Prediction of survival in oropharyngeal squamous cell carcinoma using machine learning algorithms: A study based on the surveillance, epidemiology, and end results database

Kim, Su Il; Kang, Ji-Won; Eun, Young‐Gyu; Lee, Young Chan

doi:10.3389/fonc.2022.974678

Cited by 11 publications

(9 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, distant metastases have an important impact on the prognosis of patients with LPADC. In conjunction with previous analyses, the findings demonstrate that patients who developed distant metastases had poorer survival rates than other patients 26 , 27 . A higher N-stage also plays a crucial role in the model, indicating poor prognosis 28 .…”

Section: Discussionsupporting

confidence: 79%

Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models

Xia,

Chen,

Jin

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Accurate prognostic prediction is crucial for treatment decision-making in lung papillary adenocarcinoma (LPADC). The aim of this study was to predict cancer-specific survival in LPADC using ensemble machine learning and classical Cox regression models. Moreover, models were evaluated to provide recommendations based on quantitative data for personalized treatment of LPADC. Data of patients diagnosed with LPADC (2004–2018) were extracted from the Surveillance, Epidemiology, and End Results database. The set of samples was randomly divided into the training and validation sets at a ratio of 7:3. Three ensemble models were selected, namely gradient boosting survival (GBS), random survival forest (RSF), and extra survival trees (EST). In addition, Cox proportional hazards (CoxPH) regression was used to construct the prognostic models. The Harrell’s concordance index (C-index), integrated Brier score (IBS), and area under the time-dependent receiver operating characteristic curve (time-dependent AUC) were used to evaluate the performance of the predictive models. A user-friendly web access panel was provided to easily evaluate the model for the prediction of survival and treatment recommendations. A total of 3615 patients were randomly divided into the training and validation cohorts (n = 2530 and 1085, respectively). The extra survival trees, RSF, GBS, and CoxPH models showed good discriminative ability and calibration in both the training and validation cohorts (mean of time-dependent AUC: > 0.84 and > 0.82; C-index: > 0.79 and > 0.77; IBS: < 0.16 and < 0.17, respectively). The RSF and GBS models were more consistent than the CoxPH model in predicting long-term survival. We implemented the developed models as web applications for deployment into clinical practice (accessible through https://shinyshine-820-lpaprediction-model-z3ubbu.streamlit.app/). All four prognostic models showed good discriminative ability and calibration. The RSF and GBS models exhibited the highest effectiveness among all models in predicting the long-term cancer-specific survival of patients with LPADC. This approach may facilitate the development of personalized treatment plans and prediction of prognosis for LPADC.

show abstract

Section: Discussionsupporting

confidence: 79%

Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models

Xia,

Chen,

Jin

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

“…In previous studies, machine learning algorithms representative of DeepSurv have outperformed the traditional Cox proportional hazard model in survival prediction [ 10 , 23 , 24 ]. In the training cohort of the present study, the DeepSurv model had a higher C-index than the CPH model; however, in the validation cohort, it did not show improved efficacy in predicting the CSS rates of patients.…”

Section: Discussionmentioning

confidence: 99%

A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study

Liu,

Xie,

Wang

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Objective Successful prognosis is crucial for the management and treatment of osteosarcoma (OSC). This study aimed to predict the cancer-specific survival rate in patients with OSC using deep learning algorithms and classical Cox proportional hazard models to provide data to support individualized treatment of patients with OSC. Methods Data on patients diagnosed with OSC from 2004 to 2017 were obtained from the Surveillance, Epidemiology, and End Results database. The study sample was then divided randomly into a training cohort and a validation cohort in the proportion of 7:3. The DeepSurv algorithm and the Cox proportional hazard model were chosen to construct prognostic models for patients with OSC. The prediction efficacy of the model was estimated using the concordance index (C-index), the integrated Brier score (IBS), the root mean square error (RMSE), and the mean absolute error (SME). Results A total of 3218 patients were randomized into training and validation groups (n = 2252 and 966, respectively). Both DeepSurv and Cox models had better efficacy in predicting cancer-specific survival (CSS) in OSC patients (C-index >0.74). In the validation of other metrics, DeepSurv did not have superiority over the Cox model in predicting survival in OSC patients. Conclusions After validation, our CSS prediction model for patients with OSC based on the DeepSurv algorithm demonstrated satisfactory prediction efficacy and provided a convenient webpage calculator.

show abstract

Prediction of survival in oropharyngeal squamous cell carcinoma using machine learning algorithms: A study based on the surveillance, epidemiology, and end results database

Cited by 11 publications

References 21 publications

Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models

Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models

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

A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study

Contact Info

Product

Resources

About