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The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction

  • Head and Neck Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

Machine learning has been increasingly used for surgical outcome prediction, yet applications in head and neck reconstruction are not well-described. In this study, we developed and evaluated the performance of ML algorithms in predicting postoperative complications in head and neck free-flap reconstruction.

Methods

We conducted a comprehensive review of patients who underwent microvascular head and neck reconstruction between January 2005 and December 2018. Data were used to develop and evaluate nine supervised ML algorithms in predicting overall complications, major recipient-site complication, and total flap loss.

Results

We identified 4000 patients who met inclusion criteria. Overall, 33.7% of patients experienced a complication, 26.5% experienced a major recipient-site complication, and 1.7% suffered total flap loss. The k-nearest neighbors algorithm demonstrated the best overall performance for predicting any complication (AUROC = 0.61, sensitivity = 0.60). Regularized regression had the best performance for predicting major recipient-site complications (AUROC = 0.68, sensitivity = 0.66), and decision trees were the best predictors of total flap loss (AUROC = 0.66, sensitivity = 0.50).

Conclusions

ML accurately identified patients at risk of experiencing postsurgical complications, including total flap loss. Predictions from ML models may provide insight in the perioperative setting and facilitate shared decision making.

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References

  1. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–58.

    Article  PubMed  Google Scholar 

  2. Bur AM, Shew M, New J. Artificial intelligence for the otolaryngologist: a state of the art review. Otolaryngol Head Neck Surg. 2019;160(4):603–11.

    Article  PubMed  Google Scholar 

  3. Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64.

    Article  PubMed  PubMed Central  Google Scholar 

  4. AI: Healthcare's new nervous system. Accenture. Jul 30, 2020. Available at: https://www.accenture.com/au-en/insights/health/artificial-intelligence-healthcare Accessed on Dec 27, 2020.

  5. Ascent of machine learning in medicine. Nat Mater. 2019;18(5):407.

    Article  Google Scholar 

  6. Chen CL, Mahjoubfar A, Tai L-C, et al. Deep learning in label-free cell classification. Sci Rep. 2016;6(1):21471.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368:m689.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1(6):e271–97.

    Article  PubMed  Google Scholar 

  9. Thomsen K, Iversen L, Titlestad TL, Winther O. Systematic review of machine learning for diagnosis and prognosis in dermatology. J Dermatolog Treat. 2020;31(5):496–510.

    Article  PubMed  Google Scholar 

  10. Senders JT, Arnaout O, Karhade AV, et al. Natural and artificial intelligence in neurosurgery: a systematic review. Neurosurgery. 2018;83(2):181–92.

    Article  PubMed  Google Scholar 

  11. Senders JT, Staples PC, Karhade AV, et al. Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg. 2018;109:476-86.e471.

    Article  PubMed  Google Scholar 

  12. Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71(23):2668–79.

    Article  PubMed  Google Scholar 

  13. Chauhan J, Goyal P. BPBSAM: body part-specific burn severity assessment model. Burns. 2020;46(6):1407–23.

    Article  PubMed  Google Scholar 

  14. Cirillo MD, Mirdell R, Sjöberg F, Pham TD. Time-independent prediction of burn depth using deep convolutional neural networks. J Burn Care Res. 2019;40(6):857–63.

    Article  PubMed  Google Scholar 

  15. Yadav DP, Sharma A, Singh M, Goyal A. Feature extraction based machine learning for human burn diagnosis from burn images. IEEE J Transl Eng Health Med. 2019;7:1800507.

    Article  CAS  PubMed  Google Scholar 

  16. Jiao C, Su K, Xie W, Ye Z. Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient. Burns Trauma. 2019;7:6.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Cobb AN, Daungjaiboon W, Brownlee SA, et al. Seeing the forest beyond the trees: predicting survival in burn patients with machine learning. Am J Surg. 2018;215(3):411–6.

    Article  PubMed  Google Scholar 

  18. Huang Y, Zhang L, Lian G, et al. A novel mathematical model to predict prognosis of burnt patients based on logistic regression and support vector machine. Burns. 2016;42(2):291–9.

    Article  PubMed  Google Scholar 

  19. Yeong EK, Hsiao TC, Chiang HK, Lin CW. Prediction of burn healing time using artificial neural networks and reflectance spectrometer. Burns. 2005;31(4):415–20.

    Article  PubMed  Google Scholar 

  20. Bhalodia R, Dvoracek LA, Ayyash AM, Kavan L, Whitaker R, Goldstein JA. Quantifying the severity of metopic craniosynostosis: a pilot study application of machine learning in craniofacial surgery. J Craniofac Surg. 2020;31(3):697–701.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Angullia F, Fright WR, Richards R, Schievano S, Linney AD, Dunaway DJ. A novel RBF-based predictive tool for facial distraction surgery in growing children with syndromic craniosynostosis. Int J Comput Assist Radiol Surg. 2020;15(2):351–67.

    Article  CAS  PubMed  Google Scholar 

  22. Mendoza CS, Safdar N, Okada K, Myers E, Rogers GF, Linguraru MG. Personalized assessment of craniosynostosis via statistical shape modeling. Med Image Anal. 2014;18(4):635–46.

    Article  PubMed  Google Scholar 

  23. Formeister EJ, Baum R, Knott PD, et al. Machine learning for predicting complications in head and neck microvascular free tissue transfer. Laryngoscope. 2020;130(12):E843–9.

    Article  PubMed  Google Scholar 

  24. Kuo PJ, Wu SC, Chien PC, et al. Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer. Oncotarget. 2018;9(17):13768–82.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Zhao EH, Nishimori K, Brady J, et al. Analysis of risk factors for unplanned reoperation following free flap surgery of the head and neck. Laryngoscope. 2018;128(12):2790–5.

    Article  PubMed  Google Scholar 

  26. Zhou W, Zhang WB, Yu Y, et al. Risk factors for free flap failure: a retrospective analysis of 881 free flaps for head and neck defect reconstruction. Int J Oral Maxillofac Surg. 2017;46(8):941–5.

    Article  CAS  PubMed  Google Scholar 

  27. Carniol ET, Marchiano E, Brady JS, et al. Head and neck microvascular free flap reconstruction: an analysis of unplanned readmissions. Laryngoscope. 2017;127(2):325–30.

    Article  PubMed  Google Scholar 

  28. Maricevich M, Lin LO, Liu J, Chang EI, Hanasono MM. Interposition vein grafting in head and neck free flap reconstruction. Plast Reconstr Surg. 2018;142(4):1025–34.

    Article  CAS  PubMed  Google Scholar 

  29. Tang Z-H, Liu J, Zeng F, Li Z, Yu X, Zhou L. Comparison of prediction model for cardiovascular autonomic dysfunction using artificial neural network and logistic regression analysis. PLoS One. 2013;8(8):e70571.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jaimes F, Farbiarz J, Alvarez D, Martínez C. Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room. Crit Care. 2005;9(2):R150–6.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol. 1996;49(11):1225–31.

    Article  CAS  PubMed  Google Scholar 

  32. Chen JH, Asch SM. Machine learning and prediction in medicine: beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507–9.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Medicine. 2015;13(1):1.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Yap B, Rani KA, Rahman HA, Fong S, Khairudin Z, Abdullah NN. An application of oversampling, undersampling, bagging and boosting in handling imbalanced datasets. Paper presented at: DaEng2013.

  35. Pfob A, Sidey-Gibbons C, Tasoulis MK, et al. Artificial intelligence to accurately identify breast cancer patients with a pathologic complete response for omission of surgery after neoadjuvant systemic therapy: An international multicenter analysis. J Clin Oncol. 2020;38(15_suppl):565.

    Article  Google Scholar 

  36. Parikh RB, Manz C, Chivers C, et al. Machine learning approaches to predict 6-month mortality among patients with cancer. JAMA Netw Open. 2019;2(10):e1915997.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Li C, Zhang S, Zhang H, et al. Using the K-nearest neighbor algorithm for the classification of lymph node metastasis in gastric cancer. Comput Math Methods Med. 2012;2012:876545.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Menon R, Bhat G, Saade GR, Spratt H. Multivariate adaptive regression splines analysis to predict biomarkers of spontaneous preterm birth. Acta Obstet Gynecol Scand. 2014;93(4):382–91.

    Article  PubMed  Google Scholar 

  39. R Core Team. R: A language and environment for statistical computing. Published online 2020. Available at: https://www.r-project.org/ Accessed Jan 12, 2021.

  40. Bertsimas D, Dunn J, Velmahos GC, Kaafarani HMA. Surgical risk is not linear: Derivation and validation of a novel, user-friendly, and machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator. Ann Surg. 2018;268(4):574–83.

    Article  PubMed  Google Scholar 

  41. Hassan AM, Rajesh A, Asaad M, et al. A surgeon's guide to artificial intelligence-driven predictive models. Am Surg. 2022:31348221103648.

  42. Hassan AM, Rajesh A, Asaad M, et al. Artificial intelligence and machine learning in prediction of surgical complications: Current state, applications, and implications. Am Surg. 2022:31348221101488.

  43. Hassan AM, Lu SC, Asaad M, et al. Novel machine learning approach for the prediction of hernia recurrence, surgical complication, and 30-day readmission after abdominal wall reconstruction. J Am Coll Surg. 2022;234(5):918–27.

    Article  PubMed  Google Scholar 

  44. Hassan AM, Biaggi AP, Asaad M, et al. Development and assessment of machine learning models for individualized risk assessment of mastectomy skin flap necrosis. Ann Surg. 2022.

  45. O’Neill AC, Yang D, Roy M, Sebastiampillai S, Hofer SOP, Xu W. Development and evaluation of a machine learning prediction model for flap failure in microvascular breast reconstruction. Ann Surg Oncol. 2020;27(9):3466–75.

    Article  PubMed  Google Scholar 

  46. Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods. 2018;15(4):233–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Breiman L. Statistical modeling: the two cultures. Stat Sci. 2001;16:199–231.

    Article  Google Scholar 

  48. Trevethan R. Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front Public Health. 2017;5:307.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Mallett S, Halligan S, Thompson M, Collins GS, Altman DG. Interpreting diagnostic accuracy studies for patient care. BMJ. 2012;345:e3999.

    Article  PubMed  Google Scholar 

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Funding

No direct or indirect financial support was used for this study.

Author information

Author notes

  1. Malke Asaad and Sheng-Chieh Lu have contributed to the manuscript equally.

Authors and Affiliations

  1. Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Malke Asaad MD, Abbas M. Hassan MD, Praneeth Kambhampati BS, David Mitchell MD, Edward I. Chang MD, Peirong Yu MD & Matthew M. Hanasono MD

  2. Department of Symptom Research, MD Anderson Center for INSPiRED Cancer Care, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Sheng-Chieh Lu PhD & C. Sidey-Gibbons PhD

  3. Baylor College of Medicine, Houston, TX, USA

    Praneeth Kambhampati BS

  4. McGovern Medical School, Houston, TX, USA

    David Mitchell MD

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  1. Malke Asaad MD
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Correspondence to David Mitchell MD.

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Asaad, M., Lu, SC., Hassan, A.M. et al. The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction. Ann Surg Oncol 30, 2343–2352 (2023). https://doi.org/10.1245/s10434-022-13053-3

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  • DOI: https://doi.org/10.1245/s10434-022-13053-3

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