Diagnostic Efficacy Research Articles

Minimum Fascia-Tumour Distance for Differentiating Deep Lobe From Superficial Lobe Benign Parotid Tumours: A Retrospective Study and Meta-Analysis.

This study was designed to evaluate the diagnostic efficacy of the minimum fascia-tumour distance (MFTD) in distinguishing deep-lobe benign parotid tumours from superficial-lobe tumours through both an original study and a meta-analysis. In this study, we performed a retrospective analysis of data from 91 patients who had been diagnosed with benign parotid tumours. The MFTD values were sourced from preoperative ultrasound examinations. The locations of these tumours were confirmed through surgical findings. We assessed the diagnostic accuracy of MFTD by utilising receiver operating characteristic (ROC) curves. Additionally, we conducted a systematic review of the pertinent literature and performed a diagnostic meta-analysis to ascertain the overall diagnostic efficacy of MFTD in identifying benign parotid tumours. Patients with tumours in the deep lobe had a significantly greater MFTD than patients with tumours in the superficial lobe. Using a cutoff value of 3.50 mm for MFTD, we found an AUC of 0.93, a sensitivity of 81.8%, and a specificity of 98.8%. Our meta-analysis included seven studies covering a total of 1689 tumours. The pooled values for sensitivity, specificity, and diagnostic odds ratio (OR) of MFTD were 81.0%, 89.0%, and 32.2, respectively. The AUC of the summarised ROC curve of MFTD was 0.90. The MFTD demonstrated reliable diagnostic accuracy in identifying deep-lobe benign parotid tumours and may be incorporated into standard evaluations before parotidectomy.

Same-Day Positron Emission Tomography/Computed Tomography with 68Ga-Radiolabeled Fibroblast Activation Protein Inhibitors and 18F-Fluorodeoxyglucose Imaging for Gastrointestinal Cancers.

Objective: We investigated the clinical practicability of same-day 68Ga-radiolabeled fibroblast activation protein inhibitors (68Ga-FAPI)-first and 18F-fluorodeoxyglucose (18F-FDG) imaging and compared it with same-day 18F-FDG-first or 2-day procedures in diagnosing gastrointestinal cancers. Materials and Methods: Sixty-five patients with confirmed gastrointestinal cancers were divided into same-day 68Ga-FAPI-first group (Group A), same-day 18F-FDG-first group (Group B), and 2-day group (Group C). Low-dose CT and low injection activity were performed on 68Ga-FAPI positron emission tomography/computed tomography (PET/CT). Interval times, radiation dose, diagnostic performance, and detectability were assessed among groups. Additionally, the uptake, tumor-to-liver ratio (TLR), diagnostic efficacy, and TNM stage were compared between the two modalities. Results: The total waiting time for Group C was significantly longer than that for Group A or B (both p < 0.001). The total dose-length product and effective dose decreased in all groups. There were comparable detectability and diagnostic performance among groups (all p > 0.05). The within-group analysis in Group B indicated that 68Ga-FAPI PET/CT had higher uptake in the primary and recurrent lesions than 18F-FDG without differences in TLR, due to higher liver background on 68Ga-FAPI PET than Group A or C (both p < 0.001).68Ga-FAPI PET/CT possessed higher accuracy than 18F-FDG and changed staging in 14 patients (14/65, 21.54%). Conclusions: The same-day 68Ga-FAPI-first and 18F-FDG imaging reduced examination waiting time without increased radiation dose, simultaneously achieving excellent diagnostic performance and improving clinical staging in diagnosing gastrointestinal cancers.

Improving early mortality risk detection in light-chain cardiac amyloidosis patients with routine echocardiographic indicators and mayo staging

Abstract Background Light-chain cardiac amyloidosis (AL-CA) is a fatal cardiomyopathy caused by the deposition of monoclonal immunoglobulin light-chain in the cardiac fibers, conduction system, valves, and other parts of the heart. The Mayo 2004 staging system is widely used to assess the prognosis of AL-CA. Those in the highest-risk categories, Mayo 2004 stage IIIb, exhibit extremely poor responses to conventional treatment regimens, resulting in high early mortality rates. However, within the same stage, prognostic outcomes among patients can still vary significantly. Hence, a more detailed stratification of high-risk patients is crucial for pinpointing those at extreme risk. Objective To investigate whether conventional echocardiographic measurements can enhance the identification of high-risk patients with early mortality in AL-CA on the basis of existing risk stratification. Methods Continuous enrollment of patients diagnosed with AL-CA at our center from May 2020 to December 2022. Clinical data, echocardiography, and follow-up results of the patients were collected. Logistic regression analyses were used to identify risk factors for early mortality, with risk factor thresholds calculated using Receiver Operating Characteristic (ROC) curves. Kaplan-Meier survival analysis was employed to assess endpoint events. The diagnostic efficacy of the model was evaluated using the area under the ROC curve and the Integrated Discrimination Improvement (IDI). Results A total of 76 patients were included with an average age of 59±10 years. Among them, 51 were male (67.1%). The median follow-up duration was 10 months (ranging from 5 to 20 months). Early mortality, defined as death within 6 months, occurred in 20 patients (26.3%). Twenty-three patients were categorized under the Mayo 2004 stage IIIb (30.3%). The 6-month mortality rate for patients in the Mayo 2004 stage IIIb was 56.5%. The sensitivity of early diastolic transmitral flow velocity to early diastolic mitral annular tissue velocity ratio divided left ventricular ejection fraction [(E/e’) /LVEF] in predicting early mortality in patients with AL-CA is 63%, and the specificity is 92%.When compared to the Mayo 2004 stage IIIb alone, (E/e')/LVEF≥0.5 combined with the Mayo 2004 stage IIIb had an area under the curve of 0.731 and 0.824, respectively, in the ROC curve. The IDI was 0.113 (95% CI: 0.013-0.213, P=0.026), representing an 11.3% enhancement in the predictive value of early mortality in AL-CA patients. Conclusion Routine echocardiography derived E/e' to LVEF ratio offered added prognostic value for early mortality beyond Mayo staging.Kaplan-Meier survival curve

Ultrasound findings of ovarian intravenous leiomyomatosis: a case report

Intravenous leiomyomatosis (IVL), an abnormal growth pattern of uterine leiomyomas, is a rare tumor characterized by masses of smooth muscle cells appearing histologically benign and proliferating within the blood vessels but not invading the tissue. Currently, there have been limited reports of early cases of IVL, and the imaging characteristics of IVL remain uncertain, resulting in frequent misdiagnosis prior to surgery. The present study utilized a case of early IVL detected through conventional ultrasound and subsequently confirmed via contrast-enhanced ultrasound (CEUS) to further investigate ultrasound’s diagnostic efficacy for early IVL detection. Here, a case of a 49-year-old woman was reported who presented with uterine leiomyoma and an echogenic mass in the left adnexal region on physical examination. Subsequent transvaginal CEUS examination revealed a left ovarian venous leiomyoma. The patient underwent resection of tumors in the uterus, bilateral ovaries, and left ovarian vein under general anesthesia. A venous plexus was identified above the left broad ligament close to the left ovary, and a myoma-like growth was detected in the posterior uterine wall during the operational procedure. Reports on pathology and immunohistochemistry verified leiomyomatosis with fatty metaplasia in the left broad ligament and uterine wall vein. The prognosis of patients with IVL is determined based on the appropriate surgical methods and the timely diagnosis of the condition. In this case, conventional ultrasound aided in the early identification of IVL, which was later verified by a CEUS examination, resulting in a successful surgical treatment. This example highlights the importance of ultrasound technology in diagnosing this uncommon condition and presents a new method for preoperative detection of IVL.

Predictive factors for recurrent tricuspid regurgitation after tricuspid annuloplasty: 3D echocardiography study

Abstract Introduction There is growing concern about the recurrence of tricuspid regurgitation (TR) after tricuspid annuloplasty (TA) in individuals with functional tricuspid regurgitation (FTR). While TA is effective in treating FTR, a comprehensive understanding of the echocardiographic factors influencing TR recurrence is crucial. Aim This study aims to employ multivariate regression analysis to investigate the prognostic significance of tricuspid valve (TV) geometry and echocardiographic parameters associated with both RV geometry and function. The focus is on comprehending these relationships in the context of recurrent TR subsequent to TA. Methods This prospective observational cohort study aimed to explore factors contributing to recurrent tricuspid regurgitation (TR) after tricuspid annuloplasty (TA). The focus was on patients with moderate or severe functional TR due to left heart valvular disease, particularly severe mitral regurgitation, excluding those with ischemic heart disease. The cohort included 66 individuals undergoing preoperative assessments with 2D and 3D echocardiography, quantifying RV and right atrial (RA) geometry, functional, and tricuspid valve (TV) parameters. TR severity followed ESC guidelines. Patients were categorized into effective TA (50 patients) and recurrent TR (16 patients) groups, defined by mild-moderate TR and significant moderate-severe TR one year post-surgery, respectively. SPSS software facilitated statistical analysis, with adjusted logistic regression considering parameters such as age and gender. Results Among the patients (54% male, mean age 68±9 years), no significant differences were found in gender, left ventricular and atrial parameters, or preoperative TV orifice area between groups (recurrent TR vs. effective TA: 38 [33] mm2 vs. 29 [15] mm2, p=0.117). Univariate logistic regression analysis identified Septal-Lateral Systolic TA Diameter (OR 1.62), Septal-Lateral Diastolic TA Diameter (OR 1.99), and Major Axis Diastolic TA Diameter (OR 1.59) as key predictors. For diagnostic efficacy, ROC analysis revealed Major Axis Diastolic TA Diameter (AUC 0.848; cut-off 52.5 mm), TV Leaflet Tenting Volume (AUC 0.778; cut-off 5.1 ml), and RV Basal Diameter (AUC 0.763; cut-off 47.5 mm) as the most predictive parameters. The combined use of predefined RV basal diameter and Major Axis Diastolic TA Diameter showed the highest odds ratio for recurrent TR. Conclusion Key predictors, including Septal-Lateral Systolic and Diastolic TA Diameters, and Major Axis Diastolic TA Diameter, were identified, emphasizing their significance in understanding TR recurrence after tricuspid annuloplasty. The combination of predefined RV basal diameter and Major Axis Diastolic TA Diameter showed the highest odds ratio for recurrent TR, offering valuable insights for clinical assessment and preventive strategies.

Evaluation of Split Hand Syndrome in Early Diagnosis of Amyotrophic Lateral Sclerosis by Nerve Conduction Study as a Diagnostic Tool

Background: Amyotrophic lateral sclerosis (ALS), a degenerative disease of the motor neurons that can present diversly. Needle electromyography (EMG) is the gold standard investigation for the diagnosis of ALS. Split hand syndrome (SHS) demonstrated by nerve conduction study (NCS); a noninvasive tool could be useful in diagnosing ALS. This study aimed to assess SHS by NCS in ALS. Methods: This diagnostic accuracy case-control study included 37 cases of ALS and 37 age and gender-matched controls. Compound muscle action potential (CMAP) and distal motor latency (DML) of median and ulnar nerves and the ratio of median and ulnar CMAP and DML were compared between study groups. The diagnostic validity testing of electrophysiological SHS was also done. Results: The mean age of the ALS and control groups were similar, 39.8±2.7 and 39.3±2.6 years, respectively. Sixteen (43.2%) ALS patients were in 30-59 years age group. Male-female ratio was 5.2. In the ALS group, median-ulnar CMAP ratio was lower and median-ulnar DML ratio was prolonged than the control. The diagnostic efficacy of median-ulnar CMAP ratio and DML ratio were calculated. A median-ulnar CMAP ratio cut-off value ≤1 showed 70% sensitivity, 97% specificity, 96% positive predictive value and 77% negative predictive value. A median-ulnar DML ratio cut-off value ≥1.5 showed 48% sensitivity, 89% specificity, 82% positive predictive value and 65% negative predictive value. Conclusion: NCS demonstrating SHS can be an important tool in the early diagnosis of ALS. J Bangladesh Coll Phys Surg 2024; 42: 350-354

Diagnostic value of T-tube cholangiography and choledochoscopy in residual calculi after biliary surgery

BackgroundT-tube cholangiography and choledochoscopy are commonly used techniques for detecting residual bile duct stones after biliary surgery. However, the utility of routine cholangiography before T-tube removal needs further investigation. This study aims to evaluate the diagnostic efficacy of various methods for detecting residual calculi following biliary surgery.MethodsWe retrospectively analyzed the clinical data of 287 adult patients who underwent common bile duct exploration with T-tube drainage, followed by T-tube cholangiography and choledochoscopy, at the Department of General Surgery, Xuanwu Hospital, Capital Medical University, between 2017 and 2022. Exclusion criteria were patients with bile duct tumors, incomplete medical records or loss to follow-up, and patients with contraindications to T-tube or choledochoscopy. McNemanr test and Kappa test were used to compare the results and consistency between choledochoscopy and T-tube cholangiography. All patients underwent both cholangiography and choledochoscopy six to eight weeks after laparoscopic cholecystectomy combined with common bile duct exploration and T-tube drainage. The results of T-tube cholangiography and choledochoscopy for each patient were recorded, analyzed, and compared.ResultsAmong the 287 patients, T-tube cholangiography detected residual stones in 38 cases, which were confirmed by choledochoscopy in 29 cases. Conversely, of the 249 patients without evidence of residual stones on T-tube angiography, 11 patient was later found to have retained stones through choledochoscopy. There was no significant difference between the results of T-tube cholangiography and choledochoscopy (P = 0.82), indicating a high level of agreement between the two methods (Kappa value: 0.70) (95% CI, 0.65–0.76).ConclusionThere is no significant difference in the diagnostic accuracy between T-tube cholangiography and choledochoscopy for detecting residual bile duct stones after surgery (P = 0.82). The two methods demonstrated a high level of consistency (Kappa value: 0.70) (95% CI, 0.65–0.76). The choice of diagnostic method for postoperative residual bile duct stones should be based on the specific condition of the patient.

Identification of diagnostic markers pyrodeath-related genes in non-alcoholic fatty liver disease based on machine learning and experiment validation

Non-alcoholic fatty liver disease (NAFLD) poses a global health challenge. While pyroptosis is implicated in various diseases, its specific involvement in NAFLD remains unclear. Thus, our study aims to elucidate the role and mechanisms of pyroptosis in NAFLD. Utilizing data from the Gene Expression Omnibus (GEO) database, we analyzed the expression levels of pyroptosis-related genes (PRGs) in NAFLD and normal tissues using the R data package. We investigated protein interactions, correlations, and functional enrichment of these genes. Key genes were identified employing multiple machine learning techniques. Immunoinfiltration analyses were conducted to discern differences in immune cell populations between NAFLD patients and controls. Key gene expression was validated using a cell model. Analysis of GEO datasets, comprising 206 NAFLD samples and 10 controls, revealed two key PRGs (TIRAP, and GSDMD). Combining these genes yielded an area under the curve (AUC) of 0.996 for diagnosing NAFLD. In an external dataset, the AUC for the two key genes was 0.825. Nomogram, decision curve, and calibration curve analyses further validated their diagnostic efficacy. These genes were implicated in multiple pathways associated with NAFLD progression. Immunoinfiltration analysis showed significantly lower numbers of various immune cell types in NAFLD patient samples compared to controls. Single sample gene set enrichment analysis (ssGSEA) was employed to assess the immune microenvironment. Finally, the expression of the two key genes was validated in cell NAFLD model using qRT-PCR. We developed a prognostic model for NAFLD based on two PRGs, demonstrating robust predictive efficacy. Our findings enhance the understanding of pyroptosis in NAFLD and suggest potential avenues for therapeutic exploration.

Differentiation of pathological subtypes and Ki-67 and TTF-1 expression by dual-energy CT (DECT) volumetric quantitative analysis in non-small cell lung cancer

BackgroundTo explore the value of dual-energy computed tomography (DECT) in differentiating pathological subtypes and the expression of immunohistochemical markers Ki-67 and thyroid transcription factor 1 (TTF-1) in patients with non-small cell lung cancer (NSCLC).MethodsBetween July 2022 and May 2024, patients suspected of lung cancer who underwent two-phase contrast-enhanced DECT were prospectively recruited. Whole-tumor volumetric and conventional spectral analysis were utilized to measure DECT parameters in the arterial and venous phase. The DECT parameters model, clinical-CT radiological features model, and combined prediction model were developed to discriminate pathological subtypes and predict Ki-67 or TTF-1 expression. Multivariate logistic regression analysis was used to identify independent predictors. The diagnostic efficacy was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong’s test.ResultsThis study included 119 patients (92 males and 27 females; mean age, 63.0 ± 9.4 years) who was diagnosed with NSCLC. When applying the DECT parameters model to differentiate between adenocarcinoma and squamous cell carcinoma, ROC curve analysis indicated superior diagnostic performance for conventional spectral analysis over volumetric spectral analysis (AUC, 0.801 vs. 0.709). Volumetric spectral analysis exhibited higher diagnostic efficacy in predicting immunohistochemical markers compared to conventional spectral analysis (both P < 0.05). For Ki-67 and TTF-1 expression, the combined prediction model demonstrated optimal diagnostic performance with AUC of 0.943 and 0.967, respectively.ConclusionsThe combined predictive model based on volumetric quantitative analysis in DECT offers valuable information to discriminate immunohistochemical expression status, facilitating clinical decision-making for patients with NSCLC.

Association between visceral lipid accumulation indicators and gallstones: a cross-sectional study based on NHANES 2017–2020

BackgroundObesity is a major contributing factor to the formation of gallstones. As early identification typically results in improved outcomes, we explored the relationship between visceral lipid accumulation indicators and the occurrence of gallstones.MethodsThis cross-sectional study involved 3,224 adults. The researchers employed multivariable logistic regression, smoothed curve fitting (SCF), threshold effects analysis, and subgroup analysis to examine the relationship between metabolic scores for visceral fat (METS-VF), waist circumference (WC), lipid accumulation products (LAP), and visceral adiposity index (VAI) and gallstones. A Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to identify key factors which were then used in the construction of a nomogram model. The diagnostic efficacy of this model in detecting gallstones was then determined using receiver operating characteristic curves.ResultsVisceral lipid accumulation indicators were strongly linked to the likelihood of having gallstones. Specific saturation effects for METS-VF, WC, LAP, and VAI and gallstones were determined using SCF. The inflection points for these effects were found to be 8.565, 108.400, 18.056, and 1.071, respectively. Subgroup analyses showed that associations remained consistent in most subgroups. The nomogram model, which was developed using critical features identified by LASSO regression, demonstrated excellent discriminatory ability, as indicated by an area under the curve value of 0.725.ConclusionsStudies have shown that increases in METS-VF, WC, LAP, and VAI are linked to increased prevalences of gallstones. The nomogram model, designed with critical parameters identified using LASSO regression, exhibits a strong association with the presence of gallstones.

Diagnostic accuracy of deep learning-based algorithms in laryngoscopy: a systematic review and meta-analysis.

Laryngoscopy is routinely used for suspicious vocal cord lesions with limited performance. Accumulated studies have demonstrated the bright prospect of deep learning in processing medical imaging. In this study, we perform a systematic review and meta-analysis to investigate diagnostic utility of deep learning in laryngoscopy. The study was performed according to the Primary Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. We comprehensively retrieved articles from the PubMed, Scopus, Embase, and Web of Science up to July 14, 2024. Eligible studies with application of deep learning algorithm in laryngoscopy were assessed and enrolled by two independent investigators. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio with 95% confidence intervals (CIs) were calculated using a random effects model. We retained 9 eligible studies adding up to 106,175 endoscopic images for the meta-analysis. The pooled sensitivity and specificity to diagnose laryngeal cancer were 0.95(95% CI: 0.85-0.98) and 0.96 (95% CI: 0.91-0.98). The area under the curve of deep learning was 0.99 (95%CI: 0.97-0.99). Deep learning demonstrated excellent diagnostic efficacy in assessing laryngeal cancer with laryngoscope images in current studies, which manifests its potential of aiding endoscopist for laryngeal cancer diagnosis and clinical decision making.

Synthetic double inversion recovery imaging for rectal cancer T staging evaluation: imaging quality and added value to T2-weighted imaging

ObjectiveTo assess the image quality of synthetic double inversion recovery (SyDIR) imaging and enhance the value of T2-weighted imaging (T2WI) in evaluating T stage for rectal cancer patients.MethodsA total of 112 pathologically confirmed rectal cancer patients were retrospectively selected after undergoing MRI, including synthetic MRI. The image quality of T2WI and SyDIR imaging was compared based on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall picture quality, presence of motion artifacts, lesion edge sharpness, and conspicuity. The concordance between MRI and pathological staging results, using T2WI alone and the combination of T2WI and SyDIR for junior and senior radiologists, was assessed using the Kappa test. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic efficacy of extramural infiltration in rectal cancer patients.ResultsNo significant differences in imaging quality were observed between conventional T2WI and SyDIR (p = 0.07–0.53). The combination of T2WI and SyDIR notably improved the staging concordance between MRI and pathology for both junior (kappa value from 0.547 to 0.780) and senior radiologists (kappa value from 0.738 to 0.834). In addition, the integration of T2WI and SyDIR increased the AUC for diagnosing extramural infiltration for both junior (from 0.842 to 0.918) and senior radiologists (from 0.917 to 0.938).ConclusionThe combination of T2WI and SyDIR increased the consistency of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration, which would benefit treatment selection.Critical relevance statementSyDIR sequence provides additional diagnostic value for T2WI in the T staging of rectal cancer, improving the agreement of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration.Key PointsSynthetic double inversion recovery (SyDIR) and T2WI have comparable image quality.SyDIR provides rectal cancer anatomical features for extramural infiltration detections.The combination of T2WI and SyDIR improves the accuracy of T staging in rectal cancer.Graphical

Diagnostic efficacy of data mining method based on multimodal ultrasound for papillary thyroid carcinoma

Diagnostic efficacy of data mining method based on multimodal ultrasound for papillary thyroid carcinoma

Castellvi classification of lumbosacral transitional vertebrae: comparison between conventional radiography, CT, and MRI.

The reliability and diagnostic accuracy of commonly used diagnostic imaging modalities in the classification of lumbosacral transitional vertebrae (LSTV) are poorly known, and comparative studies are scarce. To compare the diagnostic performance of conventional radiography (CR), computed tomography (CT), and magnetic resonance imaging (MRI) in classifying LSTVs. In this retrospective cross-sectional study, a total of 852 patients undergoing lumbar imaging studies using all three modalities were initially assessed for the presence of LSTV using CT scans. In total, 100 patients with LSTV anatomy were identified. Four readers performed blinded and independent evaluations of these 100 patients on each modality, and an experienced fellowship-trained radiologist performed a gold standard read using all three modalities. Inter-reader reliability metrics were analyzed in comparison to the gold standard. Statistical software R (4.2.1) was used for the analyses. We found superior diagnostic efficacy for CT: the sensitivity, specificity, accuracy, and balanced accuracy were 76%, 93%, 77%, and 84%, respectively. For MRI, the metrics were 54%, 88%, 56%, and 68%, and for CR 32%, 85%, 42%, and 59%, respectively. Inter-reader reliability was found to be good for CT (κ = 0.63-0.71) and fair for both CR (κ = 0.16-0.32) and MRI (κ = 0.24-0.56). CT had the highest diagnostic performance in all measured metrics with good inter-reader reliability. MRI and CR showed fairly poor sensitivity and accuracy, and thus consideration should be used when classifying LSTVs with these two modalities.

Synergizing traditional CT imaging with radiomics: a novel model for preoperative diagnosis of gastric neuroendocrine and mixed adenoneuroendocrine carcinoma

ObjectiveTo develop diagnostic models for differentiating gastric neuroendocrine carcinoma (g-NEC) and gastric mixed adeno-neuroendocrine carcinoma (g-MANEC) from gastric adenocarcinoma (g-ADC) based on traditional contrast enhanced CT imaging features and radiomics features.MethodsWe retrospectively analyzed 90 g-(MA)NEC (g-MANEC and g-NEC) patients matched 1:1 by T-stage with 90 g-ADC patients. Traditional CT features were analyzed using univariable and multivariable logistic regression. Tumor segmentation and radiomics features extraction were performed with Slicer and PyRadiomics. Feature selection was conducted through univariable analysis, correlation analysis, LASSO, and multivariable stepwise logistic. The combined model incorporated clinical and radiomics predictors. Diagnostic performance was assessed with ROC curves and DeLong’s test. The models’ diagnostic efficacy was further validated in subgroup of g-NEC vs. g-ADC and g-MANEC vs. g-ADC cases.ResultsTumor necrosis and lymph node metastasis were independent predictors for differentiating g-(MA)NEC from g-ADC (P &amp;lt; 0.05). The clinical model’s AUC was 0.700 (training) and 0.667(validation). Five radiomics features were retained, with the radiomics model showing AUC of 0.809 (training) and 0.802 (validation). The combined model’s AUCs were 0.853 (training) and 0.812 (validation), significantly outperforming the clinical model (P &amp;lt; 0.05). Subgroup analysis revealed that the combined model exhibited acceptable performance in differentiating g-NEC from g-ADC and g-MANEC from g-ADC, with AUC of 0.887 and 0.823 in the training cohort and 0.852 and 0.762 in the validation cohort.ConclusionA combined model based on traditional CT imaging and radiomic features provides a non-invasive and effective preoperative diagnostic method for differentiating g-(MA)NEC from g-ADC.

Automated deep learning-based bone mineral density assessment for opportunistic osteoporosis screening using various CT protocols with multi-vendor scanners

This retrospective study examined the diagnostic efficacy of automated deep learning-based bone mineral density (DL-BMD) measurements for osteoporosis screening using 422 CT datasets from four vendors in two medical centers, encompassing 159 chest, 156 abdominal, and 107 lumbar spine datasets. DL-BMD values on L1 and L2 vertebral bodies were compared with manual BMD (m-BMD) measurements using Pearson’s correlation and intraclass correlation coefficients. Strong agreement was found between m-BMD and DL-BMD in total CT scans (r = 0.953, p < 0.001). The diagnostic performance of DL-BMD was assessed using receiver operating characteristic analysis for osteoporosis and low BMD by dual-energy x-ray absorptiometry (DXA) and m-BMD. Compared to DXA, DL-BMD demonstrated an AUC of 0.790 (95% CI 0.733–0.839) for low BMD and 0.769 (95% CI 0.710–0.820) for osteoporosis, with sensitivity, specificity, and accuracy of 80.8% (95% CI 74.2–86.3%), 56.3% (95% CI 43.4–68.6%), and 74.3% (95% CI 68.3–79.7%) for low BMD and 65.4% (95% CI 50.9–78.0%), 70.9% (95% CI 63.8–77.3%), and 69.7% (95% CI 63.5–75.4%) for osteoporosis, respectively. Compared to m-BMD, DL-BMD showed an AUC of 0.983 (95% CI 0.973–0.993) for low BMD and 0.972 (95% CI 0.958–0.987) for osteoporosis, with sensitivity, specificity, and accuracy of 97.3% (95% CI 94.5–98.9%), 85.2% (95% CI 78.8–90.3%), and 92.7% (95% CI 89.7–95.0%) for low BMD and 94.4% (95% CI 88.3–97.9%), 89.5% (95% CI 85.6–92.7%), and 90.8% (95% CI 87.6–93.4%) for osteoporosis, respectively. The DL-based method can provide accurate and reliable BMD assessments across diverse CT protocols and scanners.

The accuracy of fine needle aspiration cytology and ultrasonography in assessing thyroid nodules in correlation with histopathology: a retrospective study

Background: Accurately diagnosing thyroid nodules is vital for preventing unnecessary surgeries and providing prompt therapy. Although fine needle aspiration cytology (FNAC) and ultrasonography (US) are widely used diagnostic methods, their reliability is questioned. This study investigates the effectiveness of US and FNAC in thyroid nodule diagnosis and differentiates benign from malignant nodules in relation to final histopathological diagnosis. Method: A retrospective study including 307 adult patients with thyroid diseases who underwent neck US and FNAC before surgery was conducted between April 2019 and May 2023. The diagnostic efficacy of US, FNAC, and their combination usage was compared to histopathological results. Result: Histopathological findings revealed that 187(61%) cases were benign, while 120 (39%) were malignant. The US features of “taller-than-wider” forms and hypoechoic appearance had the highest diagnostic accuracy in characterizing malignant thyroid nodules, with 83% and 73% accuracy, respectively. The combination of US parameters demonstrated high sensitivity, specificity, positive predictive value, and negative predictive value (NPV) of 88.33%, 63.10%, 60.6%, and 89.4%, with a statistically significant area under the ROC curve (AUC: 0.828, P&lt;0.001) than individual parameters. FNAC’s sensitivity, specificity, PPV NPV, and accuracy in detecting malignant lesions were 50%, 95%, 86%, 75%, and 77%, respectively, with acceptable discrimination and statistical significance (AUC: 0.723, P&lt;0.0001). The combination of US parameters and FNAC significantly improved the AUC value (AUC: 0.878, P&lt;0.0001), sensitivity (83.33%), and specificity (79.14%). Univariate analysis showed that hypoechoic appearance, heterogenicity, large mass size (&gt;4 cm), “taller-than-wider,” infiltrative margins, and microcalcifications were risk factors for malignancy in thyroid nodules and were statistically significant (all P values &lt;0.05). Conclusion: Combining US characteristics with FNAC results can afford the maximum analytical accuracy in distinguishing benign from malignant thyroid nodules. This strategy is practical due to its simplicity, minimal invasiveness, and cost-effectiveness, enabling robust management regimens and avoiding additional surgical procedures.

Review on Next-Gen Healthcare: The Role of MEMS and Nanomaterials in Enhancing Diagnostic and Therapeutic Outcomes

The convergence of microelectromechanical systems (MEMS) and nanomaterials is transforming healthcare by enabling breakthroughs in diagnostics, drug delivery, and biosensing. This synergistic integration offers unprecedented precision, miniaturization, and biocompatibility, overcoming critical challenges in detecting disease markers and delivering therapies. MEMS-based devices, when combined with nanomaterials, achieve heightened sensitivity and specificity, allowing for early diagnosis and targeted treatments across various medical applications. From cancer detection to neurotransmitter monitoring for mental health management, this fusion holds immense potential in advancing personalized medicine. As research in this domain continues to evolve, MEMS-nanomaterial technologies are poised to significantly enhance healthcare outcomes by improving diagnostic accuracy, treatment efficacy, and patient well-being. This review discusses recent advances, key challenges, and future perspectives on the role of MEMS and nanotechnology in shaping the future of healthcare innovation.

The diagnostic accuracy of exosomes for glioma: A meta-analysis.

Glioma is one of the most prevalent primary intracranial tumors, and biomarker testing offers a non-invasive modality with high diagnostic efficiency. The aim of this meta-analysis is to evaluate the diagnostic effectiveness of exosomes as biomarkers for glioma. We included 16 studies on exosomes as biomarkers for gliomas. The pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) for 25 biomarkers across these 16 studies were as follows: 82% (95% CI: 0.77-0.86), 91% (95% CI: 0.86-0.94), 9.10 (95% CI: 5.64-14.68), 0.20 (95% CI: 0.16-0.25), 45.94 (95% CI: 25.40-83.09), and 0.92 (95% CI: 0.89-0.94), respectively. Meta-regression indicated that biomarker analysis, biomarker type, and sample size may be sources of heterogeneity. Subgroup analysis suggested that ultracentrifugation (UC) was a better method for extracting exosomes. miRNA and other RNA groups (sncRNA, lncRNA, circRNA) provided higher SEN (0.88 vs. 0.84 vs. 0.78) compared to proteins. This study demonstrates the superior diagnostic efficacy of exosomes as biomarkers for gliomas, with high accuracy in diagnosing gliomas.

Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings

BackgroundConventional MR acceleration techniques, such as compressed sensing, parallel imaging, and half Fourier often face limitations, including noise amplification, reduced signal-to-noise ratio (SNR) and increased susceptibility to artifacts, which can compromise image quality, especially in high-speed acquisitions. Artificial intelligence (AI)-assisted compressed sensing (ACS) has emerged as a novel approach that combines the conventional techniques with advanced AI algorithms. The objective of this study was to examine the imaging quality of the ACS approach by qualitative and quantitative analysis for brain, spine, kidney, liver, and knee MR imaging, as well as compare the performance of this method with conventional (non-ACS) MR imaging.MethodsThis study included 50 subjects. Three radiologists independently assessed the quality of MR images based on artefacts, image sharpness, overall image quality and diagnostic efficacy. SNR, contrast-to-noise ratio (CNR), edge content (EC), enhancement measure (EME), scanning time were used for quantitative evaluation. The Cohen’s kappa correlation coefficient (k) was employed to measure radiologists’ inter-observer agreement, and the Mann Whitney U-test used for comparison between non-ACS and ACS.ResultsThe qualitative analysis of three radiologists demonstrated that ACS images showed superior clinical information than non-ACS images with a mean k of ~ 0.70. The images acquired with ACS approach showed statistically higher values (p < 0.05) for SNR, CNR, EC, and EME compared to the non-ACS images. Furthermore, the study’s findings indicated that ACS-enabled images reduced scan time by more than 50% while maintaining high imaging quality.ConclusionIntegrating ACS technology into routine clinical settings has the potential to speed up image acquisition, improve image quality, and enhance diagnostic procedures and patient throughput.