Celebrating the career and contributions of Dr Henry T. Lynch (1928–2019)

Butel-Simoes, Grace; Macrae, Finlay; Spigelman, Allan D.

doi:10.1111/imj.14688

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Thus, in addition to Lynch syndrome, which is associated with the endometrium, the hereditary ovary and breast cancer syndrome, which affects these organs but also a small portion of the ovary, has received considerable attention for a long time Lynch syndrome is a hereditary genome-driven cancer syndrome affecting multiple organs, primarily the bowel and endometrium. This syndrome originates from mismatch repair (MMR) gene mutations as reported by Dr. Henry Lynch [11]. As a result, prior to confirmative tests, several screening factors, such as family history, based on the Amsterdam criteria, in addition to tumor-based tests, such as immunohistochemistry (IHC) or microsatellite instability (MSI), have been proposed and are still used to treat patients around the world.…”

Section: Introductionmentioning

confidence: 99%

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

Kim

Choi

Kim

et al. 2021

Cancers

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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.

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Section: Introductionmentioning

confidence: 99%