Sun Ting-Guan, Mao Liang, Chai Zi-Kang, Shen Xue-Meng, Sun Zhi-Jun
The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) and Key Laboratory of Oral Biomedicine, Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
Department of Oral Maxillofacial-Head Neck Oncology, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
Front Oncol. 2022 Apr 8;12:841262. doi: 10.3389/fonc.2022.841262. eCollection 2022.
Tongue squamous cell carcinoma (TSCC) is the most common oral malignancy. The proliferation status of tumor cells as indicated with the 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 through 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). Patients were divided into a high proliferation status group and a low proliferation status group according to the Ki-67 index of patients with the median 20% as cutoff. The model was trained and then the test set was automatically classified. Results of the test set showed an accuracy of 65.38% and an 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.
舌鳞状细胞癌（TSCC）是最常见的口腔恶性肿瘤。用Ki-67指数表示的肿瘤细胞增殖状态对肿瘤微环境、治疗策略制定和患者预后有很大影响。然而，获得增殖状态最常用的方法是通过活检或手术免疫组织化学染色。术前的非侵入性方法仍然是一个挑战。因此，在本研究中，我们旨在验证一种基于人工智能（AI）的使用对比增强CT（CECT）预测TSCC增殖状态的新方法。使用卷积神经网络（CNN）分析了179例TSCC患者病变区域的CECT图像。根据Ki-67指数，以中位数20%为界值将患者分为高增殖状态组和低增殖状态组。对模型进行训练，然后对测试集进行自动分类。测试集结果显示准确率为65.38%，AUC为0.7172，表明大多数样本分类正确且模型稳定。我们的研究提供了一种在术前通过CECT使用人工智能非侵入性预测TSCC增殖状态的可能性。
