Development and Validation of Contrast-Enhanced CT-Based Deep Transfer Learning and Combined Clinical-Radiomics Model to Discriminate Thymomas and Thymic Cysts: A Multicenter Study

Yang, Yuhua; Cheng, Jia; Peng, Zhiwei; Yi, Li; Lin, Ze; He, Anjing; Jin, Mengni; Cui, Can; Liu, Ying; Zhong, QiWen; Zuo, Minjing

doi:10.1016/j.acra.2023.10.018

Academic Radiology

2024

DOI: 10.1016/j.acra.2023.10.018

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Development and Validation of Contrast-Enhanced CT-Based Deep Transfer Learning and Combined Clinical-Radiomics Model to Discriminate Thymomas and Thymic Cysts: A Multicenter Study

Yuhua Yang,

Jia Cheng,

Zhiwei Peng

et al.

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Cited by 5 publications

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Unveiling Thymoma Typing Through Hyperspectral Imaging and Deep Learning

Lv,

Liang,

Tian

et al. 2024

Journal of Biophotonics

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Thymoma, a rare tumor from thymic epithelial cells, presents diagnostic challenges because of the subjective nature of traditional methods, leading to high false‐negative rates and long diagnosis times. This study introduces a thymoma classification technique that integrates hyperspectral imaging with deep learning. We initially capture pathological slice images of thymoma using a hyperspectral camera and delineate regions of interest to extract spectral data. This data undergoes reflectance calibration and noise reduction. Subsequently, we transform the spectral data into two‐dimensional images via the Gramian Angular Field (GAF) method. A variant residual network is then utilized to extract features and classify these images. Our results demonstrate that this model significantly enhances classification accuracy and efficiency, achieving an average accuracy of 95%. The method proves highly effective in automated thymoma diagnosis, optimizing data utilization, and feature representation learning.

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Unveiling Thymoma Typing Through Hyperspectral Imaging and Deep Learning

Lv,

Liang,

Tian

et al. 2024

Journal of Biophotonics

View full text Add to dashboard Cite

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Development and Validation of a CT-Radiomics Nomogram for the Diagnosis of Small Prevascular Mediastinal Nodules: Reducing Nontherapeutic Surgeries

Ai,

Wang,

et al. 2024

Academic Radiology

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Deep Learning Algorithm‑Based MRI Radiomics and Pathomics for Predicting Microsatellite Instability Status in Rectal Cancer: A Multicenter Study

Yao,

Deng,

Han

et al. 2024

Academic Radiology

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Development and Validation of Contrast-Enhanced CT-Based Deep Transfer Learning and Combined Clinical-Radiomics Model to Discriminate Thymomas and Thymic Cysts: A Multicenter Study

Cited by 5 publications

References 40 publications

Unveiling Thymoma Typing Through Hyperspectral Imaging and Deep Learning

Unveiling Thymoma Typing Through Hyperspectral Imaging and Deep Learning

Development and Validation of a CT-Radiomics Nomogram for the Diagnosis of Small Prevascular Mediastinal Nodules: Reducing Nontherapeutic Surgeries

Deep Learning Algorithm‑Based MRI Radiomics and Pathomics for Predicting Microsatellite Instability Status in Rectal Cancer: A Multicenter Study

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