High Diagnostic Performance Research Articles

Flexed elbow, abducted shoulder, forearm supinated (FABS) reconstruction from three-dimensional elbow MRI: diagnostic performance assessment in biceps head anatomy and pathology

To determine inter-reader analysis and diagnostic performance on digitally reconstructed virtual flexed, abducted, supinated (FABS) imaging from three-dimensional (3D) isotropic elbow magnetic resonance imaging (MRI). Six musculoskeletal radiologists independently evaluated elbow MRI images with virtual FABS reconstructions, blinded to clinical findings and final diagnoses. Each radiologist recorded a binary result as to whether the tendon was intact and if both heads were visible, along with a categorical value to the type of tear and extent of retraction in centimetres where applicable. Kappa and interclass correlation (ICC) were reported with 95% confidence intervals. Areas under the receiver operating curve (AUC) were reported. FABS reconstructions were obtained successfully in all 48 cases. With respect to tendon intactness, visibility of both heads, and type of tear, the Kappa values were 0.66 (0.53-0.78), 0.24 (0.12-0.37), and 0.55 (0.43-0.66), respectively. For the extent of retraction, the ICC was 0.85 (0.79-0.91) when including the tendons with and without retraction and 0.78 (0.61-0.91) when only including tendons with retraction. For tear versus no tear, AUC values were 0.82 (0.74-0.89) to 0.96 (0.91-1.01). Digital reconstruction of FABS positioning is feasible and allows good assessment of individual tendon head tears and retraction with high diagnostic performance.

Value of high frame rate contrast enhanced ultrasound in gallbladder wall thickening in non-acute setting

BackgroundUltrasound (US) has been widely used in screening and differential diagnosis of gallbladder wall thickening (GWT). However, the sensitivity and specificity for diagnosing wall-thickening type gallbladder cancer are limited, leading to delayed treatment or overtreatment. We aim to explore the value of high frame rate contrast enhanced ultrasound (H-CEUS) in distinguishing wall-thickening type gallbladder cancer (malignant) from GWT mimicking malignancy (benign).MethodsThis retrospective study enrolled consecutive patients with non-acute GWT who underwent US and H-CEUS examination before cholecystectomy. Clinical information, US image and H-CEUS image characteristics between malignant and benign GWT were compared. The independent risk factors for malignant GWT on H-CEUS images were selected by multivariate logistic regression analysis. The diagnostic performance of H-CEUS in determining malignant GWT was compared with that of the gallbladder reporting and data system (GB-RADS) score.ResultsForty-six patients included 30 benign GWTs and 16 malignant GWTs. Only mural layering and interface with liver on US images were significantly different between malignant and benign GWT (P < 0.05). Differences in enhancement direction, vascular morphology, serous layer continuity, wash-out time and mural layering in the venous phase of GWT on H-CEUS images were significant between malignant and benign GWT (P < 0.05). The sensitivity, specificity and accuracy of H-CEUS based on enhancement direction, vascular morphology and wash-out time in the diagnosis of malignant GWT were 93.75%, 90.00%, and 91.30%, respectively. However, the sensitivity, specificity and accuracy of the GB-RADS score were only 68.75%, 73.33% and 71.74%, respectively. The area under ROC curve (AUC) of H-CEUS was significantly higher than that of the GB-RADS score (AUC = 0.965 vs. 0.756).ConclusionsH-CEUS can accurately detect enhancement direction, vascular morphology and wash-out time of GWT, with a higher diagnostic performance than the GB-RADS score in determining wall-thickening type gallbladder cancer. This study provides a novel imaging means with high accuracy for the diagnosis of wall-thickening type gallbladder cancer, thus may be better avoiding delayed treatment or overtreatment.

Validation of Diagnostic Accuracy and Disease Severity Correlation of Chest Computed Tomography Severity Scores in Patients with COVID-19 Pneumonia.

The aim of our study was to establish and compare the diagnostic accuracy and clinical applicability of published chest CT severity scoring systems used for COVID-19 pneumonia assessment and to propose the most efficient CT scoring system with the highest diagnostic performance and the most accurate prediction of disease severity. This retrospective study included 218 patients with PCR-confirmed SARS-CoV-2 infection and chest CT. Two radiologists blindly evaluated CT scans and calculated nine different CT severity scores (CT SSs). The diagnostic validity of CT SSs was tested by ROC analysis. Interobserver agreement was excellent (intraclass correlation coefficient: 0.982-0.995). The predominance of either consolidations or a combination of consolidations and ground-glass opacities (GGOs) was a predictor of more severe disease (both p < 0.005), while GGO prevalence alone was not. Correlation between all CT SSs was high, ranging from 0.848 to 0.971. CT SS 30 had the highest diagnostic accuracy (AUC = 0.805) in discriminating mild from severe COVID-19 disease compared to all the other proposed scoring systems (AUC range 0.755-0.788). In conclusion, CT SS 30 achieved the highest diagnostic accuracy in predicting the severity of COVID-19 disease while maintaining simplicity, reproducibility, and applicability in complex clinical settings.

Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.

Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.

Diagnostic Performance and Accuracy of Ovarian-Adnexal Reporting and Data System Ultrasound Risk Score- A Prospective Cohort Study

Introduction: Adnexal malignancies are a leading cause of mortality in gynaecologic cancers, posing significant diagnostic challenges. Ultrasonography (USG) is the primary imaging modality, and the development of the Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) aims to provide standardised interpretations to assist in management decisions. Aim: To evaluate the effectiveness of using the O-RADS classification system in diagnosing and characterising adnexal lesions. Materials and Methods: This single-centre prospective cohort study was conducted at the Department of Radiodiagnosis, Sri Guru Ram Das Charitable Hospital attached to Sri Guru Ram Das Institute of Medical Sciences and Research, Amritsar, Punjab, India from January 1, 2023, to March 31, 2024. The study included 50 patients with adnexal mass lesions from all age groups. All participants underwent transabdominal/ transvaginal ultrasonographic examinations, with Colour Doppler assessment included. The O-RADS classification system was employed to assess and characterise the adnexal mass lesions. The diagnostic accuracy of the O-RADS US risk score was determined. Statistical analysis was conducted using Statistical Packages for Social Sciences (SPSS) version 24.0 to calculate the sensitivity, specificity, accuracy, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) for O-RADS US results in diagnosing ovarian masses. Results: The patients’ ages ranged from 18 to 83 years, with a mean age of 41.68±15.51 years. The cohort included 35 premenopausal and 15 postmenopausal individuals. The most common indication for USG was pelvic pain, observed in 15 out of 50 patients, representing 30% of the study cohort. The O-RADS sensitivity for the detection of ovarian cancer was 88.89%, with a specificity of 93.75%, NPV of 93.75%, and PPV of 88.89%, with an accuracy of 92%. These results were achieved by keeping O-RADS score of 4 as the cutoff for malignancy. Conclusion: The O-RADS US is effective in the risk stratification of ovarian lesions and has a high diagnostic performance. Implementing these guidelines in clinical practice could be beneficial for managing adnexal lesions.

Comparison the accuracy of thyroid sono-elastography vs. ultrasound-guided fine needle aspiration cytology with thyroid malignancy diagnosis histopathology.

Objective. The intend of the present study was to assess the diagnostic performance of strain elastography in investigating the thyroid nodule malignancy taking the surgical biopsy as a gold standard reference test. Methods. The study included 120 patients with 123 thyroid nodules, of which 67 had total thyroidectomy. The American College of Radiology Thyroid Imaging Reporting and Data Systems (ACR-TIRADS) were evaluated for all nodules. All suspicious nodules were referred for a fine needle aspiration cytology (FNAC) if they fulfilled the required size. Strain elastography was performed for each suspicious nodule. Ultrasound-guided FNAC was performed for all suspicious nodules. Total thyroidectomy was performed in those whom the suspicious nodules were proven by FNAC. Results. Strain ratio had a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of 84%, 81%, 95%, 85%, and 84%, respectively, with a cut point 1.96. Elasticity score had a sensitivity, specificity, PPV, NPV, and diagnostic accuracy of 100%, 80%, 95%, 85% and 87%, respectively, with a cut point 0.96. The elasticity score had a statistically significantly odds ratio for detecting the benignity 3.9 C. I (1.6-9.3). Conclusion. Strain elastography has a high diagnostic performance in detecting the malignant as well as benign nodules, thus it can limit the rate of unneeded FNAC or surgery especially among B3 and B4 groups with indeterminate cytology.

Layer-specific fast strain-encoded cardiac magnetic resonance imaging aids in the identification and discrimination of acute myocardial injury: a prospective proof-of-concept study

Acute myocardial injury is a common diagnosis in the emergency department and differential diagnoses are numerous. Cardiac magnetic resonance (CMR) strain sequences, such as fast strain ENCoded (fSENC), are early predictors of myocardial function loss. This study assessed the potential diagnostic and prognostic benefits of a layer-specific approach. For this prospective study, patients in the emergency department fulfilling rule-in criteria for non-ST-elevation myocardial infarction (NSTEMI) received an ultra-fast fSENC CMR. Volunteers without cardiac diseases (controls) were recruited for comparison. Measurements were performed in a single heartbeat acquisition to measure global longitudinal strain (GLS) and segmental longitudinal strain and dysfunctional segments. The GLS was measured in two layers and a difference (GLSdifference =GLSepicardial -GLSendocardial) was calculated. The performance of those strain features was compared to standard care (physical examination, cardiac biomarkers, electrocardiogram). According to the final diagnosis after discharge, patients were divided into groups and followed up for 2years. A total of 114 participants, including 50 controls, were included. The 64 patients (51 male) were divided into a NSTEMI (25), myocarditis (16), and other myocardial injury group (23). GLS served as a potent predictor of myocardial injury (area under the curve (AUC) 91.8%). The GLSdifference provided an excellent diagnostic performance to identify a NSTEMI (AUC 83.2%), further improved by including dysfunctional segments (AUC 87.5%, p=0.01). An optimal test was achieved by adding fSENC to standard care (AUC 95.5%, sensitivity 96.0%, specificity 86.5%, p=0.03). No death occurred in 2years for patients with normal GLS and ≤5 dysfunctional segments, while three patients died that showed abnormal GLS or >5 dysfunctional segments. Layer-specific strain is a potential new marker with high diagnostic performance in the identification and differentiation of acute myocardial injuries.

MR Elastography: Practical Questions, From the AJR Special Series on Imaging of Fibrosis.

MR elastography (MRE), first described in 1995 and FDA-cleared in 2009, has emerged as an important tool for noninvasively detecting and staging liver fibrosis in patients with known or suspected chronic liver disease. This review focuses on a series of practical questions about the clinical use of MRE. Most head-to-head comparison studies with other laboratory and imaging-based tests have concluded that MRE has the highest diagnostic performance among tests for staging liver fibrosis. Limitations in the accuracy of biopsy as a standard of truth in staging liver fibrosis are increasingly being recognized. MRE-based measurements show promise as quantitative surrogates of disease severity and predictors of important clinical outcomes. The appropriate role of MRE in the management of patients with chronic liver disease is being actively incorporated into recognized clinical guidelines. Growing evidence shows that MRI measurement of elevated liver fat is the most important single biomarker for detecting nonalcoholic steatohepatitis (NASH) and that MRE-based liver stiffness is the most important single biomarker for detecting at-risk NASH (i.e., NASH with stage ≥ F2 fibrosis). Advances in MRE technology are offering higher precision and new biomarkers, which have potential to allow independent assessment of inflammation and other histologic processes in addition to fibrosis.

Dynamic contrast-enhanced mammography and breast MRI in the diagnosis of breast cancer and detection of tumor size.

The aim of this study is to evaluate the performance of contrast-enhanced mammography (CEM) and dynamic breast MRI techniques for diagnosing breast lesions, assess the diagnostic accuracy of CEM's using histopathological findings, and compare lesion size measurements obtained from both methods with pathological size. This prospective study included 120 lesions, of which 70 were malignant, in 104 patients who underwent CEM and MRI within a week. Two radiologists independently evaluated the MR and CEM images in separate sessions, using the BI-RADS classification system. Additionally, the maximum sizes of lesion were measured. Diagnostic accuracy parameters and the receiver operating characteristics (ROC) curves were constructed for the two modalities. The correlation between the maximum diameter of breast lesions observed in MRI, CEM, and pathology was analyzed. The overall diagnostic values for MRI were as follows: sensitivity 97.1%, specificity 60%, positive predictive value (PPV) 77.3%, negative predictive value (NPV) 93.8%, and accuracy 81.7%. Correspondingly, for CEM, the sensitivity, accuracy, specificity, PPV, and NPV were 97.14%, 81.67%, 60%, 77.27%, and 93.75%, respectively. The ROC analysis of CEM revealed an area under the curve (AUC) of 0.907 for observer 1 and 0.857 for observer 2, whereas MRI exhibited an AUC of 0.910 for observer 1 and 0.914 for observer 2. Notably, CEM showed the highest correlation with pathological lesion size (r = 0.660 for observer 1 and r = 0.693 for observer 2, p < 0.001 for both). CEM can be used with high sensitivity and similar diagnostic performance comparable to MRI for diagnosing breast cancer. CEM proves to be a successful diagnostic method for precisely determining tumor size.

Main Coronary Flow Calculation With the Assistance of Physiological Side Branch Flow.

Fractional flow reserve (FFR) derived from coronary angiography, referred to as ICA-FFR, is a less-invasive alternative for invasive FFR measurement based on computational fluid dynamics. Blood flow into side branches influences the accuracy of ICA-FFR. However, properly compensating for side branch flow in ICAFFR analysis is challenging. In this study, we proposed a physiological side branch flow model to comprehensively compensate side branch flow for ICA-FFR analysis with no need for reconstructing side branch geometry. the physiological side branch flow model employed a reduced-order model to calculate the pressure distribution in vessel segments. The main coronary artery (without side branches) was delineated and divided based on bifurcation nodes. The model compensates for flow to invisible side branches within each segment and flow to visible side branches at each bifurcation node. Lastly, ICA-FFR based on physiological side branch flow model (ICA-FFRPSBF) was calculated from a single angiographic view. Functional stenosis is defined by FFR ≤ 0.80. Our study involved 223 vessels from 172 patients. Using invasive FFR as a reference, the Pearson correlation coefficient of ICAFFRPSBF was 0.93. ICA-FFRPSBF showed a high AUC (AUC = 0.96) and accuracy (91.9%) in predicting functional stenosis. The proposed model accurately compensates for flow to side branches without their geometry in ICA-FFR analysis. ICA-FFR analysis exhibits high feasibility and diagnostic performance in identifying functional stenosis.

Computed tomographic evaluation of portal vein indices in cats with the extrahepatic portosystemic shunts.

The portal vein to aorta (PV/Ao) ratio is used to assess the clinical significance of extrahepatic portosystemic shunt (EHPSS). Previous studies using computed tomography (CT) were conducted in dogs but not in cats. This study aimed to establish normal reference values for PV indices (PV/Ao ratio and PV diameter) in cats and determine the usefulness of these for predicting symptomatic EHPSS. This study included 95 dogs and 114 cats that underwent abdominal CT. The canine normal (CN) group included dogs without EHPSS. The cats were classified into feline normal (FN, 88/114), feline asymptomatic (FA, 16/114), and feline symptomatic (FS, 10/114) groups. The PV and Ao diameters were measured in axial cross-sections. The group FN had a higher PV/Ao ratio than the group CN (p < 0.001). Within the feline groups, the PV indices were in the order FN > FA > FS (both p < 0.001). The mean PV diameter and PV/Ao ratio for group FN were 5.23 ± 0.77 mm and 1.46 ± 0.19, respectively. The cutoff values between groups FN and FS were 4.115 mm for PV diameter (sensitivity, 100%; specificity, 97.7%) and 1.170 for PV/Ao ratio (90%, 92.1%). The cutoff values between group FA and FS were 3.835 mm (90%, 93.8%) and 1.010 (70%, 100%), respectively. The results demonstrated significant differences in PV indices between dogs and cats. In cats, the PV/Ao ratio demonstrated high diagnostic performance for symptomatic EHPSS. The PV diameter also performed well, in contrast to dogs.

Serum Aldo-Keto Reductase Family 1 Member B10 (AKR1B10) as a Potential Biomarker for Diagnosis of Hepatocellular Carcinoma.

To evaluate the diagnostic performance of aldo-keto reductase family 1 member B10 (AKR1B10) in a Beijing cohort with hepatocellular carcinoma (HCC). This study included 521 subjects who visited Peking Union Medical College Hospital from June 2017 to May 2023, including 109 cases of HCC, 165 cases of healthy controls, 106 cases of benign liver diseases, and 141 cases of other cancers. Serum AKR1B10 levels were measured and compared across various groups. Diagnostic performances of serum AKR1B10 and other tumor markers were assessed using receiver operator characteristic (ROC) curves. In addition, a subset of HCC patients who underwent surgical resection were recruited for clinical follow-up study. We found that serum AKR1B10 expression was higher in patients with HCC relative to other control groups. The association between serum AKR1B10 and clinical features of HCC was not observed. Serum AKR1B10 showed a high diagnostic performance for HCC, and when combined with AFP, the diagnostic effectiveness was significantly improved. Specifically, serum AKR1B10 showed superior diagnostic effectiveness for AFP-negative HCC. The clinical follow-up study indicated a gradual decrease in serum AKR1B10 after surgery. Our study demonstrated that serum AKR1B10 is a promising biomarker for HCC, and when used in combination with AFP can significantly improve the detection rate of HCC.

Comparative Analysis of AI-SONICTM Thyroid System and Six Thyroid Risk Stratification Guidelines in Papillary Thyroid Cancer: A Retrospective Cohort Study.

The study aimed to compare the diagnostic performance of AI-SONICTM Thyroid System (AI-SONICTM) with six thyroid nodule ultrasound risk stratification systems, as well as the interobserver agreement among different-year ultrasound examiners using the same diagnostic approach. This retrospective study included patients who underwent thyroid ultrasound examination and surgery between 2010 and 2022. Three ultrasound examiners with 2, 5, and 10 years of experience, respectively, used AI-SONICTM and six guidelines to risk-stratify the nodules. The diagnostic performance and interobserver agreement were assessed. A total of 370 thyroid nodules were included, including 195 papillary thyroid carcinomas (PTC) and 175 benign nodules. For physicians of varying seniority from low to high, AI-SONICTM had a moderate sensitivities of 82.56%, 83.08%, 84.62%, respectively, while AACE/ACE/AME had the highest diagnostic sensitivities (96.41%, 95.38%, 96.41%, respectively); And relatively higher specificities were 85.14%, 85.71%, 85.71% for KSThR, while moderate specificities with values of 84.0%, 85.14%, and 85.71%, respectively were found for AI-SONICTM; The accuracy was highest for ATA (excluding non-classifiable nodules), with values of 87.26%, 87.93%, and 88.82%, respectively, while the accuracy for AI-SONICTM were 83.24%, 84.05%, and 85.14%, respectively. The Kendall's tau coefficient indicated strong or moderate interobserver agreement among all examiners using different diagnostic methods (Kendall's tau coefficient >0.6, P<0.001). AI-SONICTM showed the highest interobserver agreement (Kendall's tau coefficient=0.995, P<0.001). A binary probit regression analysis showed that nodules with cystic components had a significantly higher regression coefficient value of 0.983 (P=0.002), indicating that AI-SONICTM may have higher accuracy for nodules with cystic components. AI-SONICTM and the six thyroid nodule ultrasound risk stratification systems showed high diagnostic performance for papillary thyroid carcinoma. All examiners showed strong or moderate interobserver agreement when using different diagnostic methods. AI-SONICTM may have higher accuracy for nodules with cystic components.

Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics.

To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT). This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set. Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59-0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%]. Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results.

Immune Profiling of Patients with Systemic Sclerosis through Targeted Proteomic Analysis.

High-throughput proteomic analysis could offer new insights into the pathogenesis of systemic sclerosis (SSc) and reveal non-invasive biomarkers for diagnosis and severity. This study aimed to assess the protein signature of patients with SSc compared to that of healthy volunteers, decipher various disease endotypes using circulating proteins, and determine the diagnostic performance of significantly expressed plasma analytes. We performed targeted proteomic profiling in a cohort of fifteen patients with SSc and eighteen controls using the Olink® (Olink Bioscience, Uppsala, Sweden)Target 96 Inflammation Panels. Seventeen upregulated proteins involved in angiogenesis, innate immunity, and co-stimulatory pathways discriminated between patients with SSc and healthy controls (HCs) and further classified them into two clusters, a low-inflammatory and a high-inflammatory endotype. Younger age, shorter disease duration, and lack of reflux esophagitis characterized patients in the low-inflammatory endotype. TNF, CXCL9, TNFRSF9, and CXCL10 positively correlated with disease progression, while the four-protein panel comprising TNF, CXCL9, CXCL10, and CX3CL1 showed high diagnostic performance. Collectively, this study identified a distinct inflammatory signature in patients with SSc that reflects a persistent T helper type 1 (Th 1) immune response irrespective of disease duration, while the multi-protein panel might improve early diagnosis in SSc.

Rare Pancreatic/Peripancreatic Cystic Lesions Can Be Accurately Characterized by EUS with Through-the-Needle Biopsy-A Unique Pictorial Essay with Clinical and Histopathological Correlations.

Due to their aspecific macroscopic appearance, uncommon pancreatic cystic lesions (PCLs) are often misdiagnosed as mucinous lesions and improperly resected. We aimed to evaluate the endoscopic ultrasound (EUS)-guided through-the-needle biopsy (TTNB) capacity of the preoperative diagnosis of uncommon PCLs. Overall, 136 patients with PCLs who underwent EUS-TTNB between 2016 and 2022 were retrospectively identified. Common histotypes (e.g., IPMN, serous cystadenoma, and mucinous cystadenoma) were excluded and 26 (19.1%) patients (15 female, mean age 52.9 ± 10.4) were analyzed. The EUS findings, adverse events (AEs), and TTNB outcomes in uncommon PCLs were evaluated. The cysts histotype was accurately diagnosed by TTNB in 24/26 (92.3%) cases (seven cystic neuroendocrine tumors, four squamoid cysts, three acinar cells cystadenomas, two lymphoepithelial cysts, two mucinous non-neoplastic cysts, two bronchogenic cysts, two cystic lymphangiomas, one solid-pseudopapillary neoplasm, and one schwannoma). In the remaining two cases, lymphangioma was eventually diagnosed after resection. Surgery was performed in 15/26 (57.7%) patients. The mean follow-up of non-surgical patients was 32.5 months. One severe acute case of pancreatitis (3.8%) that required surgery occurred after EUS-TTNB. Uncommon pancreatic/peripancreatic lesions represent the 19.1% of PCLs in our series, with mainly benign histotypes. TTNB demonstrated a high diagnostic performance with a low rate of AEs in this setting, representing a reliable tool with which to avoid useless surgery.

Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis

BackgroundThe gold standard to diagnose fatty liver is pathology. Recently, image-based artificial intelligence (AI) has been found to have high diagnostic performance. We systematically reviewed studies of image-based AI in the diagnosis of fatty liver.MethodsWe searched the Cochrane Library, Pubmed, Embase and assessed the quality of included studies by QUADAS-AI. The pooled sensitivity, specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated using a random effects model. Summary receiver operating characteristic curves (SROC) were generated to identify the diagnostic accuracy of AI models.Results15 studies were selected in our meta-analysis. Pooled sensitivity and specificity were 92% (95% CI: 90–93%) and 94% (95% CI: 93–96%), PLR and NLR were 12.67 (95% CI: 7.65–20.98) and 0.09 (95% CI: 0.06–0.13), DOR was 182.36 (95% CI: 94.85-350.61). After subgroup analysis by AI algorithm (conventional machine learning/deep learning), region, reference (US, MRI or pathology), imaging techniques (MRI or US) and transfer learning, the model also demonstrated acceptable diagnostic efficacy.ConclusionAI has satisfactory performance in the diagnosis of fatty liver by medical imaging. The integration of AI into imaging devices may produce effective diagnostic tools, but more high-quality studies are needed for further evaluation.

Accuracy of the Enhanced Liver Fibrosis Test in Patients With Type 2 Diabetes Mellitus and Its Clinical Implications

Background and AimsThe diagnostic performance of the Fibrosis-4 (FIB-4) index and nonalcoholic fatty liver disease (NAFLD) fibrosis score (NFS) is poor in patients with type 2 diabetes mellitus (T2DM). We determined the usefulness of the enhanced liver fibrosis (ELF) test in patients with T2DM. MethodsA total of 1,228 patients with biopsy-proven NAFLD were enrolled. The diagnostic performance of the ELF test for predicting advanced fibrosis in participants with or without T2DM was evaluated in comparison with the FIB-4 index and NFS. ResultsOverall, the area under the curve of the ELF test for predicting advanced fibrosis was greater (0.828) than that of the FIB-4 index (0.727) and NFS (0.733). The diagnostic performance of the ELF test (area under the curve 0.820) was also superior to that of the FIB-4 index (0.698) and NFS (0.700) in patients with T2DM. With the low cutoff values for each non-invasive test, the ELF test provided an acceptable false-negative rate (cutoff value 9.8, 6.7%) in this population, unlike the FIB-4 index (1.30, 14.5%) and NFS (−1.455, 12.4%). After propensity score matching to avoid selection bias including age, sex, body mass index, and the prevalence of advanced fibrosis, the ELF test with a low cutoff value showed a high sensitivity (≥91.4%) and a high negative predictive value (≥96.8%), irrespective of the presence or absence of T2DM. ConclusionThe high diagnostic performance of the ELF test for predicting advanced fibrosis in individuals with or without T2DM could address an unmet medical need for accurate assessment of liver fibrosis in patients with diabetes and NAFLD.

Routine 4D Cardiac CT to Identify Concomitant Transthyretin Amyloid Cardiomyopathy in Older Adults with Severe Aortic Stenosis.

Background Transthyretin amyloid cardiomyopathy (ATTR-CM) often coexists with severe aortic stenosis (AS). Although strain analysis from cardiac MRI and echocardiography was demonstrated to predict coexisting ATTR-CM, comparable data from four-dimensional (4D) cardiac CT are lacking despite wide availability. Purpose To evaluate the diagnostic performance of 4D cardiac CT-derived parameters in identifying ATTR-CM in older adults considered for transcatheter aortic valve implantation (TAVI). Materials and Methods This prospective single-center screening study for ATTR-CM included consecutive patients with severe AS considered for TAVI who underwent 4D cardiac CT between August 2019 and August 2021 approximately 1 day before technetium 99m (99mTc) 3,3-diphosphono-1,2-propanodicarboxylic-acid (DPD) scintigraphy. The diagnostic performance of CT-based left ventricular (LV), right ventricular, and left atrial dimensions, ejection fraction (EF), and myocardial strain were evaluated against 99mTc-DPD scintigraphy as the reference standard to identify ATTR-CM. Predictors and an unweighted cardiac CT score were validated with internal bootstrapping. The assignment of variables to the score was based on cutoff values achieving the highest Youden index J. Results Among 263 participants (mean age, 83 years ± 4.6 [SD]; 149 male and 114 female participants), 99mTc-DPD scintigraphy (Perugini grade 2 or 3) confirmed coexisting ATTR-CM in 27 (10.3%). CT-derived LV mass index, LV and LA global longitudinal strain (GLS), and relative apical longitudinal strain each predicted the presence of ATTR-CM with an area under the curve (AUC) of at least 0.70. Implementing these parameters with cutoff values of 81 g/m2 or higher, -14.9% or higher, less than 11.5%, and 1.7 or higher in the CT score, respectively, yielded high diagnostic performance (AUC = 0.89; 95% CI: 0.81, 0.94; P < .001) robust to internal bootstrapping validation (AUC = 0.88; 95% CI: 0.82, 0.94). If two criteria were fulfilled, the sensitivity and specificity in the detection of ATTR-CM were 96.3% (95% CI: 81.0, 99.9) and 58.9% (95% CI: 52.3, 65.2), respectively. Conclusion When compared against 99mTc-DPD scintigraphy as the reference standard, routine 4D cardiac CT in older adults considered for TAVI provided high diagnostic performance in the detection of concomitant ATTR-CM by assessing LV and left atrial GLS, relative apical longitudinal strain, and LV mass index. ClinicalTrials.gov registration no.: NCT04061213 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Tavakoli and Onder in this issue.

Benefit of Using Both Ultrasound Imaging and Clinical Information for Predicting Malignant Soft Tissue Tumors.

Ultrasonography (US) is the primary imaging method for soft tissue tumors (STTs), the diagnostic performance of which still requires improvement. To achieve an accurate evaluation of STTs, we built the diagnostic nomogram for STTs using the clinical and US features of patients with STTs. A total of 613 patients with 195 malignant and 418 benign STTs were retrospectively recruited. We used a blend of clinical and ultrasonic features, as well as exclusively US features, to develop two distinct diagnostic models for STTs: the clinical-US model and the US-only model, respectively. The two models were evaluated and compared by measuring their areas under the receiver operating characteristic curve (AUC), calibration, integrated discrimination improvement (IDI) and decision curve analysis. The performance of the clinical-US model was also compared with that of two radiologists. The clinical-US model had better diagnostic performance than the model based on US imaging features alone (AUCs of the clinical-US and US-only models: 0.95 [0.93-0.97] vs. 0.89 [0.87-0.92], p < 0.001; IDI of the two models: 0.15 ± 0.03, p < 0.001). The clinical-US model was also superior to the two radiologists in diagnosing STTs (AUCs of clinical-US model and two radiologists: 0.95 [0.93-0.97] vs. 0.79 [0.75-0.82] and 0.83 [0.80-0.85], p < 0.001). The diagnostic model based on clinical and US imaging features had high diagnostic performance in STTs, which could help identify malignant STTs for radiologists.