Matches in SemOpenAlex for { <https://semopenalex.org/work/W4281572116> ?p ?o ?g. }
- W4281572116 abstract "Machine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features.One hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters-four clinical factors, four laboratory indicators, and 91 [18F]F-FDG PET/CT radiomic features-were used for data analysis and model construction. The Boruta algorithm was used to screen the features. The retained minimum optimal feature subset was input into ten machine learning to construct a classifier for distinguishing between LUAD and LUSC. Univariate and multivariate analyses were used to identify the independent risk factors of the NSCLC subtype and constructed the Clinical model. Finally, the area under the receiver operating characteristic curve (AUC) values, sensitivity, specificity, and accuracy (ACC) was used to validate the machine learning model with the best performance effect and Clinical model in the validation group, and the DeLong test was used to compare the model performance.Boruta algorithm selected the optimal subset consisting of 13 features, including two clinical features, two laboratory indicators, and nine PEF/CT radiomic features. The Random Forest (RF) model and Support Vector Machine (SVM) model in the training group showed the best performance. Gender (P=0.018) and smoking status (P=0.011) construct the Clinical model. In the validation group, the SVM model (AUC: 0.876, ACC: 0.800) and RF model (AUC: 0.863, ACC: 0.800) performed well, while Clinical model (AUC:0.712, ACC: 0.686) performed moderately. There was no significant difference between the RF and Clinical models, but the SVM model was significantly better than the Clinical model.The proposed SVM and RF models successfully identified LUAD and LUSC. The results indicate that the proposed model is an accurate and noninvasive predictive tool that can assist clinical decision-making, especially for patients who cannot have biopsies or where a biopsy fails." @default.
- W4281572116 created "2022-05-27" @default.
- W4281572116 creator A5001189194 @default.
- W4281572116 creator A5017666478 @default.
- W4281572116 creator A5024830909 @default.
- W4281572116 creator A5035884959 @default.
- W4281572116 creator A5045496546 @default.
- W4281572116 creator A5049714662 @default.
- W4281572116 creator A5053356631 @default.
- W4281572116 creator A5066171204 @default.
- W4281572116 creator A5084387802 @default.
- W4281572116 creator A5085095982 @default.
- W4281572116 date "2022-05-26" @default.
- W4281572116 modified "2023-09-26" @default.
- W4281572116 title "The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer" @default.
- W4281572116 cites W1918373897 @default.
- W4281572116 cites W1988778997 @default.
- W4281572116 cites W1995192702 @default.
- W4281572116 cites W2031729603 @default.
- W4281572116 cites W2121138562 @default.
- W4281572116 cites W2129590156 @default.
- W4281572116 cites W2139939379 @default.
- W4281572116 cites W2150575159 @default.
- W4281572116 cites W2737578470 @default.
- W4281572116 cites W2763355946 @default.
- W4281572116 cites W2769635882 @default.
- W4281572116 cites W2788673917 @default.
- W4281572116 cites W2809848353 @default.
- W4281572116 cites W2883202249 @default.
- W4281572116 cites W2922315137 @default.
- W4281572116 cites W2950572455 @default.
- W4281572116 cites W2953404953 @default.
- W4281572116 cites W2954296981 @default.
- W4281572116 cites W2971404736 @default.
- W4281572116 cites W2981380606 @default.
- W4281572116 cites W2985928856 @default.
- W4281572116 cites W3005289617 @default.
- W4281572116 cites W3007829463 @default.
- W4281572116 cites W3009697680 @default.
- W4281572116 cites W3014109059 @default.
- W4281572116 cites W3014376713 @default.
- W4281572116 cites W3021898219 @default.
- W4281572116 cites W3048173156 @default.
- W4281572116 cites W3092833543 @default.
- W4281572116 cites W3111937690 @default.
- W4281572116 cites W3128646645 @default.
- W4281572116 cites W3157911666 @default.
- W4281572116 cites W4235965497 @default.
- W4281572116 doi "https://doi.org/10.3389/fonc.2022.875761" @default.
- W4281572116 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35692759" @default.
- W4281572116 hasPublicationYear "2022" @default.
- W4281572116 type Work @default.
- W4281572116 citedByCount "5" @default.
- W4281572116 countsByYear W42815721162022 @default.
- W4281572116 countsByYear W42815721162023 @default.
- W4281572116 crossrefType "journal-article" @default.
- W4281572116 hasAuthorship W4281572116A5001189194 @default.
- W4281572116 hasAuthorship W4281572116A5017666478 @default.
- W4281572116 hasAuthorship W4281572116A5024830909 @default.
- W4281572116 hasAuthorship W4281572116A5035884959 @default.
- W4281572116 hasAuthorship W4281572116A5045496546 @default.
- W4281572116 hasAuthorship W4281572116A5049714662 @default.
- W4281572116 hasAuthorship W4281572116A5053356631 @default.
- W4281572116 hasAuthorship W4281572116A5066171204 @default.
- W4281572116 hasAuthorship W4281572116A5084387802 @default.
- W4281572116 hasAuthorship W4281572116A5085095982 @default.
- W4281572116 hasBestOaLocation W42815721161 @default.
- W4281572116 hasConcept C11413529 @default.
- W4281572116 hasConcept C119857082 @default.
- W4281572116 hasConcept C121608353 @default.
- W4281572116 hasConcept C12267149 @default.
- W4281572116 hasConcept C126322002 @default.
- W4281572116 hasConcept C143998085 @default.
- W4281572116 hasConcept C154945302 @default.
- W4281572116 hasConcept C161584116 @default.
- W4281572116 hasConcept C169258074 @default.
- W4281572116 hasConcept C199163554 @default.
- W4281572116 hasConcept C2776256026 @default.
- W4281572116 hasConcept C2781182431 @default.
- W4281572116 hasConcept C38180746 @default.
- W4281572116 hasConcept C41008148 @default.
- W4281572116 hasConcept C58471807 @default.
- W4281572116 hasConcept C71924100 @default.
- W4281572116 hasConceptScore W4281572116C11413529 @default.
- W4281572116 hasConceptScore W4281572116C119857082 @default.
- W4281572116 hasConceptScore W4281572116C121608353 @default.
- W4281572116 hasConceptScore W4281572116C12267149 @default.
- W4281572116 hasConceptScore W4281572116C126322002 @default.
- W4281572116 hasConceptScore W4281572116C143998085 @default.
- W4281572116 hasConceptScore W4281572116C154945302 @default.
- W4281572116 hasConceptScore W4281572116C161584116 @default.
- W4281572116 hasConceptScore W4281572116C169258074 @default.
- W4281572116 hasConceptScore W4281572116C199163554 @default.
- W4281572116 hasConceptScore W4281572116C2776256026 @default.
- W4281572116 hasConceptScore W4281572116C2781182431 @default.
- W4281572116 hasConceptScore W4281572116C38180746 @default.
- W4281572116 hasConceptScore W4281572116C41008148 @default.
- W4281572116 hasConceptScore W4281572116C58471807 @default.
- W4281572116 hasConceptScore W4281572116C71924100 @default.
- W4281572116 hasLocation W42815721161 @default.