Diagnostic Performance Research Articles

Predicting early rebleeding and mortality after endoscopic hemostasis of esophagogastric varices: Diagnostic performance of aspartate aminotransferase-to-platelet ratio index and model for end-stage liver disease-Na score.

Endoscopic variceal ligation and sclerotherapy are recommended for esophagogastric variceal bleeding (EGVB) in cirrhosis but can be complicated by early rebleeding and death. This study aimed to identify noninvasive markers accurately predicting early rebleeding and mortality after endoscopic hemostasis for EGVB. Among 116 patients with endoscopically confirmed EGVB and endoscopic hemostasis, various noninvasive markers were calculated, and their predictive accuracy was compared by receiver-operating characteristic curve analysis. Endpoints included 5-day rebleeding, 5-day mortality, 6-week rebleeding, and 6-week mortality. The median age was 63 years. Child-Pugh class B and C patients accounted for 40.5% and 34.5%, respectively. Only the aspartate aminotransferase-to-platelet ratio index (APRI) significantly predicted 5-day rebleeding, with an area under the curve (AUC) of 0.777 (95% confidence interval [CI]: 0.537-1). The model for end-stage liver disease-Na (MELD-Na) score showed good predictive accuracy for 5-day mortality (AUC: 0.839, 95% CI: 0.681-0.997), 6-week rebleeding (AUC: 0.797, 95% CI: 0.663-0.932), and 6-week mortality (AUC: 0.888, 95% CI: 0.797-0.979). Patients with cirrhosis with a high APRI and MELD-Na score were at high risk of early rebleeding and death after EGVB. Allocating appropriate monitoring and care for those patients is necessary.

Pancreatic Stone Protein in the Diagnosis of Sepsis in Children Admitted to High-Dependency Care: A Single-Center Prospective Cohort Study.

Blood level of pancreatic stone protein (PSP) is a promising biomarker of sepsis both in adults and children. The aim of our study was to investigate the diagnostic accuracy of PSP in children with suspected sepsis and to compare diagnostic performance with other sepsis biomarkers approved for clinical use, that is, procalcitonin (PCT) and C-reactive protein (CRP). Prospective study. PICU and pediatric emergency department. Blood levels of PSP were measured using a nanofluidic point-of-care immunoassay (abioSCOPE, Abionic SA, Switzerland) within 24 hours of admission. We studied 99 children aged between older than 1 month and younger than 18 years with signs and symptoms of systemic inflammatory response syndrome (irrespective of associated organ dysfunction). The prevalence of sepsis was 35 of 99 (35.4%). Patients with sepsis had higher PSP levels (p < 0.001) than patients with systemic inflammation of noninfectious cause. In this analysis, the optimal cutoff for the diagnosis of sepsis using PSP was 123 ng/mL, which resulted in a sensitivity of 0.63 (95% CI, 0.43-0.80), specificity of 0.89 (95% CI, 0.77-0.95). The PSP test area under the receiver operating characteristic curve (AUROC) was 0.82 (95% CI, 0.73-0.91) and, by comparison, procalcitonin and CRP AUROC were 0.70 (95% CI, 0.58-0.82) and 0.72 (95% CI, 0.60-0.84), respectively. Overall, the pretest to posttest probability of sepsis with a positive test changed from 0.35 to 0.73. In this single-center prospective pediatric cohort, admitted to the high intensive care and to the PICU, our findings suggested the potential use of PSP as a sepsis biomarker. However, because of the clinical diagnostic uncertainty with a positive result, further investigation is needed particularly in combination with other biomarkers.

Collaborative CRISPR-Cas System-Enabled Detection of Circulating Circular RNA for Reliable Monitoring of Acute Myocardial Infarction.

Acute myocardial infarction (AMI) is one of the major causes of death worldwide, posing significant global health challenges. Circular RNA (circRNA) has recently emerged as a potential diagnostic biomarker for AMI, providing valuable information for timely medical care. In this work, a new electrochemical method for circRNA detection by engineering a collaborative CRISPR-Cas system is developed. This system integrates the unique circRNA-targeting ability with cascade trans-cleavage activities of Cas effectors, using an isothermal primer exchange reaction as the bridge. Using cZNF292, a circulating circRNA biomarker for AMI is identified by this group; as a model, the collaborative CRISPR-Cas system-based method exhibits excellent accuracy and sensitivity with a low detection limit of 2.13 × 10-15 m. Moreover, the method demonstrates a good diagnostic performance for AMI when analyzing whole blood samples. Therefore, the method may provide new insight into the detection of circRNA biomarkers and is expected to have great potential in AMI diagnosis in the future.

Head-to-Head Comparison of the Diagnostic Performance of FDG PET/CT and FDG PET/MR in Patients With Cancer: A Systematic Review and Meta-Analysis.

Background: The available evidence on the use of FDG PET/MRI performed using an integrated system in patients with cancer has grown substantially. Objective: To perform a systematic review and meta-analysis comparing the diagnostic performance of FDG PET/CT and FDG PET/MRI in patients with cancer. Evidence Acquisition: MEDLINE, EMBASE, and Cochrane Database of Systematic Reviews were searched from July 1, 2015 to January 25, 2023 for studies reporting a head-to-head comparison of the diagnostic performance of FDG PET/CT and FDG PET/MRI in patients with cancer. The two modalities' diagnostic performance was summarized, stratified by performance endpoint. For endpoints with sufficient data, meta-analysis was performed using bivariate modeling to produce summary estimates of pooled sensitivity and specificity. For remaining endpoints, reported performance in individual studies was recorded. Evidence Synthesis: The systematic review included 29 studies with a total of 1656 patients. For patient-level detection of regional nodal metastases (5 studies), pooled sensitivity and specificity for PET/MRI were 88% (95% CI, 74-95%) and 92% (95% CI, 71-98%), respectively, and for PET/CT were 86% (95% CI, 70-94%) and 86% (95% CI, 68-95%), respectively. For lesion-level detection of recurrence and/or metastases (5 studies), pooled sensitivity and specificity for PET/MRI were 94% (95% CI, 78-99%) and 83% (95% CI, 76-88%), respectively, and for PET/CT were 91% (95% CI, 77-96%) and 81% (95% CI, 72-88%), respectively. In individual studies not included in meta-analysis, PET/MRI in comparison with PET/CT showed staging accuracy in breast cancer of 98.0% versus 74.5% and in colorectal cancer of 96.2% versus 69.2%, sensitivity for primary tumor detection in cervical cancer of 93.2% versus 66.2%, and sensitivity, specificity, and accuracy for lesion-level liver metastasis detection of 91.1-98.0% versus 42.3-71.1%, 100.0% versus 83.3-98.6%, and 96.5-98.2% versus 44.7-86.7%, respectively. In three studies, management was more commonly impacted by information from PET/MRI (5.2-11.1%) than PET/CT (0.0-2.6%). Conclusions: PET/MRI showed comparable or superior diagnostic performance versus PET/CT across a range of cancers and endpoints. Clinical Impact: The findings help to identify clinical settings where PET/MRI may provide particular clinical benefit for oncologic evaluation.

Prospective and multi-reader evaluation of deep learning reconstruction-based accelerated rectal MRI: image quality, diagnostic performance, and reading time.

To evaluate deep learning reconstruction (DLR)-based accelerated rectal magnetic resonance imaging (MRI) compared with standard MRI. Patients with biopsy-confirmed rectal adenocarcinoma between November/2022 and May/2023 in a single centre were prospectively enrolled for an intra-individual comparison between standard fast spin-echo (FSEstandard) and DLR-based FSE (FSEDL) sequences. Quantitative and qualitative image quality metrics of the pre-therapeutic MRIs were evaluated in all patients; diagnostic performance and evaluating time for T-staging, N-staging, extramural vascular invasion (EMVI), and mesorectal fascia (MRF) status was further analysed in patients undergoing curative surgery, with histopathologic results as the diagnostic gold standard. A total of 117 patients were enrolled, with 60 patients undergoing curative surgery. FSEDL reduced the acquisition time by 65% than FSEstandard. FSEDL exhibited higher signal-to-noise ratios, contrast-to-noise ratio, and subjective scores (noise, tumour margin clarity, visualisation of bowel wall layering and MRF, overall image quality, and diagnostic confidence) than FSEstandard (p < 0.001). Reduced artefacts were observed in FSEDL for patients without spasmolytics (p < 0.05). FSEDL provided higher T-staging accuracy by junior readers than FSEstandard (reader 1, 58.33% vs 70.00%, p = 0.016; reader 3, 60.00% vs 76.67%, p = 0.021), with similar N-staging, EMVI, and MRF performance. No significant difference was observed for senior readers. FSEDL exhibited shorter diagnostic time in all readers' T-staging and overall evaluation, and junior readers' EMVI and MRF (p < 0.05). FSEDL provided improved image quality, reading time, and junior radiologists' T-staging accuracy than FSEstandard, while reducing the acquisition time by 65%. DLR is clinically applicable for rectal MRI, providing improved image quality with shorter scanning time, which may ease the examination burden. It is beneficial for diagnostic optimisation in improving junior radiologists' T-staging accuracy and reading time. The rising incidence of rectal cancer has demanded enhanced efficiency and quality in imaging examinations. FSEDL demonstrated superior image quality and had a 65% reduced acquisition time. FSEDL can improve the diagnostic accuracy of T-staging and reduce the reading time for assessing rectal cancer.

Dose-response relationship between serum N-glycan markers and liver fibrosis in chronic hepatitis B.

Evaluation of liver fibrosis played a monumental role in the diagnosis and monitoring of chronic hepatitis B (CHB). We aimed to explore the value of serum N-glycan markers in liver fibrosis. This multi-center (33 hospitals) study recruited 760 treatment-naïve CHB patients who underwent liver biopsy. Serum N-glycan markers were analyzed by DNA sequencer-assisted fluorophore-assisted with capillary electrophoresis (DSA-FACE) technology. First, we explore the relationship between 12 serum N-glycan markers and the fibrosis stage. Then, we developed a Px score for diagnosing significant fibrosis using the LASSO regression. Next, we compared the diagnostic performances between Px, LSM, APRI, and FIB-4. Finally, we explored the relationships between glycosyltransferase gene and liver fibrosis with RNA-transcriptome sequencing. We included 622 CHB participants: male-dominated (69.6%); median age 42.0 (IQR 34.0-50.0); 287 with normal ALT; 73.0% with significant fibrosis. P5(NA2), P8(NA3), and P10(NA4) were opposite to the degree of fibrosis, while other profiles (except for P0[NGA2]) increased with the degree of fibrosis. Seven profiles (P1[NGA2F], P2[NGA2FB], P3[NG1A2F], P4[NG1A2F], P7[NA2FB], P8[NA3], and P9[NA3Fb]) were selected into Px score. Px score was associated with an increased risk of significant fibrosis (for per Px score increase, the risk of significant fibrosis was increased by 3.54 times (OR = 4.54 [2.63-7.82]) in the fully-adjusted generalized linear model. p for trend was <0.001. The diagnostic performance of the Px score was superior to others. Glycosyltransferase genes were overexpressed in liver fibrosis, and glycosylation and glycosyltransferase-related pathways were significantly enriched. Serum N-glycan markers were positively correlated with liver fibrosis. Px score had good performance in distinguishing significant fibrosis.

Serum proteome signatures associated with liver steatosis in adolescents with obesity.

Childhood obesity, a pressing global health issue, significantly increases the risk of metabolic complications, including metabolic dysfunction associated with steatotic liver disease (MASLD). Accurate non-invasive tests for early detection and screening of steatosis are crucial. In this study, we explored the serum proteome, identifying proteins as potential biomarkers for inclusion in non-invasive steatosis diagnosis tests. Fifty-nine obese adolescents underwent ultrasonography to assess steatosis. Serum samples were collected and analyzed by targeted proteomics with the Proximity Extension Assay technology. Clinical and biochemical parameters were evaluated, and correlations among them, the individuated markers, and steatosis were performed. Receiver operating characteristic (ROC) curves were used to determine the steatosis diagnostic performance of the identified candidates, the fatty liver index (FLI), and their combination in a logistic regression model. Significant differences were observed between subjects with and without steatosis in various clinical and biochemical parameters. Gender-related differences in the serum proteome were also noted. Five circulating proteins, including Cathepsin O (CTSO), Cadherin 2 (CDH2), and Prolyl endopeptidase (FAP), were identified as biomarkers for steatosis. CDH2, CTSO, Leukocyte Immunoglobulin Like Receptor A5 (LILRA5), BMI, waist circumference, HOMA-IR, and FLI, among others, significantly correlated with the steatosis degree. CDH2, FAP, and LDL combined in a logit model achieved a diagnostic performance with an AUC of 0.91 (95% CI 0.75-0.97, 100% sensitivity, 84% specificity). CDH2 and FAP combined with other clinical parameters, represent useful tools for accurate diagnosis of fatty liver, emphasizing the importance of integrating novel markers into diagnostic algorithms for MASLD.

Deep learning with convolution neural network detecting mesiodens on panoramic radiographs: comparing four models.

The aim of this study was to develop an optimal, simple, and lightweight deep learning convolutional neural network (CNN) model to detect the presence of mesiodens on panoramic radiographs. A total of 628 panoramic radiographs with and without mesiodens were used as training, validation, and test data. The training, validation, and test dataset were consisted of 218, 51, and 40 images with mesiodens and 203, 55, and 61 without mesiodens, respectively. Unclear panoramic radiographs for which the diagnosis could not be accurately determined and other modalities were required for the final diagnosis were retrospectively identified and employed as the training dataset. Four CNN models provided within software supporting the creation of neural network models for deep learning were modified and developed. The diagnostic performance of the CNNs was evaluated according to accuracy, precision, recall and F1 scores, receiver operating characteristics (ROC) curves, and area under the ROC curve (AUC). In addition, we used SHapley Additive exPlanations (SHAP) to attempt to visualize the image features that were important in the classifications of the model that exhibited the best diagnostic performance. A binary_connect_mnist_LeNet model exhibited the best performance of the four deep learning models. Our results suggest that a simple lightweight model is able to detect mesiodens. It is worth referring to AI-based diagnosis before an additional radiological examination when diagnosis of mesiodens cannot be made on unclear images. However, further revaluation by the specialist would be also necessary for careful consideration because children are more radiosensitive than adults.

Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI.

Meniscal injury is a common cause of knee joint pain and a precursor to knee osteoarthritis (KOA). The purpose of this study is to develop an automatic pipeline for meniscal injury classification and localization using fully and weakly supervised networks based on MRI images. In this retrospective study, data were from the osteoarthritis initiative (OAI). The MR images were reconstructed using a sagittal intermediate-weighted fat-suppressed turbo spin-echo sequence. (1) We used 130 knees from the OAI to develop the LGSA-UNet model which fuses the features of adjacent slices and adjusts the blocks in Siam to enable the central slice to obtain rich contextual information. (2) One thousand seven hundred and fifty-six knees from the OAI were included to establish segmentation and classification models. The segmentation model achieved a DICE coefficient ranging from 0.84 to 0.93. The AUC values ranged from 0.85 to 0.95 in the binary models. The accuracy for the three types of menisci (normal, tear, and maceration) ranged from 0.60 to 0.88. Furthermore, 206 knees from the orthopedic hospital were used as an external validation data set to evaluate the performance of the model. The segmentation and classification models still performed well on the external validation set. To compare the diagnostic performances between the deep learning (DL) models and radiologists, the external validation sets were sent to two radiologists. The binary classification model outperformed the diagnostic performance of the junior radiologist (0.82-0.87 versus 0.74-0.88). This study highlights the potential of DL in knee meniscus segmentation and injury classification which can help improve diagnostic efficiency.

Accelerated cardiac magnetic resonance imaging using deep learning for volumetric assessment in children.

Ventricular volumetry using a short-axis stack of two-dimensional (D) cine balanced steady-state free precession (bSSFP) sequences is crucial in any cardiac magnetic resonance imaging (MRI) examination. This task becomes particularly challenging in children due to multiple breath-holds. To assess the diagnostic performance of accelerated 3-RR cine MRI sequences using deep learning reconstruction compared with standard 2-D cine bSSFP sequences. Twenty-nine consecutive patients (mean age 11 ± 5, median 12, range 1-17years) undergoing cardiac MRI were scanned with a conventional segmented 2-D cine and a deep learning accelerated cine (three heartbeats) acquisition on a 1.5-tesla scanner. Short-axis volumetrics were performed (semi-)automatically in both datasets retrospectively by two experiencedreaderswho visually assessed image quality employing a 4-point grading scale. Scan times and image quality were compared using the Wilcoxon rank-sum test. Volumetrics were assessed with linear regression and Bland-Altman analyses, and measurement agreement with intraclass correlation coefficient (ICC). Mean acquisition time was significantly reduced with the 3-RR deep learning cine compared to the standard cine sequence (45.5 ± 13.8s vs. 218.3 ± 44.8s; P < 0.001). No significant differences in biventricular volumetrics were found. Left ventricular (LV) mass was increased in the deep learning cine compared with the standard cine sequence (71.4 ± 33.1g vs. 69.9 ± 32.5g; P < 0.05). All volumetric measurements had an excellent agreement with ICC > 0.9 except for ejection fraction (EF) (LVEF 0.81, RVEF 0.73). The image quality of deep learning cine images was decreased for end-diastolic and end-systolic contours, papillary muscles, and valve depiction (2.9 ± 0.5 vs. 3.5 ± 0.4; P < 0.05). Deep learning cine volumetrics did not differ significantly from standard cine results except for LV mass, which was slightly overestimated with deep learning cine. Deep learning cine sequences result in a significant reduction in scan time with only slightly lower image quality.

The diagnostic value of serum exosomal SNORD116 and SNORA21 for NSCLC patients.

Non-small cell lung cancer (NSCLC) is a widespread and serious global malignancy. This study aimed to examine the clinical relevance of serum exosomal SNORD116 and SNORA21 as novel diagnostic biomarkers for NSCLC. Serum exosomes from 226 healthy controls and 305 NSCLC patients were isolated by ultracentrifugation. Characterization of exosomes was conducted by qNano, transmission electron microscopy (TEM) and Western immunoblotting. RT-PCR revealed snoRNAs that were differentially expressed. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic performance. In NSCLC patients, the levels of serum exosomal SNORD116 and SNORA21 were significantly reduced compared to those in healthy controls (P < 0.0001 for both). ROC curves showed AUC values of 0.738 and 0.761. By combining SNORD116 and SNORA21 with traditional blood biomarkers CYFRA21-1 and carcinoembryonic antigen (CEA), the AUC increased to 0.917. Moreover, these two exosomal snoRNAs distinguished between patients with metastatic NSCLC (n = 132) and those with non-metastatic NSCLC (n = 173) significantly (P < 0.0001 for both). The ROC curves gave AUC values of 0.743 and 0.694, respectively. The combined analysis raised the AUC to 0.751. The diagnostic power of these two exosomal snoRNAs combined with CYFRA21-1 and CEA increased to 0.784. This study demonstrated that serum exosomal SNORD116 and SNORA21 can be used as potential promising non-invasive diagnostic biomarkers for NSCLC.

Diagnostic yield of head-up tilt table test in hypertrophic cardiomyopathy and unexplained syncope: the knot does not seem to be unraveling.

Conflicting results have been published considering the diagnostic performance of head-up tilt test (HUTT) in patients with hypertrophic cardiomyopathy (HCM). We aimed to conduct a meta-analysis to evaluate the diagnostic value of HUTT in the evaluation of unexplained syncope in patients with HCM. We performed a structured systematic database search using the following keywords: hypertrophic cardiomyopathy, syncope, unexplained syncope, head-up tilt test, tilt table test, tilt testing, orthostatic stress, autonomic function, autonomic response. Studies in which the HUTT was used to define autonomic dysfunction in patients with syncope at baseline or without syncope were included in the final analysis. A total of 252 HCM patients from 6 studies (159 patients without a history of syncope and 93 with a history of syncope, respectively) were evaluated. HUTT was positive in 50 (19.84%) of 252 patients (in 21 of 93 patients (22.58%) with a history of syncope and in 29 of 159 patients (18.24%) without a history of syncope, respectively). The pooled total sensitivity and specificity of the HUTT for detecting syncope were 22.1% (14.8-35.1%) and 83.6% (73.2-91.6%), respectively. The summary receiver operator curve showed that HUTT had an only modest discriminative ability for syncope with an area under the curve value of 0.565 (0.246-0.794). Although HUTT has significant limitations in diagnosis of unexplained syncope in patients with HCM, it may still be used to determine hypotensive susceptibility. Other autonomic tests can be used in diagnostic workflow in this population.

Deep Learning to Detect Intracranial Hemorrhage in a National Teleradiology Program and the Impact on Interpretation Time.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. The diagnostic performance of an artificial intelligence (AI) clinical decision support (CDS) solution for acute intracranial hemorrhage (ICH) detection was assessed in a large teleradiology practice. The impact on radiologist read times and system efficiency was also quantified. A total of 61,704 consecutive noncontrast head CTs (NCHCT) were retrospectively evaluated. System performance was calculated along with mean and median read time values for NCHCT pre-AI (baseline; August 2021-May 2022) and post-AI (January 2023-February 2024). The AI solution had a sensitivity of 75.6%, specificity of 92.1%, accuracy of 91.7%, prevalence of 2.70%, and positive predictive value of 21.1%. Of the 56,745 post-AI NCHCT with no bleed identified by a radiologist, examinations falsely flagged as suspected ICH by the AI solution (n = 4,464) took 9min40sec on average/8min7sec median to interpret as compared with 8min25sec average/6min48ec median for unremarkable NCHCT pre-AI (n = 49,007) (P < .001) and 8min38sec average/6min53sec median post-AI when ICH was not suspected by the AI solution (n = 52,281) (P < .001). NCHCT with no bleed identified by the AI but reported as positive for ICH by the radiologist (n = 384) took 14min23sec on average/13min35sec median to interpret as compared with a read time of 13min34sec mean/12min30sec median for NCHCT correctly reported as a bleed by the AI (n = 1192) (P = .04). With lengthened read times for falsely flagged examinations, system inefficiencies may outweigh the potential benefits of using the tool in a high volume, low prevalence environment. ©RSNA, 2024.

Ultrafast sequence-based prediction model and nomogram to differentiate additional suspicious lesions on preoperative breast MRI.

To investigate whether ultrafast sequence improves the diagnostic performance of conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating additional suspicious lesions (ASLs) on preoperative breast MRI. A retrospective database search identified 668 consecutive patients who underwent preoperative breast DCE-MRI with ultrafast sequence between June 2020 and July 2021. Among these, 107 ASLs from 98 patients with breast cancer (36 multifocal, 42 multicentric, and 29 contralateral) were identified. Clinical, pathological, conventional MRI findings, and ultrafast sequence-derived parameters were collected. A prediction model that adds ultrafast sequence-derived parameters to clinical, pathological, and conventional MRI findings was developed and validated internally. Decision curve analysis and net reclassification index statistics were performed. A nomogram was constructed. The ultrafast model adding time to peak enhancement, time to enhancement, and maximum slope showed a significantly increased area under the receiver operating characteristic curve compared with the conventional model which includes age, human epidermal growth factor receptor 2 expression of index cancer, size of index cancer, lesion type of index cancer, location of ASL, and size of ASL (0.92 vs. 0.82; p = 0.002). The decision curve analysis showed that the ultrafast model had a higher overall net benefit than the conventional model. The net reclassification index of ultrafast model was 23.3% (p = 0.001). A combination of ultrafast sequence-derived parameters with clinical, pathological, and conventional MRI findings can aid in the differentiation of ASL on preoperative breast MRI. Our prediction model and nomogram that was based on ultrafast sequence-derived parameters could help radiologists differentiate ASLs on preoperative breast MRI. Ultrafast MRI can diminish background parenchymal enhancement and possibly improve diagnostic accuracy for additional suspicious lesions (ASLs). Locationof ASL, larger size of ASL, and highermaximum slope were associated with malignant ASL. The ultrafast model and nomogram can help preoperatively differentiate additional malignancies.

Diagnostic Accuracy of Ultra-Low Dose CT Compared to Standard Dose CT for Identification of Fresh Rib Fractures by Deep Learning Algorithm.

The present study aimed to evaluate the diagnostic accuracy of ultra-low dose computed tomography (ULD-CT) compared to standard dose computed tomography (SD-CT) in discerning recent rib fractures using a deep learning algorithm detection of rib fractures (DLADRF). A total of 158 patients undergoing forensic diagnosis for rib fractures were included in this study: 50 underwent SD-CT, and 108 were assessed using ULD-CT. Junior and senior radiologists independently evaluated the images to identify and characterize the rib fractures. The sensitivity of rib fracture diagnosis by radiologists and radiologist + DLADRF was better using SD-CT than ULD-CT. However, the diagnosis sensitivity of DLADRF using ULD-CT alone was slightly more than SD-CT. Nonetheless, no substantial differences were observed in specificity, positive predictive value, and negative predictive value between SD-CT and ULD-CT by the same radiologist, radiologist + DLADRF, and DLADRF (P > 0.05). The area under the curve (AUC) of receiver operating characteristic indicated that senior radiologist + DLADRF was significantly better than senior and junior radiologists, junior radiologists + DLADRF, and DLADRF alone using SD-CT or ULD-CT (all P < 0.05). Also, junior radiologists + DLADRF was better with ULD-CT than senior and junior radiologists (P < 0.05). The AUC of the rib fracture diagnosed by senior radiologists did not differ from DLADRF using ULD-CT. Also, no significant differences were observed between junior + AI and senior and between junior and DLADRF using SD-CT. DLADRF enhanced the diagnostic performance of radiologists in detecting recent rib fractures. The diagnostic outcomes between SD-CT and ULD-CT across radiologists' experience and DLADRF did not differ significantly.

GLIM Achieves Best Diagnostic Performance in Non-Cancer Patients with Low BMI: A Hierarchical Bayesian Latent-Class Meta-Analysis.

Global Leadership Initiative on Malnutrition (GLIM) and Patient-Generated Subjective Global Assessment (PG-SGA) are commonly used nutrition assessment tools, whose performance does not reach a consensus due to different and imperfect reference standards. This study aimed to evaluate and compare the diagnostic accuracy of GLIM and PG-SGA, using a hierarchical Bayesian latent class model, in the absence of a gold standard. A systematic search was undertaken in PubMed, Embase, and Web of Science from inception to October 2022. Diagnostic test studies comparing (1) the GLIM and/or (2) PG-SGA with "semi-gold" standard assessment tools for malnutrition were included. Two authors independently extracted data on sensitivity, specificity, and other key characteristics. The methodological quality of each included study was appraised according to the criteria in the Quality Assessment of Diagnostic Accuracy Studies-2. A total of 45 studies, comprising 20 876 individuals evaluated for GLIM and 11 575 for PG-SGA, were included. The pooled sensitivity was 0.833 (95% CI 0.744 to 0.896) for GLIM and 0.874 (0.797 to 0.925) for PG-SGA, while the pooled specificity was 0.837 (0.780 to 0.882) for GLIM and 0.778 (0.707 to 0.836) for PG-SGA. GLIM showed slightly better performance than PG-SGA, with a higher diagnostic odds ratio (25.791 vs 24.396). The diagnostic performance of GLIM was most effective in non-cancer patients with an average body mass index (BMI) of <24kg/m2, followed by non-cancer patients with an average age of ≥60 years. PG-SGA was most powerful in cancer patients with an average age of <60 years, followed by cancer patients with an average BMI of <24 kg/m2. Both GLIM and PG-SGA had moderately high diagnostic capabilities. GLIM was most effective in non-cancer patients with a low BMI, while PG-SGA was more applicable in cancer patients. PROSPERO registration No. CRD42022380409.

Impact of the adjunction of a short video to an original article for the recognition of newly described tumor entities in pathology: An interventional prospective study.

Merkel cell carcinoma diagnosis is often based on microscopic examination by pathologists. While histopathologic diagnosis primarily hinges on conscious and analytical cognition, the pathologist's decision-making process is also influenced by a rapid "gist" or "gestalt" approach. In this study, using cases of Merkel cell carcinoma as a model, we aim to assess how pathologists' viewing short videos containing conceptual clues and visual aids, in conjunction with reading an original article as a reference, may enhance their diagnostic performance. Sixteen pathologists were included in the present work. After participants had read the original article, their ability to distinguish Merkel cell polyomavirus (MCPyV)+ and MCPyV- Merkel cell carcinoma cases was evaluated on a first preliminary series of 20 cases. Following this test, the participants watched the video and then evaluated a second "experimental" series of 20 independent cases. After reading the original article, for each case, a median number of 12 participants (75%, Q1-Q3: 10-13) classified the specimen in the correct category (92 incorrect answers in the whole series). An important interobserver variability was observed in this setting (Kappa coefficient = 0.465). By contrast, following the video, all cases were correctly classified by most of the participants, with only 12 incorrect answers on the whole series and excellent interobserver reproducibility (Kappa coefficient = 0.846). Our study demonstrated that providing a short video together with an original article may enhance pathologists' performance in diagnosing Merkel cell carcinoma.

Diagnostic accuracy of deep learning using speech samples in depression: a systematic review and meta-analysis.

This study aims to conduct a systematic review and meta-analysis of the diagnostic accuracy of deep learning (DL) using speech samples in depression. This review included studies reporting diagnostic results of DL algorithms in depression using speech data, published from inception to January 31, 2024, on PubMed, Medline, Embase, PsycINFO, Scopus, IEEE, and Web of Science databases. Pooled accuracy, sensitivity, and specificity were obtained by random-effect models. The diagnostic Precision Study Quality Assessment Tool (QUADAS-2) was used to assess the risk of bias. A total of 25 studies met the inclusion criteria and 8 of them were used in the meta-analysis. The pooled estimates of accuracy, specificity, and sensitivity for depression detection models were 0.87 (95% CI, 0.81-0.93), 0.85 (95% CI, 0.78-0.91), and 0.82 (95% CI, 0.71-0.94), respectively. When stratified by model structure, the highest pooled diagnostic accuracy was 0.89 (95% CI, 0.81-0.97) in the handcrafted group. To our knowledge, our study is the first meta-analysis on the diagnostic performance of DL for depression detection from speech samples. All studies included in the meta-analysis used convolutional neural network (CNN) models, posing problems in deciphering the performance of other DL algorithms. The handcrafted model performed better than the end-to-end model in speech depression detection. The application of DL in speech provided a useful tool for depression detection. CNN models with handcrafted acoustic features could help to improve the diagnostic performance. The study protocol was registered on PROSPERO (CRD42023423603).

Identification of novel immune-related signatures for keloid diagnosis and treatment: insights from integrated bulk RNA-seq and scRNA-seq analysis

BackgroundKeloid is a disease characterized by proliferation of fibrous tissue after the healing of skin tissue, which seriously affects the daily life of patients. However, the clinical treatment of keloids still has limitations, that is, it is not effective in controlling keloids, resulting in a high recurrence rate. Thus, it is urgent to identify new signatures to improve the diagnosis and treatment of keloids.MethodBulk RNA seq and scRNA seq data were downloaded from the GEO database. First, we used WGCNA and MEGENA to co-identify keloid/immune-related DEGs. Subsequently, we used three machine learning algorithms (Randomforest, SVM-RFE, and LASSO) to identify hub immune-related genes of keloid (KHIGs) and investigated the heterogeneous expression of KHIGs during fibroblast subpopulation differentiation using scRNA-seq. Finally, we used HE and Masson staining, quantitative reverse transcription-PCR, western blotting, immunohistochemical, and Immunofluorescent assay to investigate the dysregulated expression and the mechanism of retinoic acid in keloids.ResultsIn the present study, we identified PTGFR, RBP5, and LIF as KHIGs and validated their diagnostic performance. Subsequently, we constructed a novel artificial neural network molecular diagnostic model based on the transcriptome pattern of KHIGs, which is expected to break through the current dilemma faced by molecular diagnosis of keloids in the clinic. Meanwhile, the constructed IG score can also effectively predict keloid risk, which provides a new strategy for keloid prevention. Additionally, we observed that KHIGs were also heterogeneously expressed in the constructed differentiation trajectories of fibroblast subtypes, which may affect the differentiation of fibroblast subtypes and thus lead to dysregulation of the immune microenvironment in keloids. Finally, we found that retinoic acid may treat or alleviate keloids by inhibiting RBP5 to differentiate pro-inflammatory fibroblasts (PIF) to mesenchymal fibroblasts (MF), which further reduces collagen secretion.ConclusionIn summary, the present study provides novel immune signatures (PTGFR, RBP5, and LIF) for keloid diagnosis and treatment, and identifies retinoic acid as potential anti-keloid drugs. More importantly, we provide a new perspective for understanding the interactions between different fibroblast subtypes in keloids and the remodeling of their immune microenvironment.

Associations between diffusion kurtosis imaging metrics and neurodevelopmental outcomes in neonates with low-grade germinal matrix and intraventricular hemorrhage

Diffusion Kurtosis Imaging (DKI)-derived metrics are recognized as indicators of maturation in neonates with low-grade germinal matrix and intraventricular hemorrhage (GMH-IVH). However, it is not yet known if these factors are associated with neurodevelopmental outcomes. The objective of this study was to acquire DKI-derived metrics in neonates with low-grade GMH-IVH, and to demonstrate their association with later neurodevelopmental outcomes. In this prospective study, neonates with low-grade GMH-IVH and control neonates were recruited, and DKI were performed between January 2020 and March 2021. These neonates underwent the Bayley Scales of Infant Development test at 18 months of age. Mean kurtosis (MK), radial kurtosis (RK) and gray matter values were measured. Spearman correlation analyses were conducted for the measured values and neurodevelopmental outcome scores. Forty controls (18 males, average gestational age (GA) 30 weeks ± 1.3, corrected GA at MRI scan 38 weeks ± 1) and thirty neonates with low-grade GMH-IVH (13 males, average GA 30 weeks ± 1.5, corrected GA at MRI scan 38 weeks ± 1). Neonates with low-grade GMH-IVH exhibited lower MK and RK values in the PLIC and the thalamus (P < 0.05). The MK value in the thalamus was associated with Mental Development Index (MDI) (r = 0.810, 95% CI 0.695–0.13; P < 0.001) and Psychomotor Development Index (PDI) (r = 0.852, 95% CI 0.722–0.912; P < 0.001) scores. RK value in the caudate nucleus significantly and positively correlated with MDI (r = 0.496, 95% CI 0.657–0.933; P < 0.001) and PDI (r = 0.545, 95% CI 0.712–0.942; P < 0.001) scores. The area under the curve (AUC) were used to assess diagnostic performance of MK and RK in thalamus (AUC = 0.866, 0.787) and caudate nucleus (AUC = 0.833, 0.671) for predicting neurodevelopmental outcomes. As quantitative neuroimaging markers, MK in thalamus and RK in caudate nucleus may help predict neurodevelopmental outcomes in neonates with low-grade GMH-IVH.