CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma

Meiyi Yang; Xiaopeng He; Lifeng Xu; Minghui Liu; Jiali Deng; Xuan Cheng; Yi Wei; Qian Li; Shang Wan; Feng Zhang; Lei Wu; Xiaomin Wang; Bin Song; Ming Liu

doi:10.3389/fonc.2022.961779

CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma

Front Oncol. 2022 Sep 28:12:961779. doi: 10.3389/fonc.2022.961779. eCollection 2022.

Authors

Meiyi Yang^{1

2}, Xiaopeng He³, Lifeng Xu⁴, Minghui Liu², Jiali Deng², Xuan Cheng², Yi Wei⁵, Qian Li⁵, Shang Wan⁵, Feng Zhang⁴, Lei Wu², Xiaomin Wang², Bin Song⁵, Ming Liu⁴

Affiliations

¹ Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
² School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
³ Department of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou, China.
⁴ Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.
⁵ Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.

Abstract

Background: Clear cell Renal Cell Carcinoma (ccRCC) is the most common malignant tumor in the urinary system and the predominant subtype of malignant renal tumors with high mortality. Biopsy is the main examination to determine ccRCC grade, but it can lead to unavoidable complications and sampling bias. Therefore, non-invasive technology (e.g., CT examination) for ccRCC grading is attracting more and more attention. However, noise labels on CT images containing multiple grades but only one label make prediction difficult. However, noise labels exist in CT images, which contain multiple grades but only one label, making prediction difficult.

Aim: We proposed a Transformer-based deep learning algorithm with CT images to improve the diagnostic accuracy of grading prediction and to improve the diagnostic accuracy of ccRCC grading.

Methods: We integrate different training models to improve robustness and predict Fuhrman nuclear grade. Then, we conducted experiments on a collected ccRCC dataset containing 759 patients and used average classification accuracy, sensitivity, specificity, and AreaUnderCurve as indicators to evaluate the quality of research. In the comparative experiments, we further performed various current deep learning algorithms to show the advantages of the proposed method. We collected patients with pathologically proven ccRCC diagnosed from April 2010 to December 2018 as the training and internal test dataset, containing 759 patients. We propose a transformer-based network architecture that efficiently employs convolutional neural networks (CNNs) and self-attention mechanisms to extract a persuasive feature automatically. And then, a nonlinear classifier is applied to classify. We integrate different training models to improve the accuracy and robustness of the model. The average classification accuracy, sensitivity, specificity, and area under curve are used as indicators to evaluate the quality of a model.

Results: The mean accuracy, sensitivity, specificity, and Area Under Curve achieved by CNN were 82.3%, 89.4%, 83.2%, and 85.7%, respectively. In contrast, the proposed Transformer-based model obtains a mean accuracy of 87.1% with a sensitivity of 91.3%, a specificity of 85.3%, and an Area Under Curve (AUC) of 90.3%. The integrated model acquires a better performance (86.5% ACC and an AUC of 91.2%).

Conclusion: A transformer-based network performs better than traditional deep learning algorithms in terms of the accuracy of ccRCC prediction. Meanwhile, the transformer has a certain advantage in dealing with noise labels existing in CT images of ccRCC. This method is promising to be applied to other medical tasks (e.g., the grade of neurogliomas and meningiomas).

Keywords: clear cell renal cell carcinoma; deep learning; ensemble learning; transformer network; tumor grading.