FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4286509875> ?p ?o ?g. }

• W4286509875 endingPage "933" @default.
• W4286509875 startingPage "921" @default.
• W4286509875 abstract "Artificial intelligence (AI) algorithms have an enormous potential to impact the field of radiology and diagnostic imaging, especially the field of cancer imaging. There have been efforts to use AI models to differentiate between benign and malignant breast lesions. However, most studies have been single-center studies without external validation. The present study examines the diagnostic efficacy of machine-learning algorithms in differentiating benign and malignant breast lesions using ultrasound images. Ultrasound images of 1259 solid non-cystic lesions from 3 different centers in 3 countries (Malaysia, Turkey, and Iran) were used for the machine-learning study. A total of 242 radiomics features were extracted from each breast lesion, and the robust features were considered for models’ development. Three machine-learning algorithms were used to carry out the classification task, namely, gradient boosting (XGBoost), random forest, and support vector machine. Sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were determined to evaluate the models. Thirty-three robust features differed significantly between the two groups from all of the features. XGBoost, based on these robust features, showed the most favorable profile for all cohorts, as it achieved a sensitivity of 90.3%, specificity of 86.7%, the accuracy of 88.4%, and AUC of 0.890. The present study results show that incorporating selected robust radiomics features into well-curated machine-learning algorithms can generate high sensitivity, specificity, and accuracy in differentiating benign and malignant breast lesions. Furthermore, our results show that this optimal performance is preserved even in external validation datasets." @default.
• W4286509875 created "2022-07-22" @default.
• W4286509875 creator A5005107290 @default.
• W4286509875 creator A5026812307 @default.
• W4286509875 creator A5029215679 @default.
• W4286509875 creator A5036052824 @default.
• W4286509875 creator A5038775624 @default.
• W4286509875 creator A5043369076 @default.
• W4286509875 creator A5045289403 @default.
• W4286509875 creator A5045783666 @default.
• W4286509875 creator A5048061802 @default.
• W4286509875 creator A5051221811 @default.
• W4286509875 creator A5061771368 @default.
• W4286509875 creator A5075548090 @default.
• W4286509875 creator A5090260885 @default.
• W4286509875 creator A5091365088 @default.
• W4286509875 date "2022-07-01" @default.
• W4286509875 modified "2023-09-23" @default.
• W4286509875 title "Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study" @default.
• W4286509875 cites W1964940342 @default.
• W4286509875 cites W1974412675 @default.
• W4286509875 cites W1985164435 @default.
• W4286509875 cites W1993947467 @default.
• W4286509875 cites W2038082385 @default.
• W4286509875 cites W2076723282 @default.
• W4286509875 cites W2088794999 @default.
• W4286509875 cites W2092128146 @default.
• W4286509875 cites W2121530587 @default.
• W4286509875 cites W2129487742 @default.
• W4286509875 cites W2155632266 @default.
• W4286509875 cites W2174661749 @default.
• W4286509875 cites W2560453529 @default.
• W4286509875 cites W2763355946 @default.
• W4286509875 cites W2772254319 @default.
• W4286509875 cites W2801665024 @default.
• W4286509875 cites W2803760365 @default.
• W4286509875 cites W2910258400 @default.
• W4286509875 cites W2922512202 @default.
• W4286509875 cites W2945020349 @default.
• W4286509875 cites W2998382186 @default.
• W4286509875 cites W3003412199 @default.
• W4286509875 cites W3007228139 @default.
• W4286509875 cites W3008603854 @default.
• W4286509875 cites W3025036301 @default.
• W4286509875 cites W3048802680 @default.
• W4286509875 cites W3109609494 @default.
• W4286509875 cites W3129661430 @default.
• W4286509875 cites W3135379929 @default.
• W4286509875 cites W3136635219 @default.
• W4286509875 cites W3151575666 @default.
• W4286509875 cites W3155053178 @default.
• W4286509875 cites W3160123822 @default.
• W4286509875 cites W3161558287 @default.
• W4286509875 cites W3167125942 @default.
• W4286509875 cites W3190437677 @default.
• W4286509875 cites W3192241663 @default.
• W4286509875 cites W3198576039 @default.
• W4286509875 cites W3201490520 @default.
• W4286509875 cites W3201701662 @default.
• W4286509875 cites W3212950234 @default.
• W4286509875 cites W3215968242 @default.
• W4286509875 cites W4200438520 @default.
• W4286509875 doi "https://doi.org/10.1016/j.bbe.2022.07.004" @default.
• W4286509875 hasPublicationYear "2022" @default.
• W4286509875 type Work @default.
• W4286509875 citedByCount "1" @default.
• W4286509875 countsByYear W42865098752023 @default.
• W4286509875 crossrefType "journal-article" @default.
• W4286509875 hasAuthorship W4286509875A5005107290 @default.
• W4286509875 hasAuthorship W4286509875A5026812307 @default.
• W4286509875 hasAuthorship W4286509875A5029215679 @default.
• W4286509875 hasAuthorship W4286509875A5036052824 @default.
• W4286509875 hasAuthorship W4286509875A5038775624 @default.
• W4286509875 hasAuthorship W4286509875A5043369076 @default.
• W4286509875 hasAuthorship W4286509875A5045289403 @default.
• W4286509875 hasAuthorship W4286509875A5045783666 @default.
• W4286509875 hasAuthorship W4286509875A5048061802 @default.
• W4286509875 hasAuthorship W4286509875A5051221811 @default.
• W4286509875 hasAuthorship W4286509875A5061771368 @default.
• W4286509875 hasAuthorship W4286509875A5075548090 @default.
• W4286509875 hasAuthorship W4286509875A5090260885 @default.
• W4286509875 hasAuthorship W4286509875A5091365088 @default.
• W4286509875 hasConcept C11413529 @default.
• W4286509875 hasConcept C119857082 @default.
• W4286509875 hasConcept C121608353 @default.
• W4286509875 hasConcept C12267149 @default.
• W4286509875 hasConcept C126322002 @default.
• W4286509875 hasConcept C126838900 @default.
• W4286509875 hasConcept C143753070 @default.
• W4286509875 hasConcept C154945302 @default.
• W4286509875 hasConcept C169258074 @default.
• W4286509875 hasConcept C2777423100 @default.
• W4286509875 hasConcept C2777432617 @default.
• W4286509875 hasConcept C2778559731 @default.
• W4286509875 hasConcept C2780472235 @default.
• W4286509875 hasConcept C41008148 @default.
• W4286509875 hasConcept C530470458 @default.
• W4286509875 hasConcept C70153297 @default.

• next