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Published on: March 21, 2018
Annotation-free genetic mutation estimation of thyroid cancer using cytological slides from multi-centers
Siping Xiong1, Shuguang Liu1, Wei Zhang1
1Department of Pathology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518000, Guangdong, China.
Abstract:
Thyroid cancer is the most common form of endocrine malignancy and fine needle aspiration (FNA) cytology is a reliable method for clinical diagnosis. Identification of genetic mutation status has been proved efficient for accurate diagnosis and prognostic risk stratification. In this study, a dataset with thyroid cytological images of 310 indeterminate (TBS3 or 4) and 392 PTC (TBS5 or 6) was collected. We introduced a multimodal cascaded network framework to estimate BARF V600E and RAS mutations directly from thyroid cytological slides. The area under the curve in the external testing set achieved 0.902 ± 0.063 and 0.801 ± 0.137 AUCs for BRAF, and RAS, respectively. The results demonstrated that deep neural networks have the potential in cytologically predicting valuable diagnosis and comprehensive genetic status.
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