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

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

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yae Won Park ◽  
Dongmin Choi ◽  
Ji Eun Park ◽  
Sung Soo Ahn ◽  
Hwiyoung Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radiation Necrosis ◽  
External Validation ◽  
Model Development ◽  
Recurrent Glioblastoma ◽  
Test Set ◽  
High Performing ◽  
Mri Sequences ◽  
Sensitivity Specificity ◽  
Recurrent Gbm

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..

scholarly journals PHOTONAI—A Python API for rapid machine learning model development

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254062
Author(s):  
Ramona Leenings ◽  
Nils Ralf Winter ◽  
Lucas Plagwitz ◽  
Vincent Holstein ◽  
Jan Ernsting ◽  
...  
Keyword(s):  
Machine Learning ◽  
External Validation ◽  
Model Development ◽  
Learning Model ◽  
Complex Data ◽  
Algorithm Selection ◽  
Iterative Model ◽  
Machine Learning Model ◽  
High Level ◽  
Learning Analysis

PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com.

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

scholarly journals Development and external validation of a deep learning-based computed tomography classification system for COVID-19

2021 ◽  
Author(s):  
Yuki KATAOKA
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Interstitial Pneumonia ◽  
External Validation ◽  
Usual Interstitial Pneumonia ◽  
Evaluation Process ◽  
High Sensitivity ◽  
Learning Model ◽  
Machine Learning Model ◽  
Ablation Study

Rationale: Currently available machine learning models for diagnosing COVID-19 based on computed tomography (CT) images are limited due to concerns regarding methodological flaws or underlying biases in the evaluation process. Objectives: We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR).Methods: We used 3128 images from a wide variety of two-gate data sources for the development and ablation study of the machine learning model. A total of 633 COVID-19 cases and 2295 non-COVID-19 cases were included in the study. We randomly divided cases into a development set and ablation set at a ratio of 8:2. For the ablation study, we used another dataset including 150 cases of interstitial pneumonia among non-COVID-19 images. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR.Result: In ablation study, using interstitial pneumonia images, the specificity of the model were 0.986 for usual interstitial pneumonia pattern, 0.820 for non-specific interstitial pneumonia pattern, 0.400 for organizing pneumonia pattern. In the external validation study, the sensitivity and specificity of the model were 0.869 and 0.432, respectively, at the low-level cutoff, and 0.724 and 0.721, respectively, at the high-level cutoff.Conclusions: Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner. Further studies are warranted to improve model specificity.

scholarly journals Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2102
Author(s):  
Eyal Klang ◽  
Robert Freeman ◽  
Matthew A. Levin ◽  
Shelly Soffer ◽  
Yiftach Barash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Acute Diverticulitis ◽  
External Validation ◽  
White Blood Cells ◽  
Learning Model ◽  
Complicated Diverticulitis ◽  
Gradient Boosting ◽  
Proof Of Concept ◽  
Machine Learning Model

Background & Aims: We aimed at identifying specific emergency department (ED) risk factors for developing complicated acute diverticulitis (AD) and evaluate a machine learning model (ML) for predicting complicated AD. Methods: We analyzed data retrieved from unselected consecutive large bowel AD patients from five hospitals from the Mount Sinai health system, NY. The study time frame was from January 2011 through March 2021. Data were used to train and evaluate a gradient-boosting machine learning model to identify patients with complicated diverticulitis, defined as a need for invasive intervention or in-hospital mortality. The model was trained and evaluated on data from four hospitals and externally validated on held-out data from the fifth hospital. Results: The final cohort included 4997 AD visits. Of them, 129 (2.9%) visits had complicated diverticulitis. Patients with complicated diverticulitis were more likely to be men, black, and arrive by ambulance. Regarding laboratory values, patients with complicated diverticulitis had higher levels of absolute neutrophils (AUC 0.73), higher white blood cells (AUC 0.70), platelet count (AUC 0.68) and lactate (AUC 0.61), and lower levels of albumin (AUC 0.69), chloride (AUC 0.64), and sodium (AUC 0.61). In the external validation cohort, the ML model showed AUC 0.85 (95% CI 0.78–0.91) for predicting complicated diverticulitis. For Youden’s index, the model showed a sensitivity of 88% with a false positive rate of 1:3.6. Conclusions: A ML model trained on clinical measures provides a proof of concept performance in predicting complications in patients presenting to the ED with AD. Clinically, it implies that a ML model may classify low-risk patients to be discharged from the ED for further treatment under an ambulatory setting.

Abstract P391: An-adcs 2 : A Novel Machine-Learning Model to Predict the Risk of Stroke-Associated Pneumonia

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Lingling Ding ◽  
Zixiao Li ◽  
Yongjun Wang
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Predictive Value ◽  
External Validation ◽  
Learning Model ◽  
Low Risk ◽  
Stroke Recurrence ◽  
Clinical Prognosis ◽  
Machine Learning Model ◽  
Validation Set

Objective: We aimed to develop and validate a machine learning-based prediction model that could assess the risk of stroke-associated pneumonia (SAP) for individual patients with acute ischemic stroke (AIS). Methods: A machine-learning model incorporating A 2 DS 2 scores and clinical features (AN-ADCS 2 ) was developed to predict the risk of SAP in patients with AIS. Two independent datasets were used for model derivation and external validation. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were estimated. The further analysis evaluated thresholds from the training set that identified patients as low-risk, intermediate-risk and high-risk, and performance at these thresholds was compared in the external validation set. Results: The AN-ADCS 2 model achieved favorable performance with a high AUC of 0.892 (95% confidence interval [CI] 0.885-0.898) in the test set and similar performance in the external validation set (AUC 0.813 [95% CI 0.812-0.814]). The AN-ADCS 2 threshold identifying low-risk was 0.03, with a NPV of 97.6% (97.2-97.9%) and sensitivity of 93.5% (92.5-94.5%). The AN-ADCS 2 threshold identifying high-risk was 0.65, with a PPV of 94.7% (93.9-95.6%) and specificity of 99.5% (99.5-99.6%). The AN-ADCS 2 model performed better than the A 2 DS 2 score (AUC 0.739, 95%CI [0.720-0.754]). Having a high risk of SAP classified by the AN-ADCS 2 was associated with unfavorable outcomes of mortality and in-hospital stroke recurrence. Conclusions: Using machine learning, the AN-ADCS 2 model provides an individualized risk prediction of SAP, which can be used as an indicator of clinical prognosis for patients with AIS.

Machine Learning Model to Predict Automated Testing Adoption

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Software Development ◽  
Software Testing ◽  
Model Development ◽  
Learning Model ◽  
Development Project ◽  
Automated Testing ◽  
Machine Learning Model ◽  
Manual Testing ◽  
Automation Testing

Software testing is an activity conducted to test the software under test. It has two approaches: manual testing and automation testing. Automation testing is an approach of software testing in which programming scripts are written to automate the process of testing. There are some software development projects under development phase for which automated testing is suitable to use and other requires manual testing. It depends on factors like project requirements nature, team which is working on the project, technology on which software is developing and intended audience that may influence the suitability of automated testing for certain software development project. In this paper we have developed machine learning model for prediction of automated testing adoption. We have used chi-square test for finding factors’ correlation and PART classifier for model development. Accuracy of our proposed model is 93.1624%.

Sign in / Sign up

Export Citation Format

Share Document