scholarly journals Integrated CT Radiomics Features Could Enhance the Efficacy of 18F-FET PET for Non-Invasive Isocitrate Dehydrogenase Genotype Prediction in Adult Untreated Gliomas: A Retrospective Cohort Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Weiyan Zhou ◽  
Qi Huang ◽  
Jianbo Wen ◽  
Ming Li ◽  
Yuhua Zhu ◽  
...  
Keyword(s):  
Predictive Model ◽  
Predictive Models ◽  
Isocitrate Dehydrogenase ◽  
Clinical Decision Making ◽  
Model Building ◽  
Machine Learning Algorithms ◽  
Clinical Feature ◽  
Fet Pet ◽  
Non Invasive ◽  
Pet Ct

PurposeWe aimed to investigate the predictive models based on O-[2-(18F)fluoroethyl]-l-tyrosine positron emission tomography/computed tomography (18F-FET PET/CT) radiomics features for the isocitrate dehydrogenase (IDH) genotype identification in adult gliomas.MethodsFifty-eight consecutive pathologically confirmed adult glioma patients with pretreatment 18F-FET PET/CT were retrospectively enrolled. One hundred and five radiomics features were extracted for analysis in each modality. Three independent radiomics models (PET-Rad Model, CT-Rad Model and PET/CT-Rad Model) predicting IDH mutation status were generated using the least absolute shrinkage and selection operator (LASSO) regression analysis based on machine learning algorithms. All-subsets regression and cross validation were applied for the filter and calibration of the predictive radiomics models. Besides, semi-quantitative parameters including maximum, peak and mean tumor to background ratio (TBRmax, TBRpeak, TBRmean), standard deviation of glioma lesion standardized uptake value (SUVSD), metabolic tumor volume (MTV) and total lesion tracer uptake (TLU) were obtained and filtered for the simple model construction with clinical feature of brain midline involvement status. The area under the receiver operating characteristic curve (AUC) was applied for the evaluation of the predictive models.ResultsThe AUC of the simple predictive model consists of semi-quantitative parameter SUVSD and dichotomized brain midline involvement status was 0.786 (95% CI 0.659-0.883). The AUC of PET-Rad Model building with three 18F-FET PET radiomics parameters was 0.812 (95% CI 0.688-0.902). The AUC of CT-Rad Model building with three co-registered CT radiomics parameters was 0.883 (95% CI 0.771-0.952). While the AUC of the combined 18F-FET PET/CT-Rad Model building with three CT and one PET radiomics features was 0.912 (95% CI 0.808-0.970). DeLong test results indicated the PET/CT-Rad Model outperformed the PET-Rad Model (p = 0.048) and simple predictive model (p = 0.034). Further combination of the PET/CT-Rad Model with the clinical feature of dichotomized tumor location status could slightly enhance the AUC to 0.917 (95% CI 0.814-0.973).ConclusionThe predictive model combining 18F-FET PET and integrated CT radiomics features could significantly enhance and well balance the non-invasive IDH genotype prediction in untreated gliomas, which is important in clinical decision making for personalized treatment.

Abstract 122: Use of Machine Learning to Accurately Predict Adverse Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Elsie G Ross ◽  
Nicholas Leeper ◽  
Nigam Shah
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Predictive Model ◽  
Predictive Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Record ◽  
Peripheral Artery ◽  
Artery Disease

Introduction: Patients with peripheral artery disease (PAD) are at high risk of major adverse cardiac and cerebrovascular events (MACCE). However, no currently available risk scores accurately delineate which patients are most likely to sustain an event, creating a missed opportunity for more aggressive risk factor management. We set out to develop a novel predictive model - based on automated machine learning algorithms using electronic health record (EHR) data - with the aim of identifying which PAD patients are most likely to have an adverse outcome during follow-up. Methods: Data were derived from patients with a diagnosis of PAD at our institution. Novel machine-learning algorithms including random forest and penalized regression predictive models were developed using structured and unstructured data that including lab values, diagnosis codes, medications, and clinical notes. Patients were matched for total follow-up time to remove length of patient records as a biasing factor in our predictive models. Results: After matching for length of follow-up, 3,807 patients were included in our models. A total of 1,269 patients had a MACCE event after PAD diagnosis. The median time to MACCE was 2.8 years after PAD diagnosis. Utilizing 1,492 different variables extracted from the EHR, our best predictive model was able to very accurately predict which patients would go on to have a MACCE event after diagnosis of PAD with an AUC of 0.98, with a sensitivity, specificity and positive predictive value of 0.90, 0.96, and 0.93, respectively. Conclusions: Hypothesis-free, machine-learning algorithms using freely available data in the EHR can accurately predict which PAD patients are most likely to go on to develop future MACCE. While these findings require validation in an independent data set, there is hope that these informatics approaches can be applied to the medical record in an automated fashion to risk stratify patients with vascular disease and identify those who might benefit from more aggressive disease management in real-time.

scholarly journals Non-invasive Early Response Monitoring of Nanoparticle-assisted Photothermal Cancer Therapy Using 18F-FDG, 18F-FLT, and 18F-FET PET/CT Imaging

2018 ◽  
Vol 2 (3) ◽  
pp. 201-210 ◽  
Author(s):  
Jesper Tranekjær Jørgensen ◽  
Kamilla Norregaard ◽  
Marina Simón Martín ◽  
Lene Broeng Oddershede ◽  
Andreas Kjaer
Keyword(s):  
Cancer Therapy ◽  
Early Response ◽  
Ct Imaging ◽  
Response Monitoring ◽  
Fet Pet ◽  
Non Invasive ◽  
Pet Ct ◽  
18F Fdg

scholarly journals Risk Assessment of Bone Metastasis for Cervical Cancer Patients by Multiple Models: A Large Population Based Real-World Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Yun Han ◽  
Bo Wang ◽  
Jinjin Zhang ◽  
Su Zhou ◽  
Jun Dai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Cervical Cancer ◽  
Bone Metastasis ◽  
Predictive Model ◽  
Cancer Patients ◽  
Roc Curve ◽  
Predictive Models ◽  
Population Based ◽  
Machine Learning Algorithms

Background: Population-based data on the risk assessment of newly diagnosed cervical cancer patients' bone metastasis (CCBM) are lacking. This study aimed to develop various predictive models to assess the risk of bone metastasis via machine learning algorithms.Materials and Methods: We retrospectively reviewed the CCBM patients from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute to risk factors of the presence of bone metastasis. Clinical usefulness was assessed by Akaike information criteria (AIC) and multiple machine learning algorithms based predictive models. Concordance index (C-index) and receiver operating characteristic (ROC) curve were used to define the predictive and discriminatory capacity of predictive models.Results: A total of 16 candidate variables were included to develop predictive models for bone metastasis by machine learning. The areas under the ROC curve (AUCs) of the random forest model (RF), generalized linear model (GL), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), artificial neutral network (ANN), decision tree (DT), and naive bayesian model (NBM) ranged from 0.85 to 0.93. The RF model with 10 variables was developed as the optimal predictive model. The weight of variables indicated the top seven factors were organ-site metastasis (liver, brain, and lung), TNM stage and age.Conclusions: Multiple machine learning based predictive models were developed to identify risk of bone metastasis in cervical cancer patients. By incorporating clinical characteristics and other candidate variables showed robust risk stratification for CCBM patients, and the RF predictive model performed best among these predictive models.

scholarly journals Development of a validated computer-based preoperative predictive model for pseudarthrosis with 91% accuracy in 336 adult spinal deformity patients

2018 ◽  
Vol 45 (5) ◽  
pp. E11 ◽  
Author(s):  
Justin K. Scheer ◽  
Taemin Oh ◽  
Justin S. Smith ◽  
Christopher I. Shaffrey ◽  
Alan H. Daniels ◽  
...  
Keyword(s):  
Predictive Model ◽  
Spinal Deformity ◽  
Clinical Decision Making ◽  
Model Building ◽  
Predictive Analytics ◽  
Adult Spinal Deformity ◽  
Graft Material ◽  
Recurrent Pain ◽  
Wide Range

OBJECTIVEPseudarthrosis can occur following adult spinal deformity (ASD) surgery and can lead to instrumentation failure, recurrent pain, and ultimately revision surgery. In addition, it is one of the most expensive complications of ASD surgery. Risk factors contributing to pseudarthrosis in ASD have been described; however, a preoperative model predicting the development of pseudarthrosis does not exist. The goal of this study was to create a preoperative predictive model for pseudarthrosis based on demographic, radiographic, and surgical factors.METHODSA retrospective review of a prospectively maintained, multicenter ASD database was conducted. Study inclusion criteria consisted of adult patients (age ≥ 18 years) with spinal deformity and surgery for the ASD. From among 82 variables assessed, 21 were used for model building after applying collinearity testing, redundancy, and univariable predictor importance ≥ 0.90. Variables included demographic data along with comorbidities, modifiable surgical variables, baseline coronal and sagittal radiographic parameters, and baseline scores for health-related quality of life measures. Patients groups were determined according to their Lenke radiographic fusion type at the 2-year follow-up: bilateral or unilateral fusion (union) or pseudarthrosis (nonunion). A decision tree was constructed, and internal validation was accomplished via bootstrapped training and testing data sets. Accuracy and the area under the receiver operating characteristic curve (AUC) were calculated to evaluate the model.RESULTSA total of 336 patients were included in the study (nonunion: 105, union: 231). The model was 91.3% accurate with an AUC of 0.94. From 82 initial variables, the top 21 covered a wide range of areas including preoperative alignment, comorbidities, patient demographics, and surgical use of graft material.CONCLUSIONSA model for predicting the development of pseudarthrosis at the 2-year follow-up was successfully created. This model is the first of its kind for complex predictive analytics in the development of pseudarthrosis for patients with ASD undergoing surgical correction and can aid in clinical decision-making for potential preventative strategies.

scholarly journals Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients

2021 ◽  
Vol 18 (12) ◽  
pp. 6228
Author(s):  
Abhinav Vepa ◽  
Amer Saleem ◽  
Kambiz Rakhshan ◽  
Alireza Daneshkhah ◽  
Tabassom Sedighi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Risk Stratification ◽  
Predictive Models ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Secondary Outcome ◽  
Control Analysis

Background: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Methods: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case–control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. Results: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.

scholarly journals A comparative study of machine learning techniques for suicide attempts predictive model

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822198939
Author(s):  
Noratikah Nordin ◽  
Zurinahni Zainol ◽  
Mohd Halim Mohd Noor ◽  
Chan Lai Fong
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Predictive Model ◽  
Predictive Models ◽  
Suicide Ideation ◽  
Suicide Attempts ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Recursive Feature Elimination

Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts.

11C-metomidate PET-CT in primary aldosteronism: five possible indications for a non-invasive alternative to adrenal vein sampling

2015 ◽  
Author(s):  
Andrew S Powlson ◽  
Olympia Koulouri ◽  
Elena Azizan ◽  
Carmela Maniero ◽  
Kevin Taylor ◽  
...  
Keyword(s):  
Primary Aldosteronism ◽  
Adrenal Vein ◽  
Adrenal Vein Sampling ◽  
Non Invasive ◽  
Pet Ct

Study of the route correction system for the on-board navigation system of an unmanned aerial vehicle on the grounds of radar images of the terrain

2020 ◽  
pp. 129-134
Keyword(s):  
Predictive Model ◽  
Unmanned Aerial Vehicle ◽  
Predictive Models ◽  
Navigation System ◽  
Error Compensation ◽  
Compensation Scheme ◽  
Radar Images ◽  
Aerial Vehicle ◽  
System Errors ◽  
Correction System

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

Comparison Between 18F-Dopa and 18F-Fet PET/CT in Patients with Suspicious Recurrent High Grade Glioma: A Literature Review and Our Experience

2019 ◽  
Vol 12 (3) ◽  
pp. 220-228 ◽  
Author(s):  
Laura Evangelista ◽  
Lea Cuppari ◽  
Luisa Bellu ◽  
Daniele Bertin ◽  
Mario Caccese ◽  
...  
Keyword(s):  
Case Series ◽  
High Grade Glioma ◽  
High Grade ◽  
True Negative ◽  
Fet Pet ◽  
Positron Emission ◽  
Pet Ct ◽  
Magnetic Resonance Imaging Mri ◽  
Definition Of ◽  
Sensitivity Specificity

Purpose: The aims of the present study were to: 1- critically assess the utility of L-3,4- dihydroxy-6-18Ffluoro-phenyl-alanine (18F-DOPA) and O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) Positron Emission Tomography (PET)/Computed Tomography (CT) in patients with high grade glioma (HGG) and 2- describe the results of 18F-DOPA and 18F-FET PET/CT in a case series of patients with recurrent HGG. Methods: We searched for studies using the following databases: PubMed, Web of Science and Scopus. The search terms were: glioma OR brain neoplasm and DOPA OR DOPA PET OR DOPA PET/CT and FET OR FET PET OR FET PET/CT. From a mono-institutional database, we retrospectively analyzed the 18F-DOPA and 18F-FET PET/CT of 29 patients (age: 56 ± 12 years) with suspicious for recurrent HGG. All patients underwent 18F-DOPA or 18F-FET PET/CT for a multidisciplinary decision. The final definition of recurrence was made by magnetic resonance imaging (MRI) and/or multidisciplinary decision, mainly based on the clinical data. Results: Fifty-one articles were found, of which 49 were discarded, therefore 2 studies were finally selected. In both the studies, 18F-DOPA and 18F-FET as exchangeable in clinical practice particularly for HGG patients. From our institutional experience, in 29 patients, we found that sensitivity, specificity and accuracy of 18F-DOPA PET/CT in HGG were 100% (95% confidence interval- 95%CI - 81-100%), 63% (95%CI: 39-82%) and 62% (95%CI: 39-81%), respectively. 18F-FET PET/CT was true positive in 4 and true negative in 4 patients. Sensitivity, specificity and accuracy for 18F-FET PET/CT in HGG were 100%. Conclusion: 18F-DOPA and 18F-FET PET/CT have a similar diagnostic accuracy in patients with recurrent HGG. However, 18F-DOPA PET/CT could be affected by inflammation conditions (false positive) that can alter the final results. Large comparative trials are warranted in order to better understand the utility of 18F-DOPA or 18F-FET PET/CT in patients with HGG.

scholarly journals Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales

2021 ◽  
Vol 13 (11) ◽  
pp. 2074
Author(s):  
Ryan R. Reisinger ◽  
Ari S. Friedlaender ◽  
Alexandre N. Zerbini ◽  
Daniel M. Palacios ◽  
Virginia Andrews-Goff ◽  
...  
Keyword(s):  
Habitat Selection ◽  
Predictive Models ◽  
Regional Variation ◽  
Large Scale ◽  
Predictive Performance ◽  
Humpback Whale ◽  
Machine Learning Algorithms ◽  
Humpback Whales ◽  
Environmental Covariates ◽  
Animal Habitat

Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection.

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