Receiver Operating Characteristic Model Research Articles

The effect of the ratio of aneurysm sac diameter to patient age on the long-term efficacy of different surgical methods for infrarenal abdominal aortic aneurysm

Objective: To investigate the effect of the ratio of the maximum diameter of aneurysm sac to age (R) on the long-term efficacy of endovascular aneurysm repair (EVAR) and open surgical repair (OSR) in patients with infrarenal abdominal aortic aneurysm (IAAA). Methods: This is a retrospective cohort study.The clinical data of 317 patients with IAAA who underwent surgical repair in the Department of Vascular Surgery,the Third Affiliated Hospital of Sun Yat-Sen University from January 2016 to October 2022 were retrospectively collected.There were 266 males and 51 females,aged (69.7±8.3) years (range:37 to 87 years).The R value of the patient was calculated and the receiver operating characteristic(ROC) curve was used to establish a model to calculate the optimal cut-off value.The propensity score matching method was used to match the baseline data of patients in the EVAR and OSR group by 3∶1 (the caliper value was 0.05),and the patients were stratified according to the cutoff value of R,and the postoperative efficacy and survival of the patients were analyzed.The primary endpoint was the total mortality rate,and the secondary endpoints included the occurrence of postoperative complications and reintervention.Pearson χ2 or Fisher 's exact test was used for categorical variables,and t test was used for continuous variables to compare differences between groups.The survival curves of the two groups were described by Kaplan-Meier method. Results: After propensity score matching,198 cases were in the EVAR group and 66 cases were in the OSR group.The ROC model showed that the best cut-off value of R value was 0.90,and the two groups were divided into two layers:R<0.90 and R≥0.90.Among them,112 patients with R<0.90 (84 cases of EVAR,28 cases of OSR);there were 152 patients with R≥0.90 (114 cases of EVAR and 38 cases of OSR).The follow-up time was (23.6±1.6) months (range:1 to 70 months).In the R≥0.90 stratification,the total mortality (26.3% vs.5.3%,P<0.05),complication rate (44.7% vs.26.3%,P<0.05), and secondary intervention rate (31.6% vs.13.2%,P<0.05) in the EVAR group were higher than those in the OSR group.In the R<0.90 stratification,there was no significant difference in the total mortality rate (13.1% vs.10.7%,P<0.05),complication rate (28.6% vs.35.7%,P>0.05) and secondary intervention rate (14.3% vs.21.4%, P>0.05) between the two groups. Conclusions: When R≥0.90 in IAAA patients,OSR maybe more beneficial to patients in terms of survival rate,postoperative complication rate and secondary intervention rate than EVAR.When R<0.90,there are no significant differences in survival rate,complication rate and secondary intervention rate between the two surgical methods.

334 Identification of novel plasma protein biomarkers for diagnosis and prediction of Alzheimer’s disease in African Americans

OBJECTIVES/GOALS: To identify novel panel of plasma protein biomarkers to improve prediction and diagnosis of Alzheimer’s disease (AD) for African Americans (AA), who are at greater risk of developing AD compared to non-Hispanic White individuals but are underrepresented in AD research. METHODS/STUDY POPULATION: Pre-existing plasma samples from 460 AA individuals with clinical diagnoses of AD, cognitively unimpaired (CU), mild cognitive impairment (MCI), or dementia with Lewy bodies (DLB) will undergo untargeted proteomics using the SomaScan assay, where modified single stranded DNA aptamers bind to protein targets which are quantified by DNA microarray. Protein expression levels will be compared between diagnostic groups to identify differentially expressed proteins. Additional clinical, genetic, and lifestyle factors will be compared with protein expression when available. Proteins of interest, identified by differential protein expression analysis results, will be included in receiver operating characteristic analyses to identify the optimal set of proteins for diagnostic classification. RESULTS/ANTICIPATED RESULTS: A pilot experiment utilizing plasma from 40 individuals identified multiple differentially expressed proteins (DEPs) between AD and non-AD groups. Eight proteins were nominated from the differential protein analysis into a receiver operating characteristic (ROC) analysis based on pvalue and previous implication in AD genome wide association studies. Proteins involved in microglial activation, neuronal adhesion, cell proliferation, and innate immunity were nominated. The ROC model achieved 100% classification accuracy of AD and CU groups using age, sex, and the eight nominated proteins. It is expected that there will be more significant associations when utilizing the full cohort of 460 AA and that DEPs between AD, CU, MCI, and DLB will be identified. DISCUSSION/SIGNIFICANCE: The nomination of a novel panel of plasma biomarkers developed from an AA cohort will directly serve the AA community by improving access to an early and accurate diagnosis of AD. Access to improved prediction and diagnosis will likely improve disease management, thus improving patient outcomes and decreasing burden on families and caregivers.

Consanguinity and Nonsuicidal Self-Injury in Depressed Patients: New Risk Factors and Risk Prediction Models.

The aim of the study was to identify the risk factors associated with nonsuicidal self-injurious (NSSI) behavior in patients with depressive disorders and develop predictive models utilizing these influencing factors as predictors, followed by validation of the constructed models for their efficacy. Patients with depression disorders admitted to Wuhan Mental Health Center from 2020 to 2021 were included using retrospective analysis. Patients who exhibited one or more items on the NSSI behavior rating questionnaire were categorized into the NSSI group, while those without any such behaviors were assigned to the non-NSSI group. Patients in both groups were categorized separately based on gender, age, personality traits, and interpersonal relationships. The above data were analyzed using multiple logistic regression analysis. Prediction models were constructed, receiver operating characteristic (ROC) curves were produced and model accuracy was calculated. A total of 237 patients were included in this study, with 122 patients assigned to the NSSI group and 115 patients assigned to the non-NSSI group. By comparing the baseline data of the patients in the 2 groups, the results revealed statistically significant differences between the 2 groups in terms of age, grades at school, early childhood parenting style, Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Scale (HAMA), and Experiences in Close Relationships Scale (ECRS) (P＜.05). However, no statistically significant differences were observed for the remaining indicators (P＞.05). The results of the multiple logistic regression model showed that grades at school, early childhood parenting style, HAMD, HAMA, and ECRS scores were risk factors. The ROC model was constructed using school performance, childhood parenting style, HAMD, HAMA, and ECRS scores as predictors. The findings indicated that the ECRS score was the best predictor of NSSI, and it had a sensitivity of 91.8% and specificity of 70.5% for an area of 0.967. ECRS was utilized as a predictor to evaluate the NSSI inclination of depressed patients with commendable sensitivity and specificity. Furthermore, early childhood parenting style, HAMD, HAMA, and ECRS scores were identified as risk factors for NSSI. For individuals at high risk who exhibit these aforementioned risk factors, clinical diagnosis and treatment should be approached with caution.

Predicting preterm birth within 2 weeks in asymptomatic women with a short cervix: Combined effects of cervicovaginal fluid cytokine levels and fetal fibronectin test.

To predict preterm birth (PTB) accurately, we conducted a comprehensive cytokine assay using cervicovaginal fluid (CVF) and evaluated the additive effects of cytokine levels on the fetal fibronectin (fFN) test. A total of 645 CVF samples were collected from 256 asymptomatic pregnant women between 24 and 35 weeks gestation, exhibiting short cervix. After selection based on specific criteria, 17 cytokines in 105 CVF samples were simultaneously measured using multiplex assay. Multivariate logistic regression analysis was performed to evaluate the association between cytokine levels and impending PTB, which is defined as PTB within 2 weeks after CVF collection. Moreover, receiver operating characteristic (ROC) analysis was performed in women with positive fFN results, which was validated using another set of 65 CVF samples. In positive fFN women, the CCL2 level was significantly higher in the impending PTB group than the other group (p < 0.01) and a predictor of impending PTB (adjusted odds ratio 1.020, 95% confidence interval [95% CI] 1.003-1.038, p = 0.020). The cutoff value of CCL2 was 64.8 pg/mL (are under the curve 0.726, p = 0.004, 95% CI 0.593-0.859, sensitivity 45.2%, specificity 91.7%). Additionally, the reliable classification performance of proposed ROC model could be validated. However, measuring cytokine levels could not help in predicting impending PTB in women with negative fFN or normal labor onset in healthy-term women. Comprehensive analysis of CVF cytokines revealed that the CCL2 level significantly improves the prediction of impending PTB in asymptomatic fFN-positive women with a short cervix, which may contribute to better clinical management.

Risk Factors for Massive Cerebral Infarction in Pediatric Patients With Moyamoya Disease

BackgroundTo explore the risk factors for preoperative massive cerebral infarction (MCI) in pediatric patients with moyamoya disease (MMD). MethodsPediatric patients with MMD treated between 2017 and 2022 were enrolled. Logistic regression analysis was performed to identify risk factors for MCI among the patients, and a nomogram was constructed to identify potential predictors of MCI. Receiver operating characteristic (ROC) curves and areas under the curves were calculated to determine the effects of different risk factors. ResultsThis study included 308 pediatric patients with MMD, including 36 with MCI. The MCI group exhibited an earlier age of onset than the non-MCI group. Significant intergroup differences were observed in familial MMD history, postcirculation involvement, duration from diagnosis to initiation of treatment, Suzuki stage, magnetic resonance angiography (MRA) score, collateral circulation score, and RNF213 p.R4810K variations. Family history, higher MRA score, lower collateral circulation score, and RNF213 p.R4810K variations were substantial risk factors for MCI in pediatric patients with MMD. The nomogram demonstrated excellent discrimination and calibration capabilities. The integrated ROC model, which included all the abovementioned four variables, showed superior diagnostic precision with a sensitivity of 67.86%, specificity of 87.01%, and accuracy of 85.11%. ConclusionsThis study showed that family history, elevated MRA score, reduced collateral circulation score, and RNF213 p.R4810K variations are risk factors for MCI in pediatric patients with MMD. The synthesized model including these variables demonstrated superior predictive efficacy; thus, it can facilitate early identification of at-risk patients and timely initiation of appropriate interventions.

Matrix variate receiver operating characteristic curve for binary classification

In recent years, the study of Receiver Operating Characteristic (ROC) curve analysis has gained significant attention as a means of accurately assessing test performance and determining optimal cutoff points. Traditionally, ROC models have been developed for bi-distributional univariate and multivariate data, such as Bi-normal, Bi-Exponential, Multivariate ROC models, and so forth. However, in current practical scenarios, the prevalence of high-dimensional matrix variate data poses a challenge for accurate test evaluation. To address this issue, this paper presents a novel ROC model that incorporates matrix variate normal distribution to effectively explain the accuracy of a test in the context of matrix data. Further, the accuracy measure, Area under the Curve (AUC) is derived, which helps in explaining the variability of the curve and provides the sensitivity at a particular value of specificity and vice versa. The proposed methodology is supported by a real data set and simulation studies.

Predicting the need for cerebrospinal fluid shunt implantation after spontaneous intracerebral hemorrhage: a challenging task.

The development of persistent hydrocephalus in patients after spontaneous intracerebral hemorrhage (ICH) is still poorly understood, and many variables predicting the need for a cerebrospinal fluid (CSF)-shunt have been described in the literature with varying results. The aim of this study is to find predictive factors for shunt dependency. We performed a retrospective, single-center study of 99 neurosurgically treated patients with spontaneous ICH. Variables, including age, Glasgow Coma Scale (GCS), intraventricular hemorrhage (IVH), location of hemorrhage, acute hydrocephalus, and volumetric analysis of IVH, ICH, and intraventricular CSF were compared between patients with and without CSF-shunt implantation. Furthermore, receiver operating characteristics (ROC) for ICH, IVH, and intraventricular CSF volume parameters were calculated. CSF-shunt implantation was performed significantly more often in patients after thalamic (p = 0.03) and cerebellar ICH (p = 0.04). Moreover, a lower ratio between the total hemorrhage volume and intraventricular CSF volume (p = 0.007), a higher IVH distribution in the third ventricle, and an acute hydrocephalus (p < 0.001) with an increased intraventricular CSF volume (p < 0.001) were associated with shunt dependency. Our ROC model demonstrated a sensitivity of 82% and a specificity of 65% to predict the necessity for a shunt at a cutoff value of 1.9 with an AUC of 0.835. Volumetric analysis of ICH, IVH, and intraventricular CSF may improve the prediction of CSF shunt implantation in patients with spontaneous ICH.

Predicting Adverse Cardiac Events After Radiotherapy for Locally Advanced Non–Small Cell Lung Cancer

BackgroundRadiotherapy may cause grade ≥3 cardiac events, necessitating a better understanding of risk factors. The potential predictive role of imaging biomarkers with radiotherapy doses for cardiac event occurrence has not been studied. ObjectivesThe aim of this study was to establish the associations between cardiac substructure dose and coronary artery calcium (CAC) scores and cardiac event occurrence. MethodsA retrospective cohort analysis included patients with locally advanced non–small cell lung cancer treated with radiotherapy (2006-2018). Cardiac substructures, including the left anterior descending coronary artery, left main coronary artery, left circumflex coronary artery, right coronary artery, and TotalLeft (left anterior descending, left main, and left circumflex coronary arteries), were contoured. Doses were measured in 2-Gy equivalent units, and visual CAC scoring was compared with automated scoring. Grade ≥3 adverse cardiac events were recorded. Time-dependent receiver-operating characteristic modeling, the log-rank statistic, and competing-risk models were used to measure prediction performance, threshold modeling, and the cumulative incidence of cardiac events, respectively. ResultsOf the 233 eligible patients, 61.4% were men, with a median age of 68.1 years (range: 34.9-90.7 years). The median follow-up period was 73.7 months (range: 1.6-153.9 months). Following radiotherapy, 22.3% experienced cardiac events, within a median time of 21.5 months (range: 1.7-118.9 months). Visual CAC scoring showed significant correlation with automated scoring (r = 0.72; P < 0.001). In a competing-risk multivariable model, TotalLeft volume receiving 15 Gy (per 1 cc; HR: 1.38; 95% CI: 1.11-1.72; P = 0.004) and CAC score >5 (HR: 2.51; 95% CI: 1.08-5.86; P = 0.033) were independently associated with cardiac events. A model incorporating age, TotalLeft CAC (score >5), and volume receiving 15 Gy demonstrated a higher incidence of cardiac events for a high-risk group (28.9%) compared with a low-risk group (6.9%) (P < 0.001). ConclusionsAdverse cardiac events associated with radiation occur in more than 20% of patients undergoing thoracic radiotherapy within a median time of <2 years. The present findings provide further evidence to support significant associations between TotalLeft radiotherapy dose and cardiac events and define CAC as a predictive risk factor.

A novel quantitative model based on gross tumor volume corresponding to anatomical distribution measured with multidetector computed tomography to determine the resectability of non‑distant metastatic esophageal squamous cell carcinoma.

It is important to accurately determine the resectability of thoracic esophageal squamous cell carcinoma (ESCC) for treatment decision-making. Previous studies have revealed that the CT-derived gross tumor volume (GTV) is associated with the staging of ESCC. The present study aimed to explore whether the anatomical distribution-based GTV of non-distant metastatic thoracic ESCC measured using multidetector computed tomography (MDCT) could quantitatively determine the resectability. For this purpose, 473 consecutive patients with biopsy-confirmed non-distant metastatic thoracic ESCC who underwent contrast-enhanced CT were randomly divided into a training cohort (TC; 376 patients) and validation cohort (VC; 97 patients). GTV was retrospectively measured using MDCT. Univariate and multivariate analyses were performed to identify the determinants of the resectability of ESCC in the TC. Receiver operating characteristic (ROC) analysis was performed to clarify whether anatomical distribution-based GTV could help quantitatively determinate resectability. Unweighted Cohen's Kappa tests in VC were used to assess the performance of the previous models. Univariate analysis demonstrated that sex, anatomic distribution, cT stage, cN stage and GTV were related to the resectability of ESCC in the TC (all P<0.05). Multivariate analysis revealed that GTV [P<0.001; odds ratio (OR) 1.158] and anatomic distribution (P=0.027; OR, 1.924) were independent determinants of resectability. ROC analysis revealed that the GTV cut-offs for the determination of the resectability of the upper, middle and lower thoracic portions were 23.57, 22.89 and 22.58 cm3, respectively, with areas under the ROC curves of >0.9. Unweighted Cohen's Kappa tests revealed an excellent performance of the ROC models in the upper, middle and lower thoracic portions with Cohen k-values of 0.913, 0.879 and 0.871, respectively. On the whole, the present study demonstrated that GTV and the anatomic distribution of non-distant metastatic thoracic ESCC may be independent determinants of resectability, and anatomical distribution-based GTV can effectively be used to quantitatively determine resectability.

Dental Occlusion Characteristics for Treatment Decision-Making Regarding Surgery-First Approach in Orthodontics.

The surgery-first approach (SFA) is conducted to decrease the difficulty and duration of orthodontic treatment by correcting the skeletal discrepancy at the initial stage of treatment. However, the indication of the SFA has not been well defined yet. This study explored the dental occlusion characteristics for treatment decision-making regarding the SFA. A total of 200 skeletal Class III patients were consecutively collected and divided into two groups: the orthodontic-first approach (OFA) group and the SFA group. The pretreatment digital dental models and lateral cephalograms were measured. Logistic regression was completed and receiver operating characteristic (ROC) curves were obtained to predict the probability of the SFA. Results showed that the ROC model with L1-MP, upper and lower arch length discrepancy, overbite, and asymmetric tooth number as influencing factors revealed that the sensitivity and specificity for determining SFA were 83.0% and 65.0%, respectively; the accuracy of prediction was 75.0%. In conclusion, our findings indicate that the six measurements from digital dental models and lateral cephalograms can be effectively applied in treatment decision-making for the SFA with satisfactory accuracy.

Role of risk factors, scoring systems, and prognostic models in predicting the functional outcome in meningioma surgery: multicentric study of 552 skull base meningiomas

Despite the importance of functional outcome, only a few scoring systems exist to predict neurologic outcome in meningioma surgery. Therefore, our study aims to identify preoperative risk factors and develop the receiver operating characteristics (ROC) models estimating the risk of a new postoperative neurologic deficit and a decrease in Karnofsky performance status (KPS). A multicentric study was conducted in a cohort of 552 consecutive patients with skull base meningiomas who underwent surgical resection from 2014 to 2019. Data were gathered from clinical, surgical, and pathology records as well as radiological diagnostics. The preoperative predictive factors of functional outcome (neurologic deficit, decrease in KPS) were analyzed in univariate and multivariate stepwise selection analyses. Permanent neurologic deficits were present in 73 (13.2%) patients and a postoperative decrease in KPS in 84 (15.2%). Surgery-related mortality was 1.3%. A ROC model was developed to estimate the probability of a new neurologic deficit (area 0.74; SE 0.0284; 95% Wald confidence limits (0.69; 0.80)) based on meningioma location and diameter. Consequently, a ROC model was developed to predict the probability of a postoperative decrease in KPS (area 0.80; SE 0.0289; 95% Wald confidence limits (0.74; 0.85)) based on the patient’s age, meningioma location, diameter, presence of hyperostosis, and dural tail. To ensure an evidence-based therapeutic approach, treatment should be founded on known risk factors, scoring systems, and predictive models. We propose ROC models predicting the functional outcome of skull base meningioma resection based on the age of the patient, meningioma size, and location and the presence of hyperostosis and dural tail.

O091 Predict factors of hepatocyte growth factor family and bone morphogenetic proteins for breast cancer nodal metastasis

Abstract Introduction Lymph node metastasis is essential for the outcome of patients with breast cancer. Bone morphogenetic proteins (BMP) and hepatocyte growth factor (HGF), key to angiogenesis and metastasis of cancers, were investigated for their roles in the predication of nodal metastasis from breast cancer. Methods Expression of BMP family ligands, BMP receptors, HGF, its receptor MET and regulatory factors of HGF activation including HGF activator (HGFA), HGFA inhibitor 1 (HAI1), HAI2, Matriptase-1 and Matriptase-2 were determined in breast cancers (n=127) using real time quantitative PCR. Transcript levels of these genes were analysed in the breast cancers for their implication in lymphatic involvement using Mann-Whitney test. Predictive potential of those genes for nodal metastasis of these molecules was evaluated using both binary logistic regression and receiver operating characteristic (ROC) with SPSS (Version 27). Results With median as a cut-off value, logistic regression analyses showed that MET, Matriptase-1 and BMP15 were positively associated with nodal metastasis whereas Matriptase-2, BMP3 and HAI1 were inversely correlated with nodal involvement. After integrating these six prospective factors, ROC model returned with a significant value against nodal status (RUC=0.657, p=0.001). With the optimal ROC cut-off value to dichotomise patients, the integrated expression produced a significant prediction of nodal metastasis (p=0.006, HR=2.929). MET and HAI1 was correlated with the lymphangiogenesis marks of LYVE1, podo, prox1. Matriptase-1 was inversely correlated with podo, whilst Matriptase-2 and BMP15 showed a positive correlation with prox1. Conclusion The MET/Matriptase-1/BMP15 and the inversed Matriptase-2/BMP3/HAI1 are connected significantly with lymph node metastasis in breast cancer.

Gut microbiota signature as predictors of adverse outcomes after acute ischemic stroke in patients with hyperlipidemia.

The alterations of gut microbiota have been associated with multiple diseases. However, the relationship between gut microbiota and adverse outcomes of hyperlipidemic stroke patients remains unclear. Here we determined the gut microbial signature to predict the poor outcome of acute ischemic stroke (AIS) with hyperlipidemia (POAH). Fecal samples from hyperlipidemic stroke patients were collected, which further analyzed by 16s rRNA gene sequencing. The diversity, community composition and differential gut microbiota were evaluated. The adverse outcomes were determined by modified Rankin Scale (mRS) scores at 3 months after admission. The diagnostic performance of microbial characteristics in predicting adverse outcomes was assessed by receiver operating characteristic (ROC) curves. Our results showed that the composition and structure of gut microbiota between POAH patients and good outcome of AIS with hyperlipidemia (GOAH) patients were different. The characteristic gut microbiota of POAH patients was that the relative abundance of Enterococcaceae and Enterococcus were increased, while the relative abundance of Lachnospiraceae, Faecalibacterium, Rothia and Butyricicoccus were decreased. Moreover, the characteristic gut microbiota were correlated with many clinical parameters, such as National Institutes of Health Stroke Scale (NIHSS) score, mean arterial pressure, and history of cerebrovascular disease. Moreover, the ROC models based on the characteristic microbiota or the combination of characteristic microbiota with independent risk factors could distinguish POAH patients and GOAH patients (area under curve is 0.694 and 0.971 respectively). These findings revealed the microbial characteristics of POAH, which highlighted the predictive capability of characteristic microbiota in POAH patients.

Gross tumor volume of adenocarcinoma of esophagogastric junction corresponding to cT and cN stages measured with computed tomography to quantitatively determine resectabiliy: A case control study.

To determine whether gross tumor volume (GTV) of adenocarcinoma of esophagogastric junction (AEG) corresponding to cT and cN stages measured on CT could help quantitatively determine resectability. 343 consecutive patients with AEG, including 279 and 64 randomly enrolled in training cohort (TC) and validation cohort (VC), respectively, underwent preoperative contrast-enhanced CT. Univariate and multivariate analyses for TC were performed to determine factors associated with resectability. Receiver operating characteristic (ROC) analyses were to determine if GTV corresponding to cT and cN stages could help determine resectability. For VC, Cohen's Kappa tests were to assess performances of the ROC models. cT stage, cN stage and GTV were independently associated with resectability of AEG with odds ratios of 4.715, 4.534 and 1.107, respectively. For differentiating resectable and unresectable AEG, ROC analyses showed that cutoff GTV of 32.77 cm3 in stage cT1-4N0-3 with an area under the ROC curve (AUC) of 0.901. Particularly, cutoffs of 27.67 and 32.77 cm3 in stages cT3 and cT4 obtained AUC values of 0.860 and 0.890, respectively; and cutoffs of 27.09, 33.32 and 37.39 cm3 in stages cN1, cN2 and cN3 obtained AUC values of 0.852, 0.821 and 0.902, respectively. In VC, Cohen's Kappa tests verified that the ROC models had good performance in distinguishing between resectable and unresectable AEG (all Cohen's K values > 0.72). GTV, cT and cN stages could be independent determinants of resectability of AEG. And GTV corresponding to cT and cN stages can help quantitatively determine resectability.

Identifying cancer cell-secreted proteins that activate cancer-associated fibroblasts as prognostic factors for patients with pancreatic cancer.

The study aimed to investigate the mechanism by which cancer-associated fibroblasts (CAFs) are activated by cancer cells and construct a risk model to predict the prognosis of patients with pancreatic cancer (PC) after surgery. Pancreatic stellate cells were isolated from human pancreatic tissue and co-cultured with cancer cells to verify their crosstalk. Liquid chromatography-tandem mass spectrometry was used to detect proteins secreted by cancer cells. The online tools Gene Expression Profiling Interactive Analysis, UALCAN, and the Human Protein Atlas were used to analyse gene expression in PC. Expression data from the cancer genome atlas and the clinical samples were used to develop a training receiver operating characteristic (ROC) model and an external validation ROC model, respectively. We identified that cancer cells promote the activation of inflammatory CAFs (iCAF) through secretory proteins, which promote PC metastasis. Six candidate proteins secreted by cancer cells were identified which promote iCAF formation. These proteins were highly expressed in tumours and were associated with a poor prognosis in patients with PC. Moreover, a 6-gene model was constructed to predict death risk in patients at 1, 2 and 3 years after surgery. The training areas under the ROC curves (AUC) of 1-, 2- and 3-year death risks were 0.780, 0.792 and 0. 825, respectively. The externally validated AUC of death at 3 years post-surgery was 0.728. In conclusion, cancer cell-secreted proteins play a vital role in iCAF formation, and the 6-gene model may be a potential marker for predicting whether PC patients will benefit from surgery.

52P Affordable cancer research for under-resourced settings to implement precision medicine: The tale of the Moroccan OVANORDEST-1 study

52P Affordable cancer research for under-resourced settings to implement precision medicine: The tale of the Moroccan OVANORDEST-1 study

Multi-Class Classification using Mixtures of Univariate and Multivariate ROC Curves

Introduction: Receiver Operating Characteristic (ROC) curve is one of the widely used supervised classification technique to allocate/classify the individuals and also instrumental in comparing diagnostic tests. Generally, to deal with classification problems we need to have knowledge on class labels. In most of the medical scenarios, most of data sets exhibit multi-model patterns in class labels which leads to multi-class classification problems. The main aim of this study is to address on the issue of constructing ROC models when there exists multimodel patterns in the class labels further, to classify the individuals for better diagnosis and also to reduce the complexity of graphical representation of ROC curves in such classification problems.&#x0D; Methods: A new version of univariate and multivariate ROC models are proposed in the framework of Finite Mixtures, due to the flexibility of identifying and modelling the subcomponents in the heterogeneous populations.&#x0D; Results: Oral Glucose Tolerance Test and Disk Hernia datasets are used and simulation studies are also performed. Results show that the proposed models possess better accuracy when compared with Bi-Normal and MROC models with reasonable low 1-Specificity and higher Sensitivity. The ROC curves are depicted in a 2D space rather than higher dimension for multi-class classification problem.&#x0D; Conclusion: It is suggested that before one proceeds to model ROC curves, it is better to take a look at the density patterns of the study variable(s), which in turn help in explaining the true information between the classes and also provides good amount of “true” accuracy.

Optimization and normalization strategies for long term untargeted HILIC-LC-qTOF-MS based metabolomics analysis: Early diagnosis of breast cancer

Although breast cancer is one of the main causes of cancer-related female deaths all over the world, more than 95% of patients survive as a result of early diagnosis and effective treatment. The discovery of new biomarkers can enable early diagnosis of breast cancer and treatment without cancer spread. Metabolomic studies are an effective research area for determining biomarkers that can be used for early diagnosis and monitoring of breast cancer. In this study, a HILIC-LC-qTOF-MS method has been developed to find new biomarkers for early detection of breast cancer from plasma. The chromatographic performance of the LC-MS method was tested using different HILIC columns to analyze as many as metabolites in a single run. After determining optimum method parameters, the method in positive and negative ionization modes was applied to a quite large group of plasma samples collected from 105 healthy volunteers, 172 early stage and 92 metastatic breast cancer patients for untargeted metabolomic screening. This long-term metabolomics data was tested with different normalization techniques in order to minimize the biological and analytical variation. The normalized data were evaluated statistically and 35 metabolites have been found significantly altered between the groups. Among these metabolites, Leukotriene A4, PC(24:1(15Z)/24:1(15Z)), TG(17:0/17:0/18:0), Hypoxanthine and cis-ACCP have been suggested as biomarkers based on receiver operating characteristic (ROC) analysis that can be applied in the early diagnosis of breast cancer. Moreover, the cross-validation results showed that the ROC model has a predictive power of over 87.4% in the early diagnosis of breast cancer.

Procalcitonin for diagnosis of asymptomatic bacteriuria in kidney transplant recipients

Background and aim. Asymptomatic bacteriuria (ASB) is a frequent finding in allograft kidney transplant recipients and may be associated with a higher incidence of urinary tract infections in this population. We aimed to investigate the potential utility of serum procalcitonin (PCT) concentrations, an early marker of infection, for ASB diagnosis. We also compared its diagnostic performance with white blood cell (WBC) count, erythrocyte sedimentation rate (ESR), and highly sensitive C-reactive protein (hsCRP). Methods. In a single-center, cross-sectional study, 37 kidney transplant recipients with no clinical signs or symptoms of urinary tract infections were included. ASB was assessed by means of urine culture. Serum PCT concentrations were determined by the electrochemiluminescence immunoassay technique. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic performance of PCT and other classifiers. Results. Seventeen patients (46%) had ASB. Serum PCT concentrations were significantly higher in ASB+ patients (0.14 vs. 0.08 ng/ml, p=0.009). PCT concentrations significantly correlated with serum creatinine (r=0.408, p=0.012) and ESR (r=0.466, p=0.004). Of the four tested classifiers (PCT, WBC count, ESR, and hsCRP), only PCT was able to significantly distinguish between ASB+ and ASB- patients [area under the curve: 0.74, (95% CI: 057-0.91) p=0.012]. Adjustment of the ROC model for serum creatinine showed that the ability of PCT in classifying patients by ASB status is not affected by creatinine concentrations (crude vs. adjusted area under the curve: 0.74 vs. 0.72, test of AUC difference: p=0.891). A cut-point of 0.10 ng/ml of PCT correctly classified ASB+ patients with a sensitivity and specificity of 64.7% and 80.0%, respectively. Conclusion. PCT might be a useful surrogate marker for ASB diagnosis among kidney transplant recipients. Diagnostic performance of PCT is superior to that of WBC count, ESR, and hsCRP. Further, diagnostic ability of PCT appears to be independent of renal function.

Diagnostic value of lacrimal scintigraphy in the evaluation of lacrimal drainage system obstruction: a systematic review and meta-analysis.

A systematic review and meta-analysis to evaluate the diagnostic performance of lacrimal scintigraphy (LS) versus anatomical methods in the evaluation of the nasolacrimal duct obstruction (NLDO). A systematic search was performed using electronic bibliographic databases until the end of May 2021. Inclusion criteria: (a) used LS as a diagnostic method to evaluate NLDO; (b) used anatomical studies [including syringing, irrigation, probing, and dacryocystography (DCG)] as reference tests; and (c) provided adequate crude data. A hierarchical method was used to pool the sensitivity and specificity. The hierarchical summary receiver-operating characteristic model was performed. Additionally, the studies' heterogeneity and publication bias were analyzed. All analyses were conducted by the 'Midas' module of STATA 16. Twelve articles (with 14 separate populations) were considered eligible to enter the meta-analysis. They were divided into two groups based on the reference standard method, called irrigation and DCG groups. In the irrigation group, the pooled sensitivity and specificity were 89% [95% confidence interval (CI), 72-96%] and 25% (95% CI, 8-56%), respectively. In DCG group, the pooled sensitivity and specificity were 97% (95% CI, 85-100%) and 27% (95% CI, 0.12-0.49), in turn. LS is a sensitive modality to evaluate the anatomical obstruction of NLD. In contrast, it shows low pooled specificity compared with anatomical methods. Thus, LS can be used as the first noninvasive modality for the evaluation of epiphora. However, in case of any abnormality, confirmatory procedures are required.