer PDX, AHCC® significantly suppressed tumor proliferation from the day 48 to 83 after transplantation. In two types of breast cancer PDX, tendency of the growth inhibitory effects of AHCC® were shown by PDX growth curve. Significant inhibitory effect was found at only one time point for during proliferation in each PDX. Super SCID-PDX model has the potential to be a useful tool to investigate for the effect of functional foods.

Calyceal Diverticulum Found Incidentally on 18 F-FDG PET/CT Imaging.

ABSTRACT: A 69-year-old man with pancreatic cancer underwent 18 F-FDG PET/CT examination for tumor staging. The PET images showed a focal mass-like FDG accumulation in the left kidney mimicking malignancy, whereas simultaneous CT and fused PET/CT images suggested a cystic lesion. On subsequent MR examination, the lesion appeared cystic on T2-weighted, contrast-enhanced arterial phase, and contrast-enhanced venous phase images. In addition, excretory phase images showed filling contrast medium to the cystic cavity, leading to a diagnosis of calyceal diverticulum. This report suggests that the possibility of a calyceal diverticulum should be considered in cases with focal FDG accumulation in renal cystic lesions.

Effect of Deep Learning Reconstruction on Respiratory-triggered T2-weighted MR Imaging of the Liver: A Comparison between the Single-shot Fast Spin-echo and Fast Spin-echo Sequences.

PURPOSE: To compare the effects of deep learning reconstruction (DLR) on respiratory-triggered T2-weighted MRI of the liver between single-shot fast spin-echo (SSFSE) and fast spin-echo (FSE) sequences. METHODS: Respiratory-triggered fat-suppressed liver T2-weighted MRI was obtained with the FSE and SSFSE sequences at the same spatial resolution in 55 patients. Conventional reconstruction (CR) and DLR were applied to each sequence, and the SNR and liver-to-lesion contrast were measured on FSE-CR, FSE-DLR, SSFSE-CR, and SSFSE-DLR images. Image quality was independently assessed by three radiologists. The results of the qualitative and quantitative analyses were compared among the four types of images using repeated-measures analysis of variance or Friedman's test for normally and non-normally distributed data, respectively, and a visual grading characteristics (VGC) analysis was performed to evaluate the image quality improvement by DLR on the FSE and SSFSE sequences. RESULTS: The liver SNR was lowest on SSFSE-CR and highest on FSE-DLR and SSFSE-DLR (P < 0.01). The liver-to-lesion contrast did not differ significantly among the four types of images. Qualitatively, noise scores were worst on SSFSE-CR but best on SSFSE-DLR because DLR significantly reduced noise (P < 0.01). In contrast, artifact scores were worst both on FSE-CR and FSE-DLR (P < 0.01) because DLR did not reduce the artifacts. Lesion conspicuity was significantly improved by DLR compared with CR in the SSFSE (P < 0.01) but not in FSE sequences for all readers. Overall image quality was significantly improved by DLR compared with CR for all readers in the SSFSE (P < 0.01) but only one reader in the FSE (P < 0.01). The mean area under the VGC curve values for the FSE-DLR and SSFSE-DLR sequences were 0.65 and 0.94, respectively. CONCLUSION: In liver T2-weighted MRI, DLR produced more marked improvements in image quality in SSFSE than in FSE.

Ultra-High-Resolution T2-Weighted PROPELLER MRI of the Rectum With Deep Learning Reconstruction: Assessment of Image Quality and Diagnostic Performance.

OBJECTIVE: The aim of this study was to evaluate the impact of ultra-high-resolution acquisition and deep learning reconstruction (DLR) on the image quality and diagnostic performance of T2-weighted periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging of the rectum. MATERIALS AND METHODS: This prospective study included 34 patients who underwent magnetic resonance imaging (MRI) for initial staging or restaging of rectal tumors. The following 4 types of oblique axial PROPELLER images perpendicular to the tumor were obtained: a standard 3-mm slice thickness with conventional reconstruction (3-CR) and DLR (3-DLR), and 1.2-mm slice thickness with CR (1.2-CR) and DLR (1.2-DLR). Three radiologists independently evaluated the image quality and tumor extent by using a 5-point scoring system. Diagnostic accuracy was evaluated in 22 patients with rectal cancer who underwent surgery after MRI without additional neoadjuvant therapy (median interval between MRI and surgery, 22 days). The signal-to-noise ratio and tissue contrast were measured on the 4 types of PROPELLER imaging. RESULTS: 1.2-DLR imaging showed the best sharpness, overall image quality, and rectal and lesion conspicuity for all readers (P < 0.01). Of the assigned scores for tumor extent, extramural venous invasion (EMVI) scores showed moderate agreement across the 4 types of PROPELLER sequences in all readers (intraclass correlation coefficient, 0.60-0.71). Compared with 3-CR imaging, the number of cases with MRI-detected extramural tumor spread was significantly higher with 1.2-DLR imaging (19.0 ± 2.9 vs 23.3 ± 0.9, P = 0.03), and the number of cases with MRI-detected EMVI was significantly increased with 1.2-CR, 3-DLR, and 1.2-DLR imaging (8.0 ± 0.0 vs 9.7 ± 0.5, 11.0 ± 2.2, and 12.3 ± 1.7, respectively; P = 0.02). For the diagnosis of histopathologic extramural tumor spread, 3-CR and 1.2-CR had significantly higher specificity than 3-DLR and 1.2-DLR imaging (0.75 and 0.78 vs 0.64 and 0.58, respectively; P = 0.02), and only 1.2-CR had significantly higher accuracy than 3-CR imaging (0.83 vs 0.79, P = 0.01). The accuracy of MRI-detected EMVI with reference to pathological EMVI was significantly lower for 3-CR and 3-DLR compared with 1.2-CR (0.77 and 0.74 vs 0.85, respectively; P < 0.01), and was not significantly different between 1.2-CR and 1.2-DLR (0.85 vs 0.80). Using any pathological venous invasion as the reference standard, the accuracy of MRI-detected EMVI was significantly the highest with 1.2-DLR, followed by 1.2-CR, 3-CR, and 3-DLR (0.71 vs 0.67 vs 0.59 vs 0.56, respectively; P < 0.01). The signal-to-noise ratio was significantly highest with 3-DLR imaging (P < 0.05). There were no significant differences in tumor-to-muscle contrast between the 4 types of PROPELLER imaging. CONCLUSIONS: Ultra-high-resolution PROPELLER T2-weighted imaging of the rectum combined with DLR improved image quality, increased the number of cases with MRI-detected extramural tumor spread and EMVI, but did not improve diagnostic accuracy with respect to pathology in rectal cancer, possibly because of false-positive MRI findings or false-negative pathologic findings.

High-quality semi-supervised anomaly detection with generative adversarial networks.

PURPOSE: The visualization of an anomaly area is easier in anomaly detection methods that use generative models rather than classification models. However, achieving both anomaly detection accuracy and a clear visualization of anomalous areas is challenging. This study aimed to establish a method that combines both detection accuracy and clear visualization of anomalous areas using a generative adversarial network (GAN). METHODS: In this study, StyleGAN2 with adaptive discriminator augmentation (StyleGAN2-ADA), which can generate high-resolution and high-quality images with limited number of datasets, was used as the image generation model, and pixel-to-style-to-pixel (pSp) encoder was used to convert images into intermediate latent variables. We combined existing methods for training and proposed a method for calculating anomaly scores using intermediate latent variables. The proposed method, which combines these two methods, is called high-quality anomaly GAN (HQ-AnoGAN). RESULTS: The experimental results obtained using three datasets demonstrated that HQ-AnoGAN has equal or better detection accuracy than the existing methods. The results of the visualization of abnormal areas using the generated images showed that HQ-AnoGAN could generate more natural images than the existing methods and was qualitatively more accurate in the visualization of abnormal areas. CONCLUSION: In this study, HQ-AnoGAN comprising StyleGAN2-ADA and pSp encoder was proposed with an optimal anomaly score calculation method. The experimental results show that HQ-AnoGAN can achieve both high abnormality detection accuracy and clear visualization of abnormal areas; thus, HQ-AnoGAN demonstrates significant potential for application in medical imaging diagnosis cases where an explanation of diagnosis is required.