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Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image

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Abstract

Objectives

Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1–T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. This paper introduces three models of radiomics, deep learning, and deep learning-based radiomics to identify T4 OCC.

Methods

We established a dataset of computed tomography (CT) images of 164 patients with pathologically confirmed OCC, from which 2537 slides were extracted. First, since T4 tumors penetrate the bowel wall and involve adjacent organs, we explored whether the peritumoral region contributes to the assessment of T4 OCC. Furthermore, we visualized the radiomics and deep learning features using the t-distributed stochastic neighbor embedding technique (t-SNE). Finally, we built a merged model by fusing radiomic features with deep learning features. In this experiment, the performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC).

Results

In the test cohort, the AUC values predicted by the radiomics model in the dilated region of interest (dROI) was 0.770. And the AUC value of the deep learning model with the patches extended 20-pixel reached 0.936. Combining the characteristics of radiomics and deep learning, our method achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification, and increased the AUC value to 0.950 after the addition of clinical features.

Conclusion

The prediction results of our merged model of deep learning radiomics outperformed the deep learning model and significantly outperformed the radiomics model. The experimental results demonstrate that combining the peritumoral region improves the prediction performance of the radiomics model and the deep learning model.

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Acknowledgements

This work was financed by Fujian Provincial Natural Science Foundation project (Grant No. 2021J02019); Fujian Provincial Health Technology Project (Grant No. 2020GGA034); National Natural Science Foundation of China (Grant No. 62271149); the Central Guidance on Local Science and Technology Development Fund of Fujian Province (Grant No. 2022L3003); Joint Funds for the Innovation of Science and Technology, Fujian Province (Grant No. 2018Y9054); the Construction Project of Fujian Province Minimally Invasive Medical Center (Grant No.[2021]76).

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Authors and Affiliations

  1. College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China

    Lin Pan, Tian He & Shaohua Zheng

  2. School of Future Technology, Harbin Institute of Technology, Harbin, 150000, China

    Zihan Huang

  3. Department of Emergency Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China

    Shuai Chen, Junrong Zhang & **anqiang Chen

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Correspondence to Shaohua Zheng or **anqiang Chen.

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Pan, L., He, T., Huang, Z. et al. Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image. Abdom Radiol 48, 1246–1259 (2023). https://doi.org/10.1007/s00261-023-03838-9

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