Survivability prediction of colon cancer patients using neural networks

2017 ◽  
Vol 25 (3) ◽  
pp. 878-891 ◽  
Author(s):  
Reda Al-Bahrani ◽  
Ankit Agrawal ◽  
Alok Choudhary
Keyword(s):  
Neural Networks ◽  
Colon Cancer ◽  
Deep Neural Networks ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Patient Survival ◽  
Characteristic Curve ◽  
Conditional Survival ◽  
Operating Characteristic Curve ◽  
Multiple Neural Network

We utilize deep neural networks to develop prediction models for patient survival and conditional survival of colon cancer. Our models are trained and validated on data obtained from the Surveillance, Epidemiology, and End Results Program. We provide an online outcome calculator for 1, 2, and 5 years survival periods. We experimented with multiple neural network structures and found that a network with five hidden layers produces the best results for these data. Moreover, the online outcome calculator provides conditional survival of 1, 2, and 5 years after surviving the mentioned survival periods. In this article, we report an approximate 0.87 area under the receiver operating characteristic curve measurements, higher than the 0.85 reported by Stojadinovic et al.

scholarly journals Traditional Cardiovascular Risk Factors and Individual Prediction of Cardiovascular Events in Childhood Cancer Survivors

2019 ◽  
Vol 112 (3) ◽  
pp. 256-265 ◽  
Author(s):  
Yan Chen ◽  
Eric J Chow ◽  
Kevin C Oeffinger ◽  
William L Border ◽  
Wendy M Leisenring ◽  
...  
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Cardiovascular Risk ◽  
Childhood Cancer ◽  
Cardiovascular Risk Factors ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Childhood Cancer Survivors ◽  
Operating Characteristic Curve

Abstract Background Childhood cancer survivors have an increased risk of heart failure, ischemic heart disease, and stroke. They may benefit from prediction models that account for cardiotoxic cancer treatment exposures combined with information on traditional cardiovascular risk factors such as hypertension, dyslipidemia, and diabetes. Methods Childhood Cancer Survivor Study participants (n = 22 643) were followed through age 50 years for incident heart failure, ischemic heart disease, and stroke. Siblings (n = 5056) served as a comparator. Participants were assessed longitudinally for hypertension, dyslipidemia, and diabetes based on self-reported prescription medication use. Half the cohort was used for discovery; the remainder for replication. Models for each outcome were created for survivors ages 20, 25, 30, and 35 years at the time of prediction (n = 12 models). Results For discovery, risk scores based on demographic, cancer treatment, hypertension, dyslipidemia, and diabetes information achieved areas under the receiver operating characteristic curve and concordance statistics 0.70 or greater in 9 and 10 of the 12 models, respectively. For replication, achieved areas under the receiver operating characteristic curve and concordance statistics 0.70 or greater were observed in 7 and 9 of the models, respectively. Across outcomes, the most influential exposures were anthracycline chemotherapy, radiotherapy, diabetes, and hypertension. Survivors were then assigned to statistically distinct risk groups corresponding to cumulative incidences at age 50 years of each target outcome of less than 3% (moderate-risk) or approximately 10% or greater (high-risk). Cumulative incidence of all outcomes was 1% or less among siblings. Conclusions Traditional cardiovascular risk factors remain important for predicting risk of cardiovascular disease among adult-age survivors of childhood cancer. These prediction models provide a framework on which to base future surveillance strategies and interventions.

scholarly journals External Validation of Five Scores to Predict Stroke-Associated Pneumonia and the Role of Selected Blood Biomarkers

Stroke ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 325-330
Author(s):  
Benjamin Hotter ◽  
Sarah Hoffmann ◽  
Lena Ulm ◽  
Christian Meisel ◽  
Alejandro Bustamante ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Literature Search ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Data Set ◽  
Biomarker Analysis ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background and Purpose: Several clinical scoring systems as well as biomarkers have been proposed to predict stroke-associated pneumonia (SAP). We aimed to externally and competitively validate SAP scores and hypothesized that 5 selected biomarkers would improve performance of these scores. Methods: We pooled the clinical data of 2 acute stroke studies with identical data assessment: STRAWINSKI and PREDICT. Biomarkers (ultrasensitive procalcitonin; mid-regional proadrenomedullin; mid-regional proatrionatriuretic peptide; ultrasensitive copeptin; C-terminal proendothelin) were measured from hospital admission serum samples. A literature search was performed to identify SAP prediction scores. We then calculated multivariate regression models with the individual scores and the biomarkers. Areas under receiver operating characteristic curves were used to compare discrimination of these scores and models. Results: The combined cohort consisted of 683 cases, of which 573 had available backup samples to perform the biomarker analysis. Literature search identified 9 SAP prediction scores. Our data set enabled us to calculate 5 of these scores. The scores had area under receiver operating characteristic curve of 0.543 to 0.651 for physician determined SAP, 0.574 to 0.685 for probable and 0.689 to 0.811 for definite SAP according to Pneumonia in Stroke Consensus group criteria. Multivariate models of the scores with biomarkers improved virtually all predictions, but mostly in the range of an area under receiver operating characteristic curve delta of 0.05. Conclusions: All SAP prediction scores identified patients who would develop SAP with fair to strong capabilities, with better discrimination when stricter criteria for SAP diagnosis were applied. The selected biomarkers provided only limited added predictive value, currently not warranting addition of these markers to prediction models. Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT01264549 and NCT01079728.

scholarly journals Validation of Three Postoperative Risk Prediction Models for Intensive Care Unit Mortality after Cardiac Surgery

2018 ◽  
Vol 66 (08) ◽  
pp. 651-660 ◽  
Author(s):  
Camila Caiado ◽  
Charles McCollum ◽  
Michael Goldstein ◽  
Ignacio Malagon ◽  
Rajamiyer Venkateswaran ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cardiac Surgery ◽  
Risk Prediction ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Icu Mortality ◽  
Risk Prediction Models ◽  
Postoperative Risk ◽  
Operating Characteristic Curve

Background Several cardiac surgery risk prediction models based on postoperative data have been developed. However, unlike preoperative cardiac surgery risk prediction models, postoperative models are rarely externally validated or utilized by clinicians. The objective of this study was to externally validate three postoperative risk prediction models for intensive care unit (ICU) mortality after cardiac surgery. Methods The logistic Cardiac Surgery Scores (logCASUS), Rapid Clinical Evaluation (RACE), and Sequential Organ Failure Assessment (SOFA) scores were calculated over the first 7 postoperative days for consecutive adult cardiac surgery patients between January 2013 and May 2015. Model discrimination was assessed using receiver operating characteristic curve analyses. Calibration was assessed using the Hosmer–Lemeshow (HL) test, calibration plots, and observed to expected ratios. Recalibration of the models was performed. Results A total of 2255 patients were included with an ICU mortality rate of 1.8%. Discrimination for all three models on each postoperative day was good with areas under the receiver operating characteristic curve of >0.8. Generally, RACE and logCASUS had better discrimination than SOFA. Calibration of the RACE score was better than logCASUS, but ratios of observed to expected mortality for both were generally <0.65. Locally recalibrated SOFA, logCASUS and RACE models all performed well. Conclusion All three models demonstrated good discrimination for the first 7 days after cardiac surgery. After recalibration, logCASUS and RACE scores appear to be most useful for daily risk prediction after cardiac surgery. If appropriately calibrated, postoperative cardiac surgery risk prediction models have the potential to be useful tools after cardiac surgery.

scholarly journals An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats

2021 ◽  
Vol 8 ◽  
Author(s):  
Tommaso Banzato ◽  
Marek Wodzinski ◽  
Federico Tauceri ◽  
Chiara Donà ◽  
Filippo Scavazza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Pleural Effusion ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Computer Aided Detection ◽  
Radiographic Findings ◽  
Computer Aided ◽  
Operating Characteristic Curve

An artificial intelligence (AI)-based computer-aided detection (CAD) algorithm to detect some of the most common radiographic findings in the feline thorax was developed and tested. The database used for training comprised radiographs acquired at two different institutions. Only correctly exposed and positioned radiographs were included in the database used for training. The presence of several radiographic findings was recorded. Consequenly, the radiographic findings included for training were: no findings, bronchial pattern, pleural effusion, mass, alveolar pattern, pneumothorax, cardiomegaly. Multi-label convolutional neural networks (CNNs) were used to develop the CAD algorithm, and the performance of two different CNN architectures, ResNet 50 and Inception V3, was compared. Both architectures had an area under the receiver operating characteristic curve (AUC) above 0.9 for alveolar pattern, bronchial pattern and pleural effusion, an AUC above 0.8 for no findings and pneumothorax, and an AUC above 0.7 for cardiomegaly. The AUC for mass was low (above 0.5) for both architectures. No significant differences were evident in the diagnostic accuracy of either architecture.

scholarly journals External Validations of Cardiovascular Clinical Prediction Models: A Large-Scale Review of the Literature

2021 ◽  
Author(s):  
Benjamin S. Wessler ◽  
Jason Nelson ◽  
Jinny G. Park ◽  
Hannah McGinnes ◽  
Gaurav Gulati ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
External Validation ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Operating Characteristic Curve ◽  
Performance Heterogeneity

Background: There are many clinical prediction models (CPMs) available to inform treatment decisions for patients with cardiovascular disease. However, the extent to which they have been externally tested, and how well they generally perform has not been broadly evaluated. Methods: A SCOPUS citation search was run on March 22, 2017 to identify external validations of cardiovascular CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry. We assessed the extent of external validation, performance heterogeneity across databases, and explored factors associated with model performance, including a global assessment of the clinical relatedness between the derivation and validation data. Results: We identified 2030 external validations of 1382 CPMs. Eight hundred seven (58%) of the CPMs in the Registry have never been externally validated. On average, there were 1.5 validations per CPM (range, 0–94). The median external validation area under the receiver operating characteristic curve was 0.73 (25th–75th percentile [interquartile range (IQR)], 0.66–0.79), representing a median percent decrease in discrimination of −11.1% (IQR, −32.4% to +2.7%) compared with performance on derivation data. 81% (n=1333) of validations reporting area under the receiver operating characteristic curve showed discrimination below that reported in the derivation dataset. 53% (n=983) of the validations report some measure of CPM calibration. For CPMs evaluated more than once, there was typically a large range of performance. Of 1702 validations classified by relatedness, the percent change in discrimination was −3.7% (IQR, −13.2 to 3.1) for closely related validations (n=123), −9.0 (IQR, −27.6 to 3.9) for related validations (n=862), and −17.2% (IQR, −42.3 to 0) for distantly related validations (n=717; P <0.001). Conclusions: Many published cardiovascular CPMs have never been externally validated, and for those that have, apparent performance during development is often overly optimistic. A single external validation appears insufficient to broadly understand the performance heterogeneity across different settings.

scholarly journals Auto-detection of strong gravitational lenses using convolutional neural networks

2018 ◽  
Vol 2 ◽  
pp. 1 ◽  
Author(s):  
James Pearson ◽  
Clara Pennock ◽  
Tom Robinson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Area Under Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Automated Detection ◽  
Gravitational Lenses ◽  
Simulated Image ◽  
Operating Characteristic Curve

We propose a method for the automated detection of strong galaxy-galaxy gravitational lenses in images, utilising a convolutional neural network (CNN) trained on 210 000 simulated galaxy-galaxy lens and non-lens images. The CNN, named LensFinder, was tested on a separate 210 000 simulated image catalogue, with 95% of images classied with at least 98.6% certainty. An accuracy of over 98% was achieved and an area under curve of 0.9975 was determined from the resulting receiver operating characteristic curve. A regional CNN, R-LensFinder, was trained to label lens positions in images, perfectly labelling 80% while partially labelling another 10% correctly.

Serum miR-21 and miR-26a Levels Negatively Correlate with Severity of Cirrhosis in Patients with Chronic Hepatitis B

MicroRNA ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 86-92 ◽  
Author(s):  
Shili Jiang ◽  
Wei Jiang ◽  
Ying Xu ◽  
Xiaoning Wang ◽  
Yongping Mu ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Pugh Class ◽  
Operating Characteristic Curve ◽  
Class C ◽  
Circulating Levels

Background and Objective: Accurately evaluating the severity of liver cirrhosis is essential for clinical decision making and disease management. This study aimed to evaluate the value of circulating levels of microRNA (miR)-26a and miR-21 as novel noninvasive biomarkers in detecting severity of cirrhosis in patients with chronic hepatitis B. </P><P> Methods: Thirty patients with clinically diagnosed chronic hepatitis B-related cirrhosis and 30 healthy individuals were selected. The serum levels of miR-26a and miR-21 were quantified by qRT-PCR. Receiver operating characteristic curve analysis was performed to evaluate the sensitivity and specificity of the miRNAs for detecting the severity of cirrhosis. Results: Serum miR-26a and miR-21 levels were found to be significantly downregulated in patients with severe cirrhosis scored at Child-Pugh class C in comparison to healthy controls (miR-26a p<0.01, and miR-21 p<0.001, respectively). The circulating miR-26a and miR-21 levels in patients were positively correlated with serum albumin concentration but negatively correlated with serum total bilirubin concentration and prothrombin time. Receiver operating characteristic curve analysis revealed that both serum miR-26a and miR-21 levels were associated with a high diagnostic accuracy for patients with cirrhosis scored at Child-Pugh class C (miR-26a Cut-off fold change at ≤0.4, Sensitivity: 84.62%, Specificity: 89.36%, P<0.0001; miR-21 Cut-off fold change at ≤0.6, Sensitivity: 84.62%, Specificity: 78.72%, P<0.0001). Our results indicate that the circulating levels of miR-26a and miR-21 are closely related to the extent of liver decompensation, and the decreased levels are capable of discriminating patients with cirrhosis at Child-Pugh class C from the whole cirrhosis cases.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

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