Machine Learning-Based Model for the Prognosis of Postoperative Gastric Cancer

Liu, Donghui; Wang, Xuyao; Long, Li; Jiang, Qingxin; Li, Xiaoxue; Liu, Meilin; Wang, Wenxin; Shi, Enhong; Zhang, Chenyao; Wang, Yinghui; Zhang, Yan; Wang, Liru

doi:10.2147/cmar.s342352

Cited by 24 publications

(10 citation statements)

References 33 publications

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“…And in this study, the best C-index of DeepSurv model reached 0.821. Liu et al., on the other hand, employed a machine learning algorithm to develop a prognostic model for postoperative gastric cancer with an AUC value of up to 0.8; however, their evaluation was solely based on the AUC value ( 42 ). Models were evaluated comprehensively, including C-index, AUC, IDI, NRI and DCA.…”

Section: Discussionmentioning

confidence: 99%

Deep learning model for predicting postoperative survival of patients with gastric cancer

Zeng,

Song,

et al. 2024

Front. Oncol.

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BackgroundPrognostic prediction for surgical treatment of gastric cancer remains valuable in clinical practice. This study aimed to develop survival models for postoperative gastric cancer patients.MethodsEleven thousand seventy-five patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and 122 patients from the Chinese database were used for external validation. The training cohort was created to create three separate models, including Cox regression, RSF, and DeepSurv, using data from the SEER database split into training and test cohorts with a 7:3 ratio. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The new risk stratification based on the best model will be compared with the AJCC stage on the test and Chinese cohorts using decision curve analysis (DCA), the net reclassification index (NRI), and integrated discrimination improvement (IDI).ResultsIt was discovered that the DeepSurv model predicted postoperative gastric cancer patients’ overall survival (OS) with a c-index of 0.787; the area under the curve reached 0.781, 0.798, 0.868 at 1-, 3- and 5- years, respectively; the Brier score was below 0.25 at different time points; showing an advantage over the Cox and RSF models. The results are also validated in the China cohort. The calibration plots demonstrated good agreement between the DeepSurv model’s forecast and actual results. The NRI values (test cohort: 0.399, 0.288, 0.267 for 1-, 3- and 5-year OS prediction; China cohort:0.399, 0.288 for 1- and 3-year OS prediction) and IDI (test cohort: 0.188, 0.169, 0.157 for 1-, 3- and 5-year OS prediction; China cohort: 0.189, 0.169 for 1- and 3-year OS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.05). DCA showed that the risk score stratification was clinically useful and had better discriminative ability than the AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of patients with postoperative gastric cancer.ConclusionIn this study, a high-performance prediction model for the postoperative prognosis of gastric cancer was developed using DeepSurv, which offers essential benefits for risk stratification and prognosis prediction for each patient.

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Section: Discussionmentioning

confidence: 99%

Deep learning model for predicting postoperative survival of patients with gastric cancer

Zeng,

Song,

et al. 2024

Front. Oncol.

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“…This study included 12 clinicopathological variables from 17,690 postoperative gastric cancer patients. The Lasso regression-based model achieved an AUC of approximately 0.8 in both the internal and external test sets [42]. Chen Y et al developed ML models for predicting major pathological responses to neoadjuvant treatment in patients with advanced gastric cancer.…”

Section: Gastric Cancermentioning

confidence: 99%

Application of Machine Learning Based on Structured Medical Data in Gastroenterology

Kim,

Gong,

Bang

2023

Biomimetics

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The era of big data has led to the necessity of artificial intelligence models to effectively handle the vast amount of clinical data available. These data have become indispensable resources for machine learning. Among the artificial intelligence models, deep learning has gained prominence and is widely used for analyzing unstructured data. Despite the recent advancement in deep learning, traditional machine learning models still hold significant potential for enhancing healthcare efficiency, especially for structured data. In the field of medicine, machine learning models have been applied to predict diagnoses and prognoses for various diseases. However, the adoption of machine learning models in gastroenterology has been relatively limited compared to traditional statistical models or deep learning approaches. This narrative review provides an overview of the current status of machine learning adoption in gastroenterology and discusses future directions. Additionally, it briefly summarizes recent advances in large language models.

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“…Individuals with low-risk gastric cancer should also be monitored to minimize the likelihood of advancing to high-risk stages. Therefore, preventing risk factors that contribute to the formation and development of gastric cancer should be a priority in healthcare system programs [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors

2023

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Background Gastric cancer is one of the leading causes of death worldwide. Screening for gastric cancer greatly relies on endoscopy and pathology biopsy, which are invasive and pose financial burdens. Thus, the prevention of the disease by modifying lifestyle-related behaviors and dietary habits or even the prevention of risk factor formation is of great importance. This study aimed to construct an inexpensive, non-invasive, fast, and high-precision diagnostic model using six machine learning (ML) algorithms to classify patients at high or low risk of developing gastric cancer by analyzing individual lifestyle factors. Methods This retrospective study used the data of 2029 individuals from the gastric cancer database of Ayatollah Taleghani Hospital in Abadan City, Iran. The data were randomly separated into training and test sets (ratio 0.7:0.3). Six ML methods, including multilayer perceptron (MLP), support vector machine (SVM) (linear kernel), SVM (RBF kernel), k-nearest neighbors (KNN) (K = 1, 3, 7, 9), random forest (RF), and eXtreme Gradient Boosting (XGBoost), were trained to construct prognostic models before and after performing the relief feature selection method. Finally, to evaluate the models’ performance, the metrics derived from the confusion matrix were calculated via a test split and cross-validation. Results This study found 11 important influence factors for the risk of gastric cancer, such as Helicobacter pylori infection, high salt intake, and chronic atrophic gastritis, among other factors. Comparisons indicated that the XGBoost had the best performance for the risk prediction of gastric cancer. Conclusions The results suggest that based on simple baseline patient data, the ML techniques have the potential to start the prescreening of gastric cancer and identify high-risk individuals who should proceed with invasive examinations. Our model could also considerably lessen the number of cases that need endoscopic surveillance. Future studies are required to validate the efficacy of the models in a larger and multicenter population.

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Machine Learning-Based Model for the Prognosis of Postoperative Gastric Cancer

Cited by 24 publications

References 33 publications

Deep learning model for predicting postoperative survival of patients with gastric cancer

Deep learning model for predicting postoperative survival of patients with gastric cancer

Application of Machine Learning Based on Structured Medical Data in Gastroenterology

Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors

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