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Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma

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Abstract

Purpose

To explore the clinical value of contrast-enhanced computed tomography (CECT) radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma.

Materials and methods

Seventy patients were retrospectively included and separated into very good partial response (VGPR) group and non-VGPR group according to the changes in primary tumor volume. The clinical features with statistical difference between the two groups were used to construct the clinical models using a logistic regression (LR) algorithm. The radiomics models based on different radiomics features selected by Kruskal–Wallis (KW) test and recursive feature elimination (RFE) were established using support vector machine (SVM) and LR algorithms. The radiomics score (Radscore) and clinical features were integrated into the combined models. Leave-one-out cross-validation (LOOCV) was used to validate the predictive performance of models in the entire dataset.

Results

The optimal clinical model achieved an area under the curve (AUC) of 0.767 [95% confidence interval (CI): 0.638, 0.896] and an accuracy of 0.771 after LOOCV. The AUCs of the best KW + SVM, KW + LR, RFE + SVM, and RFE + LR radiomics models were 0.816, 0.826, 0.853, and 0.850, respectively, and the corresponding AUCs after LOOCV were 0.780, 0.785, 0.755, and 0.772, respectively. The AUC and accuracy after LOOCV of the optimal combined model was 0.804 (95% CI: 0.694, 0.915) and 0.814, respectively. The Delong test showed a statistical difference in predictive performance between the optimal clinical and combined models after LOOCV (Z = 2.003, P = 0.045). The decision curve analysis showed that the combined model performs better than the clinical model.

Conclusion

The CECT radiomics models have a favorable predictive performance in predicting VGPR of high-risk neuroblastoma to neoadjuvant chemotherapy. When integrating radiomics features and clinical features, the predictive performance of the combined models can be further improved.

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Data availability

Available from the authors upon reasonable request.

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Funding

The project was funded by Basic Research and Frontier Exploration Project (Yuzhong District, Chongqing, China) (Grant No. 20200155).

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Author notes

    Authors and Affiliations

    1. Department of Radiology, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China

      Haoru Wang, Jinjie Qin, Xin Chen, Ting Zhang, Li Zhang, Hao Ding & Ling He

    2. Department of Cardiothoracic Surgery, Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China

      Zhengxia Pan

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    Contributions

    Conceptualization: HW. Data curation: HW, JQ, XC, TZ. Formal analysis: HW. Investigation: HW, LZ, HD. Methodology: HW. Project administration: HW, ZP, LH. Supervision: LH. Validation: HW, JQ. Visualization: HW. Writing: HW, JQ.

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    Correspondence to Zhengxia Pan or Ling He.

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    The requirement for patient informed consent was waived.

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    This retrospective study was approved by the ethics committee of our institution.

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    Haoru Wang and Jinjie Qin have been contributed equally to this work.

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    Wang, H., Qin, J., Chen, X. et al. Contrast-enhanced computed tomography radiomics in predicting primary site response to neoadjuvant chemotherapy in high-risk neuroblastoma. Abdom Radiol 48, 976–986 (2023). https://doi.org/10.1007/s00261-022-03774-0

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