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Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer

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

Abstract

Background

The ability to provide accurate prognostic and predictive information to patients is becoming increasingly important as clinicians enter an era of personalized medicine. For a disease as heterogeneous as epithelial ovarian cancer, conventional algorithms become too complex for routine clinical use. This study therefore investigated the potential for an artificial intelligence model to provide this information and compared it with conventional statistical approaches.

Methods

The authors created a database comprising 668 cases of epithelial ovarian cancer during a 10-year period and collected data routinely available in a clinical environment. They also collected survival data for all the patients, then constructed an artificial intelligence model capable of comparing a variety of algorithms and classifiers alongside conventional statistical approaches such as logistic regression.

Results

The model was used to predict overall survival and demonstrated that an artificial neural network (ANN) algorithm was capable of predicting survival with high accuracy (93 %) and an area under the curve (AUC) of 0.74 and that this outperformed logistic regression. The model also was used to predict the outcome of surgery and again showed that ANN could predict outcome (complete/optimal cytoreduction vs. suboptimal cytoreduction) with 77 % accuracy and an AUC of 0.73.

Conclusions

These data are encouraging and demonstrate that artificial intelligence systems may have a role in providing prognostic and predictive data for patients. The performance of these systems likely will improve with increasing data set size, and this needs further investigation.

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References

  1. ESMO Minimum clinical recommendations for diagnosis, treatment, and follow-up of ovarian cancer. Ann Oncol. 2001;12:1205–7.

  2. Thigpen T, et al. Age as a prognostic factor in ovarian carcinoma. Cancer. 1993;71:606–14.

    Article  CAS  PubMed  Google Scholar 

  3. Benedet J, et al. FIGO staging classifications and clinical practice gudelines in the management of gynecologic cancers. Int J Gynecol Obstet. 2000;70:209–62.

    Article  CAS  Google Scholar 

  4. van Houwelingen J, et al. Predictability of the survival of patients with advanced ovarian cancer. J Clin Oncol. 1989;7:769–73.

    PubMed  Google Scholar 

  5. Makar AP, et al. The prognostic significance of residual disease, FIGO substage, tumor histology, and grade in patients with FIGO stage III ovarian cancer. Gynecol Oncol. 1995;56:175–80.

    Article  CAS  PubMed  Google Scholar 

  6. Hogdall E. Cancer antigen 125 and prognosis. Curr Opin Obstet Gynecol. 2008;20:4–8.

    Article  PubMed  Google Scholar 

  7. Bristow RE, et al. Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis. J Clin Oncol. 2002;20:1248–59. DOI:10.1200/JCO.20.5.1248.

    Article  PubMed  Google Scholar 

  8. du Bois, A, Harter P. The role of surgery in advanced and recurrent ovarian cancer. Ann Oncol. 2006;17(Suppl 10):x235–40.

    Article  PubMed  Google Scholar 

  9. Perren TJ, Swart AM, Pfisterer J, Ledermann JA, Pujade-Lauraine E, Kristensen G, Carey MS, Beale P, Cervantes A, Kurzeder C, du Bois A, Sehouli J, Kimmig R, Stähle A, Collinson F, Essapen S, Gourley C, Lortholary A, Selle F, Mirza MR, Leminen A, Plante M, Stark D, Qian W, Parmar MK, Oza AM, ICON7 Investigators. A phase 3 trial of bevacizumab in ovarian cancer. N Engl J Med. 2011;365(26):2484–96.

    Article  CAS  PubMed  Google Scholar 

  10. Ledermann JA, Harter P, Gourley C, Friedlander M, Vergote IB, Rustin GJS, et al. Phase II randomized placebo-controlled study of olaparib (AZD2281) in patients with platinum-sensitive relapsed serous ovarian cancer (PSR SOC). J Clin Oncol. 2011;29(Suppl 15):5003.

    Google Scholar 

  11. Sjoquist K, et al. The Role of Hormonal therapy in Gynaecological Cancers – Current Status and Future Directions. Int J Gynecol Cancer. 2011;21:1328–33.

    PubMed  Google Scholar 

  12. Collinson F, et al. Predicting response to bevacizumab in ovarian cancer: a panel of potential biomarkers informing treatment selection. Clin Cancer Res. 2013;19:5227–39.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Mukhopadhyay A. et al. Development of a functional assay for homologous recombination status in primary cultures of epithelial ovarian tumor and correlation with sensitivity to poly(ADP-ribose) polymerase inhibitors. Clin Cancer Res. 2010;16:2344–51.

    Article  CAS  PubMed  Google Scholar 

  14. Elattar A, et al. Androgen receptor expression is a biological marker for androgen sensitivity in high-grade serous epithelial ovarian cancer. Gynecol Oncol. 2012;124:142–47.

    Article  CAS  PubMed  Google Scholar 

  15. Narayanan A, Keedwell E, Olsson B. Artificial intelligence techniques for bioinformatics. Appl. Bioinformat. 2002;1:191–222.

    CAS  Google Scholar 

  16. Wilkinson SJ, et al. Expression of gonadotrophin releasing hormone receptor I is a favorable prognostic factor in epithelial ovarian cancer. Hum Pathol. 2008;39:1197–204.

    Article  CAS  PubMed  Google Scholar 

  17. Dziuda D. Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data. Wiley, New Jersey, 2010.

    Book  Google Scholar 

  18. Cort JW, Kenji M. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 2005;30:79–82.

    Article  Google Scholar 

  19. Bristow RE. Predicting surgical outcome for advanced ovarian cancer, surgical standards of care, and the concept of kaizen. Gynecol Oncol. 2009;112:1–3.

    Article  PubMed  Google Scholar 

  20. Salani R, et al. Limited utility of conventional criteria for predicting unresectable disease in patients with advanced stage epithelial ovarian cancer. Gynecol Oncol. 2008;108:271–5.

    Article  PubMed  Google Scholar 

  21. Jefferson MF, et al. Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinoma. Cancer. 1997;79:1338–42.

    Article  CAS  PubMed  Google Scholar 

  22. CG Gerestein ME, de Jong D, van der Burg MEL, Dykgraaf RHM, Kooi GS, Baalbergen A, Burger CW, Ansink AC. The prediction of progression-free and overall survival in women with an advanced stage of epithelial ovarian carcinoma. BJOG Int J Obstet Gynaecol. 2009;116:372–80.

    Article  CAS  Google Scholar 

  23. Teramukai S, et al. PIEPOC: A new prognostic index for Advanced Epithelial Ovarian Cancer Japan Multinational Trial Organization OC01-01. J Clin Oncol. 2007;25:3302–6. DOI:10.1200/JCO.2007.11.0114.

    Article  PubMed  Google Scholar 

  24. Clark TG, et al. Validation of a new prognostic index for advanced epithelial ovarian cancer: results from its application to a UK-based cohort. J Clin Oncol. 2007;25:5669–70. DOI:10.1200/JCO.2007.14.2976.

    Article  PubMed  Google Scholar 

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Acknowledgments

Support for A. Enshaei was provided by a Grant from the EPSRC and Catherine Cookson Foundation. The data set was created using a Grant from the Northern Cancer Care and Research Society.

Conflict of interest

There are no conflicts of interest.

Author information

Authors and Affiliations

  1. Medical School, Northern Institute for Cancer Research, University of Newcastle Upon Tyne, Newcastle upon Tyne, UK

    A. Enshaei & C. N. Robson

  2. Chair of Gynaecological Oncology, Faculty Institute for Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, St. Mary’s Hospital, Manchester, UK

    R. J. Edmondson

Authors
  1. A. Enshaei
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  2. C. N. Robson
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  3. R. J. Edmondson
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Corresponding author

Correspondence to R. J. Edmondson.

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Enshaei, A., Robson, C.N. & Edmondson, R.J. Artificial Intelligence Systems as Prognostic and Predictive Tools in Ovarian Cancer. Ann Surg Oncol 22, 3970–3975 (2015). https://doi.org/10.1245/s10434-015-4475-6

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  • DOI: https://doi.org/10.1245/s10434-015-4475-6

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