The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study

Lin, Jiaxi; Yin, Minyue; Liu, Lu; Gao, Jingwen; Yu, Chenyan; Liu, Xiaolin; Xu, Chunfang; Zhu, Jinzhou

doi:10.3390/cancers14194667

Cited by 27 publications

(17 citation statements)

References 31 publications

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“…In our study, the random survival forest did not perform well as Lin J, et al's (C-index= 0.678 vs 0.723) (23), I think this is mainly because the two features in the dataset after one hot The attribution score of all input features in the deep learning model. The x-axis represents the name of the input features, and the y-axis represents value of attribution score for each feature.…”

Section: Discussioncontrasting

confidence: 79%

“…In our study, the random survival forest did not perform well as Lin J, et al’s (C-index= 0.678 vs 0.723) ( 23 ), I think this is mainly because the two features in the dataset after one hot encoding, the Histologic type and Radiation, generate lots of sparse variables, including Radioactive implants, Signet ring cell carcinoma and so on, which eventually cause harm to the formation of different estimator trees. The result that the deep learning model’s C-index is higher than the Cox Proportional hazard model(C-index= 0.834 vs 0.640) meets our expectations, mainly because deep learning could formulate the complex relationships between clinical baseline characteristics and the patient’s hazard rate, which is more accurate than the linear relationship assumption of the Cox proportional hazard model.…”

Section: Discussioncontrasting

confidence: 71%

“…In the current medical practice, there is a lack of consensus regarding the principles of adjuvant therapy for stage-iii NSCLC patients. For instance, According to the latest version of NCCN Guidelines for NSCLC(Version5.2022), one major controversy is inconsistent results among different randomized controlled trials of stage-III NSCLC ( 23 – 26 ). The one reason for the inconsistent results among different randomized controlled trials is the RCT lacks external validity ( 27 ), which means there might be neglected features that are effective for the prognosis.…”

Section: Discussionmentioning

confidence: 99%

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Development and validation of machine learning models to predict survival of patients with resected stage-III NSCLC

Jin

Zhao

Fu³

et al. 2023

Front. Oncol.

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ObjectiveTo compare the performance of three machine learning algorithms with the tumor, node, and metastasis (TNM) staging system in survival prediction and validate the individual adjuvant treatment recommendations plan based on the optimal model.MethodsIn this study, we trained three machine learning madel and validated 3 machine learning survival models-deep learning neural network, random forest and cox proportional hazard model- using the data of patients with stage-al3 NSCLC patients who received resection surgery from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) database from 2012 to 2017,the performance of survival predication from all machine learning models were assessed using a concordance index (c-index) and the averaged c-index is utilized for cross-validation. The optimal model was externally validated in an independent cohort from Shaanxi Provincial People’s Hospital. Then we compare the performance of the optimal model and TNM staging system. Finally, we developed a Cloud-based recommendation system for adjuvant therapy to visualize survival curve of each treatment plan and deployed on the internet.ResultsA total of 4617 patients were included in this study. The deep learning network performed more stably and accurately in predicting stage-iii NSCLC resected patients survival than the random survival forest and Cox proportional hazard model on the internal test dataset (C-index=0.834 vs. 0.678 vs. 0.640) and better than TNM staging system (C-index=0.820 vs. 0.650) in the external validation. The individual patient who follow the reference from recommendation system had superior survival compared to those who did not. The predicted 5-year-survival curve for each adjuvant treatment plan could be accessed in the recommender system via the browser.ConclusionDeep learning model has several advantages over linear model and random forest model in prognostic predication and treatment recommendations. This novel analytical approach may provide accurate predication on individual survival and treatment recommendations for resected Stage-iii NSCLC patients.

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Section: Discussioncontrasting

confidence: 79%

Section: Discussioncontrasting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

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Development and validation of machine learning models to predict survival of patients with resected stage-III NSCLC

Jin

Zhao

Fu³

et al. 2023

Front. Oncol.

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“…There is a slight difference between the values of the three models on the C-index. We reviewed the relevant literature, and the gap between their models`c indices was between 0.005 and 0.024 (23)(24)(25)(26). Therefore, the DeepSurv model is advantageous in predicting the survival rate of gastric adenocarcinoma patients.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study

Zeng

Cao

et al. 2023

Front. Oncol.

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BackgroundThe currently available prediction models, such as the Cox model, were too simplistic to correctly predict the outcome of gastric adenocarcinoma patients. This study aimed to develop and validate survival prediction models for gastric adenocarcinoma patients using the deep learning survival neural network.MethodsA total of 14,177 patients with gastric adenocarcinoma from the Surveillance, Epidemiology, and End Results (SEER) database were included in the study and randomly divided into the training and testing group with a 7:3 ratio. Two algorithms were chosen to build the prediction models, and both algorithms include random survival forest (RSF) and a deep learning based-survival prediction algorithm (DeepSurv). Also, a traditional Cox proportional hazard (CoxPH) model was constructed for comparison. The consistency index (C-index), Brier score, and integrated Brier score (IBS) were used to evaluate the model’s predictive performance. The accuracy of predicting survival at 1, 3, 5, and 10 years was also assessed using receiver operating characteristic curves (ROC), calibration curves, and area under the ROC curve (AUC).ResultsGastric adenocarcinoma patients were randomized into a training group (n = 9923) and a testing group (n = 4254). DeepSurv showed the best performance among the three models (c-index: 0.772, IBS: 0.1421), which was superior to that of the traditional CoxPH model (c-index: 0.755, IBS: 0.1506) and the RSF with 3-year survival prediction model (c-index: 0.766, IBS: 0.1502). The DeepSurv model produced superior accuracy and calibrated survival estimates predicting 1-, 3- 5- and 10-year survival (AUC: 0.825-0.871).ConclusionsA deep learning algorithm was developed to predict more accurate prognostic information for gastric cancer patients. The DeepSurv model has advantages over the CoxPH and RSF models and performs well in discriminative performance and calibration.

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“…Even among patients who undertake surgical interventions, poor prognosis is still high. Of the 10%–20% of pancreatic cancer patients who undergo surgical resection after diagnosis, only about 20% have a 5‐year survival rate 1,2 . Comparatively, patients without surgical resection have far worse outcomes with a 5‐year survival rate of <5% 3 .…”

Section: Introductionmentioning

confidence: 99%

A novel prediction model of the risk of pancreatic cancer among diabetes patients using multiple clinical data and machine learning

Chen,

Phuc,

Nguyen

et al. 2023

Cancer Medicine

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IntroductionPancreatic cancer is associated with poor prognosis. Considering the increased global incidence of diabetes cases and that individuals with diabetes are considered a high‐risk subpopulation for pancreatic cancer, it is critical to detect the risk of pancreatic cancer within populations of person living = with diabetes. This study aimed to develop a novel prediction model for pancreatic cancer risk among patients with diabetes, using = a real‐world database containing clinical features and employing numerous artificial intelligent approach algorithms.MethodsThis retrospective observational study analyzed data on patients with Type 2 diabetes from a multisite Taiwanese EMR database between 2009 and 2019. Predictors were selected in accordance with the literature review and clinical perspectives. The prediction models were constructed using machine learning algorithms such as logistic regression, linear discriminant analysis, gradient boosting machine, and random forest.ResultsThe cohort consisted of 66,384 patients. The Linear Discriminant Analysis (LDA) model generated the highest AUROC of 0.9073, followed by the Voting Ensemble and Gradient Boosting machine models. LDA, the best model, exhibited an accuracy of 84.03%, a sensitivity of 0.8611, and a specificity of 0.8403. The most significant predictors identified for pancreatic cancer risk were glucose, glycated hemoglobin, hyperlipidemia comorbidity, antidiabetic drug use, and lipid‐modifying drug use.ConclusionThis study successfully developed a highly accurate 4‐year risk model for pancreatic cancer in patients with diabetes using real‐world clinical data and multiple machine‐learning algorithms. Potentially, our predictors offer an opportunity to identify pancreatic cancer early and thus increase prevention and invention windows to impact survival in diabetic patients.

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The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study

Cited by 27 publications

References 31 publications

Development and validation of machine learning models to predict survival of patients with resected stage-III NSCLC

Development and validation of machine learning models to predict survival of patients with resected stage-III NSCLC

Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study

A novel prediction model of the risk of pancreatic cancer among diabetes patients using multiple clinical data and machine learning

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