1. Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
2. Department of Oncology, Shanghai Medical Colloge, Fudan University, Shanghai, 200032, China
3. Depertment of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China
4. Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
Qing Guan and Yunjun Wang contributed equally to the work and should be regarded as co-first authors.
Objective: In this study, we exploited a VGG-16 deep convolutional neural network (DCNN) model to differentiate papillary thyroid carcinoma (PTC) from benign thyroid nodules using cytological images.
Methods: A pathology-proven dataset was built from 279 cytological images of thyroid nodules. The images were cropped into fragmented images and divided into a training dataset and a test dataset. VGG-16 and Inception-v3 DCNNs were trained and tested to make differential diagnoses. The characteristics of tumor cell nucleus were quantified as contours, perimeter, area and mean of pixel intensity and compared using independent Student's t-tests.
Results: In the test group, the accuracy rates of the VGG-16 model and Inception-v3 on fragmented images were 97.66% and 92.75%, respectively, and the accuracy rates of VGG-16 and Inception-v3 in patients were 95% and 87.5%, respectively. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules, which were 61.01±17.10 vs 47.00±24.08, p=0.000, 134.99±21.42 vs 62.40±29.15, p=0.000, 1770.89±627.22 vs 1157.27±722.23, p=0.013, 165.84±26.33 vs 132.94±28.73, p=0.000), respectively.
Conclusion: In summary, after training with a large dataset, the DCNN VGG-16 model showed great potential in facilitating PTC diagnosis from cytological images. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules.
Keywords: Deep convolutional neural network, papillary thyroid carcinoma, cytological images, fine-needle aspiration, liquid-based cytology