Predicting metastasis in Gastric cancer patients: machine learning-based approaches

Abstract

Background Gastric cancer (GC), with a 5-year survival rate of less than 40%, is known as the fourth principal reason of cancer-related mortality over the world. The study aimed to perform machine learning (ML) methods in GC patients. Methods The data applied in this study including 733 of GC patients diagnosed at Taleghani hospital. In order to predict metastasis in GC, machine learning approaches, including Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Regression Tree (RT) and Logistic Regression (LR), with 5-fold cross validation were performed. To assess the model performance, precision, sensitivity, specificity and AUC of Receiver operating characteristic (ROC) curve were obtained. Results 262 (36%) experienced metastasis among 733 patients with GC. The RF of ML-based models, with size of tomur and age as two essential variables, is considered as efficient model, because of higher specificity and AUC (84% and 87%). Also, the sensitivity in SVM model seems to be better (93%). Conclusion According to AUC, sensitivity and specificity, both RF and SVM can be regarded as better ML-based algorithms among six applied ML-based methods.

Development of an Artificial Intelligence Model for the Classification of Gastric Carcinoma Stages Using Pathology Slides

“…Achieving perfect accuracy and precision rates of 100% sets an impressive standard in diagnostic accuracy for distinguishing between stage one and stage two gastric carcinoma [20]. Additionally, the high sensitivity, specificity, and F1-score attained by the model surpass or rival those reported in existing literature, indicating superior discriminatory power and reliability [21]. This study builds upon existing research in several key ways.…”

Machine learning-based classifiers to predict metastasis in colorectal cancer patients

“…Additionally, ML models can automatically handle noise in datasets, non-linearity, complex interactions, large sample sizes, and numerous features. Overall, ML approaches have shown promise in improving treatment outcomes in cancer research (Zhou et al, 2021;Greener et al, 2022;Talebi et al, 2023). ML-based approaches have also been employed to predict metastatic relapse in breast cancer in several studies (Tapak et al, 2019;Nicol et al, 2020).…”

“…ML-based approaches have also been employed to predict metastatic relapse in breast cancer in several studies (Tapak et al, 2019;Nicol et al, 2020). Furthermore, numerous epidemiological studies have investigated specific hypotheses related to CRC risk factors (Talebi et al, 2019(Talebi et al, , 2020Borumandnia et al, 2021). These studies encompass survival analysis techniques such as Cox proportional hazards, time-dependent Cox, cure models, and other types of survival analysis using clinical datasets.…”

“…These studies encompass survival analysis techniques such as Cox proportional hazards, time-dependent Cox, cure models, and other types of survival analysis using clinical datasets. Moreover, other investigations have utilized ML models such as decision tree (DT), support vector machine (SVM), neural network (NN), and Naive Bayes (NB) methods (Talebi et al, 2023).…”