Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

Abstract Objectives To investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning to differentiate benign from malignant lesions using model-free parameter maps. Methods In this retrospective study, BRCA-positive patients who had an MRI from November 2013 to February 2019 that led to a biopsy (BI-RADS 4) or imaging follow-up (BI-RADS 3) for sub-centimeter lesions were included. Two radiologists assessed all lesions independently and in consensus according to BI-RADS. Radiomics features were calculated using open-source CERR software. Univariate analysis and multivariate modeling were performed to identify significant radiomics features and clinical factors to be included in a machine learning model to differentiate malignant from benign lesions. Results Ninety-six BRCA mutation carriers (mean age at biopsy = 45.5 ± 13.5 years) were included. Consensus BI-RADS classification assessment achieved a diagnostic accuracy of 53.4%, sensitivity of 75% (30/40), specificity of 42.1% (32/76), PPV of 40.5% (30/74), and NPV of 76.2% (32/42). The machine learning model combining five parameters (age, lesion location, GLCM-based correlation from the pre-contrast phase, first-order coefficient of variation from the 1st post-contrast phase, and SZM-based gray level variance from the 1st post-contrast phase) achieved a diagnostic accuracy of 81.5%, sensitivity of 63.2% (24/38), specificity of 91.4% (64/70), PPV of 80.0% (24/30), and NPV of 82.1% (64/78). Conclusions Radiomics analysis coupled with machine learning improves the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses as benign or malignant compared with qualitative morphological assessment with BI-RADS classification alone in BRCA mutation carriers. Key Points • Radiomics and machine learning can help differentiate benign from malignant breast masses even if the masses are small and morphological features are benign. • Radiomics and machine learning analysis showed improved diagnostic accuracy, specificity, PPV, and NPV compared with qualitative morphological assessment alone.

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MRI-based Radiomics for Prognosis of Pediatric Diffuse Intrinsic Pontine Glioma: An International Study

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab042 ◽

2021 ◽

Author(s):

Lydia T Tam ◽

Kristen W Yeom ◽

Jason N Wright ◽

Alok Jaju ◽

Alireza Radmanesh ◽

...

Keyword(s):

Machine Learning ◽

International Study ◽

Learning Model ◽

Image Feature ◽

Training Dataset ◽

Post Contrast ◽

Clinical Variables ◽

Testing Dataset ◽

Machine Learning Model ◽

Independent Testing Dataset

Abstract Background Diffuse Intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model. Methods We isolated tumor volumes of T1-post contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of LASSO Cox regression selected optimal features to predict overall survival (OS) in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. Results All selected features were intensity and texture-based on the wavelet filtered images (three T1 grey-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first order-mean). This multivariable Cox model demonstrated a concordance of 0.68 [95% CI: 0.61-0.74] in the training dataset, significantly outperforming the clinical-only model (C=0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C=0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C=0.59 [95% CI: 0.51-0.67], Noether’s test p=0.02). Conclusion In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.

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Design of Machine Learning Model for Urban Planning and Management Improvement

International Journal of Performability Engineering ◽

10.23940/ijpe.20.06.p14.958967 ◽

2020 ◽

Vol 16 (6) ◽

pp. 958 ◽

Cited By ~ 1

Author(s):

Zhou Jiafeng ◽

Liu Tian ◽

Zou Lin

Keyword(s):

Machine Learning ◽

Urban Planning ◽

Learning Model ◽

Planning And Management ◽

Machine Learning Model ◽

Urban Planning And Management ◽

Management Improvement

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A Novel Machine Learning Model for Early Operational Anomaly Detection Using LWD/MWD Data

10.2523/iptc-19230-ms ◽

2019 ◽

Author(s):

Mohammed Al-Ghazal ◽

Viranchi Vedpathak

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Learning Model ◽

Machine Learning Model

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Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

10.26434/chemrxiv.7411172 ◽

2018 ◽

Author(s):

Steen Lysgaard ◽

Paul C. Jennings ◽

Jens Strabo Hummelshøj ◽

Thomas Bligaard ◽

Tejs Vegge

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Genetic Algorithms ◽

Au Nanoparticles ◽

Learning Model ◽

Energy Calculations ◽

Atomic Distribution ◽

Machine Learning Model ◽

Fold Reduction ◽

Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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Faculty of 1000 evaluation for The poly(ADP-ribose) polymerase inhibitor niraparib (MK4827) in BRCA mutation carriers and patients with sporadic cancer: a phase 1 dose-escalation trial.

F1000 - Post-publication peer review of the biomedical literature ◽

10.3410/f.718052087.793484800 ◽

2013 ◽

Author(s):

Steve Plaxe ◽

Kristy Ward

Keyword(s):

Dose Escalation ◽

Brca Mutation ◽

Phase 1 ◽

Sporadic Cancer ◽

Mutation Carriers ◽

Brca Mutation Carriers ◽

Polymerase Inhibitor

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AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200325104317 ◽

2020 ◽

Vol 13 ◽

Author(s):

Dhilsath Fathima.M ◽

S. Justin Samuel ◽

R. Hari Haran

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Decision Tree ◽

Learning Model ◽

Training Dataset ◽

Data Set ◽

Machine Learning Model ◽

Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

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