Guo Hongpeng, Zhang Junjie, Li You, Pan Xinghe, Sun Chenglin
Department of General Surgery, The Second Hospital Affiliated to Shenyang Medical College, No.64, Qishan West Road, Huanggu District, Shenyang, Liaoning, 110002, China.
Department of Pathology, Central Hospital Affiliated to Shenyang Medical College, Shenyang, Liaoning, 110024, China.
Diagn Pathol. 2025 Mar 7;20(1):28. doi: 10.1186/s13000-025-01621-6.
Thyroid cancer is a prevalent malignancy requiring accurate subtype identification for effective treatment planning and prognosis evaluation. Deep learning has emerged as a valuable tool for analyzing tumor microenvironment features and distinguishing between pathological subtypes, yet the interplay between microenvironment characteristics and clinical outcomes remains unclear.
Pathological tissue slices, gene expression data, and protein expression data were collected from 118 thyroid cancer patients with various subtypes. The data underwent preprocessing, and 10 AI models, including EfficientNetB0, were compared. EfficientNetB0 was selected, trained, and validated, with microenvironment features such as tumor-immune cell interactions and extracellular matrix (ECM) composition extracted from the samples.
The study demonstrated the high accuracy of the EfficientNetB0 model in differentiating papillary, follicular, medullary, and anaplastic thyroid carcinoma subtypes, surpassing other models in performance metrics. Additionally, the model revealed significant correlations between microenvironment features and pathological subtypes, impacting disease progression, treatment response, and patient prognosis.
The research establishes the effectiveness of the EfficientNetB0 model in identifying thyroid cancer subtypes and analyzing tumor microenvironment features, providing insights for precise diagnosis and personalized treatment. The results enhance our understanding of the relationship between microenvironment characteristics and pathological subtypes, offering potential molecular targets for future treatment strategies.
甲状腺癌是一种常见的恶性肿瘤，需要准确识别亚型以进行有效的治疗规划和预后评估。深度学习已成为分析肿瘤微环境特征和区分病理亚型的有价值工具，但微环境特征与临床结果之间的相互作用仍不清楚。
收集了118例不同亚型甲状腺癌患者的病理组织切片、基因表达数据和蛋白质表达数据。对数据进行预处理，并比较了包括EfficientNetB0在内的10种人工智能模型。选择EfficientNetB0进行训练和验证，并从样本中提取肿瘤-免疫细胞相互作用和细胞外基质（ECM）组成等微环境特征。
该研究证明了EfficientNetB0模型在区分乳头状、滤泡状、髓样和未分化甲状腺癌亚型方面具有很高的准确性，在性能指标上超过了其他模型。此外，该模型揭示了微环境特征与病理亚型之间的显著相关性，影响疾病进展、治疗反应和患者预后。
该研究证实了EfficientNetB0模型在识别甲状腺癌亚型和分析肿瘤微环境特征方面的有效性，并为精确诊断和个性化治疗提供了见解。研究结果加深了我们对微环境特征与病理亚型之间关系的理解，为未来的治疗策略提供了潜在的分子靶点。
