Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics with machine learning model development and external validation

AbstractThe purpose of this study was to establish a high-performing radiomics strategy with machine learning from conventional and diffusion MRI to differentiate recurrent glioblastoma (GBM) from radiation necrosis (RN) after concurrent chemoradiotherapy (CCRT) or radiotherapy. Eighty-six patients with GBM were enrolled in the training set after they underwent CCRT or radiotherapy and presented with new or enlarging contrast enhancement within the radiation field on follow-up MRI. A diagnosis was established either pathologically or clinicoradiologically (63 recurrent GBM and 23 RN). Another 41 patients (23 recurrent GBM and 18 RN) from a different institution were enrolled in the test set. Conventional MRI sequences (T2-weighted and postcontrast T1-weighted images) and ADC were analyzed to extract 263 radiomic features. After feature selection, various machine learning models with oversampling methods were trained with combinations of MRI sequences and subsequently validated in the test set. In the independent test set, the model using ADC sequence showed the best diagnostic performance, with an AUC, accuracy, sensitivity, specificity of 0.80, 78%, 66.7%, and 87%, respectively. In conclusion, the radiomics models models using other MRI sequences showed AUCs ranging from 0.65 to 0.66 in the test set. The diffusion radiomics may be helpful in differentiating recurrent GBM from RN..

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Differentiation of Recurrent Glioblastoma from Radiation Necrosis Using Diffusion Radiomics: Machine Learning Model Development and External Validation

Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - Lecture Notes in Computer Science ◽

10.1007/978-3-030-66843-3_27 ◽

2020 ◽

pp. 276-283

Author(s):

Yae Won Park ◽

Ji Eun Park ◽

Sung Soo Ahn ◽

Hwiyoung Kim ◽

Ho Sung Kim ◽

...

Keyword(s):

Machine Learning ◽

Radiation Necrosis ◽

External Validation ◽

Model Development ◽

Learning Model ◽

Recurrent Glioblastoma ◽

Machine Learning Model

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Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Diagnostics ◽

10.3390/diagnostics11071263 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1263

Author(s):

Samy Ammari ◽

Raoul Sallé de Chou ◽

Tarek Assi ◽

Mehdi Touat ◽

Emilie Chouzenoux ◽

...

Keyword(s):

Machine Learning ◽

Therapeutic Option ◽

Binary Classification ◽

Progression Free Survival ◽

Recurrent Glioblastoma ◽

Machine Learning Algorithms ◽

Survival Prediction ◽

Classification Models ◽

Angiogenic Therapy ◽

Recurrent Gbm

Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18–80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients.

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Machine Learning for Mortality Prediction in Pediatric Myocarditis

Frontiers in Pediatrics ◽

10.3389/fped.2021.644922 ◽

2021 ◽

Vol 9 ◽

Author(s):

Fu-Sheng Chou ◽

Laxmi V. Ghimire

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Regression Model ◽

Current Knowledge ◽

External Validation ◽

Model Development ◽

Kidney Injury ◽

High Specificity ◽

Mortality Prediction ◽

Linear Regression Models

Background: Pediatric myocarditis is a rare disease. The etiologies are multiple. Mortality associated with the disease is 5–8%. Prognostic factors were identified with the use of national hospitalization databases. Applying these identified risk factors for mortality prediction has not been reported.Methods: We used the Kids' Inpatient Database for this project. We manually curated fourteen variables as predictors of mortality based on the current knowledge of the disease, and compared performance of mortality prediction between linear regression models and a machine learning (ML) model. For ML, the random forest algorithm was chosen because of the categorical nature of the variables. Based on variable importance scores, a reduced model was also developed for comparison.Results: We identified 4,144 patients from the database for randomization into the primary (for model development) and testing (for external validation) datasets. We found that the conventional logistic regression model had low sensitivity (~50%) despite high specificity (>95%) or overall accuracy. On the other hand, the ML model struck a good balance between sensitivity (89.9%) and specificity (85.8%). The reduced ML model with top five variables (mechanical ventilation, cardiac arrest, ECMO, acute kidney injury, ventricular fibrillation) were sufficient to approximate the prediction performance of the full model.Conclusions: The ML algorithm performs superiorly when compared to the linear regression model for mortality prediction in pediatric myocarditis in this retrospective dataset. Prospective studies are warranted to further validate the applicability of our model in clinical settings.

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Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study

Cancers ◽

10.3390/cancers12123817 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3817

Author(s):

Shi-Jer Lou ◽

Ming-Feng Hou ◽

Hong-Tai Chang ◽

Chong-Chi Chiu ◽

Hao-Hsien Lee ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Learning Algorithms ◽

External Validation ◽

Model Development ◽

Cancer Surgery ◽

Machine Learning Algorithms ◽

Breast Cancer Surgery ◽

Training Dataset

No studies have discussed machine learning algorithms to predict recurrence within 10 years after breast cancer surgery. This study purposed to compare the accuracy of forecasting models to predict recurrence within 10 years after breast cancer surgery and to identify significant predictors of recurrence. Registry data for breast cancer surgery patients were allocated to a training dataset (n = 798) for model development, a testing dataset (n = 171) for internal validation, and a validating dataset (n = 171) for external validation. Global sensitivity analysis was then performed to evaluate the significance of the selected predictors. Demographic characteristics, clinical characteristics, quality of care, and preoperative quality of life were significantly associated with recurrence within 10 years after breast cancer surgery (p < 0.05). Artificial neural networks had the highest prediction performance indices. Additionally, the surgeon volume was the best predictor of recurrence within 10 years after breast cancer surgery, followed by hospital volume and tumor stage. Accurate recurrence within 10 years prediction by machine learning algorithms may improve precision in managing patients after breast cancer surgery and improve understanding of risk factors for recurrence within 10 years after breast cancer surgery.

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REDIAL-2020: A Suite of Machine Learning Models to Estimate Anti-SARS-CoV-2 Activities

10.26434/chemrxiv.12915779.v2 ◽

2020 ◽

Author(s):

Govinda KC ◽

Giovanni Bocci ◽

Srijan Verma ◽

Mahmudulla Hassan ◽

Jayme Holmes ◽

...

Keyword(s):

Machine Learning ◽

High Throughput Screening ◽

Web Application ◽

External Validation ◽

Model Development ◽

Machine Learning Algorithms ◽

Virus Infectivity ◽

Learning Models ◽

Live Virus ◽

Machine Learning Models

Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The “best models” are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (<a href="https://drugdiscovery.utep.edu/redial">http://drugcentral.org/Redial</a>). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (viral entry, viral replication, and live virus infectivity) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (<a href="https://github.com/sirimullalab/ncats_covid">https://github.com/sirimullalab/</a>redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.

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Prediction of Genotype Positivity in Patients with Hypertrophic Cardiomyopathy Using Machine Learning

Circulation Genomic and Precision Medicine ◽

10.1161/circgen.120.003259 ◽

2021 ◽

Author(s):

Lusha W. Liang ◽

Michael A. Fifer ◽

Kohei Hasegawa ◽

Mathew S. Maurer ◽

Muredach P. Reilly ◽

...

Keyword(s):

Machine Learning ◽

Genetic Testing ◽

Hypertrophic Cardiomyopathy ◽

Predictive Value ◽

External Validation ◽

Scoring Systems ◽

Training Set ◽

Test Set ◽

Net Reclassification Improvement ◽

Mayo Score

Background - Genetic testing can determine family screening strategies and has prognostic and diagnostic value in hypertrophic cardiomyopathy (HCM). However, it can also pose a significant psychosocial burden. Conventional scoring systems offer modest ability to predict genotype positivity. The aim of our study was to develop a novel prediction model for genotype positivity in patients with HCM by applying machine learning (ML) algorithms. Methods - We constructed three ML models using readily available clinical and cardiac imaging data of 102 patients from Columbia University with HCM who had undergone genetic testing (the training set). We validated model performance on 76 patients with HCM from Massachusetts General Hospital (the test set). Within the test set, we compared the area under the receiver operating characteristic curves (AUCs) for the ML models against the AUCs generated by the Toronto HCM Genotype Score ("the Toronto score") and Mayo HCM Genotype Predictor ("the Mayo score") using the Delong test and net reclassification improvement (NRI). Results - Overall, 63 of the 178 patients (35%) were genotype positive. The random forest ML model developed in the training set demonstrated an AUC of 0.92 (95% CI 0.85-0.99) in predicting genotype positivity in the test set, significantly outperforming the Toronto score (AUC 0.77, 95% CI 0.65-0.90, p=0.004, NRI: p<0.001) and the Mayo score (AUC 0.79, 95% CI 0.67-0.92, p=0.01, NRI: p=0.001). The gradient boosted decision tree ML model also achieved significant NRI over the Toronto score (p<0.001) and the Mayo score (p=0.03), with an AUC of 0.87 (95% CI 0.75-0.99). Compared to the Toronto and Mayo scores, all three ML models had higher sensitivity, positive predictive value, and negative predictive value. Conclusions - Our ML models demonstrated a superior ability to predict genotype positivity in patients with HCM compared to conventional scoring systems in an external validation test set.

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Using Automated-Machine Learning to Predict COVID-19 Patient Survival: Identify Influential Biomarkers (Preprint)

10.2196/preprints.23458 ◽

2020 ◽

Author(s):

Kenji Ikemura ◽

D.Y. Goldstein ◽

James Szymanski ◽

Eran Bellin ◽

Lindsay Stahl ◽

...

Keyword(s):

Machine Learning ◽

Patient Survival ◽

Binary Classifier ◽

Rt Pcr ◽

Threshold Probability ◽

Test Set ◽

Medical Practitioners ◽

Automated Machine Learning ◽

Binary Classifiers ◽

Sensitivity Specificity

BACKGROUND In a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. OBJECTIVE In this study, we used automated machine learning (autoML) to develop and compare between multiple machine learning (ML) models that predict the chance of patient survival from COVID-19 infection and identified the best-performing model. In addition, we investigated which biomarkers are the most influential in generating an accurate model. We believe an ML model such as this could be a useful tool for clinicians stratifying hospitalized SARS-CoV-2 patients. METHODS The data was retrospectively collected from Clinical Looking Glass (CLG) on all patients testing positive for COVID-19 through a nasopharyngeal specimen by real-time RT-PCR and admitted between 3/1/2020-7/3/2020 (4376 patients) at our institution. We collected 47 biomarkers from each patient within 36 hours before or after the index time: RT-PCR positivity, and tracked whether a patient survived or not for one month following this time. We utilized the autoML from H2O.ai, an open source package for R language. The autoML generated 20 ML models and ranked them by area under the precision-recall curve (AUCPR) on the test set. We selected the best model (model_var_47) and chose a threshold probability that maximized F2 score to make a binary classifier: dead or alive. Subsequently, we ranked the relative importance of variables that generated model_var_47 and chose the 10 most influential variables. Next, we reran the autoML with these 10 variables and likewise selected the model with the best AUCPR on the test set (model_var_10). Again, threshold probability that maximized F2 score for model_var_10 was chosen to make a binary classifier. We calculated and compared the sensitivity, specificity, and positive predicate value (PPV) for model_var_10 and model_var_47. RESULTS The best model that autoML generated using all 47 variables was the stacked ensemble model of all models (AUCPR = 0.836). The most influential variables were: systolic and diastolic blood pressure, age, respiratory rate, pulse oximetry, blood urea nitrogen, lactate dehydrogenase, d-dimer, troponin, and glucose. When the autoML was retrained with these 10 most important variables, it did not significantly affect the performance (AUCPR= 0.82). For the binary classifiers, sensitivity, specificity, and PPV of model_var_47 was 83.5%, 87.7%, and 69.8% respectively, while for model_var_10 they were 90.1%, 71.1%, and 51.8% respectively. CONCLUSIONS By using autoML, we developed high-performing models that predict patient mortality from COVID-19 infection. In addition, we identified the most important biomarkers correlated with mortality. This ML model can be used as a decision supporting tool for medical practitioners to efficiently triage COVID-19 infected patients. From our literature review, this will be the largest COVID-19 patient cohort to train ML models and the first to utilize autoML.

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Machine Learning Approach Using Routine Immediate Postoperative Laboratory Values for Predicting Postoperative Mortality

Journal of Personalized Medicine ◽

10.3390/jpm11121271 ◽

2021 ◽

Vol 11 (12) ◽

pp. 1271

Author(s):

Jaehyeong Cho ◽

Jimyung Park ◽

Eugene Jeong ◽

Jihye Shin ◽

Sangjeong Ahn ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Prediction Models ◽

External Validation ◽

Model Development ◽

Postoperative Mortality ◽

Random Forest Model ◽

Forest Model ◽

Laboratory Values ◽

Increased Risk

Background: Several prediction models have been proposed for preoperative risk stratification for mortality. However, few studies have investigated postoperative risk factors, which have a significant influence on survival after surgery. This study aimed to develop prediction models using routine immediate postoperative laboratory values for predicting postoperative mortality. Methods: Two tertiary hospital databases were used in this research: one for model development and another for external validation of the resulting models. The following algorithms were utilized for model development: LASSO logistic regression, random forest, deep neural network, and XGBoost. We built the models on the lab values from immediate postoperative blood tests and compared them with the SASA scoring system to demonstrate their efficacy. Results: There were 3817 patients who had immediate postoperative blood test values. All models trained on immediate postoperative lab values outperformed the SASA model. Furthermore, the developed random forest model had the best AUROC of 0.82 and AUPRC of 0.13, and the phosphorus level contributed the most to the random forest model. Conclusions: Machine learning models trained on routine immediate postoperative laboratory values outperformed previously published approaches in predicting 30-day postoperative mortality, indicating that they may be beneficial in identifying patients at increased risk of postoperative death.

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Using Automated-Machine Learning to Predict COVID-19 Patient Survival: Identify Influential Biomarkers

10.1101/2020.10.12.20211086 ◽

2020 ◽

Author(s):

Kenji Ikemura ◽

D.Y. Goldstein ◽

James Szymanski ◽

Eran Bellin ◽

Lindsay Stahl ◽

...

Keyword(s):

Machine Learning ◽

Patient Survival ◽

Binary Classifier ◽

Rt Pcr ◽

Threshold Probability ◽

Test Set ◽

Medical Practitioners ◽

Automated Machine Learning ◽

Binary Classifiers ◽

Sensitivity Specificity

AbstractBackgroundIn a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. In this study, we used automated machine learning (autoML) to develop and compare between multiple machine learning (ML) models that predict the chance of patient survival from COVID-19 infection and identified the best-performing model. In addition, we investigated which biomarkers are the most influential in generating an accurate model. We believe an ML model such as this could be a useful tool for clinicians stratifying hospitalized SARS-CoV-2 patients.MethodsThe data was retrospectively collected from Clinical Looking Glass (CLG) on all patients testing positive for COVID-19 through a nasopharyngeal specimen by real-time RT-PCR and admitted between 3/1/2020-7/3/2020 (4376 patients) at our institution. We collected 47 biomarkers from each patient within 36 hours before or after the index time: RT-PCR positivity, and tracked whether a patient survived or not for one month following this time. We utilized the autoML from H2O.ai, an open source package for R language. The autoML generated 20 ML models and ranked them by area under the precision-recall curve (AUCPR) on the test set. We selected the best model (model_var_47) and chose a threshold probability that maximized F2 score to make a binary classifier: dead or alive. Subsequently, we ranked the relative importance of variables that generated model_var_47 and chose the 10 most influential variables. Next, we reran the autoML with these 10 variables and likewise selected the model with the best AUCPR on the test set (model_var_10). Again, threshold probability that maximized F2 score for model_var_10 was chosen to make a binary classifier. We calculated and compared the sensitivity, specificity, and positive predicate value (PPV) for model_var_10 and model_var_47.ResultsThe best model that autoML generated using all 47 variables was the stacked ensemble model of all models (AUCPR = 0.836). The most influential variables were: systolic and diastolic blood pressure, age, respiratory rate, pulse oximetry, blood urea nitrogen, lactate dehydrogenase, d-dimer, troponin, and glucose. When the autoML was retrained with these 10 most important variables, it did not significantly affect the performance (AUCPR= 0.828). For the binary classifiers, sensitivity, specificity, and PPV of model_var_47 was 83.6%, 87.7%, and 69.8% respectively, while for model_var_10 they were 90.9%, 71.1%, and 51.8% respectively.ConclusionsBy using autoML, we developed high-performing models that predict patient mortality from COVID-19 infection. In addition, we identified the most important biomarkers correlated with mortality. This ML model can be used as a decision supporting tool for medical practitioners to efficiently triage COVID-19 infected patients. From our literature review, this will be the largest COVID-19 patient cohort to train ML models and the first to utilize autoML. The COVID-19 survival calculator based on this study can be found at https://www.tsubomitech.com/.

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