Cargando…
Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers
SIMPLE SUMMARY: Recurrent patients with gynecologic cancer experience a difficult situation when using immune checkpoint inhibitors based on mismatch repair gene immunohistochemistry and microsatellite instability. Six machine learning algorithms were used to create predictive models with seven pros...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8616351/ https://www.ncbi.nlm.nih.gov/pubmed/34830824 http://dx.doi.org/10.3390/cancers13225670 |
_version_ | 1784604326641008640 |
---|---|
author | Kim, Byung Wook Choi, Min Chul Kim, Min Kyu Lee, Jeong-Won Kim, Min Tae Noh, Joseph J. Park, Hyun Jung, Sang Geun Joo, Won Duk Song, Seung Hun Lee, Chan |
author_facet | Kim, Byung Wook Choi, Min Chul Kim, Min Kyu Lee, Jeong-Won Kim, Min Tae Noh, Joseph J. Park, Hyun Jung, Sang Geun Joo, Won Duk Song, Seung Hun Lee, Chan |
author_sort | Kim, Byung Wook |
collection | PubMed |
description | SIMPLE SUMMARY: Recurrent patients with gynecologic cancer experience a difficult situation when using immune checkpoint inhibitors based on mismatch repair gene immunohistochemistry and microsatellite instability. Six machine learning algorithms were used to create predictive models with seven prospective features (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This provides novel and baseline results of patients with recurrent gynecologic cancer using immune checkpoint inhibitors by using machine learning methods based on Lynch syndrome-related screening markers. ABSTRACT: To support the implementation of genome-based precision medicine, we developed machine learning models that predict the recurrence of patients with gynecologic cancer in using immune checkpoint inhibitors (ICI) based on clinical and pathologic characteristics, including Lynch syndrome-related screening markers such as immunohistochemistry (IHC) and microsatellite instability (MSI) tests. To accomplish our goal, we reviewed the patient demographics, clinical data, and pathological results from their medical records. Then we identified seven potential characteristics (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). Following that, predictive models were built based on these variables using six machine learning algorithms: logistic regression (LR), support vector machine (SVM), naive Bayes (NB), random forest (RF), gradient boosting (GB), and extreme gradient boosting (EGB) (XGBoost). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This study provides novel and baseline results about predicting the recurrence of gynecologic cancer in patients using ICI by using machine learning methods based on Lynch syndrome-related screening markers. |
format | Online Article Text |
id | pubmed-8616351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86163512021-11-26 Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers Kim, Byung Wook Choi, Min Chul Kim, Min Kyu Lee, Jeong-Won Kim, Min Tae Noh, Joseph J. Park, Hyun Jung, Sang Geun Joo, Won Duk Song, Seung Hun Lee, Chan Cancers (Basel) Article SIMPLE SUMMARY: Recurrent patients with gynecologic cancer experience a difficult situation when using immune checkpoint inhibitors based on mismatch repair gene immunohistochemistry and microsatellite instability. Six machine learning algorithms were used to create predictive models with seven prospective features (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This provides novel and baseline results of patients with recurrent gynecologic cancer using immune checkpoint inhibitors by using machine learning methods based on Lynch syndrome-related screening markers. ABSTRACT: To support the implementation of genome-based precision medicine, we developed machine learning models that predict the recurrence of patients with gynecologic cancer in using immune checkpoint inhibitors (ICI) based on clinical and pathologic characteristics, including Lynch syndrome-related screening markers such as immunohistochemistry (IHC) and microsatellite instability (MSI) tests. To accomplish our goal, we reviewed the patient demographics, clinical data, and pathological results from their medical records. Then we identified seven potential characteristics (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). Following that, predictive models were built based on these variables using six machine learning algorithms: logistic regression (LR), support vector machine (SVM), naive Bayes (NB), random forest (RF), gradient boosting (GB), and extreme gradient boosting (EGB) (XGBoost). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This study provides novel and baseline results about predicting the recurrence of gynecologic cancer in patients using ICI by using machine learning methods based on Lynch syndrome-related screening markers. MDPI 2021-11-12 /pmc/articles/PMC8616351/ /pubmed/34830824 http://dx.doi.org/10.3390/cancers13225670 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kim, Byung Wook Choi, Min Chul Kim, Min Kyu Lee, Jeong-Won Kim, Min Tae Noh, Joseph J. Park, Hyun Jung, Sang Geun Joo, Won Duk Song, Seung Hun Lee, Chan Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers |
title | Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers |
title_full | Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers |
title_fullStr | Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers |
title_full_unstemmed | Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers |
title_short | Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers |
title_sort | machine learning for recurrence prediction of gynecologic cancers using lynch syndrome-related screening markers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8616351/ https://www.ncbi.nlm.nih.gov/pubmed/34830824 http://dx.doi.org/10.3390/cancers13225670 |
work_keys_str_mv | AT kimbyungwook machinelearningforrecurrencepredictionofgynecologiccancersusinglynchsyndromerelatedscreeningmarkers AT choiminchul machinelearningforrecurrencepredictionofgynecologiccancersusinglynchsyndromerelatedscreeningmarkers AT kimminkyu machinelearningforrecurrencepredictionofgynecologiccancersusinglynchsyndromerelatedscreeningmarkers AT leejeongwon machinelearningforrecurrencepredictionofgynecologiccancersusinglynchsyndromerelatedscreeningmarkers AT kimmintae machinelearningforrecurrencepredictionofgynecologiccancersusinglynchsyndromerelatedscreeningmarkers AT nohjosephj machinelearningforrecurrencepredictionofgynecologiccancersusinglynchsyndromerelatedscreeningmarkers AT parkhyun machinelearningforrecurrencepredictionofgynecologiccancersusinglynchsyndromerelatedscreeningmarkers AT jungsanggeun machinelearningforrecurrencepredictionofgynecologiccancersusinglynchsyndromerelatedscreeningmarkers AT joowonduk machinelearningforrecurrencepredictionofgynecologiccancersusinglynchsyndromerelatedscreeningmarkers AT songseunghun machinelearningforrecurrencepredictionofgynecologiccancersusinglynchsyndromerelatedscreeningmarkers AT leechan machinelearningforrecurrencepredictionofgynecologiccancersusinglynchsyndromerelatedscreeningmarkers |