Advanced pathological subtype classification of thyroid cancer using efficientNetB0.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.