A mathematical observer study for evaluation of a model-based compensation method for crosstalk in simultaneous dual isotope SPECT

Anemia is one of the common adverse reactions after hip fracture surgery. The traditional method to solve anemia is allogeneic transfusion. However, the transfusion may lead to some complications such as septicemia and fever. So far, few studies have reported roles of machine learning in predicting whether blood transfusion is needed or not after hip fracture surgery. Therefore, the purpose of this study is to develop machine learning models to predict the likelihood of postoperative blood transfusion in patients undergoing hip fracture surgery. This study enrolled 1355 patients who underwent hip fracture surgery at the Affiliated Hospital of Qingdao University from January 2016 to December 2021. Among all patients, 210 cases received postoperative blood transfusion. All patients were randomly divided into a training group and a testing group at a ratio of 7:3. In the training group, univariate and multivariate logistic regression analyses were used to determine independent risk factors for the postoperative transfusion. Then, based on these independent risk factors, tenfold cross-validation method was utilized to develop five machine learning models, including logistic, multilayer perceptron (MLP), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The receiver operating characteristic (ROC) curve, area under ROC curve (AUC), and Matthews correlation coefficient (MCC) were generated to evaluate the performance of the models. Calibration plot and decision curve analysis (DCA) were used to test the performance, stability, and clinical applicability of the models. The models were validated using the testing group; and the ROC curve, MCC, calibration plot, and DCA curves were also generated to validate the performance, stability, and clinical applicability of the models. To further verify the robustness of the model, we randomly grabbed 70% of the samples in the testing set, performed 1000 iterations, and calculated the AUC and confidence interval of the five models. Finally, we used SHapley Additive exPlanations (SHAP) to explain these models. Multivariate logistic regression analysis showed that there were 8 independent risk factors, including age, blood transfusion history, albumin (ALB), globulin (GLO), total bilirubin (TBIL), indirect bilirubin (IBIL), hemoglobin (HB), and blood loss > 200ml. We finally selected five independent risk factors including HB, GLO, age, IBIL, and blood loss > 200ml. Based on these five independent risk factors, we generated six characteristic variables, namely HB, HB × HB, HB × blood loss, GLO × HB, age, age × IBIL, and established five machine learning models using a tenfold cross-validation method. In the training group, the AUC values of logistic, RF, MLP, SVM, and XGB were 0.9320, 0.8911, 0.9327, 0.9225, and 0.8825, respectively, and the average AUC was 0.9122 ± 0.0212. The MCC values were 0.65, 0.77, 0.65, 0.66, and 0.68, respectively, and the calibration plot and DCA performed well. In the testing group the AUC values of logistic, RF, MLP, SVM, and XGB were 0.8483, 0.7978, 0.8576, 0.8598, and 0.8216, respectively. The average AUC was 0.8370 ± 0.0238, and the MCC values were 0.41, 0.35, 0.40, 0.41, and 0.41, respectively. The calibration plot and DCA in the testing group also showed good performance. The AUC values and confidence intervals of the 1000-iteration model were: logistic (AUC, min confidence interval [CI]-max confidence interval [CI] 0.848, 0.804-0.903), RF (AUC, minCI-maxCI 0.797, 0.734-0.857), MLP (AUC, minCI-maxCI 0.858, 0.812-0.902), SVM (AUC, minCI-maxCI 0.859, 0.819-0.910), and XGB (AUC, minCI-maxCI 0.821, 0.764-0.894). The model performed well. Finally, according to SHAP, among all five models, HB played the most important role in model prediction and interpretation. The five models we developed all performed well in predicting the likelihood of blood transfusion after hip fracture surgery. Therefore, we believed that the prediction model based on machine learning had great application prospects in clinical practice, which could help clinicians better predict the risk of blood transfusion after hip fracture surgery.

OP0257 VALUE OF COLOR DOPPLER ULTRASOUND ASSESSMENT OF SACROILIAC JOINTS IN NON-RADIOGRAPHIC AXIAL SPONDYLOARTHRITIS:A COMPARISON WITH ANKYLOSING SPONDYLITIS

Purpose To demonstrate methods for receiver-operating characteristic (ROC) analysis of correlated eye data. Methods We applied the Obuchowski’s nonparametric approach and cluster bootstrap for estimating and comparing the area under ROC curve (AUC) between different sets of predictors to three datasets with varying inter-eye correlation. Results In an optic neuritis (ON) study of 152 eyes (80 patients), the AUC of optical coherence tomography retinal nerve fiber layer thickness for diagnosing ON (inter-eye kappa = 0.13) was 0.71 [95% confidence interval (95% CI): 0.622, 0.792] from the naïve approach without accounting for inter-eye correlation was narrower than from nonparametric (95% CI: 0.613, 0.801) or cluster bootstrap (95% CI: 0.614, 0.797) approaches. In an analysis of 198 eyes (135 patients), the baseline Age-related Eye disease Study scale predicted 5-year incidence of advanced age-related macular degeneration (inter-eye kappa = 0.23) with AUC of 0.72. The 95% CI from the naïve approach was slightly narrower (0.645, 0.794) than from the nonparametric (0.641, 0.797) or cluster bootstrap (0.641, 0.793) approaches. In an analysis of 1542 eyes (771 infants), birthweight and gestational age predicted treatment-requiring retinopathy of prematurity (inter-eye kappa = 0.98) with AUC of 0.80. Furthermore, the 95% CI from the naïve approach was narrower (0.769, 0.835) than from the nonparametric (0.755, 0.848) or cluster bootstrap (0.755, 0.845) approaches. 95% CIs for AUC differences between different models were narrower in the naïve approach than the nonparametric or cluster bootstrap approaches. Conclusion In ROC analysis of correlated eye data, ignoring inter-eye correlation leads to narrower 95% CI with underestimation dependent on magnitude of inter-eye correlation. Nonparametric and cluster bootstrap approaches properly account for inter-eye correlation.

ObjectiveHypoxia-inducible factor 1alpha (HIF-1α) functions as a crucial transcriptional mediator in hypoxic and ischemic brain response. We endeavored to assess the prognostic significance of serum HIF-1α in human aneurysmal subarachnoid hemorrhage (aSAH).MethodsIn this prospective, longitudinal, multicenter, and observational study of 257 patients with aSAH and 100 healthy controls, serum HIF-1α levels were quantified. Univariate analyses, followed by multivariate analyses, were performed to discern the relationship between serum HIF-1α levels and severity and delayed cerebral ischemia (DCI) plus poststroke 6-month poor outcome [extended Glasgow outcome scale (GOSE) scores of 1–4]. Predictive efficiency was determined under the receiver operating characteristic (ROC) curve.ResultsThere were significantly increased serum HIF-lα levels after aSAH, in comparison to controls (median, 288.0 vs. 102.6 pg/ml; P < 0.001). Serum HIF-lα levels were independently correlated with Hunt–Hess scores [β, 78.376; 95% confidence interval (CI): 56.446–100.305; P = 0.001] and modified Fisher scores (β, 52.037; 95% CI: 23.461–80.614; P = 0.002). Serum HIF-lα levels displayed significant efficiency for discriminating DCI risk [area under ROC curve (AUC), 0.751; 95% CI: 0.687–0.815; P < 0.001] and poor outcome (AUC, 0.791; 95% CI: 0.736–0.846; P < 0.001). Using the Youden method, serum HIF-1α levels >229.3 pg/ml predicted the development of DCI with 92.3% sensitivity and 48.4% specificity and serum HIF-1α levels >384.0 pg/ml differentiated the risk of a poor prognosis with 71.4% sensitivity and 81.1% specificity. Serum HIF-1α levels >229.3 pg/ml were independently predictive of DCI [odds ratio (OR), 3.061; 95% CI: 1.045–8.965; P = 0.041] and serum HIF-1α levels >384.0 pg/ml were independently associated with a poor outcome (OR, 2.907; 95% CI: 1.403–6.024; P = 0.004). The DCI predictive ability of their combination was significantly superior to those of Hunt–Hess scores (AUC, 0.800; 95% CI: 0.745–0.855; P = 0.039) and modified Fisher scores (AUC, 0.784; 95% CI: 0.726–0.843; P = 0.004). The prognostic predictive ability of their combination substantially exceeded those of Hunt–Hess scores (AUC, 0.839; 95% CI: 0.791–0.886; P < 0.001) and modified Fisher scores (AUC, 0.844; 95% CI: 0.799–0.890; P < 0.001).ConclusionElevated serum HIF-lα levels after aSAH, in independent correlation with stroke severity, were independently associated with DCI and 6-month poor outcome, substantializing serum HIF-lα as a potential prognostic biomarker of aSAH.

To analyze the risk factors of patients with trauma in intensive care unit (ICU), a new warning scoring system is established for predicting the incidence of sepsis in traumatic patients; and to provide a new simple method of clinical score, which could provide a reference for clinical prevention and treatment of sepsis. The clinical data of 591 patients with trauma in the ICU of the Army Specialized Medical Center of Army Medical University and Affiliated Hospital of Zunyi Medical University from January 2012 to December 2017 were retrospectively analyzed. The patients were divided into sepsis group (n = 382) and non-sepsis group (n = 209) according to their clinical outcome. The basic clinical data of all ICU trauma patients were collected, and the differences in gender, age, underlying diseases, and vital signs, critical illness scores, blood culture results and laboratory biochemical examinations within 24 hours of ICU admission between the two groups were analyzed. Univariate Logistic regression analysis was used to screen the related factors leading to sepsis. The indexes with P < 0.12 analyzed by univariate Logistic regression analysis were included in multivariate Logistic regression analysis. The risk factors of sepsis in traumatic patients were screened and assigned, and the total score was sepsis early warning score. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive value of the warning score of sepsis in patients with trauma. The incidence of sepsis in ICU trauma patients was 64.6% (382/591), and the ICU mortality was 10.5% (40/382). The traffic accident was a common cause of ICU trauma patients. Compared with non-sepsis patients, Glasgow coma score (GCS), proportion of past history, red blood cell (RBC), platelet (PLT), albumin (Alb) were lower in patients with sepsis, and body temperature, pulse, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), injury severity score (ISS), new injury severity score (NISS), fraction of inspired oxygen (FiO2), blood sodium, activated partial thromboplastin time (APTT), prothrombin time (PT), procalcitonin (PCT), C-reactive protein (CRP) levels were higher, blood transfusion, central venous catheterization, mechanical ventilation, shock, multiple organ dysfunction syndrome (MODS), open injury and multiple injuries were more common, the duration of mechanical ventilation, ICU days and total hospital days were longer, and all the differences were statistically significant. Most of the traumatic patients with sepsis were undergone with multiple trauma. Compared with non-sepsis patients, the proportion of multiple position trauma was significantly higher than patients without sepsis. And most traumatic patients were insulted in head, face and neck. The risk factors were screened by univariate and multivariate Logistic stepwise regression analysis, the indexes into the regression model were pulse > 100 bpm [odds ratio (OR) = 1.617, 95% confidence interval (95%CI) = 0.992-2.635, P = 0.044], APTT > 36 s (OR = 2.164, 95%CI = 1.056-4.435, P = 0.035), shock (OR = 1.798, 95%CI = 1.056-3.059, P = 0.031), mechanical ventilation (OR = 5.144, 95%CI = 2.302-11.498, P < 0.001), APACHE II > 21 (OR = 3.348, 95%CI = 1.724-6.502, P < 0.001), NISS > 25 (OR = 3.332, 95%CI = 1.154-9.624, P = 0.026), assigning scores were 0.5, 1.0, 0.5, 1.5, 1.5, 1.5, respectively, which were included in the new warning score of sepsis. ROC curve analysis showed that the area under ROC curve (AUC) of warning score for predicting sepsis in patients with trauma was 0.782, which was significantly higher than the APACHE II (AUC = 0.672), APTT (AUC = 0.574) and NISS (AUC = 0.515) with significant difference (all P < 0.01). When the cut-off value of sepsis warning score was 4.0, the sensitivity and specificity were 71.7% and 61.9%, respectively. Close monitoring and stabilization of vital signs of traumatic patients within 24 hours of ICU admission and reduction of unreasonable invasive mechanical ventilation time are expected to reduce the incidence of sepsis in traumatic patients. New warning score of sepsis consisted of six factors: pulse, APTT, shock, mechanical ventilation, APACHE II and NISS. Rational use of warning score of sepsis would help us to assess the prognosis of traumatic patients more easily and effectively, and the predicted effect is much better than APACHE II, APTT and NISS.

BackgroundCracked teeth may cause various clinical symptoms depending on the extension depth of the crack and the subsequent bacterial infections. However, techniques to reliably determine the extension depths of cracks in teeth before treatment are lacking. The aim of this study was to develop a new technique based on contrast-enhanced cone beam computed tomography (CBCT) to improve the accuracy of crack depth evaluation in vitro.MethodsWe developed an in vitro artificial simulation model of cracked teeth. Pre-experimental CBCT (pre-CBCT), and micro-computed tomography (micro-CT) were first performed for all cracked teeth (n = 31). Contrast-enhanced CBCT was then performed by infiltrating the crack with ioversol under vacuum conditions. The sensitivities of pre-CBCT and contrast-enhanced CBCT for the diagnosis of cracked teeth were calculated. According to the K-means clusters, crack depths measured by micro-CT were changed into categorical variables. Bland–Altman plot and the intraclass correlation coefficient (ICC) were used to analyze the consistency of the crack depths between the pre-CBCT and contrast-enhanced CBCT, as well as the ICC between the contrast-enhanced CBCT and micro-CT. Receiver operating characteristic (ROC) curves were generated to assess the ability for predicting crack depth in the differential diagnosis using pre-CBCT and contrast-enhanced CBCT. Restricted cubic splines were also used to model the non-linear relationship between the crack depths of contrast-enhanced CBCT and micro-CT.ResultsThe sensitivities of pre-CBCT and contrast-enhanced CBCT were 48.4%, and 67.7%, respectively. The ICC value of crack depth as measured by pre-CBCT and contrast-enhanced CBCT was 0.847 (95% confidence interval [CI] 0.380–0.960; P < 0.001). The areas under ROC curves (AUC) of pre-CBCT and contrast-enhanced CBCT were different: the AUC of pre-CBCT was 0.958 (P = 0.000, 95% CI 0.843–1.074), and the AUC of contrast-enhanced CBCT was 0.979 (P = 0.000, 95% CI 0.921–1.037), and the difference was not statistically significant (Z = − 0.707, P = 0.480). The ICC value of crack depth as measured by contrast-enhanced CBCT and micro-CT was 0.753 (95% CI 0.248–0.911; P < 0.001).ConclusionContrast-enhanced CBCT under vacuum conditions with a contrast medium can significantly improve the crack detection rate of cracked teeth; however, it cannot measure the crack depths accurately.

BackgroundsTo investigate the value of prolactin (PRL) in diagnosing non-alcoholic fatty liver disease (NAFLD).MethodsMetabolic parameters and serum PRL levels were measured in 452 males and 421 females, who were randomized to the estimation or the validation group as a 1:1 ratio. Hepatic steatosis was diagnosed via abdominal ultrasound. Variables that significantly associated with NAFLD in univariate analysis were included in multiple logistic regression. We used the receiver operator characteristic (ROC) curves to test the model performance. Besides, 147 patients underwent metabolic and liver biopsy were analyzed to validate the diagnostic value of this model.ResultsBody mass index, alanine aminotransferase, prolactin, high density lipoprotein cholesterol and HbA1c were included into models. In males, the area under ROC curve (AUC) was 0.86 (95%CI: 0.82–0.91) for the validation group. With two cut-off points (− 0.79 and 1.71), the sensitivity and specificity for predicting NALFD was 95.2 and 91.1% in the validation group, respectively. In females, the AUC was 0.82 (95%CI: 0.76–0.88) for the validation group. With two cut-off points (− 0.68 and 2.16), the sensitivity and specificity for predicting NALFD was 97.1 and 91.4% in the validation group, respectively. In subjects with liver pathology, the AUC was higher than that of fatty liver index. A positive correlation between the scores of the model and the severities of NAFLD was observed. Importantly, we demonstrated a potential value of this model in predicting nonalcoholic steatohepatitis.ConclusionWe established a mathematic model that can conveniently and effectively diagnose the existence and severities of NAFLD.

This study was aimed to assess if combining the evaluation of blood glucose level (BGL) and the Triage Revised Trauma Score (T-RTS) may result in a more accurate prediction of the actual clinical outcome, both in general adult population and in elderly patients with trauma. This is a retrospective cohort study, conducted in the emergency department (ED) of an urban teaching hospital, with an average ED admission rate of 75,000 patients per year. Those excluded: known diagnosis of diabetes, age <18 years old, pregnancy, and mild trauma (classified as isolate trauma of upper or lower limb, in absence of exposed fractures). A combined Revised Trauma Score Glucose (RTS-G) score was obtained adding to T-RTS: two for BGL <160mg/dL (8.9mmol/L); one for BGL ≥160mg/dL and < 200mg/dL (11.1mmol/L); and zero for BGL ≥ 200mg/dL. The primary outcome was a composite of patient's death in ED or admission to intensive care unit (ICU). Receiver Operating Characteristic (ROC) curve analysis was used to evaluate the overall performance of T-RTS and of the combined RTS-G score. Among a total of 68,933 traumas, 9,436 patients (4,407 females) were enrolled, aged from 18 to 103 years; 4,288 were aged ≥65 years. A total of 577 (6.1%) met the primary endpoint: 38 patients died in ED (0.4%) and 539 patients were admitted to ICU. The T-RTS and BGL were independently associated to primary endpoint at multivariate analysis. The cumulative RTS-G score was significantly more accurate than T-RTS and reached the best accuracy in elderly patients. In general population, ROC area under curve (AUC) for T-RTS was 0.671 (95% CI, 0.661 - 0.680) compared to RTS-G ROC AUC 0.743 (95% CI, 0.734 - 0.752); P <.001. In patients ≥65 years, T-RTS ROC AUC was 0.671 (95% CI, 0.657 - 0.685) compared to RTS-G ROC AUC 0.780 (95% CI, 0.768 - 0.793); P <.001. Results showed RTS-G could be used effectively at ED triage for the risk stratification for death in ED and ICU admission of trauma patients, and it could reduce under-triage of approximately 20% compared to T-RTS. Comparing ROC AUCs, the combined RTS-G score performs significantly better than T-RTS and gives best results in patients ≥65 years.

Introduction: Staging of liver fibrosis is essential for making therapeutic decisions in patients with Chronic Hepatitis B (CHB) having raised Hepatitis B Virus Deoxyribonucleic Acid (HBV DNA) levels (&gt;2000 IU/ml) and normal or mildly elevated Alanine Transaminase (ALT). Though the gold standard for assessment of liver fibrosis has been liver biopsy, many non invasive models have been developed to mitigate the risks associated with liver biopsy and overcome its limitations. Aim: To evaluate the non invasive models predictive of significant fibrosis in this selected subgroup of Chronic Hepatitis B patients. Materials and Methods: Fifty-six CHB patients were evaluated. This longitudinal observational study was conducted at Sir Sunderlal Hospital, Institute of Medical Sciences, Banaras Hindu University from February 2017 to July 2018 on 56 patients. Liver Stiffness Measurement (LSM), Aspartate Aminotransferase (AST) to Platelet Ratio Index (APRI), FIBROSIS-4 (FIB-4) and Gamma-Glutamyl Transpeptidase (GGT) to platelet ratio (GPR) were estimated. Liver fibrosis staging was done using Metavir score. Significant fibrosis corresponds to Metavir score F2-F4 and advanced fibrosis as more than F3.The performance of non invasive methods was assessed using Receiver Operating Characteristic (ROC) curves. Z -test was used to compare Area Under ROC Curves (AUROCs). Results: Twenty-one patients (37.5%) had significant fibrosis, out of which seven had F3-F4 fibrosis. Patients with F2-F4 fibrosis had higher age, Hepatitis B e antigen (HBeAg) positivity, HBV DNA, ALT, AST, GGT, LSM, APRI, FIB-4 and GPR values than patients with F0-F1 fibrosis. Metavir fibrosis stages positively correlated with LSM values (r=0.831, p&lt;0.0001), APRI (r=0.338, p=0.011), FIB-4 (r=0.375, p=0.003) and GPR (r=0.36, p=0.012). To predict advanced fibrosis, the AUROC of LSM had higher AUROC than APRI (0.956 vs 0.755, p=0.01), FIB-4 (0.956vs 0.786, p=0.01) and was comparable to GPR (0.956 vs 0.895, p=0.2). Conclusion: Transient Elastography (TE) is a reliable non invasive test for the diagnosis of liver fibrosis. GPR is a new model which is comparable to APRI and FIB-4 but inferior to TE.

Purpose: To evaluate the performance of radiomic features (RF) derived from PSMA PET for intraprostatic tumor discrimination and non-invasive characterization of Gleason score (GS) and pelvic lymph node status.Patients and methods: Patients with prostate cancer (PCa) who underwent [68Ga]-PSMA-11 PET/CT followed by radical prostatectomy and pelvic lymph node dissection were prospectively enrolled (n=20). Coregistered histopathological gross tumor volume (GTV-Histo) in the prostate served as reference. 133 RF were derived from GTV-Histo and from manually created segmentations of the intraprostatic tumor volume (GTV-Exp). Spearman´s correlation coefficients (ρ) were assessed between RF derived from the different GTVs. We additionally analyzed the differences in RF values for PCa and non-PCa tissues. Furthermore, areas under receiver-operating characteristics curves (AUC) were calculated and uni- and multivariate analyses were performed to evaluate the RF based discrimination of GS 7 and ≥8 disease and of patients with nodal spread (pN1) and non-nodal spread (pN0) in surgical specimen. The results found in the latter analyses were validated by a retrospective cohort of 40 patients.Results: Most RF from GTV-Exp showed strong correlations with RF from GTV-Histo (86% with ρ>0.7). 81% and 76% of RF from GTV-Exp and GTV-Histo significantly discriminated between PCa and non-PCa tissue. The texture feature QSZHGE discriminated between GS 7 and ≥8 considering GTV-Histo (AUC=0.93) and GTV-Exp (prospective cohort: AUC=0.91 / validation cohort: AUC=0.84). QSZHGE also discriminated between pN1 and pN0 disease considering GTV-Histo (AUC=0.85) and GTV-Exp (prospective cohort: AUC=0.87 / validation cohort: AUC=0.85). In uni- and multivariate analyses including patients of both cohorts QSZHGE was a statistically significant (p<0.01) predictor for PCa patients with GS ≥8 tumors and pN1 status.Conclusion: RF derived from PSMA PET discriminated between PCa and non-PCa tissue within the prostate. Additionally, the texture feature QSZHGE discriminated between GS 7 and GS ≥8 tumors and between patients with pN1 and pN0 disease. Our results support the role of RF in PSMA PET as a new tool for non-invasive PCa discrimination and characterization of its biological properties.

This work is aimed at evaluating the image reconstruction techniques used in single photon emission computed tomography (SPECT/CT) imaging. The reconstruction techniques considered in this work are: the ordered subset expectation maximization (OSEM) and the Filtered Back Projection (FBP). An anthropomorphic torso phantom with lung and liver inserts was injected with 99mTc. The liver and the background were given 0.2 MBq/ml and 0.07 MBq/ml respectively. The lungs inserts were filled with polystyrene beads for humanization. A mimicked 30 ml tumor (27 mm) in the liver insert was given 2 MBq/ml sequential SPECT/CT (step and shot, 10 mins over 180° per head) and CT (120 kVp, 80 mAs) projections were acquired using dual head SPECT/16 slice CT system. The projections were reconstructed using OSEM (10 subsets for 2–10 numbers of iterations, Butterworth post filtering, 10 orders and 0.48 frequencies) and FBP (using Butterworth post filtering at 10 order and cutoff frequencies of 0.38–0.58, 0.05 gaps). Image J software was used for the image analysis. Statistical t-test tested the mean gray values of the tumor and its background. The OSEM and FBP reconstructed images were evaluated from their contrast-to-noise ratio (CNR). The optimal parameter was selected based on 3–5 CNR detection range set by the Rose criterion. The t-test showed the background and the tumors mean values to be statistically significant (P ≪ 0.05). The CNR values for FBP images were all below the detection range, maximum value at the cutoff frequency 0.48 (1.69). In OSEM images at cutoff frequency 0.48, the CNR values were within the detection range for 2–8 numbers of iteration with maximum value (4.89) at 4 numbers of iteration. OSEM was found to be the best for tumor detection and administered activity can be reduced at 4 OSEM’s of iterations (40 MLEM iterations) for patient safety. Keywords: Anthropomorphic torso phantom, 99mTc, SPECT, iterative reconstruction, CNR, P-Value, liver Cite this Article F. Usman, R. Zainon, A. Saidu, A. Bala. Evaluation of image reconstruction techniques in Single photon emission computed tomography (SPECT/CT) imaging. Research & Reviews: Journal of Physics. 2016; 5(2): 13–22p.

Although pneumonia is a major complication after acute ischaemic stroke (AIS), pneumonia prediction scores have not been extensively validated. This study aimed to compare the discrimination performance of 5 pneumonia prediction scores in AIS patients. We retrospectively reviewed all consecutive adult AIS patients whom presented to our emergency department within 4.5 hours of symptom-onset between January 2012 and February 2015. Diagnosis had to be made by a neurologist and infarcts confirmed by neuroimaging. We excluded patients with pneumonia on presentation. Pneumonia predictors were based on the 5 prediction scoring models: Kwon's score, Chumbler's score, Acute Ischaemic Stroke-Associated Pneumonia Score (AIS-APS), A2DS2 score and ISAN score. The definition of stroke-associated pneumonia was based on the criteria by the Pneumonia in Stroke Consensus Group. Analysis using area under receiver operating characteristics curve (AUROC) was performed. Forty (5.5%) out of 731 patients analysed had stroke-associated pneumonia (SAP). A2DS2 score had the highest discrimination capacity (AUROC 0.88; 95% CI, 0.84 to 0.92), followed by AIS-APS (AUROC 0.87; 95% CI, 0.83 to 0.91), Kwon's score (AUROC 0.86; 95% CI, 0.82 to 0.92), Prestroke Independence, Sex, Age and National Institutes of Health Stroke Scale (ISAN) score (AUROC 0.85; 95% CI, 0.80 to 0.90) and Chumbler's score (AUROC 0.79; 95% CI, 0.74 to 0.84). However, there was no statistical difference of discrimination capacity among A2DS2 score, AIS-APS and Kwon's score. A2DS2 , AIS-APS and Kwon's scores performed comparably in discriminating SAP in AIS patients.

Background Crohn’s disease (CD) is characterized by a heterogeneous disease course and treatment response. There is a clinical need to identify CD patients at diagnosis who are at risk for developing a severe disease course. Patient stratification using state-of-the-art clinical, serological or genetic markers does not predict disease course sufficiently to facilitate clinical decision making. The current study aimed to investigate the additive predictive value of histopathological features at diagnosis to discriminate between patients with a long-term mild and severe disease course. Methods Diagnostic biopsies from treatment-naïve CD patients with mild or severe disease courses in the first 10 years after diagnosis (i.e. based on the number of quarterly flares) were reviewed by two senior gastrointestinal pathologists after developing a standardized form comprising 15 histopathological features related to acute and chronic inflammation. Multivariable logistic regression models were built to identify predictive features and compute receiver operating characteristics (ROC) curves. Model 1 included clinically relevant baseline characteristics (Montreal classification, smoking status and gender). Next, histopathological were added by applying two different model-building strategies (forward selection and purposeful selection algorithm)(Model 2). Prediction models were internally validated using bootstrapping to obtain optimism-corrected performance estimates. Results In total, 817 biopsies from 137 CD patients (64 mild disease course, 73 severe disease course) were included. Based on clinical baseline characteristics alone, disease course could only be moderately predicted (Model 1 Area under ROC (AUROC): 0.738 (optimism 0.018), 95%CI 0.65–0.83, sensitivity 83.6%, specificity 53.1%). When adding histopathological features, in colonic, but not ileal, biopsies a combination of (1) basal plasmacytosis, (2) severe lymphocyte and plasma cell infiltration in the lamina propria, (3) Paneth cell metaplasia and (4) absence of ulcers were identified and resulted in significantly better prediction of a severe disease course (Model 2 AUROC: 0.883 (optimism 0.033), 95%CI 0.82–0.94, sensitivity 80.4%, specificity 84.2%, model 2 vs. model 1 AUROC p = 0.001)[Figure 1]. Conclusion In this first study investigating the additive predictive value of multiple histopathological features in biopsies at CD diagnosis, we found that certain features of chronic inflammation in colonic biopsies contributed to prediction of a severe disease course, thereby presenting a novel approach to improve stratification and facilitate clinical decision making.

This study aimed to explore the value of cardiac magnetic resonance tissue tracking (CMR-TT) technology in evaluating heart failure with preserved ejection fraction (HFpEF) in patients with chronic myocardial infarction (CMI). Between June 2016 and March 2022, we included a consecutive series of 92 patients with CMI and 40 healthy controls in this retrospective study. The CMI patients enrolled were divided into different subgroups [HFpEF-CMI group (n=54) and non- heart failure (HF)-CMI group (n=38)] according to the Heart Failure Association (HFA)-PEFF (step 1: P, pre-test assessment; step 2: E, echocardiography and natriuretic peptide score; step 3: F1, functional testing; step 4: F2, final aetiology) diagnostic algorithm. CMR scan was performed at the First Hospital of China Medical University. Quantitative measurements of myocardial damage, such as myocardial strain parameters of both ventricles derived by CMR-TT and infarct size and transmurality by late gadolinium enhancement (LGE), were assessed. One-way analysis of variance, independent samples t-test, and rank sum test were used to compare myocardial impairment among groups. Pearson or Spearman correlation coefficient was used to measure correlations between left ventricular (LV) strains and clinical and functional parameters. Logistic regression analysis and receiver operating characteristic (ROC) curve were performed to identify the best parameter for diagnosing HFpEF-CMI. HFpEF-CMI patients demonstrated significantly impaired LV strains and strain rates in all of the three directions (radial, circumferential and longitudinal) compared to non-HF-CMI patients and healthy controls (P<0.001 for all), whereas only global longitudinal strain (GLS) was significantly impaired in HFpEF-CMI patients vs. controls for right ventricular strain parameters (P<0.001). LV strains showed moderate correlation with N-terminal pro-brain natriuretic peptide (radial, circumferential and longitudinal strain, R=-0.401, R=0.408, R=0.407, respectively, P<0.001 for all). LV strains in the three directions (radial, circumferential and longitudinal) [area under ROC curve (AUC) =0.707, 95% confidence interval (CI): 0.603-0.797; AUC =0.708, 95% CI: 0.604-0.798; AUC =0.731, 95% CI: 0.628-0.818; respectively, P<0.01 for all] were discriminators for HFpEF-CMI and non-HF-CMI. LV strains and myocardial infarction volume were independent factors in multi-logistic regression analysis after adjusting for body mass index, age, and sex (P<0.05 for all). CMR-TT provides clinicians with useful additional imaging parameters to facilitate the assessment of CMI patients with HFpEF. LV strain parameters can detect early cardiac insufficiency in patients with HFpEF-CMI and have potential value for discriminating between HFpEF and non-HF patients post-CMI.

The purpose of the study was to explore the differences of conventional ultrasound characteristics, thyroid imaging reporting and data system (TI-RADS) category and shear wave speed (SWS) measurement between follicular adenoma (FA) and follicular thyroid carcinoma (FTC). Twenty-eight FTCs and 67 FAs proven by surgery were retrospectively included for analysis. Conventional ultrasound and point-shear wave elastography (p-SWE) were performed in all of the included patients. The ultrasound features, American Thyroid Association (ATA) TI-RADS category and American College of Radiology (ACR) TI-RADS category, SWS measurement were compared between the two groups. Receiver operating characteristic (ROC) curve was performed and area under ROC curve (AUC) was obtained for significant features. There were no statistical differences in mean age (46.9±15.7years vs. 48.6±13.6years, P = 0.639), gender (9 males, 32.1% vs. 18 males, 29.0%, P = 0.766) and mean diameter (28.3±16.2 mm vs. 33.8±11.9 mm, P = 0.077) between FTCs and FAs. Hypoechogenicity, lobulated or irregular margin, macrocalcification were more common in FTCs than FAs (all P < 0.05). Mean SWS of FTCs (2.29±0.64 m/s) was slightly higher than that of FAs (1.94±0.68 m/s) (P = 0.023). The AUCs were 0.655, 0.744, and 0.744 with the cut-off SWS≥1.89 m/s, ACR TI-RADS category 4 and intermediate suspicion of ATA TI-RADS category. The sensitivity and AUC were 82.1% and 0.812 with combined ultrasound features of hypoechogenicity, lobulated or irregular margin and macrocalcification. In Conclusion, SWS measurement and TI-RADS categories were useful for the identification of FTCs from FAs.