External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens.

OBJECTIVES: To externally validate the performance of the DeepDx Prostate artificial intelligence (AI) algorithm (Deep Bio Inc., Seoul, South Korea) for Gleason grading on whole-mount prostate histopathology, considering potential variations observed when applying AI models trained on biopsy samples to radical prostatectomy (RP) specimens due to inherent differences in tissue representation and sample size. MATERIALS AND METHODS: The commercially available DeepDx Prostate AI algorithm is an automated Gleason grading system that was previously trained using 1133 prostate core biopsy images and validated on 700 biopsy images from two institutions. We assessed the AI algorithm's performance, which outputs Gleason patterns (3, 4, or 5), on 500 1-mm2 tiles created from 150 whole-mount RP specimens from a third institution. These patterns were then grouped into grade groups (GGs) for comparison with expert pathologist assessments. The reference standard was the International Society of Urological Pathology GG as established by two experienced uropathologists with a third expert to adjudicate discordant cases. We defined the main metric as the agreement with the reference standard, using Cohen's kappa. RESULTS: The agreement between the two experienced pathologists in determining GGs at the tile level had a quadratically weighted Cohen's kappa of 0.94. The agreement between the AI algorithm and the reference standard in differentiating cancerous vs non-cancerous tissue had an unweighted Cohen's kappa of 0.91. Additionally, the AI algorithm's agreement with the reference standard in classifying tiles into GGs had a quadratically weighted Cohen's kappa of 0.89. In distinguishing cancerous vs non-cancerous tissue, the AI algorithm achieved a sensitivity of 0.997 and specificity of 0.88; in classifying GG ≥2 vs GG 1 and non-cancerous tissue, it demonstrated a sensitivity of 0.98 and specificity of 0.85. CONCLUSION: The DeepDx Prostate AI algorithm had excellent agreement with expert uropathologists and performance in cancer identification and grading on RP specimens, despite being trained on biopsy specimens from an entirely different patient population.

Highly potent anti-melanogenic effect of 2-thiobenzothiazole derivatives through nanomolar tyrosinase activity inhibition.

Compounds with sulfhydryl substituents and azole compounds exhibit potent anti-tyrosinase potency. 2-Thiobenzothiazole (2-TBT), a hybrid structure of sulfhydryl and azole, exists in two tautomeric forms, with the thione form being predominant according to several studies. 2-TBT derivatives were synthesized as potential tyrosinase inhibitors as the thione tautomeric form has the same N-CS moiety as phenylthiourea (PTU), which is suitable for chelation with the copper ions present in the tyrosinase active site. Eight of the ten 2-TBT derivatives inhibited the monophenolase and diphenolase activities of mushroom tyrosinase, with IC50 values of 0.02-0.83 µM. Kinetic studies and molecular dynamics simulations were performed to determine their mode of action and confirm that the 2-TBT derivatives bind to the tyrosinase active site with high stability. Derivatives 3, 4, 8, and 10 strongly inhibited melanogenesis in B16F10 cells in a pattern similar to the results of cellular tyrosinase inhibition, thereby suggesting that their ability to inhibit melanogenesis was due to their tyrosinase inhibitory activity. In a depigmentation experiment using zebrafish embryos, all 2-TBT derivatives showed better potency than kojic acid, even at 400 to 2000 times lower concentration, and 1 and 10 reduced zebrafish larva pigmentation more strongly than PTU even at 20 times lower concentration. Experiments investigating the changes in tyrosinase inhibitory activity of 2-TBT derivatives in the presence and absence of CuSO4 and their copper chelating ability supported that these derivatives exert their anti-melanogenic effect by chelating the copper ions of tyrosinase. These results suggest that 2-TBT derivatives are promising candidates for the treatment of hyperpigmentation-related disorders.

Sex-specific survival gene mutations are discovered as clinical predictors of clear cell renal cell carcinoma.

Although sex differences have been reported in patients with clear cell renal cell carcinoma (ccRCC), biological sex has not received clinical attention and genetic differences between sexes are poorly understood. This study aims to identify sex-specific gene mutations and explore their clinical significance in ccRCC. We used data from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC), The Renal Cell Cancer-European Union (RECA-EU) and Korean-KIRC. A total of 68 sex-related genes were selected from TCGA-KIRC through machine learning, and 23 sex-specific genes were identified through verification using the three databases. Survival differences according to sex were identified in nine genes (ACSS3, ALG13, ASXL3, BAP1, JADE3, KDM5C, KDM6A, NCOR1P1, and ZNF449). Female-specific survival differences were found in BAP1 in overall survival (OS) (TCGA-KIRC, p = 0.004; RECA-EU, p = 0.002; and Korean-KIRC, p = 0.003) and disease-free survival (DFS) (TCGA-KIRC, p = 0.001 and Korean-KIRC, p = 0.000004), and NCOR1P1 in DFS (TCGA-KIRC, p = 0.046 and RECA-EU, p = 0.00003). Male-specific survival differences were found in ASXL3 (OS, p = 0.017 in TCGA-KIRC; and OS, p = 0.005 in RECA-EU) and KDM5C (OS, p = 0.009 in RECA-EU; and DFS, p = 0.016 in Korean-KIRC). These results suggest that biological sex may be an important predictor and sex-specific tailored treatment may improve patient care in ccRCC.

Investigation of the Efficacy of Benzylidene-3-methyl-2-thioxothiazolidin-4-one Analogs with Antioxidant Activities on the Inhibition of Mushroom and Mammal Tyrosinases.

Based on the fact that substances with a ß-phenyl-α,ß-unsaturated carbonyl (PUSC) motif confer strong tyrosinase inhibitory activity, benzylidene-3-methyl-2-thioxothiazolidin-4-one (BMTTZD) analogs 1-8 were prepared as potential tyrosinase inhibitors. Four analogs (1-3 and 5) inhibited mushroom tyrosinase strongly. Especially, analog 3 showed an inhibitory effect that was 220 and 22 times more powerful than kojic acid in the presence of l-tyrosine and l-dopa, respectively. A kinetic study utilizing mushroom tyrosinase showed that analogs 1 and 3 competitively inhibited tyrosinase, whereas analogs 2 and 5 inhibited tyrosinase in a mixed manner. A docking simulation study indicated that analogs 2 and 5 could bind to both the tyrosinase active and allosteric sites with high binding affinities. In cell-based experiments using B16F10 cells, analogs 1, 3, and 5 effectively inhibited melanin production; their anti-melanogenic effects were attributed to their ability to inhibit intracellular tyrosinase activity. Moreover, analogs 1, 3, and 5 inhibited in situ B16F10 cellular tyrosinase activity. In three antioxidant experiments, analogs 2 and 3 exhibited strong antioxidant efficacy, similar to that of the positive controls. These results suggest that the BMTTZD analogs are promising tyrosinase inhibitors for the treatment of hyperpigmentation-related disorders.

Discovery and Validation of Survival-Specific Genes in Papillary Renal Cell Carcinoma Using a Customized Next-Generation Sequencing Gene Panel.

PURPOSE: Papillary renal cell carcinoma (PRCC), the second most common kidney cancer, is morphologically, genetically, and molecularly heterogeneous with diverse clinical manifestations. Genetic variations of PRCC and their association with survival are not yet well-understood. This study aimed to identify and validate survival-specific genes in PRCC and explore their clinical utility. MATERIALS AND METHODS: Using machine learning, 293 patients from the Cancer Genome Atlas-Kidney Renal Papillary Cell Carcinoma (TCGA-KIRP) database were analyzed to derive genes associated with survival. To validate these genes, DNAs were extracted from the tissues of 60 Korean PRCC patients. Next generation sequencing was conducted using a customized PRCC gene panel of 202 genes, including 171 survival-specific genes. Kaplan-Meier and Log-rank tests were used for survival analysis. Fisher's exact test was performed to assess the clinical utility of variant genes. RESULTS: A total of 40 survival-specific genes were identified in the TCGA-KIRP database through machine learning and statistical analysis. Of them, 10 (BAP1, BRAF, CFDP1, EGFR, ITM2B, JAK1, NODAL, PCSK2, SPATA13, and SYT5) were validated in the Korean-KIRP database. Among these survival gene signatures, three genes (BAP1, PCSK2, and SPATA13) showed survival specificity in both overall survival (OS) (p = 0.00004, p = 1.38 × 10-7, and p = 0.026, respectively) and disease-free survival (DFS) (p = 0.00002, p = 1.21 × 10-7, and p = 0.036, respectively). Notably, the PCSK2 mutation demonstrated survival specificity uniquely in both the TCGA-KIRP (OS: p = 0.010 and DFS: p = 0.301) and Korean-KIRP (OS: p = 1.38 × 10-7 and DFS: p = 1.21 × 10-7) databases. CONCLUSIONS: We discovered and verified genes specific for the survival of PRCC patients in the TCGA-KIRP and Korean-KIRP databases. The survival gene signature, including PCSK2 commonly obtained from the 40 gene signature of TCGA and the 10 gene signature of the Korean database, is expected to provide insight into predicting the survival of PRCC patients and developing new treatment.

Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.

BACKGROUND: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. PURPOSE: To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs. STUDY TYPE: Retrospective. SUBJECTS: 395 patients with 1149 FLLs. FIELD STRENGTH/SEQUENCE: The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. ASSESSMENT: The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. STATISTICAL TESTS: McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant. RESULTS: The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). DATA CONCLUSION: AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. TECHNICAL EFFICACY: Stage 2.

Assessment of Deep Learning-Based Triage Application for Acute Ischemic Stroke on Brain MRI in the ER.

RATIONALE AND OBJECTIVES: To assess a deep learning application (DLA) for acute ischemic stroke (AIS) detection on brain magnetic resonance imaging (MRI) in the emergency room (ER) and the effect of T2-weighted imaging (T2WI) on its performance. MATERIALS AND METHODS: We retrospectively analyzed brain MRIs taken through the ER from March to October 2021 that included diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) sequences. MRIs were processed by the DLA, and sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were evaluated, with three neuroradiologists establishing the gold standard for detection performance. In addition, we examined the impact of axial T2WI, when available, on the accuracy and processing time of DLA. RESULTS: The study included 947 individuals (mean age ± standard deviation, 64 years ± 16; 461 men, 486 women), with 239 (25%) positive for AIS. The overall performance of DLA was as follows: sensitivity, 90%; specificity, 89%; accuracy, 89%; and AUROC, 0.95. The average processing time was 24 s. In the subgroup with T2WI, T2WI did not significantly impact MRI assessments but did result in longer processing times (35 s without T2WI compared to 48 s with T2WI, p < 0.001). CONCLUSION: The DLA successfully identified AIS in the ER setting with an average processing time of 24 s. The absence of performance acquire with axial T2WI suggests that the DLA can diagnose AIS with just axial DWI and FLAIR sequences, potentially shortening the exam duration in the ER.

Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography.

Computed tomography (CT) has been used worldwide as a non-invasive test to assist in diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation doses has driven researchers to improve reconstruction quality. Although previous studies on low-dose computed tomography (LDCT) denoising have demonstrated the effectiveness of learning-based methods, most were developed on the simulated data. However, the real-world scenario differs significantly from the simulation domain, especially when using the multi-slice spiral scanner geometry. This paper proposes a two-stage method for the commercially available multi-slice spiral CT scanners that better exploits the complete reconstruction pipeline for LDCT denoising across different domains. Our approach makes good use of the high redundancy of multi-slice projections and the volumetric reconstructions while leveraging the over-smoothing issue in conventional cascaded frameworks caused by aggressive denoising. The dedicated design also provides a more explicit interpretation of the data flow. Extensive experiments on various datasets showed that the proposed method could remove up to 70% of noise without compromised spatial resolution, while subjective evaluations by two experienced radiologists further supported its superior performance against state-of-the-art methods in clinical practice. Code is available at https://github.com/YCL92/TMD-LDCT.

Advanced MRI techniques in abdominal imaging.

Magnetic resonance imaging (MRI) is a crucial modality for abdominal imaging evaluation of focal lesions and tissue properties. However, several obstacles, such as prolonged scan times, limitations in patients' breath-hold capacity, and contrast agent-associated artifacts, remain in abdominal MR images. Recent techniques, including parallel imaging, three-dimensional acquisition, compressed sensing, and deep learning, have been developed to reduce the scan time while ensuring acceptable image quality or to achieve higher resolution without extending the scan duration. Quantitative measurements using MRI techniques enable the noninvasive evaluation of specific materials. A comprehensive understanding of these advanced techniques is essential for accurate interpretation of MRI sequences. Herein, we therefore review advanced abdominal MRI techniques.

Comparison of non-contrast abbreviated MRI and ultrasound as surveillance modalities for HCC.

BACKGROUND & AIMS: Ultrasonography (US) is recommended for HCC surveillance in high-risk patients but has limited performance in detecting early-stage HCC. We aimed to compare the diagnostic performance of biannual US and annual non-contrast abbreviated magnetic resonance imaging (NC-AMRI) as HCC surveillance modalities in high-risk patients. METHODS: This prospective, multicenter cohort study enrolled participants with an estimated annual risk of HCC greater than 5% between October 2015 and April 2017. Participants underwent six rounds of HCC surveillance at 6-month intervals, with both US and NC-AMRI at rounds 1, 3, and 5, and only US at rounds 2, 4, and 6. The sensitivity, diagnostic yield (DY), and false referral rate (FRR) for HCC detection by US and NC-AMRI were compared. RESULTS: In total, 208 participants underwent 980 US and 516 NC-AMRI examinations during 30 months of follow-up. Among them, 34 HCCs were diagnosed in 31 participants, with 20 (64.5%) classified as very early-stage and 11 (35.5%) as early-stage HCC. The sensitivity of annual NC-AMRI (71.0%, 22/31) was marginally higher than that of biannual US (45.2%, 14/31; p = 0.077). NC-AMRI showed a significantly higher DY than US (4.26% vs. 1.43%, p <0.001), with a similar FRR (2.91% vs. 3.06%, p = 0.885). A simulation of alternating US and NC-AMRI at 6-month intervals yielded a sensitivity of 83.9% (26/31), significantly exceeding that of biannual US (p = 0.006). CONCLUSIONS: Annual NC-AMRI showed a marginally higher sensitivity than biannual US for HCC detection in high-risk patients. The DY of annual NC-AMRI was significantly higher than that of biannual US, without increasing the FRR. Thus, alternating US and NC-AMRI at 6-month intervals could be an optimal surveillance strategy for high-risk patients. IMPACT AND IMPLICATIONS: Current guidelines permit the use of magnetic resonance imaging (MRI) as a surveillance tool for hepatocellular carcinoma in patients in whom ultrasonography (US) is inadequate. However, the specific indications, imaging sequences, and intervals for MRI surveillance remain unclear. In our study, we found that annual non-contrast abbreviated MRI exhibited marginally higher sensitivity and significantly better diagnostic yield than biannual US in patients at high risk of hepatocellular carcinoma. Alternating US and non-contrast abbreviated MRI at 6-month intervals led to significantly improved sensitivity compared to biannual US, making it a potentially optimal surveillance strategy for high-risk patients. GOV IDENTIFIER: NCT02551250.

Development and Validation of a Deep-Learning-Based Algorithm for Detecting and Classifying Metallic Implants in Abdominal and Spinal CT Topograms.

PURPOSE: To develop and validate a deep-learning-based algorithm (DLA) that is designed to segment and classify metallic objects in topograms of abdominal and spinal CT. METHODS: DLA training for implant segmentation and classification was based on a U-net-like architecture with 263 annotated hip implant topograms and 2127 annotated spine implant topograms. The trained DLA was validated with internal and external datasets. Two radiologists independently reviewed the external dataset consisting of 2178 abdomen anteroposterior (AP) topograms and 515 spine AP and lateral topograms, all collected in a consecutive manner. Sensitivity and specificity were calculated per pixel row and per patient. Pairwise intersection over union (IoU) was also calculated between the DLA and the two radiologists. RESULTS: The performance parameters of the DLA were consistently >95% in internal validation per pixel row and per patient. DLA can save 27.4% of reconstruction time on average in patients with metallic implants compared to the existing iMAR. The sensitivity and specificity of the DLA during external validation were greater than 90% for the detection of spine implants on three different topograms and for the detection of hip implants on abdominal AP and spinal AP topograms. The IoU was greater than 0.9 between the DLA and the radiologists. However, the DLA training could not be performed for hip implants on spine lateral topograms. CONCLUSIONS: A prototype DLA to detect metallic implants of the spine and hip on abdominal and spinal CT topograms improves the scan workflow with good performance for both spine and hip implants.

Perioperative textbook outcomes of minimally invasive pancreatoduodenectomy: a multicenter retrospective cohort analysis in a Korean minimally invasive pancreatic surgery registry.

BACKGROUND: The aim of this study is to investigate the perioperative composite textbook outcomes of pancreatic surgery after minimally invasive pancreatoduodenectomy (MIPD). MATERIALS AND METHODS: The cohort study was conducted across 10 institutions and included 1552 patients who underwent MIPD registered with the Korean Study Group on Minimally Invasive Pancreatic Surgery between May 2007 and April 2020. We analyzed perioperative textbook outcomes of pancreatic surgery after MIPD. Subgroup analyses were performed to assess outcomes based on the hospital volume of MIPD. RESULTS: Among all patients, 21.8% underwent robotic pancreatoduodenectomy. High-volume centers (performing >20 MIPD/year) performed 88.1% of the procedures. The incidence of clinically relevant postoperative pancreatic fistula was 11.5%. Severe complications (Clavien-Dindo grade ≥IIIa) occurred in 15.1% of the cases. The 90-day mortality rate was 0.8%. The mean hospital stay was 13.7 days. Textbook outcomes of pancreatic surgery success were achieved in 60.4% of patients, with higher rates observed in high-volume centers than in low-volume centers (62.2% vs. 44.7%, P<0.001). High-volume centers exhibited significantly lower conversion rates (5.4% vs. 12.5%, P=0.001), lower 90-day mortality (0.5% vs. 2.7%, P=0.001), and lower 90-day readmission rates (4.5% vs. 9.6%, P=0.006) than those low-volume centers. CONCLUSION: MIPD could be performed safely with permissible perioperative outcomes, including textbook outcomes of pancreatic surgery, particularly in experienced centers. The findings of this study provided valuable insights for guiding surgical treatment decisions in periampullary disease.

Rim enhancement of pancreatic ductal adenocarcinoma: investigating the relationship with DCE-MRI-based radiomics and next-generation sequencing.

Purpose: To identify the clinical and genetic variables associated with rim enhancement of pancreatic ductal adenocarcinoma (PDAC) and to develop a dynamic contrast-enhanced (DCE) MRI-based radiomics model for predicting the genetic status from next-generation sequencing (NGS). Materials and methods: Patients with PDAC, who underwent pretreatment pancreatic DCE-MRI between November 2019 and July 2021, were eligible in this prospective study. Two radiologists evaluated presence of rim enhancement in PDAC, a known radiological prognostic indicator, on DCE MRI. NGS was conducted for the tissue from the lesion. The Mann-Whitney U and Chi-square tests were employed to identify clinical and genetic variables associated with rim enhancement in PDAC. For continuous variables predicting rim enhancement, the cutoff value was set based on the Youden's index from the receiver operating characteristic (ROC) curve. Radiomics features were extracted from a volume-of-interest of PDAC on four DCE maps (Ktrans, Kep, Ve, and iAUC). A random forest (RF) model was constructed using 10 selected radiomics features from a pool of 392 original features. This model aimed to predict the status of significant NGS variables associated with rim enhancement. The performance of the model was validated using test set. Results: A total of 55 patients (32 men; median age 71 years) were randomly assigned to the training (n = 41) and test (n = 14) sets. In the training set, KRAS, TP53, CDKN2A, and SMAD4 mutation rates were 92.3%, 61.8%, 14.5%, and 9.1%, respectively. Tumor size and KRAS variant allele frequency (VAF) differed between rim-enhancing (n = 12) and nonrim-enhancing (n = 29) PDACs with a cutoff of 17.22%. The RF model's average AUC from 10-fold cross-validation for predicting KRAS VAF status was 0.698. In the test set comprising 6 tumors with low KRAS VAF and 8 with high KRAS VAF, the RF model's AUC reached 1.000, achieving a sensitivity of 75.0%, specificity of 100% and accuracy of 87.5%. Conclusion: Rim enhancement of PDAC is associated with KRAS VAF derived from NGS-based genetic information. For predicting the KRAS VAF status in PDAC, a radiomics model based on DCE maps showed promising results.

Low-dose versus conventional CT urography using dual-source CT with different time-current product values and the same tube voltage: image quality and diagnostic performance in various diagnoses.

OBJECTIVES: To compare the image quality and diagnostic performance of low-dose CT urography to that of concurrently acquired conventional CT using dual-source CT. METHODS: This retrospective study included 357 consecutive CT urograms performed by third-generation dual-source CT in a single institution between April 2020 and August 2021. Two-phase CT images (unenhanced phase, excretory phase with split bolus) were obtained with two different tube current-time products (280 mAs for the conventional-dose protocol and 70 mAs for the low-dose protocol) and the same tube voltage (90 kVp) for the two X-ray tubes. Iterative reconstruction was applied for both protocols. Two radiologists independently performed quantitative and qualitative image quality analysis and made diagnoses. The correlation between the noise level or the effective radiation dose and the patients' body weight was evaluated. RESULTS: Significantly higher noise levels resulting in a significantly lower liver signal-to-noise ratio and contrast-to-noise ratio were noted in low-dose images compared to conventional images (P < .001). Qualitative analysis by both radiologists showed significantly lower image quality in low-dose CT than in conventional CT images (P < .001). Patient's body weight was positively correlated with noise and effective radiation dose (P < .001). Diagnostic performance for various diseases, including urolithiasis, inflammation, and mass, was not different between the two protocols. CONCLUSIONS: Despite inferior image quality, low-dose CT urography with 70 mAs and 90 kVp and iterative reconstruction demonstrated diagnostic performance equivalent to that of conventional CT for identifying various diseases of the urinary tract. ADVANCES IN KNOWLEDGE: Low-dose CT (25% radiation dose) with low tube current demonstrated diagnostic performance comparable to that of conventional CT for a variety of urinary tract diseases.

Automated classification of liver fibrosis stages using ultrasound imaging.

BACKGROUND: Ultrasound imaging is the most frequently performed for the patients with chronic hepatitis or liver cirrhosis. However, ultrasound imaging is highly operator dependent and interpretation of ultrasound images is subjective, thus well-trained radiologist is required for evaluation. Automated classification of liver fibrosis could alleviate the shortage of skilled radiologist especially in low-to-middle income countries. The purposed of this study is to evaluate deep convolutional neural networks (DCNNs) for classifying the degree of liver fibrosis according to the METAVIR score using US images. METHODS: We used ultrasound (US) images from two tertiary university hospitals. A total of 7920 US images from 933 patients were used for training/validation of DCNNs. All patient were underwent liver biopsy or hepatectomy, and liver fibrosis was categorized based on pathology results using the METAVIR score. Five well-established DCNNs (VGGNet, ResNet, DenseNet, EfficientNet and ViT) was implemented to predict the METAVIR score. The performance of DCNNs for five-level (F0/F1/F2/F3/F4) classification was evaluated through area under the receiver operating characteristic curve (AUC) with 95% confidential interval, accuracy, sensitivity, specificity, positive and negative likelihood ratio. RESULTS: Similar mean AUC values were achieved for five models; VGGNet (0.96), ResNet (0.96), DenseNet (0.95), EfficientNet (0.96), and ViT (0.95). The same mean accuracy (0.94) and specificity values (0.96) were yielded for all models. In terms of sensitivity, EffcientNet achieved highest mean value (0.85) while the other models produced slightly lower values range from 0.82 to 0.84. CONCLUSION: In this study, we demonstrated that DCNNs can classify the staging of liver fibrosis according to METAVIR score with high performance using conventional B-mode images. Among them, EfficientNET that have fewer parameters and computation cost produced highest performance. From the results, we believe that DCNNs based classification of liver fibrosis may allow fast and accurate diagnosis of liver fibrosis without needs of additional equipment for add-on test and may be powerful tool for supporting radiologists in clinical practice.

Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance.

BACKGROUND: The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition. PURPOSE: To compare the image quality and diagnostic performance of conventional turbo spin-echo (TSE) T2-weighted (T2W) imaging with DL-accelerated sagittal T2W imaging in the female pelvic cavity. METHODS: This study evaluated 149 consecutive female pelvic MRI examinations, including conventional T2W imaging with TSE (acquisition time = 2:59) and DL-accelerated T2W imaging with breath hold (DL-BH) (1:05 [0:14 × 3 breath-holds]) in the sagittal plane. In 294 randomly ordered sagittal T2W images, two radiologists independently assessed image quality (sharpness, subjective noise, artifacts, and overall image quality), made a diagnosis for uterine leiomyomas, and scored diagnostic confidence. For the uterus and piriformis muscle, quantitative imaging analysis was also performed. Wilcoxon signed rank tests were used to compare the two sets of T2W images. RESULTS: In the qualitative analysis, DL-BH showed similar or significantly higher scores for all features than conventional T2W imaging (P <0.05). In the quantitative analysis, the noise in the uterus was lower in DL-BH, but the noise in the muscle was lower in conventional T2W imaging. In the uterus and muscle, the signal-to-noise ratio was significantly lower in DL-BH than in conventional T2W imaging (P <0.001). The diagnostic performance of the two sets of T2W images was not different for uterine leiomyoma. CONCLUSIONS: DL-accelerated sagittal T2W imaging obtained with three breath-holds demonstrated superior or comparable image quality to conventional T2W imaging with no significant difference in diagnostic performance for uterine leiomyomas.

In vitro and in vivo anti-pigmentation effects of 2-mercaptobenzimidazoles as nanomolar tyrosinase inhibitors on mammalian cells and zebrafish embryos: Preparation of pigment-free zebrafish embryos.

Recently, 10 2-mercaptobenzo[d]imidazole (2-MBI) compounds (1-10) were synthesized. Although all 2-MBI compounds are tyrosinase inhibitors that inhibit mushroom tyrosinase at extremely low concentrations (IC50 values: 20-740 nM) and effectively inhibit the browning of apples, to our knowledge, no studies have determined whether 2-MBI compounds inhibit mammalian tyrosinase. Mammalian tyrosinase is different from mushroom tyrosinase in its distribution within the cell and has structural characteristics that are different from mushroom tyrosinase in amino acid sequence and in the presence of a quaternary structure. Thus, the effect of the 10 2-MBI compounds on mammalian tyrosinase activity was investigated in B16F10 cells. Six compounds (1-6) exhibited stronger intracellular tyrosinase inhibition than that of kojic acid and phenylthiourea (PTU), which are known to be the most potent tyrosinase inhibitors; their strong tyrosinase inhibitory activity robustly inhibited intracellular melanin production in B16F10 cells. None of the tested 2-MBI compounds exhibited appreciable cytotoxicity in HaCaT and B16F10 cells. To confirm the anti-melanogenic efficacy of the 2-MBI compounds in vivo, a zebrafish embryo model was used. At concentrations 100 times lower than kojic acid, most 2-MBI compounds demonstrated much stronger depigmentation efficacy than that of kojic acid, and three 2-MBI compounds (2-4) showed depigmentation activity similar to or more potent than that of PTU, resulting in nearly pigment-free zebrafish embryos. These results suggest that 2-MBI compounds may be potential therapeutic agents for hyperpigmentation-related disorders.

Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence.

(1) Background: Advancements in the field of liver surgery have led to a critical need for precise estimations of preoperative liver function to prevent post-hepatectomy liver failure (PHLF), a significant cause of morbidity and mortality. This study introduces a novel application of artificial intelligence (AI) in determining safe resection volumes according to a patient's liver function in major hepatectomies. (2) Methods: We incorporated a deep learning approach, incorporating a unique liver-specific loss function, to analyze patient characteristics, laboratory data, and liver volumetry from computed tomography scans of 52 patients. Our approach was evaluated against existing machine and deep learning techniques. (3) Results: Our approach achieved 68.8% accuracy in predicting safe resection volumes, demonstrating superior performance over traditional models. Furthermore, it significantly reduced the mean absolute error in under-predicted volumes to 23.72, indicating a more precise estimation of safe resection limits. These findings highlight the potential of integrating AI into surgical planning for liver resections. (4) Conclusion: By providing more accurate predictions of safe resection volumes, our method aims to minimize the risk of PHLF, thereby improving clinical outcomes for patients undergoing hepatectomy.

Thiazol-4(5H)-one analogs as potent tyrosinase inhibitors: Synthesis, tyrosinase inhibition, antimelanogenic effect, antioxidant activity, and in silico docking simulation.

As the ß-phenyl-α,ß-unsaturated carbonyl (PUSC) structure was previously identified to play a key role in tyrosinase inhibition, 14 analogs with a PUSC structure built on a thiazol-4(5H)-one scaffold were synthesized using Knoevenagel condensation to serve as potential tyrosinase inhibitors. Through mushroom tyrosinase inhibition experiments, two analogs 9 and 11 were identified as potent tyrosinase inhibitors, with 11 exhibiting an IC50 value of 0.4 ± 0.01 µM, which indicates its 26-fold greater potency than kojic acid. Kinetic studies using Lineweaver-Burk plots revealed that 9 and 11 are competitive and mixed-type inhibitors, respectively; these kinetic results were supported by docking simulations. According to the B16F10 cell-based experiments, 9 and 11 inhibited melanogenesis more effectively than kojic acid due to their potent cellular tyrosinase inhibitory activity. In addition, analogs 9 and 11 exhibited moderate-to-strong antioxidant capacity, scavenging ABTS+, DPPH, and ROS radicals. In particular, analog 12 with a catechol moiety exhibited very strong ROS-scavenging activity, similar to Trolox. These results suggest that analogs 9 and 11, which exhibit potent tyrosinase inhibitory activity in mushroom and mammalian cells and anti-melanogenic effects in B16F10 cells, are promising antibrowning agents for crops and skin lightening agents for hyperpigmentation-related diseases.

Real-world comparison between mechanical and manual cardiopulmonary resuscitation during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has posed significant challenges to healthcare systems worldwide, including an increase in out-of-hospital cardiac arrests (OHCA). Healthcare providers are now required to use personal protective equipment (PPE) during cardiopulmonary resuscitation (CPR). Additionally, mechanical CPR devices have been introduced to reduce the number of personnel required for resuscitation. This study aimed to compare the outcomes of CPR performed with a mechanical device and the outcomes of manual CPR performed by personnel wearing PPE. METHODS: This multicenter observational study utilized data from the Korean Cardiac Arrest Research Consortium registry. The study population consisted of OHCA patients who underwent CPR in emergency departments (EDs) between March 2020 and June 2021. Patients were divided into two equal propensity score matched groups: mechanical CPR group (n = 421) and PPE-equipped manual CPR group (n = 421). Primary outcomes included survival rates and favorable neurological outcomes at discharge. Total CPR duration in the ED was also assessed. RESULTS: There were no significant between-group differences with respect to survival rate at discharge (mechanical CPR: 7.4% vs PPE-equipped manual CPR: 8.3%) or favorable neurological outcomes (3.3% vs. 3.8%, respectively). However, the mechanical CPR group had a longer duration of CPR in the ED compared to the manual CPR group. CONCLUSION: This study found no significant differences in survival rates and neurological outcomes between mechanical CPR and PPE-equipped manual CPR in the ED setting. However, a longer total CPR duration was observed in the mechanical CPR group. Further research is required to explore the impact of PPE on healthcare providers' performance and fatigue during CPR in the context of the pandemic and beyond.