Machine learning based models for predicting presentation delay risk among gastric cancer patients
ObjectivePresentation delay of cancer patients prevents the patient from timely diagnosis and treatment leading to poor prognosis. Predicting the risk of presentation delay is crucial to improve the treatment outcomes. This study aimed to develop and validate prediction models of presentation delay...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1503047/full |
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| author | Huali Zhou Huali Zhou Qiong Gu Rong Bao Liping Qiu Yuhan Zhang Fang Wang Wenlian Liu Lingling Wu Li Li Yihua Ren Lei Qiu Qian Wang Gaomin Zhang Xiaoqing Qiao Wenjie Yuan Juan Ren Min Luo Rong Huang Qing Yang Qing Yang |
| author_facet | Huali Zhou Huali Zhou Qiong Gu Rong Bao Liping Qiu Yuhan Zhang Fang Wang Wenlian Liu Lingling Wu Li Li Yihua Ren Lei Qiu Qian Wang Gaomin Zhang Xiaoqing Qiao Wenjie Yuan Juan Ren Min Luo Rong Huang Qing Yang Qing Yang |
| author_sort | Huali Zhou |
| collection | DOAJ |
| description | ObjectivePresentation delay of cancer patients prevents the patient from timely diagnosis and treatment leading to poor prognosis. Predicting the risk of presentation delay is crucial to improve the treatment outcomes. This study aimed to develop and validate prediction models of presentation delay risk in gastric cancer patients by using various machine learning models.Methods875 cases of gastric cancer patients admitted to a tertiary oncology hospital from July 2023 to June 2024 were used as derivation cohort, 200 cases of gastric cancer patients admitted to other 4 tertiary hospital were used as external validation cohort. After collecting the data, statistical analysis was performed to identify discriminative variables for the prediction of presentation delay and 13 statistically significant variables are selected to develop machine learning models. The derivation cohort was randomly assigned to the training and internal validation set by the ratio of 7:3. Prediction models were developed based on six machine learning algorithms, which are logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosted trees (GBDT), extremely gradient boosting (XGBoost) and muti-layer perceptron (MLP). The discrimination and calibration of each model were assessed based on various metrics including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-Score and area under curve (AUC), calibration curves and Brier scores. The best model was selected based on comparing of various metrics. Based on the selected best model, the impact of features to the prediction result was analyzed with the permutation feature importance method.ResultsThe incidence of presentation delay for gastric cancer patients was 39.3%. The developed models achieved performance metrics as AUC (0.893-0.925), accuracy (0.817-0.847), sensitivity (0.857-0.905), specificity (0.783-0.854), PPV (0.728-0.798), NPV (0.897-0.927), F1 score (0.791-0.826) and Brier score (0.107-0.138) in internal validation set, which indicated good discrimination and calibration for the prediction of presentation delay in gastric cancer patients. Among all models, RF based model was selected as the best one as it achieved good discrimination and calibration performance on both of internal and external validation set. Feature ranking results indicated that both of subjective and objective factors have significant impact on the occurrence of presentation delay in gastric cancer patients.ConclusionThis study demonstrated that the RF based model has favorable performance for the prediction of presentation delay in gastric cancer patients. It can help medical staffs to screen out high-risk gastric cancer patients for presentation delay, and to take appropriate and specific interventions to reduce the risk of presentation delay. |
| format | Article |
| id | doaj-art-14dd2b36c91c4bf3acbd5a3de0a063a6 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-14dd2b36c91c4bf3acbd5a3de0a063a62025-01-13T05:10:47ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011410.3389/fonc.2024.15030471503047Machine learning based models for predicting presentation delay risk among gastric cancer patientsHuali Zhou0Huali Zhou1Qiong Gu2Rong Bao3Liping Qiu4Yuhan Zhang5Fang Wang6Wenlian Liu7Lingling Wu8Li Li9Yihua Ren10Lei Qiu11Qian Wang12Gaomin Zhang13Xiaoqing Qiao14Wenjie Yuan15Juan Ren16Min Luo17Rong Huang18Qing Yang19Qing Yang20School of Nursing, Chengdu Medical College, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of General Surgery, Fourth People’s Hospital of Zigong City, Zigong, ChinaGastroenterology, Chengdu Seventh People’s Hospital, Chengdu, ChinaDepartment of General Surgery, Meishan Hospital of Traditional Chinese Medicine, Affiliated Meishan Hospital of Chengdu University of Traditional Chinese Medicine, Meishan, ChinaSchool of Nursing, Chuanbei Medical College, Nanchong, ChinaSchool of Nursing, Chengdu Medical College, Chengdu, ChinaNursing Department, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, ChinaObjectivePresentation delay of cancer patients prevents the patient from timely diagnosis and treatment leading to poor prognosis. Predicting the risk of presentation delay is crucial to improve the treatment outcomes. This study aimed to develop and validate prediction models of presentation delay risk in gastric cancer patients by using various machine learning models.Methods875 cases of gastric cancer patients admitted to a tertiary oncology hospital from July 2023 to June 2024 were used as derivation cohort, 200 cases of gastric cancer patients admitted to other 4 tertiary hospital were used as external validation cohort. After collecting the data, statistical analysis was performed to identify discriminative variables for the prediction of presentation delay and 13 statistically significant variables are selected to develop machine learning models. The derivation cohort was randomly assigned to the training and internal validation set by the ratio of 7:3. Prediction models were developed based on six machine learning algorithms, which are logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosted trees (GBDT), extremely gradient boosting (XGBoost) and muti-layer perceptron (MLP). The discrimination and calibration of each model were assessed based on various metrics including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-Score and area under curve (AUC), calibration curves and Brier scores. The best model was selected based on comparing of various metrics. Based on the selected best model, the impact of features to the prediction result was analyzed with the permutation feature importance method.ResultsThe incidence of presentation delay for gastric cancer patients was 39.3%. The developed models achieved performance metrics as AUC (0.893-0.925), accuracy (0.817-0.847), sensitivity (0.857-0.905), specificity (0.783-0.854), PPV (0.728-0.798), NPV (0.897-0.927), F1 score (0.791-0.826) and Brier score (0.107-0.138) in internal validation set, which indicated good discrimination and calibration for the prediction of presentation delay in gastric cancer patients. Among all models, RF based model was selected as the best one as it achieved good discrimination and calibration performance on both of internal and external validation set. Feature ranking results indicated that both of subjective and objective factors have significant impact on the occurrence of presentation delay in gastric cancer patients.ConclusionThis study demonstrated that the RF based model has favorable performance for the prediction of presentation delay in gastric cancer patients. It can help medical staffs to screen out high-risk gastric cancer patients for presentation delay, and to take appropriate and specific interventions to reduce the risk of presentation delay.https://www.frontiersin.org/articles/10.3389/fonc.2024.1503047/fullgastric cancerpresentation delayrisk predictionmachine learningprediction model |
| spellingShingle | Huali Zhou Huali Zhou Qiong Gu Rong Bao Liping Qiu Yuhan Zhang Fang Wang Wenlian Liu Lingling Wu Li Li Yihua Ren Lei Qiu Qian Wang Gaomin Zhang Xiaoqing Qiao Wenjie Yuan Juan Ren Min Luo Rong Huang Qing Yang Qing Yang Machine learning based models for predicting presentation delay risk among gastric cancer patients Frontiers in Oncology gastric cancer presentation delay risk prediction machine learning prediction model |
| title | Machine learning based models for predicting presentation delay risk among gastric cancer patients |
| title_full | Machine learning based models for predicting presentation delay risk among gastric cancer patients |
| title_fullStr | Machine learning based models for predicting presentation delay risk among gastric cancer patients |
| title_full_unstemmed | Machine learning based models for predicting presentation delay risk among gastric cancer patients |
| title_short | Machine learning based models for predicting presentation delay risk among gastric cancer patients |
| title_sort | machine learning based models for predicting presentation delay risk among gastric cancer patients |
| topic | gastric cancer presentation delay risk prediction machine learning prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1503047/full |
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