Development and validation of a deep learning model to predict survival of patients with esophageal cancer

Huang, Chen; Dai, Yongmei; Chen, Qianshun; Chen, Hongchao; Lin, Yuanfeng; Wu, Jingyu; Xu, Xunyu; Chen, Xiao

doi:10.3389/fonc.2022.971190

Cited by 9 publications

(8 citation statements)

References 34 publications

(48 reference statements)

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“…developed software to select adjuvant radiotherapy and chemotherapy treatment plan according to the corresponding output hazard rate. Our software has two major points different from their product ( 22 ). One is the output page for oncology specialists.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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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“…For example, She et al [44] found that DeepSurv model was signi cantly better than the traditional AJCC TNM staging system in non-small-cell lung-cancer-speci c survival (C-index = 0.739 vs 0.706). Huang et al [45] demonstrated that the DeepSurv model was superior to the TNM staging model in predicting esophageal CSS with the internal test dataset (C-index = 0.753 vs 0.638) and external validation dataset (C-index = 0.687 vs 0.643). These suggested that the deep learning neural network model could be more widely used as a potential tool to assist clinicians with prognosis prediction.…”

Section: Discussionmentioning

confidence: 99%

Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study

Zhang

Cai

et al. 2022

Preprint

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Background: Gastric cancer (GC) is one of the most common cancers and the main cause of tumor-related death worldwide. Moreover, the incidence of gastric cardiac cancer (GCC) has increased obviously, with the potentially different prognosis from other sites of GC (non-gastric cardiac cancer, NGCC). We will analyze the prognosis between GCC and NGCC, and set up an effective prognostic model based on neural network for GCC. Methods: In the population-based cohort study, we firstly enrolled the clinical features from the Surveillance, Epidemiology and End Results (SEER) data (n=31397) as well as the public Chinese data from different hospitals (n=1049). Then according to the diagnostic time, the SEER data were then divided into two cohorts, the train cohort (patients were diagnosed as GCC in 2010-2014, n=4414) and the test cohort (diagnosed in 2015, n=957). Age, sex, pathology, tumor, node, and metastasis (TNM) stage, tumor size, surgery or not, radiotherapy or not, chemotherapy or not and history of malignancy were chosen as the predictive clinical features. Results: The prognosis of GCC patients in SEER database was worse than that of NGCC patients, while it was not worst in the Chinese data. The total of 5371 patients were used to conduct the development model, following inclusion and exclusion criteria. Neural network-based prognostic predictive model had a satisfactory performance for GCC overall survival (OS) prediction, which owned 0.7431 AUC in the train cohort (95% confidence intervals, CIs, 0.7423-0.7439) and 0.7419 in the test cohort (95% CIs, 0.7411-0.7428). Conclusion: GCC patients indeed have the different survival time compared with NGCC patients. And this neural network-based prognostic predictive tool is a novel and promising software for the outcome of GCC patients.

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“…We think that the smaller the value of loss function in this model, the better the stability. The Adam optimization algorithm was used and obtained current optimal parameters ( 18 ). Supplementary Table 1 shows the hyperparameters of DeepSurv.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma

et al. 2023

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BackgroundDistal cholangiocarcinoma (dCCA), originating from the common bile duct, is greatly associated with a dismal prognosis. A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. In this study, we explored and compared several novel machine learning models that might lead to an improvement in prediction accuracy and treatment options for patients with dCCA.MethodsIn this study, 169 patients with dCCA were recruited and randomly divided into the training cohort (n = 118) and the validation cohort (n = 51), and their medical records were reviewed, including survival outcomes, laboratory values, treatment strategies, pathological results, and demographic information. Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). We measured and compared the performance of models using the receiver operating characteristic (ROC) curve, integrated Brier score (IBS), and concordance index (C-index) following cross-validation. The machine learning model with the best performance was screened out and compared with the TNM Classification using ROC, IBS, and C-index. Finally, patients were stratified based on the model with the best performance to assess whether they benefited from postoperative chemotherapy through the log-rank test.ResultsAmong medical features, five variables, including tumor differentiation, T-stage, lymph node metastasis (LNM), albumin-to-fibrinogen ratio (AFR), and carbohydrate antigen 19-9 (CA19-9), were used to develop machine learning models. In the training cohort and the validation cohort, C-index achieved 0.763 vs. 0.686 (SVM), 0.749 vs. 0.692 (SurvivalTree), 0.747 vs. 0.690 (Coxboost), 0.745 vs. 0.690 (RSF), 0.746 vs. 0.711 (DeepSurv), and 0.724 vs. 0.701 (CoxPH), respectively. The DeepSurv model (0.823 vs. 0.754) had the highest mean area under the ROC curve (AUC) than other models, including SVM (0.819 vs. 0.736), SurvivalTree (0.814 vs. 0.737), Coxboost (0.816 vs. 0.734), RSF (0.813 vs. 0.730), and CoxPH (0.788 vs. 0.753). The IBS of the DeepSurv model (0.132 vs. 0.147) was lower than that of SurvivalTree (0.135 vs. 0.236), Coxboost (0.141 vs. 0.207), RSF (0.140 vs. 0.225), and CoxPH (0.145 vs. 0.196). Results of the calibration chart and decision curve analysis (DCA) also demonstrated that DeepSurv had a satisfactory predictive performance. In addition, the performance of the DeepSurv model was better than that of the TNM Classification in C-index, mean AUC, and IBS (0.746 vs. 0.598, 0.823 vs. 0.613, and 0.132 vs. 0.186, respectively) in the training cohort. Patients were stratified and divided into high- and low-risk groups based on the DeepSurv model. In the training cohort, patients in the high-risk group would not benefit from postoperative chemotherapy (p = 0.519). In the low-risk group, patients receiving postoperative chemotherapy might have a better prognosis (p = 0.035).ConclusionsIn this study, the DeepSurv model was good at predicting prognosis and risk stratification to guide treatment options. AFR level might be a potential prognostic factor for dCCA. For the low-risk group in the DeepSurv model, patients might benefit from postoperative chemotherapy.

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Development and validation of a deep learning model to predict survival of patients with esophageal cancer

Cited by 9 publications

References 34 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

Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study

Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma

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