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A machine learning risk model based on preoperative computed tomography scan to predict postoperative outcomes after pancreatoduodenectomy

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Abstract

Clinically relevant postoperative pancreatic fistula (CR-POPF) is a life-threatening complication following pancreaticoduodenectomy (PD). Individualized preoperative risk assessment could improve clinical management and prevent or mitigate adverse outcomes. The aim of this study is to develop a machine learning risk model to predict occurrence of CR-POPF after PD from preoperative computed tomography (CT) scans. A total of 100 preoperative high-quality CT scans of consecutive patients who underwent pancreaticoduodenectomy in our institution between 2011 and 2019 were analyzed. Radiomic and morphological features extracted from CT scans related to pancreatic anatomy and patient characteristics were included as variables. These data were then assessed by a machine learning classifier to assess the risk of developing CR-POPF. Among the 100 patients evaluated, 20 had CR-POPF. The predictive model based on logistic regression demonstrated specificity of 0.824 (0.133) and sensitivity of 0.571 (0.337), with an AUC of 0.807 (0.155), PPV of 0.468 (0.310) and NPV of 0.890 (0.084). The performance of the model minimally decreased utilizing a random forest approach, with specificity of 0.914 (0.106), sensitivity of 0.424 (0.346), AUC of 0.749 (0.209), PPV of 0.502 (0.414) and NPV of 0.869 (0.076). Interestingly, using the same data, the model was also able to predict postoperative overall complications and a postoperative length of stay over the median with AUCs of 0.690 (0.209) and 0.709 (0.160), respectively. These findings suggest that preoperative CT scans evaluated by machine learning may provide a novel set of information to help clinicians choose a tailored therapeutic pathway in patients candidated to pancreatoduodenectomy.

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Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

Code availability (software application or custom code)

Software code could be available from the corresponding author on reasonable request under written agreement.

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Authors and Affiliations

  1. Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20090, Milan, Italy

    Giovanni Capretti, Martina Nebbia & Alessandro Zerbi

  2. Pancreatic Surgery Unit, Humanitas Clinical and Research Center-IRCCS, Via Manzoni 56, 20089, Rozzano, MI, Italy

    Giovanni Capretti & Alessandro Zerbi

  3. Department of Diagnostic and Interventional Radiology, Humanitas Clinical and Research Center-IRCCS, Via A. Manzoni 56, Rozzano, 20089, Milan, Italy

    Cristiana Bonifacio, Caterina Giannitto & Luca Balzarini

  4. Artificial Intelligence Center, Humanitas Clinical and Research Center–IRCCS, Via A. Manzoni 56, Rozzano, 20089, Milan, Italy

    Crescenzo De Palma, Pierandrea Cancian, Maria Elena Laino & Victor Savevski

  5. Computational Clinical Imaging Group, Champalimaud Foundation, Lisbon, Portugal

    Nickolas Papanikolaou

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  1. Giovanni Capretti
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Contributions

All authors contributed to the study concept and design, data interpretation, drafting, final approval, and accountability for all aspects of the work.

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Correspondence to Victor Savevski.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the institutional Ethics Committee of Humanitas Clinical and Research Center-IRCCS, Milan.

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The study was approved by the institutional research committee. This article does not contain any studies on animals performed by any of the authors.

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Capretti, G., Bonifacio, C., De Palma, C. et al. A machine learning risk model based on preoperative computed tomography scan to predict postoperative outcomes after pancreatoduodenectomy. Updates Surg 74, 235–243 (2022). https://doi.org/10.1007/s13304-021-01174-5

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  • DOI: https://doi.org/10.1007/s13304-021-01174-5

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