Predicting the Proliferation of Tongue Cancer With Artificial Intelligence in Contrast-Enhanced CT - Sun, Ting-Guan; Mao, Liang; Chai, Zi-Kang; Shen, Xue-Meng; Sun, Zhi-Jun (Frontiers in Oncology, 2022)
Tongue squamous cell carcinoma (TSCC) is the most common oral malignancy. The proliferation status of tumor cells as indicated with Ki-67 index has great impact on tumor microenvironment, therapeutic strategy making and patients’ prognosis. However, the most commonly-used method to obtain the proliferation status is biopsy or surgical immunohistochemical staining. Noninvasive method before operation remains a challenge. Hence, in this study, we aimed to validate a novel method to predict the proliferation status of TSCC using contrast-enhanced CT (CECT) based on artificial intelligence (AI). CECT images of the lesion area from 179 TSCC patients were analyzed using a convolutional neural network (CNN). High proliferation status group and low proliferation status group were divided according to the Ki-67 index of patients regarding the median 20% as cutoff. The model was trained and then automatically classified the test set. Results of the test set showed an accuracy of 65.38%, and AUC of 0.7172 suggesting that the majority of samples were classified correctly and the model was stable. Our study provided a possibility of predicting the proliferation status of TSCC using AI in CECT noninvasively before operation.