NIMG-63. MACHINE LEARNING USING MRI RADIOMIC ANALYSIS TO PREDICT KI-67 IN WHO GRADE I MENINGIOMAS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi143-vi144
Author(s):  
Omaditya Khanna ◽  
Anahita Fathi Kazerooni ◽  
Jose A Garcia ◽  
Chiharu Sako ◽  
Sherjeel Arif ◽  
...  
Keyword(s):  
Machine Learning ◽  
Skull Base ◽  
Predictive Model ◽  
Patient Treatment ◽  
Support Vector ◽  
Benign Tumors ◽  
Replication Cohort ◽  
Discovery Cohort ◽  
Ki 67 ◽  
Who Grade

Abstract PURPOSE Although WHO grade I meningiomas are considered ‘benign’ tumors, an elevated Ki-67 is one crucial factor that has been shown to influence clinical outcomes. In this study, we use standard pre-operative MRI and develop a machine learning (ML) model to predict the Ki-67 in WHO grade I meningiomas. METHODS A retrospective analysis was performed of 306 patients that underwent surgical resection. The mean and median Ki-67 of tumor specimens were 4.84 ± 4.03% (range: 0.3–33.6) and 3.7% (Q1:2.3%, Q3:6%), respectively. Pre-operative MRI was used to perform radiomic feature extraction (N=2,520) followed by ML modeling using least absolute shrinkage and selection operator (LASSO) wrapped with support vector machine (SVM) through nested cross-validation on a discovery cohort (N=230), to stratify tumors based on Ki-67 < 5% and ≥ 5%. A replication cohort (N=76) was kept ‘unseen’ in order to provide insights regarding the generalizability of our predictive model. RESULTS A total of 60 radiomic features extracted from seven different MRI sequences were used in the final model. With this model, an AUC of 0.84 (95% CI: 0.78-0.90), with associated sensitivity and specificity of 84.1% and 73.3%, respectively, were achieved in the discovery cohort. The selected features in the trained predictive model were then applied to the subjects of the replication cohort and the model was applied independently in this cohort. An AUC of 0.83 (95% CI: 0.73-0.94), with a sensitivity of 82.6% and specificity of 85.5% was obtained for this independent testing. Furthermore, the model performed commendably when applied to all skull base and non-skull base tumors in our patient cohort, evidenced by comparable AUC values of 0.86 and 0.83, respectively. CONCLUSION The results of this study may provide enhanced diagnostics to the surgeon pre-operatively such that it can guide surgical strategy and individual patient treatment paradigms.

scholarly journals Predicting Unplanned Medical Visits Among Patients with Diabetes: Translation From Machine Learning to Clinical Implementation

2020 ◽  
Author(s):  
Arielle Selya ◽  
Drake Anshutz ◽  
Emily Griese ◽  
Tess L Weber ◽  
Benson Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Prediction Accuracy ◽  
Density Lipoprotein ◽  
The United States ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Medical Visits ◽  
Percentage Points ◽  
Patients With Diabetes

Abstract Background: Diabetes is common and an economic burden in the United States. In this study, a machine learning predictive model was developed to predict unplanned medical visits among patients with diabetes. Methods: Data were drawn from electronic medical records (EMRs) from a large healthcare organization in the Northern Plans region of the US, from adult (≥18 years old) patients with type 1 or type 2 diabetes who received care at least once during the 3 year period. A variety of machine-learning classification models were run using standard EMR variables as predictors (age, body mass index (BMI), Systolic blood pressure (BP), Diastolic BP, low-density lipoprotein (LDL), high-density lipoprotein (HDL), glycohemoglobin (A1C), smoking status, number of diagnoses and number of prescriptions). The best-performing model after cross-validation testing was analyzed to identify strongest predictors.Results: The best-performing model was a radial-basis support vector machine, which achieved a prediction accuracy (average of sensitivity and specificity) of 66.2%. This outperformed a conventional logistic regression by 1.5 percentage points. High BP and low HDL were identified as the strongest predictors, such that eliminating these from the model decreased its overall prediction accuracy by 1.9 and 1.8 percentage points, respectively.Conclusion: Our machine-learning predictive model more accurately predicted unplanned medical visits among patients with diabetes, relative to conventional models. Post-hoc analysis of the model was used for hypothesis generation, namely that HDL and BP are the strongest contributors to unplanned medical visits among patients with diabetes. In this way, this predictive model can be used in moving from prediction to implementation and improved diabetes care management in clinical settings.

scholarly journals Prediction of immune checkpoint inhibition with immune oncology-related gene expression in gastrointestinal cancer using a machine learning classifier

2020 ◽  
Vol 8 (2) ◽  
pp. e000631 ◽  
Author(s):  
Zhihao Lu ◽  
Huan Chen ◽  
Xi Jiao ◽  
Wei Zhou ◽  
Wenbo Han ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gastrointestinal Cancer ◽  
Immune Checkpoint ◽  
Learning Strategy ◽  
Validation Cohort ◽  
Support Vector ◽  
Related Gene ◽  
Discovery Cohort ◽  
Immune Oncology

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of gastrointestinal cancer. However, biomarkers correlated with the efficacy of ICIs in gastrointestinal cancer are still lacking. In this study, we performed 395-plex immune oncology (IO)-related gene target sequencing in tumor samples from 96 patients with metastatic gastrointestinal cancer patients treated with ICIs, and a linear support vector machine learning strategy was applied to construct a predictive model. ResultsAll 96 patients were randomly assigned into the discovery (n=72) and validation (n=24) cohorts. A 24-gene RNA signature (termed the IO-score) was constructed from 395 immune-related gene expression profiling using a machine learning strategy to identify patients who might benefit from ICIs. The durable clinical benefit rate was higher in patients with a high IO-score than in patients with a low IO-score (discovery cohort: 92.0% vs 4.3%, p<0.001; validation cohort: 85.7% vs 17.6%, p=0.004). The IO-score may exhibit a higher predictive value in the discovery (area under the receiver operating characteristic curve (AUC)=0.97)) and validation (AUC=0.74) cohorts compared with the programmed death ligand 1 positivity (AUC=0.52), tumor mutational burden (AUC=0.69) and microsatellite instability status (AUC=0.59) in the combined cohort. Moreover, patients with a high IO-score also exhibited a prolonged overall survival compared with patients with a low IO-score (discovery cohort: HR, 0.29; 95% CI 0.15 to 0.56; p=0.003; validation cohort: HR, 0.32; 95% CI 0.10 to 1.05; p=0.04). Taken together, our results indicated the potential of IO-score as a biomarker for immunotherapy in patients with gastrointestinal cancers.

scholarly journals Predicting unplanned medical visits among patients with diabetes: translation from machine learning to clinical implementation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Arielle Selya ◽  
Drake Anshutz ◽  
Emily Griese ◽  
Tess L. Weber ◽  
Benson Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Density Lipoprotein ◽  
Clinical Intervention ◽  
The United States ◽  
Healthcare Organization ◽  
Support Vector ◽  
Medical Visits ◽  
Percentage Points ◽  
Patients With Diabetes

Abstract Background Diabetes is a medical and economic burden in the United States. In this study, a machine learning predictive model was developed to predict unplanned medical visits among patients with diabetes, and findings were used to design a clinical intervention in the sponsoring healthcare organization. This study presents a case study of how predictive analytics can inform clinical actions, and describes practical factors that must be incorporated in order to translate research into clinical practice. Methods Data were drawn from electronic medical records (EMRs) from a large healthcare organization in the Northern Plains region of the US, from adult (≥ 18 years old) patients with type 1 or type 2 diabetes who received care at least once during the 3-year period. A variety of machine-learning classification models were run using standard EMR variables as predictors (age, body mass index (BMI), systolic blood pressure (BP), diastolic BP, low-density lipoprotein, high-density lipoprotein (HDL), glycohemoglobin (A1C), smoking status, number of diagnoses and number of prescriptions). The best-performing model after cross-validation testing was analyzed to identify strongest predictors. Results The best-performing model was a linear-basis support vector machine, which achieved a balanced accuracy (average of sensitivity and specificity) of 65.7%. This model outperformed a conventional logistic regression by 0.4 percentage points. A sensitivity analysis identified BP and HDL as the strongest predictors, such that disrupting these variables with random noise decreased the model’s overall balanced accuracy by 1.3 and 1.4 percentage points, respectively. These recommendations, along with stakeholder engagement, behavioral economics strategies, and implementation science principles helped to inform the design of a clinical intervention targeting behavioral changes. Conclusion Our machine-learning predictive model more accurately predicted unplanned medical visits among patients with diabetes, relative to conventional models. Post-hoc analysis of the model was used for hypothesis generation, namely that HDL and BP are the strongest contributors to unplanned medical visits among patients with diabetes. These findings were translated into a clinical intervention now being piloted at the sponsoring healthcare organization. In this way, this predictive model can be used in moving from prediction to implementation and improved diabetes care management in clinical settings.

scholarly journals Transcranial magnetic stimulation language mapping analysis revisited: Machine learning classification of 90 patients reveals distinct reorganization pattern in aphasic patients

2020 ◽  
Author(s):  
Ziqian Wang ◽  
Lucius Fekonja ◽  
Felix Dreyer ◽  
Peter Vajkoczy ◽  
Thomas Picht
Keyword(s):  
Machine Learning ◽  
Inferior Frontal Gyrus ◽  
Univariate Analysis ◽  
World Health ◽  
Support Vector ◽  
Who Grade ◽  
Machine Learning Classification ◽  
Svm Model ◽  
The Right ◽  
Language Network

AbstractRepetitive TMS (rTMS) allows to non-invasively and transiently disrupt local neuronal functioning. Its potential for mapping of language function is currently explored. Given the inter-individual heterogeneity of tumor impact on the language network and resulting rTMS derived functional mapping, we propose to use machine learning strategies to classify potential patterns of functional reorganization. We retrospectively included 90 patients with left perisylvian glioma tumors, world health organization (WHO) grade II-IV, affecting the language network. All patients underwent navigated rTMS language mappings. The severity of aphasia was assessed preoperatively using the Berlin Aphasia Score (BAS), which is adapted to the Aachener Aphasia Test (AAT). After spatial normalization to MNI 152 of all rTMS spots, we calculated the error rate (ER) in each cortical area by automated anatomical labeling parcellation (AAL) and used support vector machine (SVM) as a classifier for significant areas in relation to aphasia. 29 of 90 (32.2%) patients suffered from aphasia. Univariate analysis revealed 11 perisylvian AVOIs’ ERs (eight left, three right hemispheric) that were significantly higher in the aphasic than non-aphasic group (p < 0.05), depicting a broad, bihemispheric language network. After feeding the significant AVOIs into the SVM model, it showed that additional to age (w = 2.95), the ERs of right Frontal_Inf_Tri (w = 2.06) and left SupraMarginal (w = 2.05) and Parietal_Inf (w= 1.80) contributed more than other features to the model. The model’s sensitivity was 89.7%, the specificity was 82.0%, the overall accuracy was 81.1% and AUC was 88.7%. Our results demonstrate an increased vulnerability of the right inferior frontal gyrus to rTMS in patients suffering from aphasia due to left perisylvian gliomas. This confirms a functional relevant involvement of the right frontal area in relation to aphasia. While age as a feature improved our SVM model the most, the tumor location feature didn’t affect the SVM model. This finding indicates that general tumor induced network disconnection is relevant to aphasia and not necessarily related to specific lesion locations. Additionally, our results emphasize the decreasing potential for neuroplasticity with age.

scholarly journals Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Jun Zhang ◽  
Hong Peng ◽  
Yu-Lin Wang ◽  
Hua-Feng Xiao ◽  
Yuan-Yuan Cui ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Image Feature ◽  
Support Vector ◽  
Who Grade ◽  
Test Set ◽  
Apparent Diffusion ◽  
Grade Ii

PurposeTo evaluate isocitrate dehydrogenase (IDH) status in clinically diagnosed grade II~IV glioma patients using the 2016 World Health Organization (WHO) classification based on MRI parameters.Materials and MethodsOne hundred and seventy-six patients with confirmed WHO grade II~IV glioma were retrospectively investigated as the study set, including lower-grade glioma (WHO grade II, n = 64; WHO grade III, n = 38) and glioblastoma (WHO grade IV, n = 74). The minimum apparent diffusion coefficient (ADCmin) in the tumor and the contralateral normal-appearing white matter (ADCn) and the rADC (ADCmin to ADCn ratio) were defined and calculated. Intraclass correlation coefficient (ICC) analysis was carried out to evaluate interobserver and intraobserver agreement for the ADC measurements. Interobserver agreement for the morphologic categories was evaluated by Cohen’s kappa analysis. The nonparametric Kruskal-Wallis test was used to determine whether the ADC measurements and glioma subtypes were related. By univariable analysis, if the differences in a variable were significant (P&lt;0.05) or an image feature had high consistency (ICC &gt;0.8; κ &gt;0.6), then it was chosen as a predictor variable. The performance of the area under the receiver operating characteristic curve (AUC) was evaluated using several machine learning models, including logistic regression, support vector machine, Naive Bayes and Ensemble. Five evaluation indicators were adopted to compare the models. The optimal model was developed as the final model to predict IDH status in 40 patients with glioma as the subsequent test set. DeLong analysis was used to compare significant differences in the AUCs.ResultsIn the study set, six measured variables (rADC, age, enhancement, calcification, hemorrhage, and cystic change) were selected for the machine learning model. Logistic regression had better performance than other models. Two predictive models, model 1 (including all predictor variables) and model 2 (excluding calcification), correctly classified IDH status with an AUC of 0.897 and 0.890, respectively. The test set performed equally well in prediction, indicating the effectiveness of the trained classifier. The subgroup analysis revealed that the model predicted IDH status of LGG and GBM with accuracy of 84.3% (AUC = 0.873) and 85.1% (AUC = 0.862) in the study set, and with the accuracy of 70.0% (AUC = 0.762) and 70.0% (AUC = 0.833) in the test set, respectively.ConclusionThrough the use of machine-learning algorithms, the accurate prediction of IDH-mutant versus IDH-wildtype was achieved for adult diffuse gliomas via noninvasive MR imaging characteristics, including ADC values and tumor morphologic features, which are considered widely available in most clinical workstations.

scholarly journals Flood and Drought Prediction Using the Machine Learning Algorithm Support Vector Regression

2019 ◽  
Vol 9 (1) ◽  
pp. 5194-5199
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Predictive Model ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Predictive Skill ◽  
Drought Prediction ◽  
Rbf Kernel ◽  
Search Approach

Flood and drought are frequently happening natural disasters in most of the countries. These disasters can cause considerable damage to agriculture, ecology and economy of the country. Mitigating the impacts of flood and drought is a valuable help to the human being. The main cause of these disasters is precipitation. If the past precipitation data are analyzed properly, the future flood and drought events can be easily found. Prediction using the Standard Precipitation Index (SPI) is a way to find the wet or dry condition of a region or country. In this paper the SPI values with different lead times are calculated for a long period of time. These SPI indices are analysed by a predictive model using the machine learning algorithm called Support Vector Regression (SVR) with RBF (Radial Basis Function) kernel. In this model the Grid Search approach is used for optimization. The forecast result of this predictive model shows the predictive skill of the SVR-RBF kernel.

scholarly journals Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

2021 ◽  
Vol 17 (02) ◽  
pp. 90
Author(s):  
Inssaf El Guabassi ◽  
Zakaria Bousalem ◽  
Rim Marah ◽  
Aimad Qazdar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Predictive Model ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Future ◽  
Log Linear

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance.

scholarly journals Using Machine Learning to Predict Bacteremia in Febrile Children Presented to the Emergency Department

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 307 ◽  
Author(s):  
Chih-Min Tsai ◽  
Chun-Hung Richard Lin ◽  
Huan Zhang ◽  
I-Min Chiu ◽  
Chi-Yung Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Blood Culture ◽  
Predictive Model ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Pediatric Emergency ◽  
High Rate ◽  
Support Vector ◽  
Retrospective Case

Blood culture is frequently used to detect bacteremia in febrile children. However, a high rate of negative or false-positive blood culture results is common at the pediatric emergency department (PED). The aim of this study was to use machine learning to build a model that could predict bacteremia in febrile children. We conducted a retrospective case-control study of febrile children who presented to the PED from 2008 to 2015. We adopted machine learning methods and cost-sensitive learning to establish a predictive model of bacteremia. We enrolled 16,967 febrile children with blood culture tests during the eight-year study period. Only 146 febrile children had true bacteremia, and more than 99% of febrile children had a contaminant or negative blood culture result. The maximum area under the curve of logistic regression and support vector machines to predict bacteremia were 0.768 and 0.832, respectively. Using the predictive model, we can categorize febrile children by risk value into five classes. Class 5 had the highest probability of having bacteremia, while class 1 had no risk. Obtaining blood cultures in febrile children at the PED rarely identifies a causative pathogen. Prediction models can help physicians determine whether patients have bacteremia and may reduce unnecessary expenses.

scholarly journals Machine-Learning Approach for Modeling Myelosuppression Attributed to Nimustine Hydrochloride

2018 ◽  
pp. 1-21 ◽  
Author(s):  
Takuma Shibahara ◽  
Soko Ikuta ◽  
Yoshihiro Muragaki
Keyword(s):  
Machine Learning ◽  
Adverse Effect ◽  
Blood Cell ◽  
Support Vector ◽  
Learning Approach ◽  
Who Grade ◽  
Hematopoietic Stem ◽  
Cell Counts ◽  
Machine Learning Approach ◽  
Nimustine Hydrochloride

Purpose A major adverse effect arising from nimustine hydrochloride (ACNU) therapy for brain tumors is myelosuppression. Because its timing and severity vary among individual patients, the ACNU dose level has been adjusted in an empiric manner at individual medical facilities. To our knowledge, ours is the first study to develop a machine-learning approach to estimate myelosuppression through analysis of patient factors before treatment and attempts to clarify the relationship between myelosuppression and hematopoietic stem cells from daily clinical data. Adverse effect prediction will allow ACNU dose adjustment for patients predicted to have decreases in blood cell counts and will enable focused follow-up of patients undergoing chemoradiotherapy. Patients and Methods Patients were newly pathologically diagnosed with WHO grade 2 or 3 tumors and were treated with ACNU-based chemoradiotherapy. For detailed analysis of the timing and intensity of adverse effects in patients, we developed a data-weighted support vector machine (SVM) based on adverse event criteria (nadir-weighted SVM [NwSVM]). To evaluate the estimation accuracy of blood cell count dynamics, the determination coefficient ( r2) between real and estimated data was calculated by three regression methods: polynomial, SVM, and NwSVM. Results Only the NwSVM-based regression enabled estimation of the dynamics of all blood cell types with high accuracy (mean r2 = 0.81). The mean timing of nadir arrival estimated using this regression was 35 days for platelets, 41 days for RBCs, 52 days for lymphocytes, 57 days for WBCs, and 62 days for neutrophils. Conclusion The NwSVM can be used to predict myelosuppression and clearly depicts nadir timing differences between platelets and other blood cells.

scholarly journals Machine learning identifies SNPs predictive of advanced coronary artery calcium in ClinSeq® and Framingham Heart Study cohorts

2017 ◽  
Author(s):  
Cihan Oguz ◽  
Shurjo K Sen ◽  
Adam R Davis ◽  
Yi-Ping Fu ◽  
Christopher J O’Donnell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery ◽  
Data Science ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Medical Decision ◽  
Learning Tools ◽  
Operating Characteristics ◽  
Replication Cohort ◽  
Discovery Cohort

ABSTRACTOne goal of personalized medicine is leveraging the emerging tools of data science to guide medical decision-making. Achieving this using disparate data sources is most daunting for polygenic traits and requires systems level approaches. To this end, we employed random forests (RF) and neural networks (NN) for predictive modeling of coronary artery calcification (CAC), which is an intermediate end-phenotype of coronary artery disease (CAD). Model inputs were derived from advanced cases in the ClinSeq® discovery cohort (n=16) and the FHS replication cohort (n=36) from 89th−99th CAC score percentile range, and age-matching controls (ClinSeq® n=16, FHS n=36) with no detectable CAC (all subjects were Caucasian males). These inputs included clinical variables (CLIN), genotypes of 57 SNPs associated with CAC in past GWAS (SNP Set-1), and an alternative set of 56 SNPs (SNP Set-2) ranked highest in terms of their nominal correlation with advanced CAC state in the discovery cohort. Predictive performance was assessed by computing the areas under receiver operating characteristics curves (AUC). Within the discovery cohort, RF models generated AUC values of 0.69 with CLIN, 0.72 with SNP Set-1, and 0.77 with their combination. In the replication cohort, SNP Set-1 was again more predictive (AUC=0.78) than CLIN (AUC=0.61), but also more predictive than the combination (AUC=0.75). In contrast, in both cohorts, SNP Set-2 generated enhanced predictive performance with or without CLIN (AUC> 0.8). Using the 21 SNPs of SNP Set-2 that produced optimal predictive performance in both cohorts, we developed NN models trained with ClinSeq® data and tested with FHS data and replicated the high predictive accuracy (AUC>0.8) with several topologies, thereby identifying several potential susceptibility loci for advanced CAD. Several CAD-related biological processes were found to be enriched in the network of genes constructed from these loci. In both cohorts, SNP Set-1 derived from past CAC GWAS yielded lower performance than SNP Set-2 derived from “extreme” CAC cases within the discovery cohort. Machine learning tools hold promise for surpassing the capacity of conventional GWAS-based approaches for creating predictive models utilizing the complex interactions between disease predictors intrinsic to the pathogenesis of polygenic disorders.

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