scholarly journals Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods

2021 ◽  
Vol 11 ◽  
Author(s):  
Xuejiao Han ◽  
Jing Yang ◽  
Jingwen Luo ◽  
Pengan Chen ◽  
Zilong Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Pancreatic Neuroendocrine Tumors ◽  
Selection Methods ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Testing Group

ObjectivesThe purpose of this study aimed at investigating the reliability of radiomics features extracted from contrast-enhanced CT in differentiating pancreatic cystadenomas from pancreatic neuroendocrine tumors (PNETs) using machine-learning methods.MethodsIn this study, a total number of 120 patients, including 66 pancreatic cystadenomas patients and 54 PNETs patients were enrolled. Forty-eight radiomic features were extracted from contrast-enhanced CT images using LIFEx software. Five feature selection methods were adopted to determine the appropriate features for classifiers. Then, nine machine learning classifiers were employed to build predictive models. The performance of the forty-five models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score in the testing group.ResultsThe predictive models exhibited reliable ability of differentiating pancreatic cystadenomas from PNETs when combined with suitable selection methods. A combination of DC as the selection method and RF as the classifier, as well as Xgboost+RF, demonstrated the best discriminative ability, with the highest AUC of 0.997 in the testing group.ConclusionsRadiomics-based machine learning methods might be a noninvasive tool to assist in differentiating pancreatic cystadenomas and PNETs.

scholarly journals Machine Learning for Detecting Early Infarction in Acute Stroke with Non–Contrast-enhanced CT

Radiology ◽  
2020 ◽  
Vol 294 (3) ◽  
pp. 638-644 ◽  
Author(s):  
Wu Qiu ◽  
Hulin Kuang ◽  
Ericka Teleg ◽  
Johanna M. Ospel ◽  
Sung Il Sohn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Stroke ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Early Infarction

scholarly journals Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods

2019 ◽  
Vol 7 ◽  
Author(s):  
Jihyeun Lee ◽  
Surendra Kumar ◽  
Sang-Yoon Lee ◽  
Sung Jean Park ◽  
Mi-hyun Kim
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials

2020 ◽  
Author(s):  
Edwin Tse ◽  
Laksh Aithani ◽  
Mark Anderson ◽  
Jonathan Cardoso-Silva ◽  
Giovanni Cincilla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Predictive Models ◽  
Structural Information ◽  
Open Data ◽  
Ion Pump ◽  
Learning Methods ◽  
Machine Learning Methods

<p>The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the problem of increasing resistance to our frontline treatments. The Open Source Malaria (OSM) consortium has been developing compounds ("Series 4") that have potent activity against <i>Plasmodium falciparum</i> <i>in vitro</i> and <i>in vivo</i> and that have been suggested to act through the inhibition of <i>Pf</i>ATP4, an essential membrane ion pump that regulates the parasite’s intracellular Na<sup>+</sup> concentration. The structure of <i>Pf</i>ATP4 is yet to be determined. In the absence of structural information about this target, a public competition was created to develop a model that would allow the prediction of anti-<i>Pf</i>ATP4 activity among Series 4 compounds, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds.</p>In the first round, in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably, all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was undertaken, in 2019, again with freely-donated models that other participants could see. The best-performing models from this second round were used to predict novel inhibitory molecules, of which several were synthesised and evaluated against the parasite. One such compound, containing a motif that the human chemists familiar with this series would have dismissed as ill-advised, was active. The project demonstrated the abilities of new machine learning methods in the prediction of active compounds where there is no biological target structure, frequently the central problem in phenotypic drug discovery. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others.

scholarly journals Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review

2021 ◽  
Vol 15 ◽  
Author(s):  
Meijie Liu ◽  
Baojuan Li ◽  
Dewen Hu
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Selection Methods ◽  
Learning Methods ◽  
Machine Learning Methods

Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD.

The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: A proof-of-principle study

Surgery ◽  
2020 ◽  
Vol 167 (2) ◽  
pp. 448-454 ◽  
Author(s):  
Patryk Kambakamba ◽  
Manoj Mannil ◽  
Paola E. Herrera ◽  
Philip C. Müller ◽  
Christoph Kuemmerli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pancreatic Fistula ◽  
Postoperative Pancreatic Fistula ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Proof Of Principle
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