岭南现代临床外科2024，Vol.24Issue(5)：308-313,6.DOI:10.3969/j.issn.1009-976X.2024.05.007
基于人工智能的甲状腺癌病理图像分析预测BRAF-V600E突变
AI-based pathological image analysis for predicting BRAF-V600E mutation in thyroid cancer
摘要
Abstract
Objective To investigate the application value of a deep learning model based on whole-slide imaging(WSI)in predicting the BRAF-V600E mutation status in thyroid cancer and to analyze the correlation between this mutation and patients' clinical characteristics.Methods A deep learning model based on a pre-trained ResNet50 network was developed using data from The Cancer Genome Atlas(TCGA).The model employed a self-attention mechanism to extract key features from WSI and con-structed a single-task binary classification model to predict the BRAF-V600E mutation status.A total of 305 papillary thyroid carcinoma(PTC)cases were used for model training,and 131 cases were used for validation.The model's performance was evaluated using the area under the receiver operating character-istic curve(AUC).Additionally,the association between BRAF-V600E mutation and clinical character-istics such as gender,age,and tumor staging were analyzed.Results The model achieved AUC values of 0.972 and 0.904 on the training and validation datasets,respectively,demonstrating high predictive accuracy.Clinical characteristic analysis revealed that the BRAF-V600E mutation was more common in females,patients under 55 years of age,and those with advanced-stage tumors.The mutation was signifi-cantly associated with higher tumor stages and lymph node metastasis.Conclusion The deep learning-based WSI model performed excellently in predicting BRAF-V600E mutation status in thyroid cancer,providing support for personalized diagnosis and treatment.Future studies should integrate multi-center datasets to further validate the model's clinical applicability and generalizability.关键词
甲状腺癌/BRAF-V600E突变/人工智能Key words
thyroid cancer/BRAF-V600E mutation/artificial intelligence分类
医药卫生引用本文复制引用
余婷婷,朱晓彤,郭丽芬,李莉..基于人工智能的甲状腺癌病理图像分析预测BRAF-V600E突变[J].岭南现代临床外科,2024,24(5):308-313,6.基金项目
广州市科学技术局项目(2023A03J0722) （2023A03J0722）
