Efficacy and safety of enavogliflozin vs. dapagliflozin as add-on therapy in patients with type 2 diabetes mellitus based on renal function: a pooled analysis of two randomized controlled trials.

BACKGROUND: We assessed the efficacy and safety of enavogliflozin (0.3 mg), a newly developed SGLT-2 inhibitor, in patients with type 2 diabetes mellitus based on kidney function via pooled analysis of two 24-week, randomized, double-blind phase III trials. METHODS: Data from 470 patients were included (enavogliflozin: 0.3 mg/day, n = 235; dapagliflozin: 10 mg/day, n = 235). The subjects were classified by mildly reduced (60 ≤ eGFR < 90 mL/min/1.73 m², n = 247) or normal eGFR (≥ 90 mL/min/1.73 m², n = 223). RESULTS: In the mildly reduced eGFR group, enavogliflozin significantly reduced the adjusted mean change of HbA1c and fasting plasma glucose levels at week 24 compared to dapagliflozin (- 0.94% vs. -0.77%, P = 0.0196). Enavogliflozin exhibited a more pronounced glucose-lowering effect by HbA1c when combined with dipeptidyl peptidase-4 inhibitors than that observed in their absence. Enavogliflozin showed potent blood glucose-lowering effects regardless of renal function. Conversely, dapagliflozin showed a significant decrease in the glucose-lowering efficacy as the renal function decreased. Enavogliflozin showed a higher urinary glucose excretion rate in both groups. The homeostatic model assessment showed that enavogliflozin markedly decreased the insulin resistance. The blood pressure, weight loss, or homeostasis model assessment of beta-cell function values did not differ significantly between enavogliflozin and dapagliflozin. Adverse events were similar between both drugs. CONCLUSIONS: The glucose-lowering efficacy of enavogliflozin is superior to that of dapagliflozin in patients with type 2 diabetes mellitus with mild renal function impairment; this is attributed to its potent urinary glucose excretion-promoting ability. The emergence of new and potent SGLT-2 inhibitors is considered an attractive option for patients with inadequate glycemic control and decreased renal function. TRIAL REGISTRATION: Not applicable (pooled analysis).

Screening in Patients With Dense Breasts: Comparison of Mammography, Artificial Intelligence, and Supplementary Ultrasound.

BACKGROUND. Screening mammography has decreased performance in patients with dense breasts. Supplementary screening ultrasound is a recommended option in such patients, although it has yielded mixed results in prior investigations. OBJECTIVE. The purpose of this article is to compare the performance characteristics of screening mammography alone, standalone artificial intelligence (AI), ultrasound alone, and mammography in combination with AI and/or ultrasound in patients with dense breasts. METHODS. This retrospective study included 1325 women (mean age, 53 years) with dense breasts who underwent both screening mammography and supplementary breast ultrasound within a 1-month interval from January 2017 to December 2017; prior mammography and prior ultrasound examinations were available for comparison in 91.2% and 91.8%, respectively. Mammography and ultrasound examinations were interpreted by one of 15 radiologists (five staff; 10 fellows); clinical reports were used for the present analysis. A commercial AI tool was used to retrospectively evaluate mammographic examinations for presence of cancer. Screening performances were compared among mammography, AI, ultrasound, and test combinations, using generalized estimating equations. Benign diagnoses required 24 months or longer of imaging stability. RESULTS. Twelve cancers (six invasive ductal carcinoma; six ductal carcinoma in situ) were diagnosed. Mammography, standalone AI, and ultrasound showed cancer detection rates (per 1000 patients) of 6.0, 6.8, and 6.0 (all p > .05); recall rates of 4.4%, 11.9%, and 9.2% (all p < .05); sensitivity of 66.7%, 75.0%, and 66.7% (all p > .05); specificity of 96.2%, 88.7%, and 91.3% (all p < .05); and accuracy of 95.9%, 88.5%, and 91.1% (all p < .05). Mammography with AI, mammography with ultrasound, and mammography with both ultrasound and AI showed cancer detection rates of 7.5, 9.1, and 9.1 (all p > .05); recall rates of 14.9, 11.7, and 21.4 (all p < .05); sensitivity of 83.3%, 100.0%, and 100.0% (all p > .05); specificity of 85.8%, 89.1%, and 79.4% (all p < .05); and accuracy of 85.7%, 89.2%, and 79.5% (all p < .05). CONCLUSION. Mammography with supplementary ultrasound showed higher accuracy, higher specificity, and lower recall rate in comparison with mammography with AI and in comparison with mammography with both ultrasound and AI. CLINICAL IMPACT. The findings fail to show benefit of AI with respect to screening mammography performed with supplementary breast ultrasound in patients with dense breasts.

Artificial intelligence assistance for women who had spot compression view: reducing recall rates for digital mammography.

BACKGROUND: Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views. PURPOSE: To explore whether the artificial intelligence (AI) algorithm for mammography can reduce false-positive recall in patients who undergo the spot compression view. MATERIAL AND METHODS: From January to December 2017, 236 breasts from 225 women who underwent the spot compression view due to focal asymmetry, mass, or architectural distortion on standard digital mammography were included. Three readers who were blinded to the study purpose, patient information, previous mammograms, following spot compression views, and any clinical or pathologic reports retrospectively reviewed 236 standard mammograms and determined the necessity of patient recall and the probability of malignancy per breast, first without and then with AI assistance. The performances of AI and the readers were evaluated with the recall rate, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. RESULTS: Among 236 examinations, 8 (3.4%) were cancers and 228 (96.6%) were benign. The recall rates of all three readers significantly decreased with AI assistance (P < 0.05). The reader-averaged recall rates significantly decreased with AI assistance regardless of breast composition (fatty breasts: 32.7% to 24.1%m P = 0.002; dense breasts: 33.6% to 21.2%, P < 0.001). The reader-averaged AUC increased with AI assistance and was comparable to that of standalone AI (0.835 vs. 0.895; P = 0.234). The reader-averaged specificity (71.2% to 79.8%, P < 0.001) and accuracy (71.3% to 79.7%, P < 0.001) significantly improved with AI assistance. CONCLUSION: AI assistance significantly reduced false-positive recall without compromising cancer detection in women with focal asymmetry, mass, or architectural distortion on standard digital mammography regardless of mammographic breast density.

Consistency of Artificial Intelligence (AI)-based Diagnostic Support Software in Short-term Digital Mammography Reimaging After Core Needle Biopsy.

To evaluate the consistency in the performance of Artificial Intelligence (AI)-based diagnostic support software in short-term digital mammography reimaging after core needle biopsy. Of 276 women who underwent short-term (<3 mo) serial digital mammograms followed by breast cancer surgery from Jan. to Dec. 2017, 550 breasts were included. All core needle biopsies for breast lesions were performed between serial exams. All mammography images were analyzed using a commercially available AI-based software providing an abnormality score (0-100). Demographic data for age, interval between serial exams, biopsy, and final diagnosis were compiled. Mammograms were reviewed for mammographic density and finding. Statistical analysis was performed to evaluate the distribution of variables according to biopsy and to test the interaction effects of variables with the difference in AI-based score according to biopsy. AI-based score of 550 exams (benign or normal in 263 and malignant in 287) showed significant difference between malignant and benign/normal exams (0.48 vs. 91.97 in first exam and 0.62 vs. 87.13 in second exam, P<0.0001). In comparison of serial exams, no significant difference was found in AI-based score. AI-based score difference between serial exams was significantly different according to biopsy performed or not (-0.25 vs. 0.07, P = 0.035). In linear regression analysis, there was no significant interaction effect of all clinical and mammographic characteristics with mammographic examinations performed after biopsy or not. The results from AI-based diagnostic support software for digital mammography was relatively consistent in short-term reimaging even after core needle biopsy.

US, Mammography, and Histopathologic Evaluation to Identify Low Nuclear Grade Ductal Carcinoma in Situ.

Background Low nuclear grade ductal carcinoma in situ (DCIS) identified at biopsy can be upgraded to intermediate to high nuclear grade DCIS at surgery. Methods that confirm low nuclear grade are needed to consider nonsurgical approaches for these patients. Purpose To develop a preoperative model to identify low nuclear grade DCIS and to evaluate factors associated with low nuclear grade DCIS at biopsy that was not upgraded to intermediate to high nuclear grade DCIS at surgery. Materials and Methods In this retrospective study, 470 women (median age, 50 years; interquartile range, 44-58 years) with 477 pure DCIS lesions at surgical histopathologic evaluation were included (January 2010 to December 2015). Patients were divided into the training set (n = 330) or validation set (n = 147) to develop a preoperative model to identify low nuclear grade DCIS. Features at US (mass, nonmass) and at mammography (morphologic characteristics, distribution of microcalcification) were reviewed. The upgrade rate of low nuclear grade DCIS was calculated, and multivariable regression was used to evaluate factors for associations with low nuclear grade DCIS that was not upgraded later. Results A preoperative model that included lesions manifesting as a mass at US without microcalcification and no comedonecrosis at biopsy was used to identify low nuclear grade DCIS, with a high area under the receiver operating characteristic curve of 0.97 (95% CI: 0.94, 1.00) in the validation set. The upgrade rate of low nuclear grade DCIS at biopsy was 38.8% (50 of 129). Ki-67 positivity (odds ratio, 0.04; 95% CI: 0.0003, 0.43; P = .005) was inversely associated with constant low nuclear grade DCIS. Conclusion The upgrade rate of low nuclear grade ductal carcinoma in situ (DCIS) at biopsy to intermediate to high nuclear grade DCIS at surgery occurred in more than a third of patients; low nuclear grade DCIS at final histopathologic evaluation could be identified if the mass was viewed at US without microcalcifications and had no comedonecrosis at histopathologic evaluation of biopsy. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Rahbar in this issue. An earlier incorrect version appeared online. This article was corrected on April 14, 2022.

Depiction of breast cancers on digital mammograms by artificial intelligence-based computer-assisted diagnosis according to cancer characteristics.

OBJECTIVE: To evaluate how breast cancers are depicted by artificial intelligence-based computer-assisted diagnosis (AI-CAD) according to clinical, radiological, and pathological factors. MATERIALS AND METHODS: From January 2017 to December 2017, 896 patients diagnosed with 930 breast cancers were enrolled in this retrospective study. Commercial AI-CAD was applied to digital mammograms and abnormality scores were obtained. We evaluated the abnormality score according to clinical, radiological, and pathological characteristics. False-negative results were defined by abnormality scores less than 10. RESULTS: The median abnormality score of 930 breasts was 87.4 (range 0-99). The false-negative rate of AI-CAD was 19.4% (180/930). Cancers with an abnormality score of more than 90 showed a high proportion of palpable lesions, BI-RADS 4c and 5 lesions, cancers presenting as mass with or without microcalcifications and invasive cancers compared with low-scored cancers (all p < 0.001). False-negative cancers were more likely to develop in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers and DCIS compared to detected cancers. CONCLUSION: Breast cancers depicted with high abnormality scores by AI-CAD are associated with higher BI-RADS category, invasive pathology, and higher cancer stage. KEY POINTS: • High-scored cancers by AI-CAD included a high proportion of BI-RADS 4c and 5 lesions, masses with or without microcalcifications, and cancers with invasive pathology. • Among invasive cancers, cancers with higher T and N stage and HER2-enriched subtype were depicted with higher abnormality scores by AI-CAD. • Cancers missed by AI-CAD tended to be in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers by radiologists.

Cancer yield and imaging features of probably benign calcifications at digital magnification view.

OBJECTIVES: To investigate the malignancy rate of probably benign calcifications assessed by digital magnification view and imaging and clinical features associated with malignancy. METHODS: This retrospective study included consecutive women with digital magnification views assessed as probably benign for calcifications without other associated mammographic findings from March 2009 to January 2014. Initial studies rendering a probably benign assessment were analyzed, with biopsy or 4-year imaging follow-up. Fisher's exact test and univariable logistic regression were performed. Cancer yields were calculated. RESULTS: A total of 458 lesions in 422 patients were finally included. The overall cancer yield was 2.2% (10 of 458, invasive cancer [n = 4] and DCIS [n = 6]). Calcification distribution (OR = 23.80, p = .041), calcification morphology (OR = 10.84, p = .005), increased calcifications (OR = 29.40, p = .001), and having a concurrent newly diagnosed breast cancer or high-risk lesion (OR = 10.24, p = .001) were associated with malignancy. Cancer yields did not significantly differ between grouped punctate calcifications vs. calcifications with other features (1.2% [2 of 162] vs. 2.7% [8 of 296], p = .506). The cancer yield was 1.6% (7 of 437) in women without newly diagnosed breast cancer or high-risk lesions. CONCLUSION: The cancer yield of probably benign calcifications assessed by digital magnification view was below the 2% threshold for grouped punctate calcifications and for women without newly diagnosed breast cancer or high-risk lesions. Calcification distribution, morphology, increase in calcifications, and the presence of newly diagnosed breast cancer/high-risk lesion were associated with malignancy. KEY POINTS: • Among 458 probably benign calcifications assessed by digital magnification view, the overall cancer yield was 2.2% (10 of 458). • The cancer yield was below the 2% threshold for grouped punctate calcifications (1.2%, 2 of 162) and in women without newly diagnosed breast cancer or high-risk lesions (1.6%, 7 of 437). • Calcification distribution, morphology, increase in calcifications, and the presence of newly diagnosed breast cancer/high-risk lesion were associated with malignancy (all p < .05).

Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment.

We evaluated and compared the mammographic density assessment of an artificial intelligence-based computer-assisted diagnosis (AI-CAD) program using inter-rater agreements between radiologists and an automated density assessment program. Between March and May 2020, 488 consecutive mammograms of 488 patients (56.2 ± 10.9 years) were collected from a single institution. We assigned four classes of mammographic density based on BI-RADS (Breast Imaging Reporting and Data System) using commercial AI-CAD (Lunit INSIGHT MMG), and compared inter-rater agreements between radiologists, AI-CAD, and another commercial automated density assessment program (Volpara®). The inter-rater agreement between AI-CAD and the reader consensus was 0.52 with a matched rate of 68.2% (333/488). The inter-rater agreement between Volpara® and the reader consensus was similar to AI-CAD at 0.50 with a matched rate of 62.7% (306/488). The inter-rater agreement between AI-CAD and Volpara® was 0.54 with a matched rate of 61.5% (300/488). In conclusion, density assessments by AI-CAD showed fair agreement with those of radiologists, similar to the agreement between the commercial automated density assessment program and radiologists.

Application of Artificial Intelligence Computer-Assisted Diagnosis Originally Developed for Thyroid Nodules to Breast Lesions on Ultrasound.

As thyroid and breast cancer have several US findings in common, we applied an artificial intelligence computer-assisted diagnosis (AI-CAD) software originally developed for thyroid nodules to breast lesions on ultrasound (US) and evaluated its diagnostic performance. From January 2017 to December 2017, 1042 breast lesions (mean size 20.2 ± 11.8 mm) of 1001 patients (mean age 45.9 ± 12.9 years) who underwent US-guided core-needle biopsy were included. An AI-CAD software that was previously trained and validated with thyroid nodules using the convolutional neural network was applied to breast nodules. There were 665 benign breast lesions (63.0%) and 391 breast cancers (37.0%). The area under the receiver operating characteristic curve (AUROC) of AI-CAD to differentiate breast lesions was 0.678 (95% confidence interval: 0.649, 0.707). After fine-tuning AI-CAD with 1084 separate breast lesions, the diagnostic performance of AI-CAD markedly improved (AUC 0.841). This was significantly higher than that of radiologists when the cutoff category was BI-RADS 4a (AUC 0.621, P < 0.001), but lower when the cutoff category was BI-RADS 4b (AUC 0.908, P < 0.001). When applied to breast lesions, the diagnostic performance of an AI-CAD software that had been developed for differentiating malignant and benign thyroid nodules was not bad. However, an organ-specific approach guarantees better diagnostic performance despite the similar US features of thyroid and breast malignancies.

Chronological Trends of Breast Ductal Carcinoma In Situ: Clinical, Radiologic, and Pathologic Perspectives.

BACKGROUND: Because no prior studies have evaluated the chronological trends of ductal carcinoma in situ (DCIS) despite the increasing number of surgeries performed for DCIS, this study analyzed how the clinical, radiologic, and pathologic characteristics of DCIS changed during a 10-year period. METHODS: Of 7123 patients who underwent primary breast cancer surgery at a single institution from 2006 to 2015, 792 patients with pure DCIS were included in this study. The chronological trends of age, symptoms, method for detecting either mammography or ultrasonography, tumor size, nuclear grade, comedonecrosis, and molecular markers were calculated using Poisson regression for all patients and asymptomatic patients. RESULTS: During 10 years, DCIS surgery rates significantly increased (p < 0.001). Despite the high percentage of DCIS detected on mammography, the detection rate for DCIS by mammography significantly decreased (97.3% in 2006 to 67.6% in 2015; p = 0.025), whereas the detection rate by ultrasound significantly increased (2.7% to 31.0%; p < 0.001). Conservation surgery rates (odds ratio [OR], 1.058), low-to-intermediate nuclear grade rates (OR, 1.069), and the absence of comedonecrosis (OR, 1.104) significantly increased over time (all p < 0.05). Estrogen receptor (ER) negativity (OR, 0.935) and human epidermal growth factor receptor 2 (HER2) positivity rates (OR, 0.953) significantly decreased (all p < 0.05). The same trends were observed for the 613 asymptomatic patients. CONCLUSION: The rate of DCIS detected on ultrasound only significantly increased during 10 years. Low-to-intermediate nuclear grade rates significantly increased, whereas ER negativity and HER2 positivity rates significantly decreased during the same period. These findings suggest that DCIS detected on screening ultrasound is less aggressive than DCIS detected on mammography.

Application of artificial intelligence-based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms.

OBJECTIVE: To compare the diagnostic agreement and performances of synthetic and conventional mammograms when artificial intelligence-based computer-assisted diagnosis (AI-CAD) is applied. MATERIAL AND METHOD: From January 2017 to April 2017, 192 patients (mean age 53.7 ± 11.7 years) diagnosed with 203 breast cancers were enrolled in this retrospective study. All patients underwent digital breast tomosynthesis (DBT) with digital mammograms (DM) simultaneously. Commercial AI-CAD was applied to the reconstructed synthetic mammograms (SM) from DBT and DM respectively and abnormality scores were calculated. We compared the median abnormality scores between DM and SM with the Wilcoxon signed-rank test and used the Bland-Altman analysis to evaluate agreements between the two mammograms and to investigate clinicopathological factors which might affect agreement. Diagnostic performances were compared using an area under the receiver operating characteristic curve (AUC). RESULT: The abnormality scores showed a mean difference (bias) of - 3.26 (95% limits of agreement: - 32.69, 26.18) between the two mammograms by the Bland-Altman analysis. The concordance correlation coefficient was 0.934 (95% CI: 0.92, 0.946), suggesting high reproducibility. SM showed higher abnormality scores in cancer with distortion and occult findings, T1 and N0 cancer, and luminal type cancer than DM (all p ≤ 0.001). Diagnostic performance did not differ between the mammograms (AUC 0.945 for conventional mammograms, 0.938 for synthetic mammograms, p = 0.499). CONCLUSION: AI-CAD can also work well on synthetic mammograms, showing good agreement and comparable diagnostic performance compared to its application to DM. KEY POINTS: • AI-CAD which was developed based on imaging findings of digital mammograms can also be applied to synthetic mammograms. • AI-CAD showed good agreement and similar diagnostic performance when applied to both synthetic and digital mammograms. • With AI-CAD, synthetic mammograms showed relatively higher abnormality scores in cancer with distortion and occult findings, T1 and N0 cancer, and luminal type cancer than digital mammograms.

BI-RADS category 3, 4, and 5 lesions identified at preoperative breast MRI in patients with breast cancer: implications for management.

OBJECTIVES: To investigate outcomes and retrospectively evaluate characteristics of additional lesions initially assessed as BI-RADS category 3, 4, and 5 at preoperative MRI to determine appropriate follow-up management. METHODS: We retrospectively reviewed 429 lesions other than primary cancer initially assessed as BI-RADS category 3, 4, and 5 at preoperative MRI in 391 patients with breast cancer from March 2012 to December 2013. We investigated their malignancy rate and outcome according to BI-RADS category assessments. We also analyzed clinical and imaging characteristics of each lesion. Pathological results and imaging follow-up of at least 2 years were used as reference standards. RESULTS: Of 429 lesions in 391 patients (mean 48.1 years ± 9.4), the malignancy rate of BI-RADS 3, 4, and 5 lesions was 1.4% (3/213), 17.8% (38/214), and 50% (1/2), respectively. Of BI-RADS 3 lesions or BI-RADS 4 or 5 lesions that were followed up after benign-concordant biopsy (n = 114), two contralateral masses (2/306, 0.7%) were diagnosed as malignancy at 13.3 and 33.2 months after initial detection, within a median follow-up of 63.3 months. None of the NME or foci or lesions followed up after benign-concordant biopsy had a delayed diagnosis of malignancy. Of the 391 patients, 97.4% (381/391) received at least one type of adjuvant therapy. CONCLUSION: The incidence of delayed cancer diagnosis among additionally detected lesions other than primary cancer is very low and short-term follow-up is unnecessary. Contralateral masses which were not confirmed by biopsy may need annual follow-up. KEY POINTS: • 1.4% (3/213) of BI-RADS 3 lesions were malignant including 2 delayed diagnoses after 13.2 months and 33.2 months, and 17.8% (38/214) of BI-RADS 4 lesions and 50% (1/2) of BI-RADS 5 lesions were malignant. • The incidence of delayed diagnosis from additional MRI-detected lesions was very low (0.7%, 2/306) during follow-up, which were all T1N0 contralateral cancer. • Annual follow-up might be adequate for preoperative MRI-detected BI-RADS 3 lesions and BI-RADS 4 lesions followed up after benign-concordant biopsy.

Guideline Implementation on Fine-Needle Aspiration for Thyroid Nodules: Focusing on Micronodules.

OBJECTIVE: We investigated patients who were referred to our institution after fine-needle aspiration (FNA) was performed at outside clinics to evaluate how many nodules satisfied the FNA indications of the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) and compare that to the number of thyroid nodules that satisfy the FNA indications of the American College of Radiology (ACR)-TIRADS and American Thyroid Association (ATA) guidelines. METHODS: Between January 2018 and December 2018, 2,628 patients were included in our study. The included patients were those referred for thyroid surgery after having a suspicious thyroid nodule. We retrospectively applied the three guidelines to each thyroid nodule and determined whether each nodule satisfied the FNA indications. We compared the proportion of nodules satisfying the FNA indications of each guideline using a generalized linear model and generalized estimating equation. RESULTS: The median size of the 2,628 thyroid nodules was 0.9 cm (range, 0.2 to 9.5 cm). We found that FNA was not indicated for 54.1%, 47.7%, and 19.1% of nodules and 87.3%, 99.0%, and 97.8% among them were micronodules (<1 cm) according to the ACR-TIRADS, ATA guideline, and K-TIRADS, respectively. The proportion of micronodules which satisfied the FNA indications was significantly higher for the K-TIRADS (65.1%) compared to the ACR TIRADS (12.1%) and ATA guideline (12.1%) (P<.001). CONCLUSION: Among patients referred for thyroid surgery to our institutions, about 35% of the micronodules underwent FNA despite not being appropriate for indications by the K-TIRADS. Systematic training for physicians as well as modifications to increase the sensitivity of the guideline may be needed to reduce the overdiagnosis of thyroid cancers, especially for micronodules.

Phytochemicals against TNFα-Mediated Neuroinflammatory Diseases.

Tumor necrosis factor-alpha (TNF-α) is a well-known pro-inflammatory cytokine responsible for the modulation of the immune system. TNF-α plays a critical role in almost every type of inflammatory disorder, including central nervous system (CNS) diseases. Although TNF-α is a well-studied component of inflammatory responses, its functioning in diverse cell types is still unclear. TNF-α functions through its two main receptors: tumor necrosis factor receptor 1 and 2 (TNFR1, TNFR2), also known as p55 and p75, respectively. Normally, the functions of soluble TNF-α-induced TNFR1 activation are reported to be pro-inflammatory and apoptotic. While TNF-α mediated TNFR2 activation has a dual role. Several synthetic drugs used as inhibitors of TNF-α for diverse inflammatory diseases possess serious adverse effects, which make patients and researchers turn their focus toward natural medicines, phytochemicals in particular. Phytochemicals targeting TNF-α can significantly improve disease conditions involving TNF-α with fewer side effects. Here, we reviewed known TNF-α inhibitors, as well as lately studied phytochemicals, with a role in inhibiting TNF-α itself, and TNF-α-mediated signaling in inflammatory diseases focusing mainly on CNS disorders.

Extracellular contrast agent-enhanced MRI: 15-min delayed phase may improve the diagnostic performance for hepatocellular carcinoma in patients with chronic liver disease.

OBJECTIVES: To determine the value of a 15-min delayed phase in extracellular contrast agent (ECA)-enhanced magnetic resonance imaging (MRI) for evaluation of hepatocellular carcinoma (HCC) in patients with chronic liver disease. METHODS: Between 2014 and 2015, 103 patients with chronic liver disease underwent ECA-enhanced MRI; 133 lesions consisting of 107 HCCs, 23 benign lesions and three non-HCC malignancies were identified with pathological or clinical diagnosis. MRI images were reviewed by two abdominal radiologists independently using the European Association for the Study of the Liver (EASL) and Liver Imaging Reporting and Data System (LI-RADS) criteria. Imaging features observed in the 15-min delayed phase were recorded. RESULTS: Of 107 HCCs, three or four additional HCCs were diagnosed according to the EASL criteria by adding the 15-min delayed phase, increasing sensitivity (Reviewer 1, from 69.2-72.0 % [P = 0.072]; Reviewer 2, from 75.7-79.4 % [P = 0.041]). Reviewers 1 and 2 upgraded one and four HCCs from LR-4 to LR-5 based on the LI-RADS, respectively. Among 23 benign lesions, no additional findings were observed in the 15-min delayed phase. CONCLUSIONS: Including the 15-min delayed phase in ECA-enhanced MRI may improve the diagnostic performance for HCC in patients with chronic liver disease. KEY POINTS: • Additional acquisition of 15-min delayed phase (FDP) requires approximately 20 s. • About 5 % of HCCs show washout or capsule appearance only in FDP. • Including FDP improves the sensitivity of extracellular contrast agent-enhanced MRI for HCC. • These results are applicable only to patients with chronic liver disease.

Accuracy of computed tomography for selecting the revascularization method based on SYNTAX score II.

OBJECTIVES: The application of SYNTAX score II based on coronary CT angiography (CCTA) for selecting further treatment options has not been studied. This study aimed to investigate the diagnostic performance of CCTA combined with SYNTAX score II for selecting the revascularization method compared with invasive coronary angiography (ICA) based on 2014 European Society of Cardiology (ESC)/European Association for Cardio-Thoracic Surgery (EACTS) guidelines. METHODS: From January-May 2011, 160 patients who underwent both CCTA and ICA within 30 interval days were included. The diagnostic performance of CCTA, CCTA plus CT-SYNTAX score I and CT-SYNTAX score II was analysed using ICA counterparts as references. RESULTS: Overall sensitivity, specificity, positive predictive value, negative predictive value and accuracy of CCTA plus CT-SYNTAX I for selecting coronary artery bypass grafting (CABG) candidates using ICA plus ICA-SYNTAX I as reference, were 70.6 %, 95.8 %, 66.7 %, 96.5 % and 93.1 %, respectively. The diagnostic performance of CCTA plus CT-SYNTAX II showed improvement with values of 83.3 %, 97.3 %, 71.4 %, 98.6 % and 96.3 %, respectively, using ICA plus ICA-SYNTAX II as reference. CONCLUSIONS: CCTA combined with CT-SYNTAX score II is an accurate method for selecting CABG surgery candidates compared with ICA-SYNTAX score II. KEY POINTS: • SYNTAX plus CCTA can be highly specific for selecting the revascularization method. • SYNTAX II was complemented by including clinical considerations to SYNTAX I. • CCTA plus CT-SYNTAX II is an accurate method for selecting CABG candidates.

Skin Barrier Restoration and Moisturization Using Horse Oil-Loaded Dissolving Microneedle Patches.

BACKGROUND: Horse oil (HO) has skin barrier restoration and skin-moisturizing effects. Although cream formulations have been used widely and safely, their limited penetration through the stratum corneum is a major obstacle to maximizing the cosmetic efficacy of HO. Therefore, we aimed to encapsulate HO in a cosmetic dissolving microneedle (DMN) for efficient transdermal delivery. METHODS: To overcome these limitations of skin permeation, HO-loaded DMN (HO-DMN) patches were developed and evaluated for their efficacy and safety using in vitro and clinical studies. RESULTS: Despite the lipophilic nature of HO, the HO-DMN patches had a sharp shape and uniform array, with an average length and tip diameter of 388.36 ± 16.73 and 38.54 ± 5.29 µm, respectively. The mechanical strength of the HO-DMN patches was sufficient (fracture force of 0.29 ± 0.01 N), and they could successfully penetrate pig skin. During the 4-week clinical evaluation, HO-DMN patches caused significant improvements in skin and dermal density, skin elasticity, and moisturization. Additionally, a brief safety assessment showed that the HO-DMN patches induced negligible adverse events. CONCLUSION: The HO-DMNs are efficient, safe, and convenient for wide use in cosmetic applications for skin barrier restoration and moisturization.

Diagnostic performance with and without artificial intelligence assistance in real-world screening mammography.

Purpose: To evaluate artificial intelligence-based computer-aided diagnosis (AI-CAD) for screening mammography, we analyzed the diagnostic performance of radiologists by providing and withholding AI-CAD results alternatively every month. Methods: This retrospective study was approved by the institutional review board with a waiver for informed consent. Between August 2020 and May 2022, 1819 consecutive women (mean age 50.8 ± 9.4 years) with 2061 screening mammography and ultrasound performed on the same day in a single institution were included. Radiologists interpreted screening mammography in clinical practice with AI-CAD results being provided or withheld alternatively by month. The AI-CAD results were retrospectively obtained for analysis even when withheld from radiologists. The diagnostic performances of radiologists and stand-alone AI-CAD were compared and the performances of radiologists with and without AI-CAD assistance were also compared by cancer detection rate, recall rate, sensitivity, specificity, accuracy and area under the receiver-operating-characteristics curve (AUC). Results: Twenty-nine breast cancer patients and 1790 women without cancers were included. Diagnostic performances of the radiologists did not significantly differ with and without AI-CAD assistance. Radiologists with AI-CAD assistance showed the same sensitivity (76.5%) and similar specificity (92.3% vs 93.8%), AUC (0.844 vs 0.851), and recall rates (8.8% vs. 7.4%) compared to standalone AI-CAD. Radiologists without AI-CAD assistance showed lower specificity (91.9% vs 94.6%) and accuracy (91.5% vs 94.1%) and higher recall rates (8.6% vs 5.9%, all p < 0.05) compared to stand-alone AI-CAD. Conclusion: Radiologists showed no significant difference in diagnostic performance when both screening mammography and ultrasound were performed with or without AI-CAD assistance for mammography. However, without AI-CAD assistance, radiologists showed lower specificity and accuracy and higher recall rates compared to stand-alone AI-CAD.