Skip to main content

Advertisement

SpringerLink
Log in

Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence

  • Original Article
  • Published:
Techniques in Coloproctology Aims and scope Submit manuscript

Abstract

Background

Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) software and develop a new predictive model for high-risk stage II CC.

Methods

The study included 259 consecutive pathological stage II CC patients undergoing curative resection at our institution between January 2000 and December 2016. Prediction One software with five-fold cross-validation was used to create a predictive model and receiver operating characteristic (ROC) curve. Predictive accuracy of AI was evaluated using the area under the ROC curve (AUC). We also evaluated the importance of variables (IOV) using a method based on permutation feature importance (IOV > 0.01 defined high-risk factors) to evaluate disease-free survival (DFS).

Results

The median observation period was 6.1 (range = 0.3–15.8) years. Thirty-seven patients had recurrence (14.3%); the AUC of the AI model was 0.775. Preoperative carcinoembryonic antigen > 5.0 ng/mL (IOV = 0.047), venous invasion (IOV = 0.014), and obstruction (IOV = 0.012) were high-risk factors contributing to cancer recurrence. Patients with 2–3 high-risk factors had lower 5-year DFS than those with 0–1 factor (87.4% vs 62.7%, p < 0.001).

Conclusions

We developed a new predictive model that could predict recurrent high-risk stage II CC with high probability using auto-AI Prediction One software. Patients with ≥ 2 of the aforementioned factors are considered to have high risks for recurrent stage II CC and may benefit from adjuvant chemotherapy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394–424

    Article  Google Scholar 

  2. O’Connell JB, Maggard MA, Ko CY (2004) Colon cancer survival rates with the new American Joint Committee on Cancer sixth edition staging. J Natl Cancer Inst 96(19):1420–1425

  3. Hashiguchi Y, Muro K, Saito Y, Ito Y, Ajioka Y, Hamaguchi T et al Japanese Society for Cancer of the C, Rectum (2020) Japanese Society for Cancer of the Colon and Rectum (JSCCR) guidelines 2019 for the treatment of colorectal cancer. Int J Clin Oncol 25(1):1–42

  4. (1995) Efficacy of adjuvant fluorouracil and folinic acid in colon cancer. International Multicentre Pooled Analysis of Colon Cancer Trials (IMPACT) investigators. Lancet 345(8955):939–944

  5. Benson AB, Schrag D, Somerfield MR, Cohen AM, Figueredo AT, Flynn PJ et al (2004) American Society of Clinical Oncology recommendations on adjuvant chemotherapy for stage II colon cancer. J Clin Oncol 22(16):3408–3419

    Article  Google Scholar 

  6. Matsuda C, Ishiguro M, Teramukai S, Kajiwara Y, Fujii S, Kinugasa Y et al SACURA Study Group (2018) A randomised-controlled trial of 1-year adjuvant chemotherapy with oral tegafur-uracil versus surgery alone in stage II colon cancer: SACURA trial. Eur J Cancer 96:54–63

  7. Zhao M, Tang Y, Kim H, Hasegawa K (2018) Machine learning with k-means dimensional reduction for predicting survival outcomes in patients with breast cancer. Cancer Inform 17:1176935118810215

    Article  Google Scholar 

  8. Mazaki J, Katsumata K, Ohno Y, Udo R, Tago T, Kasahara K et al (2021) A novel predictive model for anastomotic leakage in colorectal cancer using auto-artificial intelligence. Anticancer Res 41(11):5821–5825

    Article  CAS  Google Scholar 

  9. Mazaki J, Katsumata K, Ohno Y, Udo R, Tago T, Kasahara K et al (2021) A novel prediction model for colon cancer recurrence using auto-artificial intelligence. Anticancer Res 41(9):4629–4636

    Article  Google Scholar 

  10. Weiser MR (2018) AJCC 8th edition: colorectal cancer. Ann Surg Oncol 25:1454–1455

    Article  Google Scholar 

  11. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  CAS  Google Scholar 

  12. Scarabelli A, Zilocchi M, Casiraghi E, Fasani P, Plensich GG, Esposito AA et al (2021) Abdominal computed tomography imaging findings in hospitalized COVID-19 patients: a year-long experience and associations revealed by explainable artificial intelligence. J Imaging 7(12):258

    Article  Google Scholar 

  13. Labianca R, Nordlinger B, Beretta GD, Mosconi S, Mandalà M, Cervantes A et al ESMO Guidelines Working Group (2013) Early colon cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 24(suppl 6):vi64–vi72

  14. Van Cutsem E, Labianca R, Bodoky G, Barone C, Aranda E, Nordlinger B et al (2009) Randomized phase III trial comparing biweekly infusional fluorouracil/leucovorin alone or with irinotecan in the adjuvant treatment of stage III colon cancer: PETACC-3. J Clin Oncol 27(19):3117–3125

    Article  Google Scholar 

  15. Tournigand C, André T, Bonnetain F, Chibaudel B, Lledo G, Hickish T et al (2012) Adjuvant therapy with fluorouracil and oxaliplatin in stage II and elderly patients (between ages 70 and 75 years) with colon cancer: subgroup analyses of the multicenter international study of oxaliplatin, fluorouracil, and leucovorin in the adjuvant treatment of colon cancer trial. J Clin Oncol 30(27):3353–3360

    Article  CAS  Google Scholar 

  16. Sabbagh C, Manceau G, Mege D, Abdalla S, Voron T, Bridoux V et al AFC (French Surgical Association) Working Group (2022) Is adjuvant chemotherapy necessary for obstructing stage II colon cancer? Results from a propensity score analysis of the French Surgical Association Database. Ann Surg 275(1):149–156

  17. O’Connell MJ, Mailliard JA, Kahn MJ, Macdonald JS, Haller DG, Mayer RJ et al (1997) Controlled trial of fluorouracil and low-dose leucovorin given for 6 months as postoperative adjuvant therapy for colon cancer. J Clin Oncol 15(1):246–250

    Article  Google Scholar 

  18. Yamazaki K, Yamanaka T, Shiozawa M, Manaka D, Kotaka M, Gamoh M et al (2021) Oxaliplatin-based adjuvant chemotherapy duration (3 versus 6 months) for high-risk stage II colon cancer: The randomized phase III ACHIEVE-2 trial. Ann Oncol 32(1):77–84

    Article  CAS  Google Scholar 

  19. Ishiguro M, Mochizuki H, Tomita N, Shimada Y, Takahashi K, Kotake K et al (2012) Study protocol of the SACURA trial: a randomized phase III trial of efficacy and safety of UFT as adjuvant chemotherapy for stage II colon cancer. BMC Cancer 12:281

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank Editage (https://www.editage.jp) for English language editing.

Funding

This study has not received specific funding from any funding agency, public, for-profit or non-profit.

Author information

Authors and Affiliations

  1. Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishi-Shinjuku, Tokyo, 160-0023, Japan

    Tetsuo Ishizaki, Junichi Mazaki, Masanobu Enomoto, Ryutaro Udo, Tomoya Tago, Kenta Kasahara & Yuichi Nagakawa

Authors
  1. Tetsuo Ishizaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junichi Mazaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masanobu Enomoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ryutaro Udo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tomoya Tago
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kenta Kasahara
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuichi Nagakawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

TI, JM and YN wrote the main manuscript text. ME, RU prepared Figs. 1, 2, 3. TT, KK created Tables 1, 2, 3. All authors reviewed the manuscript.

Corresponding author

Correspondence to Tetsuo Ishizaki.

Ethics declarations

Conflicts of interest

The authors have no conflicts of interest to declare regarding this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ishizaki, T., Mazaki, J., Enomoto, M. et al. Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence. Tech Coloproctol 27, 183–188 (2023). https://doi.org/10.1007/s10151-022-02685-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10151-022-02685-y

Keywords

Search

Navigation