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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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Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers.

Byung Wook Kim¹, Min Chul Choi², Min Kyu Kim³

¹Department of Information and Communication Engineering, Changwon National University, Changwon 51140, Korea.

|November 27, 2021

Summary

This summary is machine-generated.

Machine learning models predict gynecologic cancer recurrence in patients receiving immune checkpoint inhibitors (ICI). Models utilize Lynch syndrome markers, improving precision medicine for cancer treatment outcomes.

Keywords:

Lynch syndrome immune checkpoint inhibitors machine learning recurrence

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Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Precision medicine in oncology requires accurate prediction of treatment response.
Immune checkpoint inhibitors (ICI) offer new therapeutic avenues for gynecologic cancers.
Lynch syndrome screening markers are crucial for identifying patients who may benefit from specific therapies.

Purpose of the Study:

To develop and validate machine learning models for predicting gynecologic cancer recurrence.
To identify key clinical and pathological predictors, including Lynch syndrome markers.
To support genome-based precision medicine strategies.

Main Methods:

Retrospective review of patient demographics, clinical data, and pathological results.
Identification of seven predictive characteristics: four MMR IHC markers (MLH1, MSH2, MSH6, PMS2), MSI, age, and tumor size.
Development of predictive models using six machine learning algorithms (LR, SVM, NB, RF, GB, XGBoost).

Main Results:

The random forest (RF) model demonstrated the highest predictive performance.
Achieved AUCs of 0.818 (5-fold CV) and 0.826 (5-fold CV with 10 repetitions).
Identified specific MMR IHC markers, MSI, age, and tumor size as significant predictors.

Conclusions:

Machine learning models effectively predict gynecologic cancer recurrence in patients treated with ICI.
Lynch syndrome-related screening markers are valuable predictors in this context.
The study provides a baseline for enhancing precision medicine through predictive modeling.