scholarly journals Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarv Priya ◽  
Yanan Liu ◽  
Caitlin Ward ◽  
Nam H. Le ◽  
Neetu Soni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Characteristic Curve ◽  
Model Performance ◽  
Feature Reduction ◽  
Flair Sequence ◽  
Contrast Enhanced ◽  
Significant Difference ◽  
Reduction Strategies ◽  
T1 Contrast ◽  
Mri Sequences

AbstractFew studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.

scholarly journals The Effect of Heterogenous Subregions in Glioblastomas on Survival Stratification: A Radiomics Analysis Using the Multimodality MRI

2021 ◽  
Vol 20 ◽  
pp. 153303382110330
Author(s):  
Lulu Yin ◽  
Yan Liu ◽  
Xi Zhang ◽  
Hongbing Lu ◽  
Yang Liu
Keyword(s):  
Poor Prognosis ◽  
Intratumor Heterogeneity ◽  
Short Term ◽  
Prognostic Information ◽  
Manual Delineation ◽  
Flair Sequence ◽  
The Poor ◽  
Contrast Enhanced ◽  
Mri Sequences

Intratumor heterogeneity is partly responsible for the poor prognosis of glioblastoma (GBM) patients. In this study, we aimed to assess the effect of different heterogeneous subregions of GBM on overall survival (OS) stratification. A total of 105 GBM patients were retrospectively enrolled and divided into long-term and short-term OS groups. Four MRI sequences, including contrast-enhanced T1-weighted imaging (T1C), T1, T2, and FLAIR, were collected for each patient. Then, 4 heterogeneous subregions, i.e. the region of entire abnormality (rEA), the regions of contrast-enhanced tumor (rCET), necrosis (rNec) and edema/non-contrast-enhanced tumor (rE/nCET), were manually drawn from the 4 MRI sequences. For each subregion, 50 radiomics features were extracted. The stratification performance of 4 heterogeneous subregions, as well as the performances of 4 MRI sequences, was evaluated both alone and in combination. Our results showed that rEA was superior in stratifying long-and short-term OS. For the 4 MRI sequences used in this study, the FLAIR sequence demonstrated the best performance of survival stratification based on the manual delineation of heterogeneous subregions. Our results suggest that heterogeneous subregions of GBMs contain different prognostic information, which should be considered when investigating survival stratification in patients with GBM.

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

2021 ◽  
pp. 197140092199897
Author(s):  
Sarv Priya ◽  
Caitlin Ward ◽  
Thomas Locke ◽  
Neetu Soni ◽  
Ravishankar Pillenahalli Maheshwarappa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Central Nervous System ◽  
Nervous System ◽  
Diagnostic Performance ◽  
Model Performance ◽  
Area Under The Curve ◽  
Central Nervous System Lymphoma ◽  
First Order ◽  
Single Slice ◽  
Contrast Enhanced

Objectives To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma. Methods Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve. Results The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909–0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice. Conclusions T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

scholarly journals Exploring the Value of Radiomics Features Based on B-Mode and Contrast-Enhanced Ultrasound in Discriminating the Nature of Thyroid Nodules

2021 ◽  
Vol 11 ◽  
Author(s):  
Shi Yan Guo ◽  
Ping Zhou ◽  
Yan Zhang ◽  
Li Qing Jiang ◽  
Yong Feng Zhao
Keyword(s):  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
New Technologies ◽  
Statistical Significance ◽  
Characteristic Curve ◽  
Logistic Models ◽  
Contrast Enhanced Ultrasound ◽  
Clinical Value ◽  
Contrast Enhanced ◽  
Significant Difference

BackgroundWith the improvement of ultrasound imaging resolution and the application of various new technologies, the detection rate of thyroid nodules has increased greatly in recent years. However, there are still challenges in accurately diagnosing the nature of thyroid nodules. This study aimed to evaluate the clinical application value of the radiomics features extracted from B-mode ultrasound (B-US) images combined with contrast-enhanced ultrasound (CEUS) images in the differentiation of benign and malignant thyroid nodules by comparing the diagnostic performance of four logistic models.MethodsWe retrospectively collected and ultimately included B-US images and CEUS images of 123 nodules from 123 patients, and then extracted the corresponding radiomics features from these images respectively. Meanwhile, a senior radiologist combined the thyroid imaging reporting and data system (TI-RADS) and the enhancement pattern of the ultrasonography to make a graded diagnosis of the malignancy of these nodules. Next, based on these radiomics features and grades, logistic regression was used to help build the models (B-US radiomics model, CEUS radiomics model, B-US+CEUS radiomics model, and TI-RADS+CEUS model). Finally, the study assessed the diagnostic performance of these radiomics features with a comparison of the area under the curve (AUC) of the receiver operating characteristic curve of four logistic models for predicting the benignity or malignancy of thyroid nodules.ResultsThe AUC in the differential diagnosis of the nature of thyroid nodules was 0.791 for the B-US radiomics model, 0.766 for the CEUS radiomics model, 0.861 for the B-US+CEUS radiomics model, and 0.785 for the TI-RADS+CEUS model. Compared to the TI-RADS+CEUS model, there was no statistical significance observed in AUC between the B-US radiomics model, CEUS radiomics model, B-US+CEUS radiomics model, and TI-RADS+CEUS model (P>0.05). However, a significant difference was observed between the single B-US radiomics model or CEUS radiomics model and B-US+CEUS radiomics model (P<0.05).ConclusionIn our study, the B-US radiomics model, CEUS radiomics model, and B-US+CEUS radiomics model demonstrated similar performance with the TI-RADS+CEUS model of senior radiologists in diagnosing the benignity or malignancy of thyroid nodules, while the B-US+CEUS radiomics model showed better diagnostic performance than single B-US radiomics model or CEUS radiomics model. It was proved that B-US radiomics features and CEUS radiomics features are of high clinical value as the combination of the two had better diagnostic performance.

Correlation between contrast-enhanced cone-beam breast computed tomography features and prognostic staging in breast cancer

2021 ◽  
Author(s):  
Wei-mei Ma ◽  
Jiao Li ◽  
Shuang-gang Chen ◽  
Pei-qiang Cai ◽  
Shen Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Cone Beam ◽  
Imaging Biomarkers ◽  
Training Set ◽  
Test Set ◽  
Ps Ii ◽  
Contrast Enhanced ◽  
Significant Difference

Objective: To evaluate whether contrast-enhanced cone-beam breast CT (CE-CBBCT) features can risk-stratify prognostic stage in breast cancer. Methods: Overall, 168 biopsy-proven breast cancer patients were analysed: 115 patients in the training set underwent scanning using v. 1.5 CE-CBBCT between August 2019 and December 2019, whereas 53 patients in the test set underwent scanning using v. 1.0 CE-CBBCT between May 2012 and August 2014. All patients were restaged according to the American Joint Committee on Cancer eighth edition prognostic staging system. Following the combination of CE-CBBCT imaging parameters and clinicopathological factors, predictors that were correlated with stratification of prognostic stage via logistic regression were analysed. Predictive performance was assessed according to the area under the receiver operating characteristic curve (AUC). Goodness-of-fit of the models was assessed using the Hosmer-Lemeshow test. Results: As regards differentiation between prognostic stage (PS) I and II/III, increased tumour-to-breast volume ratio (TBR), rim enhancement pattern, and the presence of penetrating vessels were significant predictors for PS II/III disease (p < 0.05). The AUCs in the training and test sets were 0.967 [95% confidence interval (CI) 0.938–0.996; p < 0.001] and 0.896 (95% CI, 0.809–0.983; p = 0.001), respectively. Two features were selected in the training set of PS II vs III, including tumour volume [odds ratio (OR)=1.817, p = 0.019] and calcification (OR = 4.600, p = 0.040), achieving an AUC of 0.790 (95% CI, 0.636–0.944, p = 0.001). However, there was no significant difference in the test set of PS II vs III (P＞0.05). Conclusion: CE-CBBCT imaging biomarkers may provide a large amount of anatomical and radiobiological information for the pre-operative distinction of prognostic stage. Advances in knowledge: CE-CBBCT features have distinctive promise for stratification of prognostic stage in breast cancer.

scholarly journals Multilevel Intrusion Detection System with Affinity Clustering and Ensemble SVM

2020 ◽  
pp. 522-529
Author(s):  
Sadhana Patidar ◽  
Priyanka Parihar ◽  
Chetan Agrawal
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Model Performance ◽  
Feature Reduction ◽  
Support Vector ◽  
Learning Tools ◽  
Security Issues ◽  
Proposed Model

Now-a-days with growing applications over internet increases the security issues over network. Many security applications are designed to cope with such security concerns but still it required more attention to improve speed as well accuracy. With advancement of technologies there is also evolution of new threats or attacks in network. So, it is required to design such detection system that can handle new threats in network. One of the network security tools is intrusion detection system which is used to detect malicious data packets. Machine learning tool is also used to improve efficiency of network-based intrusion detection system. In this paper, an intrusion detection system is proposed with an application of machine learning tools. The proposed model integrates feature reduction, affinity clustering and multilevel Ensemble Support Vector Machine. The proposed model performance is analyzed over two datasets i.e. NSL-KDD and UNSW-NB 15 dataset and achieved approx. 12% of efficiency over other existing work.

scholarly journals Predicting Depression in Community Dwellers Using a Machine Learning Algorithm

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1429
Author(s):  
Seo-Eun Cho ◽  
Zong Woo Geem ◽  
Kyoung-Sae Na
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Feature Reduction ◽  
Classification Model ◽  
Depression Screening ◽  
Test Set ◽  
Depression Group ◽  
Selection Operator ◽  
Community Dwellers

Depression is one of the leading causes of disability worldwide. Given the socioeconomic burden of depression, appropriate depression screening for community dwellers is necessary. We used data from the 2014 and 2016 Korea National Health and Nutrition Examination Surveys. The 2014 dataset was used as a training set, whereas the 2016 dataset was used as the hold-out test set. The synthetic minority oversampling technique (SMOTE) was used to control for class imbalances between the depression and non-depression groups in the 2014 dataset. The least absolute shrinkage and selection operator (LASSO) was used for feature reduction and classifiers in the final model. Data obtained from 9488 participants were used for the machine learning process. The depression group had poorer socioeconomic, health, functional, and biological measures than the non-depression group. From the initial 37 variables, 13 were selected using LASSO. All performance measures were calculated based on the raw 2016 dataset without the SMOTE. The area under the receiver operating characteristic curve and overall accuracy in the hold-out test set were 0.903 and 0.828, respectively. Perceived stress had the strongest influence on the classifying model for depression. LASSO can be practically applied for depression screening of community dwellers with a few variables. Future studies are needed to develop a more efficient and accurate classification model for depression.

scholarly journals Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection

2019 ◽  
Vol 6 (5) ◽  
Author(s):  
Benjamin Y Li ◽  
Jeeheh Oh ◽  
Vincent B Young ◽  
Krishna Rao ◽  
Jenna Wiens
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Characteristic Curve ◽  
Model Performance ◽  
Health Record ◽  
Data Set ◽  
Icu Admission ◽  
Clostridioides Difficile ◽  
Electronic Health ◽  
Health Care Associated Infection

Abstract Background Clostridium (Clostridioides) difficile infection (CDI) is a health care–associated infection that can lead to serious complications. Potential complications include intensive care unit (ICU) admission, development of toxic megacolon, need for colectomy, and death. However, identifying the patients most likely to develop complicated CDI is challenging. To this end, we explored the utility of a machine learning (ML) approach for patient risk stratification for complications using electronic health record (EHR) data. Methods We considered adult patients diagnosed with CDI between October 2010 and January 2013 at the University of Michigan hospitals. Cases were labeled complicated if the infection resulted in ICU admission, colectomy, or 30-day mortality. Leveraging EHR data, we trained a model to predict subsequent complications on each of the 3 days after diagnosis. We compared our EHR-based model to one based on a small set of manually curated features. We evaluated model performance using a held-out data set in terms of the area under the receiver operating characteristic curve (AUROC). Results Of 1118 cases of CDI, 8% became complicated. On the day of diagnosis, the model achieved an AUROC of 0.69 (95% confidence interval [CI], 0.55–0.83). Using data extracted 2 days after CDI diagnosis, performance increased (AUROC, 0.90; 95% CI, 0.83–0.95), outperforming a model based on a curated set of features (AUROC, 0.84; 95% CI, 0.75–0.91). Conclusions Using EHR data, we can accurately stratify CDI cases according to their risk of developing complications. Such an approach could be used to guide future clinical studies investigating interventions that could prevent or mitigate complicated CDI.

scholarly journals Predicting the Impact of Neurosurgery on TBI Patients in the Low Resource Setting: A Machine Learning Approach in Uganda

Neurosurgery ◽  
2020 ◽  
Vol 67 (Supplement_1) ◽  
Author(s):  
Syed M Adil ◽  
Cyrus Elahi ◽  
Robert Gramer ◽  
Charis A Spears ◽  
Anthony Fuller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Characteristic Curve ◽  
Model Performance ◽  
Treatment Benefit ◽  
Entire Cohort ◽  
Low Resource ◽  
Poor Outcome ◽  
Resource Setting ◽  
Combining Data ◽  
The Impact

Abstract INTRODUCTION Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these low-resource settings, effective triage of TBI patients-including the decision of whether or not to perform neurosurgery-is critical in optimizing both patient outcomes and healthcare resource utilization. METHODS Data from TBI patients of all ages were prospectively collected at Mulago National Referral Hospital in Kampala, Uganda, from 2016 to 2019. Seven different machine learning models (based on 1 linear and 6 non-linear algorithms) designed to predict good vs poor outcome near hospital discharge were developed and internally validated using 5-fold cross-validation. Predictors included clinical variables easily acquired on admission-demographics, physical exam, and mechanism of injury-and whether or not the patient received surgery. Using the elastic-net regularized logistic regression model (GLMnet), the probability of poor outcome was calculated for each patient both with and without surgery (quantifying the “treatment benefit”). A relative treatment benefit was then calculated, equaling this benefit of surgery divided by the probability of bad outcome with no surgery. Predictions were calibrated using Platt scaling. RESULTS Ultimately, 1766 patients were included. Areas under the receiver operating characteristic curve (AUCs) ranged from 81.7% (k-nearest neighbors) to 88.0% (random forest). The GLMnet had the second-best AUC at 87.7%. For the entire cohort, the median relative treatment benefit was 37.6% (IQR, 31.0% to 46.0%); similarly, in just those receiving surgery, it was 38.0% (IQR, 31.4% to 47.0%). The top four variables promoting good outcomes in the GLMnet model were high GCS, being fully alert, having both pupils reactive, and receiving surgery. CONCLUSION We provide the first deployable machine learning-based model to predict TBI outcomes with and without surgery in LMICs, thus enabling more effective surgical decision making in the resource-limited setting. Currently, patients are not being optimally chosen for neurosurgical intervention. Future studies should externally validate the model, improve model performance by combining data across countries, and explore use of more advanced algorithms.

scholarly journals Comparison of Regression and Machine Learning Methods in Depression Forecasting Among Home-Based Elderly Chinese: A Community Based Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Shaowu Lin ◽  
Yafei Wu ◽  
Ya Fang
Keyword(s):  
Machine Learning ◽  
Life Satisfaction ◽  
Logistic Regression ◽  
Random Forest ◽  
Cognitive Ability ◽  
Characteristic Curve ◽  
Model Performance ◽  
Predictive Performance ◽  
Home Based ◽  
Elderly Chinese

BackgroundDepression is highly prevalent and considered as the most common psychiatric disorder in home-based elderly, while study on forecasting depression risk in the elderly is still limited. In an endeavor to improve accuracy of depression forecasting, machine learning (ML) approaches have been recommended, in addition to the application of more traditional regression approaches.MethodsA prospective study was employed in home-based elderly Chinese, using baseline (2011) and follow-up (2013) data of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort study. We compared four algorithms, including the regression-based models (logistic regression, lasso, ridge) and ML method (random forest). Model performance was assessed using repeated nested 10-fold cross-validation. As the main measure of predictive performance, we used the area under the receiver operating characteristic curve (AUC).ResultsThe mean AUCs of the four predictive models, logistic regression, lasso, ridge, and random forest, were 0.795, 0.794, 0.794, and 0.769, respectively. The main determinants were life satisfaction, self-reported memory, cognitive ability, ADL (activities of daily living) impairment, CESD-10 score. Life satisfaction increased the odds ratio of a future depression by 128.6% (logistic), 13.8% (lasso), and 13.2% (ridge), and cognitive ability was the most important predictor in random forest.ConclusionsThe three regression-based models and one ML algorithm performed equally well in differentiating between a future depression case and a non-depression case in home-based elderly. When choosing a model, different considerations, however, such as easy operating, might in some instances lead to one model being prioritized over another.

scholarly journals Better  efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

2020 ◽  
Author(s):  
Sha-Sha Zhao ◽  
Xiu-Long Feng ◽  
Yu-Chuan Hu ◽  
Yu Han ◽  
Qiang Tian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inversion Recovery ◽  
World Health ◽  
Diagnostic Efficacy ◽  
Who Grade ◽  
Superior Efficacy ◽  
Contrast Enhanced ◽  
Fluid Attenuated Inversion Recovery ◽  
T1 Contrast ◽  
Grade Ii

Abstract Abstract Background: The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. Methods: Thirty-six patients with histologically confirmed ODGs underwent T1CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist’s assessment were also compared. Results: Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. Conclusions: Machine-learning based on radiomics of T1CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

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