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Maximum Value on Arterial Phase Computed Tomography Predicts Prognosis and Treatment Efficacy of Sunitinib for Pancreatic Neuroendocrine Tumours

  • Pancreatic Tumors
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Purpose

This study was designed to assess the computed tomography maximum (CTmax) value on pretherapeutic arterial phase computed tomography (APCT) images to predict pancreatic neuroendocrine tumours (pNETs) recurrence and clarify its role in predicting the outcome of tumour therapy.

Methods

This retrospective study enrolled 250 surgical patients and 24 nonsurgical patients with sunitinib-based treatment in our hospital from 2008 to 2019. CT images were assessed, the maximum value was defined as “CTmax,” and recurrence-free survival (RFS) or progression-free survival (PFS) was compared between a high-CTmax group and a low-CTmax group among patients who underwent surgical resection or nonsurgical, sunitinib-based treatment according to the CTmax cutoff value.

Results

In ROC curve analysis, a CTmax of 108 Hounsfield units, as the cutoff value, achieved an AUC of 0.796 in predicting recurrence. Compared with the low-CTmax group, the high-CTmax group had a longer RFS (p < 0.001). Low CTmax was identified as an independent factor for RFS (p < 0.001) in multivariate analysis; these results were confirmed using the internal validation set. The CTmax value was significantly correlated with the microvascular density (MVD) value (p < 0.001) and the vascular endothelial growth factor receptor 2 (VEGFR2) score (p < 0.001). Furthermore, the high-CTmax group had a better PFS than the low-CTmax group among the sunitinib treatment group (p = 0.007).

Conclusions

The tumour CTmax on APCT might be a potential and independent indicator for predicting recurrence in patients who have undergone surgical resection and assessing the efficacy of sunitinib for patients with advanced metastatic pNETs.

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Acknowledgment

This work was supported in part by the National Natural Science Foundation of China (U21A20374), the Shanghai Municipal Science and Technology Major Project (21JC1401500), the Scientific Innovation Project of Shanghai Education Committee (2019-01-07-00-07-E00057), the Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR1006A), the Xuhui District Artificial Intelligence Medical Hospital Cooperation Project (2021-011), the Shanghai Municipal Science and Technology Commission (20ZR1471100), and the National Science Foundation of China (No. 82141129, 82173281, 82173282, 82172577 and 82172948).

Author information

Author notes

  1. Haidi Chen, Zheng Li, Yuheng Hu and Xiaowu Xu have contributed equally to this work.

Authors and Affiliations

  1. Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China

    Haidi Chen MD, Zheng Li MD, Yuheng Hu MD, Xiaowu Xu MD, PhD, Zeng Ye MD, Xin Lou MD, Wuhu Zhang MD, Heli Gao MD, PhD, Yi Qin MD, PhD, Jie Chen MD, PhD, Xianjun Yu MD, PhD, FACS & Shunrong Ji MD, PhD

  2. Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China

    Haidi Chen MD, Zheng Li MD, Yuheng Hu MD, Xiaowu Xu MD, PhD, Zeng Ye MD, Xin Lou MD, Wuhu Zhang MD, Heli Gao MD, PhD, Yi Qin MD, PhD, Xianjun Yu MD, PhD, FACS & Shunrong Ji MD, PhD

  3. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

    Haidi Chen MD, Zheng Li MD, Yuheng Hu MD, Xiaowu Xu MD, PhD, Zeng Ye MD, Xin Lou MD, Wuhu Zhang MD, Heli Gao MD, PhD, Yi Qin MD, PhD, Jie Chen MD, PhD, Xianjun Yu MD, PhD, FACS & Shunrong Ji MD, PhD

  4. Shanghai Pancreatic Cancer Institute, Shanghai, China

    Haidi Chen MD, Zheng Li MD, Yuheng Hu MD, Xiaowu Xu MD, PhD, Zeng Ye MD, Xin Lou MD, Wuhu Zhang MD, Heli Gao MD, PhD, Yi Qin MD, PhD, Jie Chen MD, PhD, Xianjun Yu MD, PhD, FACS & Shunrong Ji MD, PhD

  5. Pancreatic Cancer Institute, Fudan University, Shanghai, China

    Haidi Chen MD, Zheng Li MD, Yuheng Hu MD, Xiaowu Xu MD, PhD, Zeng Ye MD, Xin Lou MD, Wuhu Zhang MD, Heli Gao MD, PhD, Yi Qin MD, PhD, Jie Chen MD, PhD, Xianjun Yu MD, PhD, FACS & Shunrong Ji MD, PhD

  6. Department of Hepatobiliary and Pancreatic Surgery, The Third Affiliated Hospital of Suzhou University, The First People’s Hospital of Changzhou, Changzhou, China

    Yue Zhang MD, PhD & Xuemin Chen MD, PhD

  7. Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China

    Wei Tang MD, PhD

  8. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

    Wei Tang MD, PhD

Authors
  1. Haidi Chen MD
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  2. Zheng Li MD
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  3. Yuheng Hu MD
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  4. Xiaowu Xu MD, PhD
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  6. Xin Lou MD
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  7. Wuhu Zhang MD
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  11. Xuemin Chen MD, PhD
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  12. Jie Chen MD, PhD
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  13. Wei Tang MD, PhD
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  14. Xianjun Yu MD, PhD, FACS
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  15. Shunrong Ji MD, PhD
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Contributions

SRJ, XJY, and WT contributed to the conception of the study; ZY, XL, WHZ, HLG, YQ, and JC contributed significantly to analysis and manuscript preparation; HDC, ZL, YHH, XMC, YZ, and XWX performed the data analyses and wrote the manuscript; HDC helped perform the analysis with constructive discussions.

Corresponding authors

Correspondence to Wei Tang MD, PhD, Xianjun Yu MD, PhD, FACS or Shunrong Ji MD, PhD.

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Chen, H., Li, Z., Hu, Y. et al. Maximum Value on Arterial Phase Computed Tomography Predicts Prognosis and Treatment Efficacy of Sunitinib for Pancreatic Neuroendocrine Tumours. Ann Surg Oncol 30, 2988–2998 (2023). https://doi.org/10.1245/s10434-022-12693-9

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