scholarly journals CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Tiansong Xie ◽  
Xuanyi Wang ◽  
Zehua Zhang ◽  
Zhengrong Zhou
Keyword(s):  
Predictive Value ◽  
Cross Validation ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Cystic Neoplasm ◽  
Cystic Neoplasms ◽  
Serous Cystadenomas ◽  
Radiological Model ◽  
Fold Cross Validation

ObjectivesTo investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN).MethodsA total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.ResultsTen screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728.ConclusionsThe CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.

scholarly journals Impact of Grade Groups on Prostate Cancer-Specific and Other-Cause Mortality: Competing Risk Analysis from a Large Single Institution Series

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1963
Author(s):  
Daimantas Milonas ◽  
Tomas Ruzgas ◽  
Zilvinas Venclovas ◽  
Mindaugas Jievaltas ◽  
Steven Joniau
Keyword(s):  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Competing Risk ◽  
Grade Group ◽  
Tertiary Center ◽  
Stable Performance ◽  
Cancer Specific Mortality ◽  
Risk Of Cancer

Objective: To assess the risk of cancer-specific mortality (CSM) and other-cause mortality (OCM) using post-operative International Society of Urological Pathology Grade Group (GG) model in patients after radical prostatectomy (RP). Patients and Methods: Overall 1921 consecutive men who underwent RP during 2001 to 2017 in a single tertiary center were included in the study. Multivariate competing risk regression analysis was used to identify significant predictors and quantify cumulative incidence of CSM and OCM. Time-depending area under the curve (AUC) depicted the performance of GG model on prediction of CSM. Results: Over a median follow-up of 7.9-year (IQR 4.4-11.7) after RP, 235 (12.2%) deaths were registered, and 52 (2.7%) of them were related to PCa. GG model showed high and stable performance (time-dependent AUC 0.88) on prediction of CSM. Cumulative 10-year CSM in GGs 1 to 5 was 0.9%, 2.3%, 7.6%, 14.7%, and 48.6%, respectively; 10-year OCM in GGs was 15.5%, 16.1%, 12.6%, 17.7% and 6.5%, respectively. The ratio between 10-year CSM/OCM in GGs 1 to 5 was 1:17, 1:7, 1:2, 1:1, and 7:1, respectively. Conclusions: Cancer-specific and other-cause mortality differed widely between GGs. Presented findings could aid in personalized clinical decision making for active treatment.

Abstract W P266: A Simple Score for Prediction of Outcome after Cerebral Venous Thrombosis

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Miguel A Barboza ◽  
Erwin Chiquete ◽  
Antonio Arauz ◽  
Jonathan Colín ◽  
Alejandro Quiroz-Compean ◽  
...  
Keyword(s):  
Decision Making ◽  
Venous Thrombosis ◽  
Predictive Value ◽  
Cerebral Venous Thrombosis ◽  
Goodness Of Fit ◽  
Clinical Decision Making ◽  
Case Fatality ◽  
Clinical Decision ◽  
Classification Systems ◽  
Cox Proportional Hazards Model

Background and purpose: Cerebral venous thrombosis (CVT) not always implies a good prognosis. There is a need for robust and simple classification systems of severity after CVT that help in clinical decision-making. Methods: We studied 467 patients (81.6% women, median age: 29 years, interquartile range: 22-38 years) with CVT who were hospitalized from 1980 to 2014 in two third-level referral hospitals. Bivariate analyses were performed to select variables associated with 30-day mortality to integrate a further multivariate analysis. The resultant model was evaluated with the Hosmer-Lemeshow test for goodness of fit, and on Cox proportional hazards model for reliability of the effect size. After the scale was configured, security and validity were tested for 30-day mortality and modified Rankin scale (mRS) >2. The prognostic performance was compared with that of the CVT risk score (CVT-RS, 0-6 points) as the reference system. Results: The 30-day case fatality rate was 8.7%. The CVT grading scale (CVT-GS, 0-9 points) was integrated by stupor/coma (4 points), parenchymal lesion >6 cm (2 points), mixed (superficial and deep systems) CVT (1 point), meningeal syndrome (1 point) and seizures (1 point). CVT-GS was categorized into mild (0-3 points, 1.1% mortality), moderate (4-6 points, 19.6% mortality) and severe (7-9 points, 61.4% mortality). For 30-day mortality prediction, as compared with CVT-RS (cut-off 4 points), CVT-GS (cut-off 5 points) was globally better in sensitivity (85% vs 37%), specificity (90% vs 95%), positive predictive value (44% vs 40%), negative predictive value (98% vs 94%), and accuracy (94% vs 80%). For 30-day mRS >2 the performance of CVT-GS over CVT-RS was comparably improved. Conclusion: The CVT-GS is a simple and reliable score for predicting outcome that may help in clinical decision-making and that could be used to stratify patients recruited into clinical trials.

scholarly journals Multiple biomarker panel to screen for severe aortic stenosis: results from the CASABLANCA study

Open Heart ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. e000916 ◽  
Author(s):  
Sammy Elmariah ◽  
Cian McCarthy ◽  
Nasrien Ibrahim ◽  
Deborah Furman ◽  
Renata Mukai ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Predictive Value ◽  
Cross Validation ◽  
Severe Aortic Stenosis ◽  
Area Under The Curve ◽  
Diagnostic Model ◽  
Aortic Valve Area ◽  
Academic Medical Centre ◽  
Diagnostic Score ◽  
Biomarker Panel

ObjectiveSevere aortic valve stenosis (AS) develops via insidious processes and can be challenging to correctly diagnose. We sought to develop a circulating biomarker panel to identify patients with severe AS.MethodsWe enrolled study participants undergoing coronary or peripheral angiography for a variety of cardiovascular diseases at a single academic medical centre. A panel of 109 proteins were measured in blood obtained at the time of the procedure. Statistical learning methods were used to identify biomarkers and clinical parameters that associate with severe AS. A diagnostic model incorporating clinical and biomarker results was developed and evaluated using Monte Carlo cross-validation.ResultsOf 1244 subjects (age 66.4±11.5  years, 28.7% female), 80 (6.4%) had severe AS (defined as aortic valve area (AVA) <1.0  cm2). A final model included age, N-terminal pro-B-type natriuretic peptide, von Willebrand factor and fetuin-A. The model had good discrimination for severe AS (OR=5.9, 95% CI 3.5 to 10.1, p<0.001) with an area under the curve of 0.76 insample and 0.74 with cross-validation. A diagnostic score was generated. Higher prevalence of severe AS was noted in those with higher scores, such that 1.6% of those with a score of 1 had severe AS compared with 15.3% with a score of 5 (p<0.001), and score values were inversely correlated with AVA (r=−0.35; p<0.001). At optimal model cut-off, we found 76% sensitivity, 65% specificity, 13% positive predictive value and 98% negative predictive value.ConclusionsWe describe a novel, multiple biomarker approach for diagnostic evaluation of severe AS.Trial registration numberNCT00842868.

scholarly journals Identification and Evaluation of Serum Protein Biomarkers Which Differentiate Psoriatic from Rheumatoid Arthritis

2020 ◽  
Author(s):  
Angela Mc Ardle ◽  
Anna Kwasnik ◽  
Agnes Szenpetery ◽  
Melissa Jones ◽  
Belinda Hernandez ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Serum Protein ◽  
Clinical Decision Making ◽  
Multiple Reaction Monitoring ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Additional Patient ◽  
Second Phase ◽  
Protein Biomarkers ◽  
Protein Discovery

AbstractObjectivesTo identify serum protein biomarkers which might separate early inflammatory arthritis (EIA) patients with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) to provide an accurate diagnosis and support appropriate early intervention.MethodsIn an initial protein discovery phase, the serum proteome of a cohort of patients with PsA and RA was interrogated using unbiased liquid chromatography mass spectrometry (LC-MS/MS) (n=64 patients), a multiplexed antibody assay (Luminex) for 48 proteins (n=64 patients) and an aptamer-based assay (SOMAscan) targeting 1,129 proteins (n=36 patients). Subsequently, analytically validated targeted multiple reaction monitoring (MRM) assays were developed to further evaluate those proteins identified as discriminatory during the discovery. During an initial verification phase, MRM assays were developed to a panel of 150 proteins (by measuring a total of 233 peptides) and used to re-evaluate the discovery cohort (n=60). During a second verification phase, the panel of proteins was expanded to include an additional 23 proteins identified in other proteomic discovery analyses of arthritis patients. The expanded panel was evaluated using a second, independent cohort of PsA and RA patients (n=167).ResultsMultivariate analysis of the protein discovery data revealed that it was possible to discriminate PsA from RA patients with an area under the curve (AUC) of 0.94 for nLC-MS/MS, 0.69 for Luminex based measurements; 0.73 for SOMAscan analysis. During the initial verification phase, random forest models confirmed that proteins measured by MRM could differentiate PsA and RA patients with an AUC of 0.79 and during the second phase of verification the expanded panel could segregate the two disease groups with an AUC of 0.85.ConclusionWe report a serum protein biomarker panel which can separate EIA patients with PsA from those with RA. We suggest that the routine use of such a panel in EIA patients will improve clinical decision making and with continued evaluation and refinement using additional patient cohorts will support the development of a diagnostic test for patients with PsA.

Likelihood Ratio, Skull Roentgenograms, and Clinical Decision-Making

PEDIATRICS ◽  
1983 ◽  
Vol 71 (4) ◽  
pp. 673-674
Author(s):  
JOHN C. LEONIDAS ◽  
ANNA BINKIEWICZ ◽  
R. MICHAEL SCOTT ◽  
STEPHEN G. PAUKER
Keyword(s):  
Decision Making ◽  
Likelihood Ratio ◽  
Predictive Value ◽  
Clinical Finding ◽  
Posterior Probability ◽  
Clinical Decision Making ◽  
Skull Fracture ◽  
Clinical Decision ◽  
Negative Finding ◽  
The Individual

In Reply.— We appreciate the thoughtful comments of Leventhal and Lembo and concur with their conclusion that the clinician needs to know "the probability of skull fracture in a patient with head trauma." Unfortunately, their proposed "clinical likelihood ratio" (CR) will not further that end because it compares the predictive value (or, more precisely, the posterior probability) of a skull fracture after a positive clinical finding to the posterior probability after a negative finding. After the patient has been examined, the patient does not have both findings; thus, the CR cannot apply to the individual patient.

Evaluating Diagnostic Tests

1993 ◽  
Vol 7 (2) ◽  
pp. 66-69 ◽  
Author(s):  
C.W. Douglass
Keyword(s):  
Predictive Value ◽  
Diagnostic Tests ◽  
Clinical Decision Making ◽  
New Technologies ◽  
Clinical Decision ◽  
Future Research ◽  
Scientific Support ◽  
Body Of Knowledge ◽  
Efficacy Parameters ◽  
Sensitivity Specificity

The presentations at this conference will discuss new technologies and rapid scientific developments that have resulted in new diagnostic tests for periodontal disease, musculoskeletal imaging, temporomandibular joint dysfunctions, and incipient coronal and root dental caries. However, for many of these claims, there has been insufficient scientific support regarding the sensitivity and specificity of the tests, or their ability to predict the percent of cases in which the disease or condition progresses to the next state of development. Research is needed that will yield the basic diagnostic parameters of new diagnostic tests, i.e., their accuracy, precision, sensitivity, specificity, positive predictive value, and negative predictive value. The purpose and methods for calculating each of these measures are described in this paper. Five questions are then presented that will need to be addressed in future research regarding new diagnostic tests: (1) Does the scientific theory of the test fit with our current body of knowledge? (2) Have the efficacy parameters of the test been reliably determined? (3) How does the test affect clinical decision-making? (4) Does using the test improve the patient's health? and (5) Is the added expense of the test justified by increased effectiveness or by avoiding other health expenditures?

The predictive value of microbiological findings on teeth, internal and external implant portions in clinical decision making

2016 ◽  
Vol 28 (5) ◽  
pp. 512-519 ◽  
Author(s):  
Luigi Canullo ◽  
Sandro Radovanović ◽  
Boris Delibasic ◽  
Juan Antonio Blaya ◽  
David Penarrocha ◽  
...  
Keyword(s):  
Decision Making ◽  
Predictive Value ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals A three year prospective study-suspected acute appendicitis: diagnostic accuracy of modified Alvarado score versus ultrasonography in adults

2017 ◽  
Vol 4 (12) ◽  
pp. 3924
Author(s):  
Murhari D. Gaikwad ◽  
Anand Auti ◽  
Avinash Magare
Keyword(s):  
Acute Appendicitis ◽  
Diagnostic Accuracy ◽  
Prospective Study ◽  
Predictive Value ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Alvarado Score ◽  
Suspected Appendicitis ◽  
Modified Alvarado Score ◽  
Suspected Acute Appendicitis

Background: To evaluate and compare diagnostic accuracy of modified Alvarado score and ultrasonography in co-relation to histopathology report for diagnosis of acute appendicitis.Methods: A prospective study of the patients who underwent appendectomy for suspected acute appendicitis at IIMS and R Medical College and Noor Hospital Warudi, Badnapur, Dist. Jalna (Maharashtra). The clinical (radiological) and ultrasonography data of 760 patients with suspected appendicitis was collected between March 2014 to Feb. 2017. These patients were evaluated by modified Alvarado score and ultrasonographically, which was corrected with histopathological finding.Results: Out of 760 patients 69.34% had acute appendicitis 63.81% had modified Alvarado score≥7 and 58.28% patients were ultrasonographically positive. In present study modified Alvarado score has sensitivity of 89.37% specificity 93.99% positive predictive value 97.11%, negative predictive value 79.64%, diagnostic accuracy of 81.32%.Conclusions: Modified Alvarado score can be used effectively in clinical decision making. When compare with ultrasonography neither one is advantageous. However, additional information provided by ultrasonography improves diagnostic accuracy.

scholarly journals Unstructured Text in EMR Improves Prediction of Death after Surgery in Children

2018 ◽  
Author(s):  
Oguz Akbilgic ◽  
Ramin Homayouni ◽  
Kevin Heinrich ◽  
Max Raymond langham, Jr ◽  
Robert Lowell Davis
Keyword(s):  
Risk Score ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Structured Data ◽  
Risk Scores ◽  
Free Text ◽  
Significant Information ◽  
C Statistic ◽  
Surgery Outcomes ◽  
Fold Cross Validation

Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6,497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children&rsquo;s Hospital EMR. We used a text mining approach on preoperative notes to obtain the text-based risk score algorithm as predictive of death within 30 days of surgery. We studied the additional performance obtained by including text-based risk score as a predictor of death along with other structured data based clinical risk factors. The C-statistic of a logistic regression model with 5-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes.

scholarly journals 275 Validation of The Radiographic Union Score for Humeral Fractures (RUSHU) For Prediction of Humeral Shaft Nonunion

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
N Arora
Keyword(s):  
Scoring System ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Roc Curve Analysis ◽  
Exclusion Criteria ◽  
Non Union ◽  
Humeral Shaft Nonunion ◽  
Surgical Fixation ◽  
Radiographic Union

Abstract Aim To validate the use of RUSHu score in prediction of humerus non union. Method All patients having radiographs of humerus performed between Jan 2016 to December 2018 were assessed based on inclusion and exclusion criteria. The RUSHu scoring system as published was used to score each 6-week radiograph, separately by 2 blinded observers. 6 months was used as end point to assess outcome. Cohort of 188 observations were used to assess utility of scoring system to predict non union. Results 94 suitable fractures were identified. Union rate of 72.3% was observed. Mean score in union group was 9.6, 6.4 for non-unions. There was substantial inter-observer reliability with an ICC of 0.73. Rate of union progressively increases with increasing RUSHu scores. ROC curve analysis identifies 8 as most suitable for use as threshold. Area under the curve is high (0.9) Conclusions A low RUSH score at 6 weeks is a reliable predictor of non union down the line. If a score 7 or lower is observed, it should trigger a discussion with the patient and review of correctable factors contributing to development of non union. Consideration of surgical fixation should be made at this stage if instability is felt to be a major contributing cause. A patient with score of 8 or higher is more likely to go on to union. Routine use of RUSHu score can aid in clinical decision making and introduce an element of objectivity in clinical assessment. It has potential to prompt earlier intervention and reduce morbidity duration.

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