Abstract
Simple SummaryRecurrent 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.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.
Highlights
Gynecologic cancers are among the emerging focuses of genome-based medicine [1].They comprise cancers of the cervix, endometrium, and ovary
The performance of the six classification models—random forest (RF), gradient boosting (GB), XGBoost, naive Bayes (NB), logistic regression (LR), and support vector machine (SVM)—when trained on the dataset is presented in Table 6 for the AUC score
The performance of the LRbased model for ovarian carcinoma prediction [21] was reported to be comparable or superior to that of the RF, SVM, or DNN. This means that the ovarian carcinoma prediction problem is linearly separable, which is uncommon in real-world scenarios. When it came to the relationship between gynecological cancer recurrence and genetic factors, this study found that the tree-based machine learning method outperformed LR
Summary
Gynecologic cancers are among the emerging focuses of genome-based medicine [1].They comprise cancers of the cervix, endometrium, and ovary. In addition to Lynch syndrome, which is associated with the endometrium, the hereditary ovary and breast cancer syndrome, which affects these organs and 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 (1928–2019) [11]. 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)
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