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

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 screen...

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Autores principales:	Byung Wook Kim, Min Chul Choi, Min Kyu Kim, Jeong-Won Lee, Min Tae Kim, Joseph J. Noh, Hyun Park, Sang Geun Jung, Won Duk Joo, Seung Hun Song, Chan Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning immune checkpoint inhibitors Lynch syndrome recurrence Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/9f28b2f6609946aca0cf30a648f4b40c
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spelling	oai:doaj.org-article:9f28b2f6609946aca0cf30a648f4b40c2021-11-25T17:02:28ZMachine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers10.3390/cancers132256702072-6694https://doaj.org/article/9f28b2f6609946aca0cf30a648f4b40c2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/22/5670https://doaj.org/toc/2072-6694To 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 [<i>MLH1, MSH2, MSH6,</i> and <i>PMS2</i>], 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.Byung Wook KimMin Chul ChoiMin Kyu KimJeong-Won LeeMin Tae KimJoseph J. NohHyun ParkSang Geun JungWon Duk JooSeung Hun SongChan LeeMDPI AGarticlemachine learningimmune checkpoint inhibitorsLynch syndromerecurrenceNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5670, p 5670 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning immune checkpoint inhibitors Lynch syndrome recurrence Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	machine learning immune checkpoint inhibitors Lynch syndrome recurrence Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Byung Wook Kim Min Chul Choi Min Kyu Kim Jeong-Won Lee Min Tae Kim Joseph J. Noh Hyun Park Sang Geun Jung Won Duk Joo Seung Hun Song Chan Lee Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers
description	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 [<i>MLH1, MSH2, MSH6,</i> and <i>PMS2</i>], 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	article
author	Byung Wook Kim Min Chul Choi Min Kyu Kim Jeong-Won Lee Min Tae Kim Joseph J. Noh Hyun Park Sang Geun Jung Won Duk Joo Seung Hun Song Chan Lee
author_facet	Byung Wook Kim Min Chul Choi Min Kyu Kim Jeong-Won Lee Min Tae Kim Joseph J. Noh Hyun Park Sang Geun Jung Won Duk Joo Seung Hun Song Chan Lee
author_sort	Byung Wook Kim
title	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_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_sort	machine learning for recurrence prediction of gynecologic cancers using lynch syndrome-related screening markers
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9f28b2f6609946aca0cf30a648f4b40c
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Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers

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