FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204192682> ?p ?o ?g. }

• W3204192682 endingPage "138761" @default.
• W3204192682 startingPage "138753" @default.
• W3204192682 abstract "Despite recent advances in precision medicine, lung cancer remains the leading cause of cancer-related mortality worldwide. To determine the prognosis of non-small cell lung cancer (NSCLC), which accounts for 85% of lung cancer, comprehensive analysis of various clinical factors are necessary. Artificial intelligence can help physician quickly identify key information from the vast amount of medical information including positron emission tomography (PET) scan. In this study, we compared image feature-extraction models and survival estimation models to determine an optimal model that effectively extracts features related to survival time. We collected PET image data of 2,685 patients who were diagnosed with NSCLC and received treatment at the Chonnam National University Hwasun Hospital in South Korea over a period of seven years. We compared four convolution neural network models, DenseNet, NFNet, EfficientNet, and ResNet, and two survival estimation models, CoxPH and CoxCC. The best model was determined based on criteria such as C-index, mean absolute error (MAE), classification accuracy for survival status, and learning time. The results show that DenseNet combined with CoxPH delivers superior performance for most of the criteria. In particular, the MAE for this combination was very low (391.50 days), and the model predicted survival days well; the five-year classification accuracy, which can indicate a cure for cancer, was high (95%). Extracted features were visualized using Score-CAM; thus, the learning process of the model could be understood without requiring expert knowledge of PET. In addition, the learning time for this model was short." @default.
• W3204192682 created "2021-10-11" @default.
• W3204192682 creator A5028037045 @default.
• W3204192682 creator A5028070594 @default.
• W3204192682 creator A5044231588 @default.
• W3204192682 creator A5056726303 @default.
• W3204192682 creator A5089902772 @default.
• W3204192682 date "2021-01-01" @default.
• W3204192682 modified "2023-10-18" @default.
• W3204192682 title "PET-Based Deep-Learning Model for Predicting Prognosis of Patients With Non-Small Cell Lung Cancer" @default.
• W3204192682 cites W1963592300 @default.
• W3204192682 cites W1984807498 @default.
• W3204192682 cites W1990003226 @default.
• W3204192682 cites W2041091602 @default.
• W3204192682 cites W2072191991 @default.
• W3204192682 cites W2075219850 @default.
• W3204192682 cites W2084139018 @default.
• W3204192682 cites W2102012860 @default.
• W3204192682 cites W2109631360 @default.
• W3204192682 cites W2117539524 @default.
• W3204192682 cites W2137960802 @default.
• W3204192682 cites W2146961916 @default.
• W3204192682 cites W2149407433 @default.
• W3204192682 cites W2194775991 @default.
• W3204192682 cites W2228221433 @default.
• W3204192682 cites W2295107390 @default.
• W3204192682 cites W2324805613 @default.
• W3204192682 cites W2330852899 @default.
• W3204192682 cites W2344779122 @default.
• W3204192682 cites W2506122519 @default.
• W3204192682 cites W2571620227 @default.
• W3204192682 cites W2737617412 @default.
• W3204192682 cites W2753919178 @default.
• W3204192682 cites W2760946358 @default.
• W3204192682 cites W2889646458 @default.
• W3204192682 cites W2905889700 @default.
• W3204192682 cites W2946185430 @default.
• W3204192682 cites W2954499361 @default.
• W3204192682 cites W2963446712 @default.
• W3204192682 cites W2994824962 @default.
• W3204192682 cites W2996698847 @default.
• W3204192682 cites W3023412939 @default.
• W3204192682 cites W3035253074 @default.
• W3204192682 cites W3036901136 @default.
• W3204192682 cites W3095569631 @default.
• W3204192682 cites W3129732569 @default.
• W3204192682 cites W4239667773 @default.
• W3204192682 cites W4293241248 @default.
• W3204192682 cites W4299564810 @default.
• W3204192682 doi "https://doi.org/10.1109/access.2021.3115486" @default.
• W3204192682 hasPublicationYear "2021" @default.
• W3204192682 type Work @default.
• W3204192682 sameAs 3204192682 @default.
• W3204192682 citedByCount "2" @default.
• W3204192682 countsByYear W32041926822023 @default.
• W3204192682 crossrefType "journal-article" @default.
• W3204192682 hasAuthorship W3204192682A5028037045 @default.
• W3204192682 hasAuthorship W3204192682A5028070594 @default.
• W3204192682 hasAuthorship W3204192682A5044231588 @default.
• W3204192682 hasAuthorship W3204192682A5056726303 @default.
• W3204192682 hasAuthorship W3204192682A5089902772 @default.
• W3204192682 hasBestOaLocation W32041926821 @default.
• W3204192682 hasConcept C108583219 @default.
• W3204192682 hasConcept C119857082 @default.
• W3204192682 hasConcept C121608353 @default.
• W3204192682 hasConcept C126322002 @default.
• W3204192682 hasConcept C126838900 @default.
• W3204192682 hasConcept C143998085 @default.
• W3204192682 hasConcept C154945302 @default.
• W3204192682 hasConcept C2776256026 @default.
• W3204192682 hasConcept C41008148 @default.
• W3204192682 hasConcept C50644808 @default.
• W3204192682 hasConcept C52622490 @default.
• W3204192682 hasConcept C544519230 @default.
• W3204192682 hasConcept C71924100 @default.
• W3204192682 hasConceptScore W3204192682C108583219 @default.
• W3204192682 hasConceptScore W3204192682C119857082 @default.
• W3204192682 hasConceptScore W3204192682C121608353 @default.
• W3204192682 hasConceptScore W3204192682C126322002 @default.
• W3204192682 hasConceptScore W3204192682C126838900 @default.
• W3204192682 hasConceptScore W3204192682C143998085 @default.
• W3204192682 hasConceptScore W3204192682C154945302 @default.
• W3204192682 hasConceptScore W3204192682C2776256026 @default.
• W3204192682 hasConceptScore W3204192682C41008148 @default.
• W3204192682 hasConceptScore W3204192682C50644808 @default.
• W3204192682 hasConceptScore W3204192682C52622490 @default.
• W3204192682 hasConceptScore W3204192682C544519230 @default.
• W3204192682 hasConceptScore W3204192682C71924100 @default.
• W3204192682 hasFunder F4320322034 @default.
• W3204192682 hasFunder F4320322107 @default.
• W3204192682 hasFunder F4320322120 @default.
• W3204192682 hasLocation W32041926821 @default.
• W3204192682 hasOpenAccess W3204192682 @default.
• W3204192682 hasPrimaryLocation W32041926821 @default.
• W3204192682 hasRelatedWork W2946016983 @default.
• W3204192682 hasRelatedWork W3014300295 @default.
• W3204192682 hasRelatedWork W3164822677 @default.
• W3204192682 hasRelatedWork W4223943233 @default.

• next