| Autor: |
Ke Sun, Shouyu Chen, Jiabi Zhao, Bin Wang, Yang Yang, Yin Wang, Chunyan Wu, Xiwen Sun |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Frontiers in Oncology, Vol 11 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2234-943X |
| DOI: |
10.3389/fonc.2021.792062 |
| Popis: |
PurposeTo establish a non-invasive diagnostic model based on convolutional neural networks (CNNs) to distinguish benign from malignant lesions manifesting as a solid, indeterminate solitary pulmonary nodule (SPN) or mass (SPM) on computed tomography (CT).MethodA total of 459 patients with solid indeterminate SPNs/SPMs on CT were ultimately included in this retrospective study and assigned to the train (n=366), validation (n=46), and test (n=47) sets. Histopathologic analysis was available for each patient. An end-to-end CNN model was proposed to predict the natural history of solid indeterminate SPN/SPMs on CT. Receiver operating characteristic curves were plotted to evaluate the predictive performance of the proposed CNN model. The accuracy, sensitivity, and specificity of diagnoses by radiologists alone were compared with those of diagnoses by radiologists by using the CNN model to assess its clinical utility.ResultsFor the CNN model, the AUC was 91% (95% confidence interval [CI]: 0.83–0.99) in the test set. The diagnostic accuracy of radiologists with the CNN model was significantly higher than that without the model (89 vs. 66%, P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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