A prognostic Bayesian network that makes personalized predictions of poor prognostic outcome post resection of pancreatic ductal adenocarcinoma
Autoři:
Alison Bradley aff001; Robert Van der Meer aff001; Colin J. McKay aff002
Působiště autorů:
Department of Management Science, Strathclyde Business School, University of Strathclyde, Glasgow, Scotland, United Kingdom
aff001; West of Scotland Pancreatic Cancer Unit, Glasgow Royal Infirmary, Glasgow, Scotland, United Kingdom
aff002
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0222270
Souhrn
Background
The narrative surrounding the management of potentially resectable pancreatic cancer is complex. Surgical resection is the only potentially curative treatment. However resection rates are low, the risk of operative morbidity and mortality are high, and survival outcomes remain poor. The aim of this study was to create a prognostic Bayesian network that pre-operatively makes personalized predictions of post-resection survival time of 12months or less and also performs post-operative prognostic updating.
Methods
A Bayesian network was created by synthesizing data from PubMed post-resection survival analysis studies through a two-stage weighting process. Input variables included: inflammatory markers, tumour factors, tumour markers, patient factors and, if applicable, response to neoadjuvant treatment for pre-operative predictions. Prognostic updating was performed by inclusion of post-operative input variables including: pathology results and adjuvant therapy.
Results
77 studies (n = 31,214) were used to create the Bayesian network, which was validated against a prospectively maintained tertiary referral centre database (n = 387). For pre-operative predictions an Area Under the Curve (AUC) of 0.7 (P value: 0.001; 95% CI 0.589–0.801) was achieved accepting up to 4 missing data-points in the dataset. For prognostic updating an AUC 0.8 (P value: 0.000; 95% CI:0.710–0.870) was achieved when validated against a dataset with up to 6 missing pre-operative, and 0 missing post-operative data-points. This dropped to AUC: 0.7 (P value: 0.000; 95% CI:0.667–0.818) when the post-operative validation dataset had up to 2 missing data-points.
Conclusion
This Bayesian network is currently unique in the way it utilizes PubMed and patient level data to translate the existing empirical evidence surrounding potentially resectable pancreatic cancer to make personalized prognostic predictions. We believe such a tool is vital in facilitating better shared decision-making in clinical practice and could be further developed to offer a vehicle for delivering personalized precision medicine in the future.
Klíčová slova:
Medicine and health sciences – Surgical and invasive medical procedures – Surgical resection – Oncology – Cancers and neoplasms – Gastrointestinal tumors – Pancreatic cancer – Cancer treatment – Surgical oncology – Clinical oncology – Clinical medicine – Diagnostic medicine – Prognosis – Research and analysis methods – Mathematical and statistical techniques – Statistical methods – Physical sciences – Mathematics – Statistics – Biology and life sciences – Neuroscience – Cognitive science – Cognitive psychology – Decision making – Cognition – Psychology – Social sciences
Zdroje
1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65:5–29 doi: 10.3322/caac.21254 25559415
2. Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JW, Comber H, et al. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer. 2013;49:1374–403. doi: 10.1016/j.ejca.2012.12.027 23485231
3. Cancer Research UK. Pancreatic cancer and treatment statistics.https://www.cancerresearchuk.org/about-cancer/pancreatic-cancer/survival. Accessed 7th January 2019.
4. Neoptolemos JP, Dunn JA, Stocken DD, Almond J, Link K, Beger H, et al. Adjuvant chemoradio- therapy and chemotherapy in resectable pancreatic cancer: a randomized controlled trial. Lancet. 2001;358:1576–85. doi: 10.1016/s0140-6736(01)06651-x 11716884
5. Winter JM, Brennan MF, Tang LH, D’Angelica MI, Dematteo RP, Fong Y, et al. Survival after resection of pancreatic adenocarcinoma: results from a single institution over three decades. Ann Surg Oncol. 2012;19:169. doi: 10.1245/s10434-011-1900-3 21761104
6. Bilimoria KY, Bentrem DJ, Ko CY, Tomlinson JS, Stewart AK, Winchester DP, et al. Multimodality therapy for pancreatic cancer in the U.S.: utilization, outcomes, and the effect of hospital volume. Cancer. 2007;110:1227–34. doi: 10.1002/cncr.22916 17654662
7. Asare EA, Evans DB, Erickson BA, Aburajab M, Tolat P, Tsai S. Neoadjuvant treatment sequencing adds value to the care of patients with operable pancreatic cancer. Journal of Surgical Oncology. 2016;114(3):291–295. doi: 10.1002/jso.24316 27264017
8. Lee J, Ahn S, Paik K, Kim HW, Kang J, Kim J, et al. Clinical impact of neoadjuvant treatment in resectable pancreatic cancer: a systematic review and meta-analysis protocol. BMJ. 2016;6(3):1–9
9. Versteijne E, Vogel JA, Besselink MG, Busch ORC, Wilmink JW, Daams JG, et al. Meta‐analysis comparing upfront surgery with neoadjuvant treatment in patients with resectable or borderline resectable pancreatic cancer. The British Journal of Surgery. 2018;105(8):946–958. doi: 10.1002/bjs.10870 29708592
10. Sharma G, Whang EE, Ruan DT, Ito H. Efficacy of neoadjuvant versus adjuvant versus adjuvant therapy for resectable pancreatic adenocarcinoma: a decision analysis. Ann Surg Oncol. 2015;22(suppl 3):1229–37.
11. Xu CP, Xue XJ, Laing N, Xu DG, Liu FJ, Yu XS, et al. Effect of chemoradiotherapy and neoadjuvant chemoradiotherapy in resectable pancreatic cancer: a systematic review and meta-analysis. J Cancer Res Clin Oncol. 2014;140:549–59. doi: 10.1007/s00432-013-1572-4 24370686
12. Andriulli A, Festa V, Botteri E, Valvano MR, Koch M, Bassi C, et al. Neoadjuvant/preoperative gemcitabine for patients with localized pancreatic cancer: a meta-analysis of prospective studies. Ann Surg Oncol. 2012;19:1644–62. doi: 10.1245/s10434-011-2110-8 22012027
13. Petrelli F, Coinu A, Borgnovo K, Cabiddu M, Ghilardi M, Lonati V, et al. FOLFIRINOX-based neoadjuvant therapy in borderline resectable or unresectable pancreatic cancer: a meta-analytical review of published studies. Pancreas. 2015;44(4):515–21. doi: 10.1097/MPA.0000000000000314 25872127
14. de Gus SW, Evans DB, Bliss LA, Eskander MF, Smith JK, Wolff RA, et al. Neoadjuvant therapy versus upfront surgical strategies in resectable pancreatic cancer: a markov decision analysis. Eur J Surg. 2016; 42(10):1552–60.
15. Van Houten JP, White RR, Jackson GP. A decision model of therapy for potentially resectable pancreatic cancer. The Journal of Surgical Research. 2012;174(2):222–230. doi: 10.1016/j.jss.2011.08.022 22079845
16. Velikova M, Scheltinga JT, Lucas PJF, Spaanderman M. Exploiting causal functional relationships in Bayesian network modeling for personalized healthcare. Int Journal of Approximate Reasoning. 2014: 55:59–73
17. School R, Kaplan D, Denissen J, Asendorpf JB, Neyer FJ, van Aken MAG. A Gentle introduction to Bayesian analysis: applications to development research. Child Development. 2013: 85:3:842–860. doi: 10.1111/cdev.12169 24116396
18. Lewis RS Jr, Vollmer CM Jr. Risk scores and prognostic models in surgery: pancreas resection as a paradigm. Curr Probl Surg. 2012. 49:12:731–95. doi: 10.1067/j.cpsurg.2012.08.002 23131540
19. Verduijn M, Peek N, Rosseel PM, de Jonge E, de Mol BAJM. Prognostic Bayesian networks I: rationale, learning procedure, and clinical use. Journal of Biomedical Informatics. 2007:609–618. doi: 10.1016/j.jbi.2007.07.003 17704008
20. Fenton N, Neil M. Risk assessment and decision analysis with Bayesian networks. 2nd ed. London: CRC Press;2019.
21. Stajduhar I, Dalbelo-Basic B. Learning Bayesian networks from survival data using weighting censored instances. Journal of Biomedical Informatics. 2010: 43:613–622. doi: 10.1016/j.jbi.2010.03.005 20332035
22. Lucas PJF, van der Gaag, Abu-Hanna A. Bayesian networks in biomedicine and health-care. Artificial Intelligence in Medicine. 2004: 201–214. doi: 10.1016/j.artmed.2003.11.001 15081072
23. Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. Open Med. 2009; 3:e123–e130. 21603045
24. Guyatt GH, Oxman AD, Visit G, Kunz R, Brozek J, Alonso-Coello P et al. GRADE guidelines: 4. Rating the qyality of evidence-study limitations (risk of bias). Journal of Clinical Epidemiology. 2011; 64:407–415. doi: 10.1016/j.jclinepi.2010.07.017 21247734
25. Zhu X., Zhou X., Zhang Y., Sun X., Liu H., & Zhang Y. Reporting and methodological quality of survival analysis in articles published in Chinese oncology journals. Medicine. 2017: 96(50), e9204. doi: 10.1097/MD.0000000000009204 29390340
26. Zhao D, Weng C. Combining PubMed knowledge and EHR data to develop a weighted Bayesian network for pancreatic cancer prediction. Journal of Biomedical Informatics. 2011: 44:5:859–868. doi: 10.1016/j.jbi.2011.05.004 21642013
27. Agenarisk. Bayesian network software for risk analysis and decision analysis. https://www.agenarisk.com.
28. Kanda M, Fujii T, Takami H, Suenaga M, Inokawa Y, Yamada S, et al. The combination of the serum carbohydrate antigen 19–9 and carcinoembryonic antigen is a simple and accurate predictor of mortality in pancreatic cancer patients. Surg Today. 2014: 44:1692–1701 doi: 10.1007/s00595-013-0752-9 24114022
29. Hsu CC, Wolfgang CL, Laheru DA, Pawlik TM, Swartz MJ, Winter JM, et al. Early mortality risk score: identification of poor outcomes following upfront surgery for resectable pancreatic cancer. J Gastrointest Surg. 2012; 16:4:753–761 doi: 10.1007/s11605-011-1811-4 22311282
30. Shen Y-N, Bai X-L, Gang J, Zhang Q, Lu JH, Quin RY, et al. A preoperative nomogram predicts prognosis of up front resectable patients with pancreatic with pancreatic head cancer and suspected venous invasion. HPB. 2018: 1–10.
31. Balzano G, Dugnani E, Crippa S, Scavini M, Pasquale V, Aleotti V, et al. A preoperative score to predict early death after pancreatic cancer resection. Digestive and Liver Disease. 2017: 49:1050–1056. doi: 10.1016/j.dld.2017.06.012 28734776
32. Walczak S & Velanovich V. An evaluation of Artificial Neural Networks in predicting pancreatic cancer survival. J Gastrointest Surg. 2017; 21:1606–1612 doi: 10.1007/s11605-017-3518-7 28776157
33. Smith BJ & Mezhir JJ. An interactive Bayesian model for prediction of lymph node ratio and survival in pancreatic cancer patients. J Am Med Inform Assoc. 2014;21:e203–e211 doi: 10.1136/amiajnl-2013-002171 24444460
34. Tonelli MR, Shirts BH. Knowledge for precision medicine mechanistic reasoning and methodological pluralism. JAMA. 2017;318:17:1649–1650. doi: 10.1001/jama.2017.11914 29052713
35. MacConaill LE, Lindeman NI, Rollins BJ. Brave-ish new world—what’s needed to make precision oncology a practical reality. JAMAOncol. 2015:1:7:879–880.
36. Dzau VJ, Ginsburg GS. Realizing the full potential of precision medicine in health and health care. JAMA.2016:316:16:1659–1660. doi: 10.1001/jama.2016.14117 27669484
37. Obermeyer ZMD, Lee TH. Lost in thought—the limits of the human mind and the future of medicine. N Engl J Med. 2017:377:1209–1211. doi: 10.1056/NEJMp1705348 28953443
Článek vyšel v časopise
PLOS One
2019 Číslo 9
- Proč jsou nemocnice nepřítelem spánku? A jak to změnit?
- Dlouhodobá ketodieta může poškozovat naše orgány
- „Jednohubky“ z klinického výzkumu – 2024/42
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- MUDr. Jana Horáková: Remise již dosahujeme u více než 80 % pacientů s myastenií