Objective: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection. Materials and Methods: This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively. Results: Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05). Conclusion The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.

Objective: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection. Materials and Methods: This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively. Results: Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05). Conclusion The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.

Objective: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection. Materials and Methods: This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively. Results: Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05). Conclusion The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.

Objective: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection. Materials and Methods: This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively. Results: Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05). Conclusion The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.

Objective: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI DL) protocol with standard AMRI (AMRI STD) of the liver in terms of image quality and malignant focal lesion detection. Materials and Methods: This retrospective study included 155 consecutive patients (110 male; mean age 62.4 ± 11 years) from two sites who underwent standard liver MRI and additional AMRIDL sequences, specifically DL-accelerated single-shot fast-spin echo (SSFSE DL) and DL-accelerated diffusion-weighted imaging (DWIDL). Additional MRI phantom experiments assessed signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values. Three reviewers evaluated AMRIDL and AMRI STD protocols for image quality using a five-point Likert scale and identified malignant hepatic lesions. Image quality scores and per-lesion sensitivities were compared between AMRIDL and AMRI STD using the Wilcoxon signed-rank test and logistic regression with generalized estimating equations, respectively. Results: Phantom experiments demonstrated comparable SNR and higher CNR for SSFSE DL compared to SSFSE STD, with similar ADC values for DWIDL and DWI STD. Among the 155 patients, 130 (83.9%) had chronic liver disease or a history of intra- or extrahepatic malignancy. Of 104 malignant focal lesions in 64 patients, 58 (55.8%) were hepatocellular carcinomas (HCCs), 38 (36.5%) were metastases, four (3.8%) were cholangiocarcinomas, and four (3.8%) were lymphomas. The pooled per-lesion sensitivity across three readers was 97.6% for AMRIDL, comparable to 97.6% for AMRI STD. Compared with AMRI STD, AMRIDL demonstrated superior image quality regarding structural sharpness, artifacts, and noise (all P < 0.001) and reduced the average scan time by approximately 50% (2 min 29 sec vs. 4 min 11 sec). In patients with chronic liver disease, AMRIDL achieved a 96.6% per-lesion sensitivity for HCC detection, similar to 96.5% for AMRI STD (P > 0.05). Conclusion The AMRIDL protocol offers comparable sensitivity for detecting malignant focal lesions, including HCC while significantly enhancing image quality and reducing scan time by approximately 50% compared to AMRI STD.

Background: We aimed to evaluate whether composite blood biomarkers including aldo-keto reductase family 1 member B10 (AKR1B10) and cytokeratin 18 (CK-18; a nonalcoholic steatohepatitis [NASH] marker) have clinically applicable performance for the diagnosis of NASH, advanced liver fibrosis, and high-risk NASH (NASH+significant fibrosis). Methods: A total of 116 subjects including healthy control subjects and patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD) were analyzed to assess composite blood-based and imaging-based biomarkers either singly or in combination. Results: A composite blood biomarker comprised of AKR1B10, CK-18, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) showed excellent performance for the diagnosis of, NASH, advanced fibrosis, and high-risk NASH, with area under the receiver operating characteristic curve values of 0.934 (95% confidence interval [CI], 0.888 to 0.981), 0.902 (95% CI, 0.832 to 0.971), and 0.918 (95% CI, 0.862 to 0.974), respectively. However, the performance of this blood composite biomarker was inferior to that various magnetic resonance (MR)-based composite biomarkers, such as proton density fat fraction/MR elastography- liver stiffness measurement (MRE-LSM)/ALT/AST for NASH, MRE-LSM+fibrosis-4 index for advanced fibrosis, and the known MR imaging-AST (MAST) score for high-risk NASH. Conclusion Our blood composite biomarker can be useful to distinguish progressive forms of NAFLD as an initial noninvasive test when MR-based tools are not available.

Purpose: The aim of this study was to demonstrate the effectiveness of a machine learning-based radiomics model for distinguishing tumor response and overall survival in patients with unresectable colorectal liver metastases (CRLM) treated with targeted biological therapy. Methods: We prospectively recruited 17 patients with unresectable liver metastases of colorectal cancer, who had been given targeted biological therapy as the first line of treatment. All patients underwent liver magnetic resonance imaging (MRI) three times up until 8 weeks after chemotherapy. We evaluated the diagnostic performance of machine learning-based radiomics model in tumor response of liver MRI compared with the guidelines for the Response Evaluation Criteria in Solid Tumors. Overall survival was evaluated using the Kaplan-Meier analysis and compared to the Cox proportional hazard ratios following univariate and multivariate analyses. Results: Performance measurement of the trained model through metrics showed the accuracy of the machine learning model to be 76.5%, and the area under the receiver operating characteristic curve was 0.857 (95% confidence interval [CI], 0.605–0.976; P < 0.001). For the patients classified as non-progressing or progressing by the radiomics model, the median overall survival was 17.5 months (95% CI, 12.8–22.2), and 14.8 months (95% CI, 14.2–15.4), respectively (P = 0.431, log-rank test). Conclusion Machine learning-based radiomics models could have the potential to predict tumor response in patients with unresectable CRLM treated with biologic therapy.

Purpose: To evaluate the outcomes of wavefront-optimized (WFO) and topography-guided (TG) transepithelial photorefractive keratectomy (transPRK) in the treatment of myopia and myopic astigmatism. Methods: Patients who underwent transPRK using the WaveLight EX500 excimer laser for the correction of myopia and myopic astigmatism between January 2022 and March 2023 were divided into groups of WFO transPRK (77 eyes of 36 patients) or TG transPRK (63 eyes of 31 patients) in this retrospective, observational cohort study. The preoperative and postoperative 3-month refractive and visual outcomes of the two groups were analyzed. Results: In both groups, the uncorrected distance visual acuity was 0.0 logarithm of the minimum angle of resolution or better in 95% of eyes 3 months postoperatively, and the mean manifest refraction spherical equivalent was within ±1.0 diopter in 90% of eyes. No significant differences were observed between the groups in terms of the uncorrected distance visual acuity or astigmatism. A significant induction of higher order aberrations (HOAs) was observed in both groups. However, the induction of total corneal HOAs (p = 0.014) and spherical aberrations (p < 0.001) was significantly lower in the TG group than that in the WFO group. Conclusions WFO and TG transPRK effectively improved the visual and refractive outcomes; however, the induction of total corneal HOAs and spherical aberration was lesser following the TG ablation.

The natural flavonoid macakurzin C (1) exhibited adiponectin biosynthesis-inducing activity during adipogenesis in human bone marrow mesenchymal stem cells and its molecular mechanism was directly associated with a pan-peroxisome proliferator-activated receptor (PPAR) modulator affecting all three PPAR subtypes α, γ, and δ. In this study, increases in adiponectin biosynthesisinducing activity by macakurzin C derivatives (2–7) were studied. The most potent adiponectin biosynthesis-inducing compound 6, macakurzin C 3,5-dimethylether, was elucidated as a dual PPARα/γ modulator. Compound 6 may exhibit the most potent activity because of the antagonistic relationship between PPARδ and PPARγ. Docking studies revealed that the O-methylation of macakurzin C to generate compound 6 significantly disrupted PPARδ binding. Compound 6 has therapeutic potential in hypoadiponectinemia-related metabolic diseases.

Adiponectin secretion-promoting compounds have therapeutic potentials in human metabolic diseases. Diallyl biphenyl-type neolignan compounds, magnolol, honokiol, and 4-O-methylhonokiol, from a Magnolia officinalis extract were screened as adiponectin-secretion promoting compounds in the adipogenic differentiation model of human bone marrow mesenchymal stem cells (hBM-MSCs). In a target identification study, magnolol, honokiol, and 4-O-methylhonokiol were elucidated as PPARα and PPARγ dual modulators. Diallyl biphenyl-type neolignans affected the transcription of lipid metabolism-associated genes in a different way compared to those of specific PPAR ligands. The diallyl biphenyl-type neolignan structure provides a novel pharmacophore of PPARα/γ dual modulators, which may have unique therapeutic potentials in diverse metabolic diseases.