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Cancer Survival Analysis

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Related Experiment Video

Updated: Dec 6, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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Using machine learning to create prognostic systems for endometrial cancer.

Aaron M Praiss¹, Yongmei Huang², Caryn M St Clair³

¹Columbia University, Vagelos College of Physicians and Surgeons, United States of America; NewYork-Presbyterian Hospital, United States of America.

Gynecologic Oncology

|October 6, 2020

Summary

This summary is machine-generated.

A new machine learning algorithm, EACCD, improves endometrial cancer prognostication. This precision system uses TNM stage, grade, and age to predict survival more accurately for patients.

Keywords:

Endometrial cancer Hysterectomy Machine learning Staging Uterine cancer

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Area of Science:

Oncology
Machine Learning
Biostatistics

Background:

Endometrial cancer prognostication traditionally relies on established staging systems.
There is a need for more precise predictive models integrating multiple clinical factors.

Purpose of the Study:

To develop a precision prognostication system for endometrial cancer using a novel machine learning algorithm.
To evaluate the performance of this system compared to existing methods.

Main Methods:

Applied the Ensemble Algorithm for Clustering Cancer Data (EACCD) unsupervised machine learning algorithm to endometrioid endometrial cancer patient data (2004-2015).
Developed prognostic groups based on TNM stage, grade, and age, using concordance index (C-index) for group creation.
Utilized Kaplan-Meier survival analysis to compare EACCD-based groups with AJCC groups.

Main Results:

Identified 46,773 women; the EACCD algorithm generated eleven prognostic groups with a C-index of 0.8380.
Five-year survival rates varied from 37.9% to 99.8% across the eleven groups.
A modified eight-group system showed a C-index of 0.8313, with distinct survival rates for each group.

Conclusions:

The novel machine learning algorithm offers improved prognostic prediction for endometrial cancer patients.
Machine learning facilitates the integration of diverse factors for precise prognostication in endometrial cancer.