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Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma

  • Pancreas
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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To develop and validate an automated magnetic resonance imaging (MRI)-based model to preoperatively differentiate pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC).

Methods

This retrospective study included patients with surgically resected, histopathologically confirmed PASC or PDAC who underwent MRI between January 2011 and December 2020. According to time of treatment, they were divided into training and validation sets. Automated deep-learning-based artificial intelligence was used for pancreatic tumor segmentation. Linear discriminant analysis was performed with conventional MRI and radiomic features to develop clinical, radiomics, and mixed models in the training set. The models’ performances were determined from their discrimination and clinical utility. Kaplan–Meier and log-rank tests were used for survival analysis.

Results

Overall, 389 and 123 patients with PDAC (age, 61.37 ± 9.47 years; 251 men) and PASC (age, 61.99 ± 9.82 years; 78 men) were included, respectively; they were split into the training (n = 358) and validation (n = 154) sets. The mixed model showed good performance in the training and validation sets (area under the curve: 0.94 and 0.96, respectively). The sensitivity, specificity, and accuracy were 76.74%, 93.38%, and 89.39% for the training set, respectively, and 67.57%, 97.44%, and 90.26% for the validation set, respectively. The mixed model outperformed the clinical (p = 0.001) and radiomics (p = 0.04) models in the validation set. Log-rank test revealed significantly longer survival in the predicted PDAC group than in the predicted PASC group (p = 0.003), according to the mixed model.

Conclusion

Our mixed model, which combined MRI and radiomic features, can be used to differentiate PASC from PDAC.

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Funding

This work was supported in part by the National Science Foundation for Scientists of China (Grant Nos. 81871352, 82171915, 82171930, U1809205, 62171230, 92159301, 62101365 and 91959207), The Natural Science Foundation of Shanghai Science and Technology Innovation Action Plan (Grant Nos. 21ZR1478500, 21Y11910300), Clinical Research Plan of SHDC (SHDC2020CR4073), and 234 Platform Discipline Consolidation Foundation Project (Grant Nos. 2019YPT001, 2020YPT001).

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Author notes

  1. Qi Li, Zhenhao Zhou and Yukun Chen are the joint first authors in the paper.

Authors and Affiliations

  1. Department of Radiology, Changhai Hospital, Naval Medical University, Changhai Road 168, Shanghai, 200434, China

    Qi Li, Yukun Chen, Jieyu Yu, Hao Zhang, Yinghao Meng, Mengmeng Zhu, Na Li, Jian Zhou, Fang Liu, Xu Fang, Jing Li, Tiegong Wang, Jianping Lu, Chengwei Shao & Yun Bian

  2. Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, No. 219 Ning Liu Road, Nanjing, 210044, Jiangsu, China

    Zhenghao Zhou, Teng Zhang & Jun Xu

  3. Department of Radiology, No. 971 Hospital of Navy, Qingdao, 266071, Shandong, China

    Yinghao Meng

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  1. Qi Li
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Correspondence to Jun Xu, Chengwei Shao or Yun Bian.

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Li, Q., Zhou, Z., Chen, Y. et al. Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma. Abdom Radiol 48, 2074–2084 (2023). https://doi.org/10.1007/s00261-023-03801-8

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