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Objective <p>The objective of this study is to investigate the value of radiomics features and deep learning features based on positron emission tomography/computed tomography (PET/CT) in predicting perineural invasion (PNI) in rectal cancer.</p> Methods <p>We retrospectively collected 120 rectal cancer (56 PNI-positive patients 64 PNI-negative patients) patients with preoperative <sup>18</sup>F-FDG PET/CT examination and randomly divided them into training and validation sets at a 7:3 ratio. We also collected 31 rectal cancer patients from two other hospitals as an independent external validation set. χ2 test and binary logistic regression were used to analyze PET metabolic parameters. PET/CT images were utilized to extract radiomics features and deep learning features. The Mann-Whitney U test and LASSO were employed to select valuable features. Metabolic parameter, radiomics, deep learning and combined models were constructed. ROC curves were generated to evaluate the performance of models.</p> Results <p>The results indicate that metabolic tumor volume (MTV) is correlated with PNI (<i>P</i> = 0.001). In the training set and validation set, the AUC values of the metabolic parameter model were 0.673 (95%CI: 0.572–0.773), 0.748 (95%CI: 0.599–0.896). We selected 16 radiomics features and 17 deep learning features as valuable factors for predicting PNI. The AUC values of radiomics model and deep learning model were 0.768 (95%CI: 0.667–0.868) and 0.860 (95%CI: 0.780–0.940) in the training set. And the AUC values in the validation set were 0.803 (95%CI: 0.656–0.950) and 0.854 (95% CI 0.721–0.987). Finally, the combined model exhibited AUCs of 0.893 (95%CI: 0.825–0.961) in the training set and 0.883 (95%CI: 0.775–0.990) in the validation set. In the external validation set, the combined model achieved an AUC of 0.829 (95% CI: 0.674–0.984), outperforming each individual model. The decision curve analysis of these models indicated that using the combined model to guide treatment provided a substantial net benefit.</p> Conclusions <p>This combined model established by integrating PET metabolic parameters, radiomics features, and deep learning features can accurately predict the PNI in rectal cancer.</p>

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The value of radiomics and deep learning based on PET/CT in predicting perineural nerve invasion in rectal cancer

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Mengzhang Jiao,
  • Zongjing Ma,
  • Zhaisong Gao,
  • Yu Kong,
  • Shumao Zhang,
  • Guangjie Yang,
  • Zhenguang Wang

摘要

Objective

The objective of this study is to investigate the value of radiomics features and deep learning features based on positron emission tomography/computed tomography (PET/CT) in predicting perineural invasion (PNI) in rectal cancer.

Methods

We retrospectively collected 120 rectal cancer (56 PNI-positive patients 64 PNI-negative patients) patients with preoperative 18F-FDG PET/CT examination and randomly divided them into training and validation sets at a 7:3 ratio. We also collected 31 rectal cancer patients from two other hospitals as an independent external validation set. χ2 test and binary logistic regression were used to analyze PET metabolic parameters. PET/CT images were utilized to extract radiomics features and deep learning features. The Mann-Whitney U test and LASSO were employed to select valuable features. Metabolic parameter, radiomics, deep learning and combined models were constructed. ROC curves were generated to evaluate the performance of models.

Results

The results indicate that metabolic tumor volume (MTV) is correlated with PNI (P = 0.001). In the training set and validation set, the AUC values of the metabolic parameter model were 0.673 (95%CI: 0.572–0.773), 0.748 (95%CI: 0.599–0.896). We selected 16 radiomics features and 17 deep learning features as valuable factors for predicting PNI. The AUC values of radiomics model and deep learning model were 0.768 (95%CI: 0.667–0.868) and 0.860 (95%CI: 0.780–0.940) in the training set. And the AUC values in the validation set were 0.803 (95%CI: 0.656–0.950) and 0.854 (95% CI 0.721–0.987). Finally, the combined model exhibited AUCs of 0.893 (95%CI: 0.825–0.961) in the training set and 0.883 (95%CI: 0.775–0.990) in the validation set. In the external validation set, the combined model achieved an AUC of 0.829 (95% CI: 0.674–0.984), outperforming each individual model. The decision curve analysis of these models indicated that using the combined model to guide treatment provided a substantial net benefit.

Conclusions

This combined model established by integrating PET metabolic parameters, radiomics features, and deep learning features can accurately predict the PNI in rectal cancer.