Deep learning in thyroid ultrasonography to predict tumor recurrence in thyroid cancers
DC Field | Value | Language |
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dc.contributor.author | Kil, Jieun | - |
dc.contributor.author | Kim, Kwang Gi | - |
dc.contributor.author | Kim, Young Jae | - |
dc.contributor.author | Koo, Hye Ryoung | - |
dc.contributor.author | Park, Jeong Seon | - |
dc.date.accessioned | 2022-07-07T15:06:48Z | - |
dc.date.available | 2022-07-07T15:06:48Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2020-09 | - |
dc.identifier.issn | 1738-2637 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145211 | - |
dc.description.abstract | Purpose To evaluate a deep learning model to predict recurrence of thyroid tumor using preoperative ultrasonography (US). Materials and Methods We included representative images from 229 US-based patients (male:female = 42:187; mean age, 49.6 years) who had been diagnosed with thyroid cancer on preoperative US and subsequently underwent thyroid surgery. After selecting each representative transverse or longitudinal US image, we created a data set from the resulting database of 898 images after augmentation. The Python 2.7.6 and Keras 2.1.5 framework for neural networks were used for deep learning with a convolutional neural network. We compared the clinical and histological features between patients with and without recurrence. The predictive performance of the deep learning model between groups was evaluated using receiver operating characteristic (ROC) analysis, and the area under the ROC curve served as a summary of the prognostic performance of the deep learning model to predict recurrent thyroid cancer. Results Tumor recurrence was noted in 49 (21.4%) among the 229 patients. Tumor size and multifocality varied significantly between the groups with and without recurrence (p < 0.05). The overall mean area under the curve (AUC) value of the deep learning model for prediction of recurrent thyroid cancer was 0.9 ± 0.06. The mean AUC value was 0.87 ± 0.03 in macrocarcinoma and 0.79 ± 0.16 in microcarcinoma. Conclusion A deep learning model for analysis of US images of thyroid cancer showed the possibility of predicting recurrence of thyroid cancer. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Korean Radiological Society | - |
dc.title | Deep learning in thyroid ultrasonography to predict tumor recurrence in thyroid cancers | - |
dc.title.alternative | 인공지능 딥러닝을 이용한 갑상선 초음파에서의 갑상선암의 재발 예측 | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Jeong Seon | - |
dc.identifier.doi | 10.3348/JKSR.2019.0147 | - |
dc.identifier.scopusid | 2-s2.0-85091951452 | - |
dc.identifier.bibliographicCitation | Journal of the Korean Society of Radiology, v.81, pp.1 - 11 | - |
dc.relation.isPartOf | Journal of the Korean Society of Radiology | - |
dc.citation.title | Journal of the Korean Society of Radiology | - |
dc.citation.volume | 81 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 11 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002631607 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Recurrence | - |
dc.subject.keywordAuthor | Thyroid Cancer, Papillary | - |
dc.subject.keywordAuthor | Ultrasonography | - |
dc.identifier.url | https://jksronline.org/DOIx.php?id=10.3348/jksr.2019.0147 | - |
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