Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Nov;126(6):1096-1103.
doi: 10.1002/jso.27008. Epub 2022 Jul 12.

Predicting recurrence and recurrence-free survival in high-grade endometrial cancer using machine learning

Sabrina Piedimonte  1 , Tomer Feigenberg  2 , Erik Drysdale  3 , Janice Kwon  4 , Walter H Gotlieb  5 , Beatrice Cormier  6 , Marie Plante  7 , Susie Lau  5 , Limor Helpman  8 , Marie-Claude Renaud  7 , Taymaa May  9 , Danielle Vicus  10
Affiliations

Predicting recurrence and recurrence-free survival in high-grade endometrial cancer using machine learning

Sabrina Piedimonte et al. J Surg Oncol. 2022 Nov.

Abstract

Objective: To develop machine-learning models to predict recurrence and time-to-recurrence in high-grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.

Methods: Data were retrospectively collected across eight Canadian centers including 1237 patients. Four models were trained to predict recurrence: random forests, boosted trees, and two neural networks. Receiver operating characteristic curves were used to select the best model based on the highest area under the curve (AUC). For time to recurrence, we compared random forests and Least Absolute Shrinkage and Selection Operator (LASSO) model to Cox proportional hazards.

Results: The random forest was the best model to predict recurrence in HGEC; the AUCs were 85.2%, 74.1%, and 71.8% in the training, validation, and test sets, respectively. The top five predictors were: stage, uterus height, specimen weight, adjuvant chemotherapy, and preoperative histology. Performance increased to 77% and 80% when stratified by Stage III and IV, respectively. For time to recurrence, there was no difference between the LASSO and Cox proportional hazards models (c-index 71%). The random forest had a c-index of 60.5%.

Conclusions: A bootstrap random forest model may be a more accurate technique to predict recurrence in HGEC using multiple clinicopathologic factors. For time to recurrence, machine-learning methods performed similarly to the Cox proportional hazards model.

Keywords: high-grade endometrial cancer; machine learning; recurrence.

PubMed Disclaimer

References

REFERENCES

    1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021;71:7-33.
    1. Bernardini MQ, Gien LT, Lau S, et al. Treatment related outcomes in high-risk endometrial carcinoma: Canadian high risk endometrial cancer consortium (CHREC). Gynecol Oncol. 2016;141:148-154.
    1. The difference between artificial intelligence, machine learning and deep learning. 2019. Accessed May 28, 2021. https://datacatchup.com/artificial-intelligence-machine-learning-and-dee...
    1. Keys HM, Roberts JA, Brunetto VL, et al. A phase III trial of surgery with or without adjunctive external pelvic radiation therapy in intermediate risk endometrial adenocarcinoma: a Gynecologic Oncology Group study. Gynecol Oncol. 2004;92:744-751.
    1. Jegatheeswaran K, Cormier B, Dube S, et al. Evaluating the diagnostic performance of preoperative endometrial biopsies in patients diagnosed with high grade endometrial cancer: a study of the Society of Gynecologic Oncology (GOC) Community of Practice (CoP). Gynecol Oncol. 2020;159:52-57.

LinkOut - more resources