FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3029373937> ?p ?o ?g. }

• W3029373937 endingPage "4136" @default.
• W3029373937 startingPage "4125" @default.
• W3029373937 abstract "Purpose Low‐dose CT screening allows early lung cancer detection, but is affected by frequent false positive results, inter/intra observer variation and uncertain diagnoses of lung nodules. Radiomics‐based models have recently been introduced to overcome these issues, but limitations in demonstrating their generalizability on independent datasets are slowing their introduction to clinic. The aim of this study is to evaluate two radiomics‐based models to classify malignant pulmonary nodules in low‐dose CT screening, and to externally validate them on an independent cohort. The effect of a radiomics features harmonization technique is also investigated to evaluate its impact on the classification of lung nodules from a multicenter data. Methods Pulmonary nodules from two independent cohorts were considered in this study; the first cohort (110 subjects, 113 nodules) was used to train prediction models, and the second cohort (72 nodules) to externally validate them. Literature‐based radiomics features were extracted and, after feature selection, used as predictive variables in models for malignancy identification. An in‐house prediction model based on artificial neural network (ANN) was implemented and evaluated, along with an alternative model from the literature, based on a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO). External validation was performed on the second cohort to evaluate models’ generalization ability. Additionally, the impact of the Combat harmonization method was investigated to compensate for multicenter datasets variabilities. A new training of the models based on harmonized features was performed on the first cohort, then tested separately on the harmonized and non‐harmonized features of the second cohort. Results Preliminary results showed a good accuracy of the investigated models in distinguishing benign from malignant pulmonary nodules with both sets of radiomics features (i.e., non‐harmonized and harmonized). The performance of the models, quantified in terms of Area Under the Curve (AUC), was > 0.89 in the training set and > 0.82 in the external validation set for all the investigated scenarios, outperforming the clinical standard (AUC of 0.76). Slightly higher performance was observed for the SVM‐LASSO model than the ANN in the external dataset, although they did not result significantly different. For both harmonized and non‐harmonized features, no statistical difference was found between Receiver operating characteristic (ROC) curves related to training and test set for both models. Conclusions Although no significant improvements were observed when applying the Combat harmonization method, both in‐house and literature‐based models were able to classify lung nodules with good generalization to an independent dataset, thus showing their potential as tools for clinical decision‐making in lung cancer screening." @default.
• W3029373937 created "2020-06-05" @default.
• W3029373937 creator A5000570208 @default.
• W3029373937 creator A5005328001 @default.
• W3029373937 creator A5009782102 @default.
• W3029373937 creator A5012034512 @default.
• W3029373937 creator A5020595951 @default.
• W3029373937 creator A5021006684 @default.
• W3029373937 creator A5039068861 @default.
• W3029373937 creator A5067850637 @default.
• W3029373937 creator A5069653985 @default.
• W3029373937 creator A5083187460 @default.
• W3029373937 date "2020-06-23" @default.
• W3029373937 modified "2023-10-15" @default.
• W3029373937 title "External validation of radiomics‐based predictive models in low‐dose CT screening for early lung cancer diagnosis" @default.
• W3029373937 cites W130099911 @default.
• W3029373937 cites W1969870772 @default.
• W3029373937 cites W1986649315 @default.
• W3029373937 cites W1994682257 @default.
• W3029373937 cites W1997706498 @default.
• W3029373937 cites W2002520519 @default.
• W3029373937 cites W2096904202 @default.
• W3029373937 cites W2148347694 @default.
• W3029373937 cites W2163814837 @default.
• W3029373937 cites W2166521422 @default.
• W3029373937 cites W2328176404 @default.
• W3029373937 cites W2333277922 @default.
• W3029373937 cites W2470491115 @default.
• W3029373937 cites W2523726602 @default.
• W3029373937 cites W2753416097 @default.
• W3029373937 cites W2757342873 @default.
• W3029373937 cites W2763355946 @default.
• W3029373937 cites W2765452832 @default.
• W3029373937 cites W2781993955 @default.
• W3029373937 cites W2790092584 @default.
• W3029373937 cites W2790414987 @default.
• W3029373937 cites W2791069488 @default.
• W3029373937 cites W2861648048 @default.
• W3029373937 cites W2885326465 @default.
• W3029373937 cites W2889506086 @default.
• W3029373937 cites W2893800736 @default.
• W3029373937 cites W2895227855 @default.
• W3029373937 cites W2898384149 @default.
• W3029373937 cites W2917696006 @default.
• W3029373937 cites W2921444773 @default.
• W3029373937 cites W2950030754 @default.
• W3029373937 cites W2981797631 @default.
• W3029373937 cites W2998789541 @default.
• W3029373937 cites W3099641252 @default.
• W3029373937 cites W3103367279 @default.
• W3029373937 cites W4235767814 @default.
• W3029373937 doi "https://doi.org/10.1002/mp.14308" @default.
• W3029373937 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7708421" @default.
• W3029373937 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32488865" @default.
• W3029373937 hasPublicationYear "2020" @default.
• W3029373937 type Work @default.
• W3029373937 sameAs 3029373937 @default.
• W3029373937 citedByCount "18" @default.
• W3029373937 countsByYear W30293739372021 @default.
• W3029373937 countsByYear W30293739372022 @default.
• W3029373937 countsByYear W30293739372023 @default.
• W3029373937 crossrefType "journal-article" @default.
• W3029373937 hasAuthorship W3029373937A5000570208 @default.
• W3029373937 hasAuthorship W3029373937A5005328001 @default.
• W3029373937 hasAuthorship W3029373937A5009782102 @default.
• W3029373937 hasAuthorship W3029373937A5012034512 @default.
• W3029373937 hasAuthorship W3029373937A5020595951 @default.
• W3029373937 hasAuthorship W3029373937A5021006684 @default.
• W3029373937 hasAuthorship W3029373937A5039068861 @default.
• W3029373937 hasAuthorship W3029373937A5067850637 @default.
• W3029373937 hasAuthorship W3029373937A5069653985 @default.
• W3029373937 hasAuthorship W3029373937A5083187460 @default.
• W3029373937 hasBestOaLocation W30293739372 @default.
• W3029373937 hasConcept C105795698 @default.
• W3029373937 hasConcept C119857082 @default.
• W3029373937 hasConcept C12267149 @default.
• W3029373937 hasConcept C126838900 @default.
• W3029373937 hasConcept C142724271 @default.
• W3029373937 hasConcept C143998085 @default.
• W3029373937 hasConcept C148483581 @default.
• W3029373937 hasConcept C154945302 @default.
• W3029373937 hasConcept C27158222 @default.
• W3029373937 hasConcept C2776256026 @default.
• W3029373937 hasConcept C2777405583 @default.
• W3029373937 hasConcept C2778321237 @default.
• W3029373937 hasConcept C2778559731 @default.
• W3029373937 hasConcept C33923547 @default.
• W3029373937 hasConcept C41008148 @default.
• W3029373937 hasConcept C534262118 @default.
• W3029373937 hasConcept C71924100 @default.
• W3029373937 hasConcept C72563966 @default.
• W3029373937 hasConceptScore W3029373937C105795698 @default.
• W3029373937 hasConceptScore W3029373937C119857082 @default.
• W3029373937 hasConceptScore W3029373937C12267149 @default.
• W3029373937 hasConceptScore W3029373937C126838900 @default.
• W3029373937 hasConceptScore W3029373937C142724271 @default.
• W3029373937 hasConceptScore W3029373937C143998085 @default.
• W3029373937 hasConceptScore W3029373937C148483581 @default.

• next