Identification of the most specific markers to differentiate primary pulmonary carcinoma from metastatic gastrointestinal carcinoma to the lung

Abstract Background A number of biomarkers have the potential of differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract, however, a standardised panel for that purpose does not exist yet. We aimed to identify the smallest panel that is most sensitive and specific at differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract. Methods A total of 170 samples were collected, including 140 primary and 30 non-primary lung tumours and staining for CK7, Napsin-A, TTF1, CK20, CDX2, and SATB2 was performed via tissue microarray. The data was then analysed using univariate regression models and a combination of multivariate regression models and Receiver Operating Characteristic (ROC) curves. Results Univariate regression models confirmed the 6 biomarkers’ ability to independently predict the primary outcome (p < 0.001). Multivariate models of 2-biomarker combinations identified 11 combinations with statistically significant odds ratios (ORs) (p < 0.05), of which TTF1/CDX2 had the highest area under the curve (AUC) (0.983, 0.960–1.000 95% CI). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 75.7, 100, 100, and 37.5% respectively. Multivariate models of 3-biomarker combinations identified 4 combinations with statistically significant ORs (p < 0.05), of which CK7/CK20/SATB2 had the highest AUC (0.965, 0.930–1.000 95% CI). The sensitivity, specificity, PPV, and NPV were 85.1, 100, 100, and 41.7% respectively. Multivariate models of 4-biomarker combinations did not identify any combinations with statistically significant ORs (p < 0.05). Conclusions The analysis identified the combination of CK7/CK20/SATB2 to be the smallest panel with the highest sensitivity (85.1%) and specificity (100%) for predicting tumour origin with an ROC AUC of 0.965 (p < 0.001; SE: 0.018, 0.930–1.000 95% CI).

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Significance of CAVI, hs-CRP and Homocysteine in subclinical arteriosclerosis among a healthy population in China

Clinical & Investigative Medicine ◽

10.25011/cim.v36i2.19570 ◽

2013 ◽

Vol 36 (2) ◽

pp. 81 ◽

Cited By ~ 1

Author(s):

Jinpeng Zhong ◽

Yonghong Wang ◽

Xiaoling Wang ◽

Fengzeng Li ◽

Yulei Hou ◽

...

Keyword(s):

Predictive Value ◽

Area Under The Curve ◽

High Sensitivity ◽

Intima Media Thickness ◽

Roc Curves ◽

Carotid Intima Media Thickness ◽

Youden Index ◽

Healthy Population ◽

Hs Crp ◽

Sensitivity Specificity

Purpose: The purpose of this study is to evaluate the ability of cardio-ankle vascular index (CAVI), high-sensitivity C-reactive protein (hs-CRP) levels and homocysteine (Hcy) levels to screen for subclinical arteriosclerosis (subAs) in an apparently healthy population, with the view to obtaining an optimal diagnostic marker or profile for subAs. Methods: Subjects (152) undergoing routine health examinations were recruited and divided into two groups: carotid arteriosclerosis (CA) and non-carotid arteriosclerosis (NCA), according to carotid intima-media thickness (CMIT). CAVI was calculated based on blood pressure and pulse wave velocity. Serum hs-CRP and Hcy levels were also measured. A Receiver Operating Characteristic (ROC) curve was plotted to evaluate the efficacy of each in carotid arteriosclerosis screening. Ten parameter combinations, designated W1 to W10, were compared in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: The levels of all three parameters were significantly higher in the CA group, compared with the NCA group. ROC curves showed that the area under the curve (AUC) for CAVI was 0.708 (95%CI: 0.615-0.800), which is significantly larger than that of either hs-CRP (0.622) or Hcy (0.630), respectively (P < 0.001). Maximum sensitivity (100%) and NPV (100%) were attained with W10, while maximum specificity (86.2%) and PPV (46.7%) were obtained with W7. With W9, the maximum Youden index (0.416) was obtained, with a sensitivity of 77.8% and specificity of 63.8%. Conclusions: CAVI is more effective than hs-CRP or Hcy for subAs screening. The optimal profile was obtained with a combination of CAVI and other parameters.

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Comparison of biparametric and multiparametric MRI in the diagnosis of prostate cancer

Cancer Imaging ◽

10.1186/s40644-019-0274-9 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 5

Author(s):

Lili Xu ◽

Gumuyang Zhang ◽

Bing Shi ◽

Yanhan Liu ◽

Tingting Zou ◽

...

Keyword(s):

Prostate Cancer ◽

Diagnostic Accuracy ◽

Predictive Value ◽

Area Under The Curve ◽

Roc Curves ◽

Multiparametric Mri ◽

Dce Mri ◽

Prostate Imaging ◽

The Difference ◽

Sensitivity Specificity

Abstract Purpose To compare the diagnostic accuracy of biparametric MRI (bpMRI) and multiparametric MRI (mpMRI) for prostate cancer (PCa) and clinically significant prostate cancer (csPCa) and to explore the application value of dynamic contrast-enhanced (DCE) MRI in prostate imaging. Methods and materials This study retrospectively enrolled 235 patients with suspected PCa in our hospital from January 2016 to December 2017, and all lesions were histopathologically confirmed. The lesions were scored according to the Prostate Imaging Reporting and Data System version 2 (PI-RADS V2). The bpMRI (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI]/apparent diffusion coefficient [ADC]) and mpMRI (T2WI, DWI/ADC and DCE) scores were recorded to plot the receiver operating characteristic (ROC) curves. The area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for each method were calculated and compared. The patients were further stratified according to bpMRI scores (bpMRI ≥3, and bpMRI = 3, 4, 5) to analyse the difference in DCE MRI between PCa and non-PCa lesions (as well as between csPCa and non-csPCa). Results The AUC values for the bpMRI and mpMRI protocols for PCa were comparable (0.790 [0.732–0.840] and 0.791 [0.733–0.841], respectively). The accuracy, sensitivity, specificity, PPV and NPV of bpMRI for PCa were 76.2, 79.5, 72.6, 75.8, and 76.6%, respectively, and the values for mpMRI were 77.4, 84.4, 69.9, 75.2, and 80.6%, respectively. The AUC values for the bpMRI and mpMRI protocols for the diagnosis of csPCa were similar (0.781 [0.722–0.832] and 0.779 [0.721–0.831], respectively). The accuracy, sensitivity, specificity, PPV and NPV of bpMRI for csPCa were 74.0, 83.8, 66.9, 64.8, and 85.0%, respectively; and 73.6, 87.9, 63.2, 63.2, and 87.8%, respectively, for mpMRI. For patients with bpMRI scores ≥3, positive DCE results were more common in PCa and csPCa lesions (both P = 0.001). Further stratification analysis showed that for patients with a bpMRI score = 4, PCa and csPCa lesions were more likely to have positive DCE results (P = 0.003 and P < 0.001, respectively). Conclusion The diagnostic accuracy of bpMRI is comparable with that of mpMRI in the detection of PCa and the identification of csPCa. DCE MRI is helpful in further identifying PCa and csPCa lesions in patients with bpMRI ≥3, especially bpMRI = 4, which may be conducive to achieving a more accurate PCa risk stratification. Rather than omitting DCE, we think further comprehensive studies are required for prostate MRI.

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Triglycerides and Glucose Index as Potential Marker of Metabolic Syndrome

International Journal of Human and Health Sciences (IJHHS) ◽

10.31344/ijhhs.v5i1.239 ◽

2020 ◽

Vol 5 (1) ◽

pp. 85

Author(s):

Mariya Tabassum ◽

Miliva Mozaffor ◽

Md Matiur Rahman ◽

Reaz Mahmud Huda

Keyword(s):

Metabolic Syndrome ◽

Predictive Value ◽

Area Under The Curve ◽

Roc Curves ◽

Cross Sectional Study ◽

Serum Glucose ◽

Cross Sectional ◽

Tyg Index ◽

Potential Marker ◽

Sensitivity Specificity

Background:Triglycerides and Glucose Index (TyG index), a product from fasting levels of triglycerides and glucose, presented promising results as apotential marker of metabolic syndrome in different ethnicity. However, no such reports are available in our population to date.Objective: To see the effectiveness of ‘Triglycerides and Glucose Index’ to predict metabolic syndromein a Bangladeshi population.Methods: This cross-sectional study was carried out in Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh, from March 2016 to February 2017. A total of 200 apparently healthy subjects (127 men and 73 women) were selected for the study, who attended the out-patient-departments of the same institution. Anthropometric measurements were recorded – height, weight, waist circumference (WC) and body mass index (BMI). Overnight fasting blood samples were collected to estimate fasting serum glucose andlipid profile. Then TyG index was calculated and evaluated as a tool in diagnosis of metabolic syndrome in the study subjects.Receiver operating characteristic (ROC) curves were plotted to assess the performance of TyG index in MetS prediction by gender. The power of MetS prediction was quantified by the area under the curve (AUC) with 95% confidence intervals.Results: Sensitivity, specificity, positive predictive value and negative predictive value of TyG index to predict metabolic syndrome were 70.45%, 82.14%, 75.61% and 77.97%in males and 25.00%, 97.32%, 88.00% and 62.29%in females respectively. ROC curve showed optimal cut off value 8.72 and area under the curve 0.72 in male study subjects; in female study subjects, the values were 8.72 and 0.96 respectively (P<0.001).Conclusion:Triglycerides and Glucose Index (TyG index) represents a simple,accessible and effective tool for assessment of metabolic syndrome in Bangladeshi population.International Journal of Human and Health Sciences Vol. 05 No. 01 January’21 Page: 85-89

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Establishment and Characterization of an Empirical Biomarker SS/PV-ROC Plot Using Results of the UBC® Rapid Test in Bladder Cancer

Entropy ◽

10.3390/e22070729 ◽

2020 ◽

Vol 22 (7) ◽

pp. 729 ◽

Cited By ~ 1

Author(s):

Peter Oehr ◽

Thorsten Ecke

Keyword(s):

Bladder Cancer ◽

Roc Curve ◽

Predictive Value ◽

Area Under The Curve ◽

Rapid Test ◽

Roc Curves ◽

Threshold Value ◽

Control Group ◽

Predictive Values ◽

Sensitivity Specificity

Background: This investigation included both a study of potential non-invasive diagnostic approaches for the bladder cancer biomarker UBC® Rapid Test and a study including comparative methods about sensitivity–specificity characteristic (SS-ROC) and predictive receiver operating characteristic (PV-ROC) curves that used bladder cancer as a useful example. Methods: The study included 289 urine samples from patients with tumors of the urinary bladder, patients with non-evidence of disease (NED) and healthy controls. The UBC® Rapid Test is a qualitative point of care assay. Using a photometric reader, quantitative data can also be obtained. Data for pairs of sensitivity/specificity as well as positive/negative predictive values were created by variation of threshold values for the whole patient cohort, as well as for the tumor-free control group. Based on these data, sensitivity–specificity and predictive value threshold distribution curves were constructed and transformed into SS-ROC and PV-ROC curves, which were included in a single SS/PV-ROC plot. Results: The curves revealed TPP-asymmetric improper curves which cross the diagonal from above. Evaluation of the PV-ROC curve showed that two or more distinct positive predictive values (PPV) can correspond to the same value of a negative predictive value (NPV) and vice versa, indicating a complexity in PV-ROC curves which did not exist in SS-ROC curves. In contrast to the SS-ROC curve, the PV-ROC curve had neither an area under the curve (AUC) nor a range from 0% to 100%. Sensitivity of the qualitative assay was 58.5% and specificity 88.2%, PPV was 75.6% and NPV 77.3%, at a threshold value of approximately 12.5 µg/L. Conclusions: The SS/PV-ROC plot is a new diagnostic approach which can be used for direct judgement of gain and loss of predictive values, sensitivity and specificity according to varied threshold value changes, enabling characterization, comparison and evaluation of qualitative and quantitative bioassays.

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Evaluation of the clot burden score (cbs) for acute ischemic stroke (ais) in intent-to-treat patients.

Journal Of Cardiovascular, Neurovascular & Stroke ◽

10.32896/cvns.v1n1.11-15 ◽

2019 ◽

Vol 1 (1) ◽

pp. 11-15 ◽

Cited By ~ 1

Author(s):

Sarah Yaziz ◽

Ahmad Sobri Muda ◽

Wan Asyraf Wan Zaidi ◽

Nik Azuan Nik Ismail

Keyword(s):

Ischemic Stroke ◽

Clinical Outcome ◽

Acute Ischemic Stroke ◽

Predictive Value ◽

Area Under The Curve ◽

Rank Correlation ◽

Treatment Decision ◽

Roc Curves ◽

Significant Negative Correlation ◽

Clot Burden

Background : The clot burden score (CBS) is a scoring system used in acute ischemic stroke (AIS) to predict patient outcome and guide treatment decision. However, CBS is not routinely practiced in many institutions. This study aimed to investigate the feasibility of CBS as a relevant predictor of good clinical outcome in AIS cases. Methods: A retrospective data collection and review of AIS patients in a teaching hospital was done from June 2010 until June 2015. Patients were selected following the inclusion and exclusion criteria. These patients were followed up after 90 days of discharge. The Modified Rankin scale (mRS) was used to assess their outcome (functional status). Linear regression Spearman Rank correlation was performed between the CBS and mRS. The quality performance of the correlations was evaluated using Receiver operating characteristic (ROC) curves. Results: A total of 89 patients with AIS were analysed, 67.4% (n=60) male and 32.6% (n=29) female. Twenty-nine (29) patients (33.7%) had a CBS ?6, 6 patients (6.7%) had CBS <6, while 53 patients (59.6%) were deemed clot free. Ninety (90) days post insult, clinical assessment showed that 57 (67.6%) patients were functionally independent, 27 (30.3%) patients functionally dependent, and 5 (5.6%) patients were deceased. Data analysis reported a significant negative correlation (r= -0.611, p<0.001). ROC curves analysis showed an area under the curve of 0.81 at the cut-off point of 6.5. This showed that a CBS of more than 6 predicted a good mRS clinical outcome in AIS patients; with sensitivity of 98.2%, specificity of 53.1%, positive predictive value (PPV) of 76%, and negative predictive value (NPV) of 21%. Conclusion: CBS is a useful additional variable for the management of AIS cases, and should be incorporated into the routine radiological reporting for acute ischemic stroke (AIS) cases.

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New screening approach to detecting congenital syphilis in China: a retrospective cohort study

Archives of Disease in Childhood ◽

10.1136/archdischild-2020-320549 ◽

2020 ◽

pp. archdischild-2020-320549

Author(s):

Fang Hu ◽

Shuai-Jun Guo ◽

Jian-Jun Lu ◽

Ning-Xuan Hua ◽

Yan-Yan Song ◽

...

Keyword(s):

Predictive Value ◽

Congenital Syphilis ◽

Area Under The Curve ◽

Diagnostic Tools ◽

Operating Characteristics ◽

Loss To Follow Up ◽

Sensitivity Specificity ◽

Mother To Child ◽

Rule Out

BackgroundDiagnosis of congenital syphilis (CS) is not straightforward and can be challenging. This study aimed to evaluate the validity of an algorithm using timing of maternal antisyphilis treatment and titres of non-treponemal antibody as predictors of CS.MethodsConfirmed CS cases and those where CS was excluded were obtained from the Guangzhou Prevention of Mother-to-Child Transmission of syphilis programme between 2011 and 2019. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using receiver operating characteristics (ROC) in two situations: (1) receiving antisyphilis treatment or no-treatment during pregnancy and (2) initiating treatment before 28 gestational weeks (GWs), initiating after 28 GWs or receiving no treatment for syphilis seropositive women.ResultsAmong 1558 syphilis-exposed children, 39 had confirmed CS. Area under the curve, sensitivity and specificity of maternal non-treponemal titres before treatment and treatment during pregnancy were 0.80, 76.9%, 78.7% and 0.79, 69.2%, 88.7%, respectively, for children with CS. For the algorithm, ROC results showed that PPV and NPV for predicting CS were 37.3% and 96.4% (non-treponemal titres cut-off value 1:8 and no antisyphilis treatment), 9.4% and 100% (non-treponemal titres cut-off value 1:16 and treatment after 28 GWs), 4.2% and 99.5% (non-treponemal titres cut-off value 1:32 and treatment before 28 GWs), respectively.ConclusionsAn algorithm using maternal non-treponemal titres and timing of treatment during pregnancy could be an effective strategy to diagnose or rule out CS, especially when the rate of loss to follow-up is high or there are no straightforward diagnostic tools.

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Value and Diagnostic Efficacy of Fetal Morphology Assessment Using Ultrasound in A Poor-Resource Setting

Diagnostics ◽

10.3390/diagnostics9030109 ◽

2019 ◽

Vol 9 (3) ◽

pp. 109 ◽

Cited By ~ 1

Author(s):

Ukweh ◽

Ugbem ◽

Okeke ◽

Ekpo

Keyword(s):

Predictive Value ◽

Performance Metrics ◽

Area Under The Curve ◽

Youden Index ◽

Likelihood Ratios ◽

Diagnostic Efficacy ◽

Predictive Values ◽

Resource Setting ◽

Post Test ◽

Sensitivity Specificity

Background: Ultrasound is operator-dependent, and its value and efficacy in fetal morphology assessment in a low-resource setting is poorly understood. We assessed the value and efficacy of fetal morphology ultrasound assessment in a Nigerian setting. Materials and Methods: We surveyed fetal morphology ultrasound performed across five facilities and followed-up each fetus to ascertain the outcome. Fetuses were surveyed in the second trimester (18th–22nd weeks) using the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) guideline. Clinical and surgical reports were used as references to assess the diagnostic efficacy of ultrasound in livebirths, and autopsy reports to confirm anomalies in terminated pregnancies, spontaneous abortions, intrauterine fetal deaths, and still births. We calculated sensitivity, specificity, positive and negative predictive values, Area under the curve (AUC), Youden index, likelihood ratios, and post-test probabilities. Results: In total, 6520 fetuses of women aged 15–46 years (mean = 31.7 years) were surveyed. The overall sensitivity, specificity, and AUC were 77.1 (95% CI: 68–84.6), 99.5 (95% CI: 99.3–99.7), and 88.3 (95% CI: 83.7–92.2), respectively. Other performance metrics were: positive predictive value, 72.4 (95% CI: 64.7–79.0), negative predictive value, 99.6 (95% CI: 99.5–99.7), and Youden index (77.1%). Abnormality prevalence was 1.67% (95% CI: 1.37–2.01), and the positive and negative likelihood ratios were 254 (95% CI: 107.7–221.4) and 0.23 (95% CI: 0.16–0.33), respectively. The post-test probability for positive test was 72% (95% CI: 65–79). Conclusion: Fetal morphology assessment is valuable in a poor economics setting, however, the variation in the diagnostic efficacy across facilities and the limitations associated with the detection of circulatory system anomalies need to be addressed.

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The role of red cell distribution width in the differential diagnosis of iron deficiency anemia and non-transfusion dependent thalassemia patients

Hematology Reports ◽

10.4081/hr.2018.7605 ◽

2018 ◽

Vol 10 (3) ◽

Cited By ~ 3

Author(s):

Pokpong Piriyakhuntorn ◽

Adisak Tantiworawit ◽

Thanawat Rattanathammethee ◽

Chatree Chai-Adisaksopha ◽

Ekarat Rattarittamrong ◽

...

Keyword(s):

Diagnostic Accuracy ◽

Predictive Value ◽

Area Under The Curve ◽

Microcytic Anemia ◽

Deficiency Anemia ◽

Operating Characteristics ◽

Training Set ◽

Distribution Width ◽

Validation Set ◽

Sensitivity Specificity

This study aims to find the cut-off value and diagnostic accuracy of the use of RDW as initial investigation in enabling the differentiation between IDA and NTDT patients. Patients with microcytic anemia were enrolled in the training set and used to plot a receiving operating characteristics (ROC) curve to obtain the cut-off value of RDW. A second set of patients were included in the validation set and used to analyze the diagnostic accuracy. We recruited 94 IDA and 64 NTDT patients into the training set. The area under the curve of the ROC in the training set was 0.803. The best cut-off value of RDW in the diagnosis of NTDT was 21.0% with a sensitivity and specificity of 81.3% and 55.3% respectively. In the validation set, there were 34 IDA and 58 NTDT patients using the cut-off value of >21.0% to validate. The sensitivity, specificity, positive predictive value and negative predictive value were 84.5%, 70.6%, 83.1% and 72.7% respectively. We can therefore conclude that RDW >21.0% is useful in differentiating between IDA and NTDT patients with high diagnostic accuracy

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Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs

Dentomaxillofacial Radiology ◽

10.1259/dmfr.20200513 ◽

2021 ◽

pp. 20200513

Author(s):

Su-Jin Jeon ◽

Jong-Pil Yun ◽

Han-Gyeol Yeom ◽

Woo-Sang Shin ◽

Jong-Hyun Lee ◽

...

Keyword(s):

Deep Learning ◽

Cone Beam Ct ◽

Area Under The Curve ◽

Roc Curves ◽

Learning System ◽

Operating Characteristics ◽

Panoramic Radiographs ◽

Test Sets ◽

Sensitivity Specificity ◽

Deep Learning Model

Objective: The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for predicting C-shaped canals in mandibular second molars on panoramic radiographs. Methods: Panoramic and cone beam CT (CBCT) images obtained from June 2018 to May 2020 were screened and 1020 patients were selected. Our dataset of 2040 sound mandibular second molars comprised 887 C-shaped canals and 1153 non-C-shaped canals. To confirm the presence of a C-shaped canal, CBCT images were analyzed by a radiologist and set as the gold standard. A CNN-based deep-learning model for predicting C-shaped canals was built using Xception. The training and test sets were set to 80 to 20%, respectively. Diagnostic performance was evaluated using accuracy, sensitivity, specificity, and precision. Receiver-operating characteristics (ROC) curves were drawn, and the area under the curve (AUC) values were calculated. Further, gradient-weighted class activation maps (Grad-CAM) were generated to localize the anatomy that contributed to the predictions. Results: The accuracy, sensitivity, specificity, and precision of the CNN model were 95.1, 92.7, 97.0, and 95.9%, respectively. Grad-CAM analysis showed that the CNN model mainly identified root canal shapes converging into the apex to predict the C-shaped canals, while the root furcation was predominantly used for predicting the non-C-shaped canals. Conclusions: The deep-learning system had significant accuracy in predicting C-shaped canals of mandibular second molars on panoramic radiographs.

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Circular RNA FUNDC1 improves prediction of stroke associated infection in acute ischemic stroke patients with high risk

Bioscience Reports ◽

10.1042/bsr20200902 ◽

2020 ◽

Vol 40 (6) ◽

Author(s):

Lei Zuo ◽

Cai Li ◽

Juan Zu ◽

Honghong Yao ◽

Fuling Yan

Keyword(s):

Ischemic Stroke ◽

High Risk ◽

Acute Ischemic Stroke ◽

Risk Model ◽

Area Under The Curve ◽

Preventive Treatment ◽

Roc Curves ◽

Circular Rna ◽

Diagnosis And Prognosis ◽

Sensitivity Specificity

Abstract Identifying those patients who were at high risk of stroke associated infection (SAI) for preventive antibiotic therapy was imperative for patients’ benefits, thus improving prediction of SAI was critical for all acute ischemic stroke (AIS) patients. Circular RNA FUNDC1 (circFUNDC1) has been reported to be the diagnosis and prognosis biomarker of AIS. Therefore, the present study aimed to figure out whether circFUNDC1 could be the potential predictor of SAI that could help to guide preventive treatment. In total, 68 patients were included in the study, 26 of which had infection and 42 without. Copy number of circFUNDC1 in plasma were quantified by quantitative real-time polymerase chain reaction (qPCR). Platelet spike-in experiment and correlation analysis were conducted to explore possible origins of circFUNDC1 in plasma. A significantly elevated level of circFUNDC1 was found in SAI patients compared with not infected AIS patients (P=0.0258). Receiver operating characteristic (ROC) curves demonstrated the prediction significance of circFUNDC1, with the area under the curve (AUC) at 0.6612 and sensitivity, specificity at 69.23%, 61.90% respectively in predicting SAI. Then, when adding circFUNDC1 in the risk model, the AUC increased from 0.7971 in model A to 0.8038 in model B. Additionally, positive correlation was observed between circFUNDC1 level and neutrophils counts. WBC and neutrophil ratios were significantly elevated in SAI patients compared with non-SAI patients. Therefore, circFUNDC1 could be used to construct a risk model for the prediction of SAI that is beneficial for AIS patients’ preventive treatment.

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