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 subtype...
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| Format: | Article |
| Language: | English |
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BMC
2025-03-01
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| Series: | Diagnostic Pathology |
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| Online Access: | https://doi.org/10.1186/s13000-025-01621-6 |
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| author | Hongpeng Guo Junjie Zhang You Li Xinghe Pan Chenglin Sun |
| author_facet | Hongpeng Guo Junjie Zhang You Li Xinghe Pan Chenglin Sun |
| author_sort | Hongpeng Guo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-604d36388b0645ca8da13bc71b21eea4 |
| institution | DOAJ |
| issn | 1746-1596 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Diagnostic Pathology |
| spelling | doaj-art-604d36388b0645ca8da13bc71b21eea42025-08-20T03:05:57ZengBMCDiagnostic Pathology1746-15962025-03-0120111310.1186/s13000-025-01621-6Advanced pathological subtype classification of thyroid cancer using efficientNetB0Hongpeng Guo0Junjie Zhang1You Li2Xinghe Pan3Chenglin Sun4Department of General Surgery, The Second Hospital Affiliated to Shenyang Medical CollegeDepartment of Pathology, Central Hospital Affiliated to Shenyang Medical CollegeDepartment of General Surgery, The Second Hospital Affiliated to Shenyang Medical CollegeDepartment of General Surgery, The Second Hospital Affiliated to Shenyang Medical CollegeDepartment of General Surgery, The Second Hospital Affiliated to Shenyang Medical CollegeAbstract 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.https://doi.org/10.1186/s13000-025-01621-6Thyroid cancerEfficientNetB0 algorithm modelPathological subtypeTumor microenvironmentPrecise diagnosisPersonalized treatment |
| spellingShingle | Hongpeng Guo Junjie Zhang You Li Xinghe Pan Chenglin Sun Advanced pathological subtype classification of thyroid cancer using efficientNetB0 Diagnostic Pathology Thyroid cancer EfficientNetB0 algorithm model Pathological subtype Tumor microenvironment Precise diagnosis Personalized treatment |
| title | Advanced pathological subtype classification of thyroid cancer using efficientNetB0 |
| title_full | Advanced pathological subtype classification of thyroid cancer using efficientNetB0 |
| title_fullStr | Advanced pathological subtype classification of thyroid cancer using efficientNetB0 |
| title_full_unstemmed | Advanced pathological subtype classification of thyroid cancer using efficientNetB0 |
| title_short | Advanced pathological subtype classification of thyroid cancer using efficientNetB0 |
| title_sort | advanced pathological subtype classification of thyroid cancer using efficientnetb0 |
| topic | Thyroid cancer EfficientNetB0 algorithm model Pathological subtype Tumor microenvironment Precise diagnosis Personalized treatment |
| url | https://doi.org/10.1186/s13000-025-01621-6 |
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