A deep learning-based model predicts survival for patients with laryngeal squamous cell carcinoma: a large population-based study

Liao, Fang; Wang, Wei; Wang, Jinyu

doi:10.1007/s00405-022-07627-w

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…in various clinical medicine subspecialties. Numerous studies have indicated that it outperforms conventional linear prediction models in survival predictions(35)(36)(37). Previous studies have demonstrated that the DeepSurv model can predict survival rates and times more accurately than can the conventional CoxPH model.…”

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confidence: 99%

Deep-learning-based survival prediction of patients with cutaneous malignant melanoma

Yang

et al. 2023

Front. Med.

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BackgroundThis study obtained data on patients with cutaneous malignant melanoma (CMM) from the Surveillance, Epidemiology, and End Results (SEER) database, and used a deep learning and neural network (DeepSurv) model to predict the survival rate of patients with CMM and evaluate its effectiveness.MethodsWe collected information on patients with CMM between 2004 and 2015 from the SEER database. We then randomly divided the patients into training and testing cohorts at a 7:3 ratio. The likelihood that patients with CMM will survive was forecasted using the DeepSurv model, and its results were compared with those of the Cox proportional-hazards (CoxPH) model. The calibration curves, time-dependent area under the receiver operating characteristic curve (AUC), and concordance index (C-index) were used to assess the prediction abilities of the model.ResultsThis study comprised 37,758 patients with CMM: 26,430 in the training cohort and 11,329 in the testing cohort. The CoxPH model demonstrated that the survival of patients with CMM was significantly influenced by age, sex, marital status, summary stage, surgery, radiotherapy, chemotherapy, postoperative lymph node dissection, tumor size, and tumor extension. The C-index of the CoxPH model was 0.875. We also constructed the DeepSurv model using the data from the training cohort, and its C-index was 0.910. We examined how well the aforementioned two models predicted outcomes. The 1-, 3-, and 5-year AUCs were 0.928, 0.837, and 0.855, respectively, for the CoxPH model, and 0.971, 0.947, and 0.942 for the DeepSurv model. The DeepSurv model presented a greater predictive effect on patients with CMM, and its reliability was better than that of the CoxPH model according to both the AUC value and the calibration curve.ConclusionThe DeepSurv model, which we developed based on the data of patients with CMM in the SEER database, was found to be more effective than the CoxPH model in predicting the survival time of patients with CMM.

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confidence: 99%

Deep-learning-based survival prediction of patients with cutaneous malignant melanoma

Yang

et al. 2023

Front. Med.

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“…For the oral cavity, the C-index for four studies conducting time-to-event analyses ranged from 0.73 to 0.89, with three of these studies also reporting on Cox PH models, showing C-indices between 0.69 and 0.77 [ 60 , 71 , 72 , 75 ]. For the larynx, five studies reported C-indices for ML models ranging from 0.71 to 0.85, and for Cox PH models in the same studies, ranging from 0.57 to 0.74 [ 72 , 75 , 78 , 80 , 86 ]. For the oropharynx, three studies reported C-indices ranging from 0.77 to 0.80, [ 72 , 73 , 75 ], but there was not enough data to consolidate the C-index for Cox PH models.…”

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confidence: 99%

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

Moharrami,

Azimian Zavareh,

Watson

et al. 2024

PLoS ONE

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Background This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data. Methods A systematic search was conducted across the Medline, Scopus, Embase, Web of Science, and Google Scholar databases. The methodological characteristics and performance metrics of studies that developed and validated ML models were assessed. The risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results Out of 5,560 unique records, 34 articles were included. For survival outcome, the ML model outperformed the Cox proportional hazards model in time-to-event analyses for HNC, with a concordance index of 0.70–0.79 vs. 0.66–0.76, and for all sub-sites including oral cavity (0.73–0.89 vs. 0.69–0.77) and larynx (0.71–0.85 vs. 0.57–0.74). In binary classification analysis, the area under the receiver operating characteristics (AUROC) of ML models ranged from 0.75–0.97, with an F1-score of 0.65–0.89 for HNC; AUROC of 0.61–0.91 and F1-score of 0.58–0.86 for the oral cavity; and AUROC of 0.76–0.97 and F1-score of 0.63–0.92 for the larynx. Disease-specific survival outcomes showed higher performance than overall survival outcomes, but the performance of ML models did not differ between three- and five-year follow-up durations. For disease progression outcomes, no time-to-event metrics were reported for ML models. For binary classification of the oral cavity, the only evaluated subsite, the AUROC ranged from 0.67 to 0.97, with F1-scores between 0.53 and 0.89. Conclusions ML models have demonstrated considerable potential in predicting post-treatment survival and disease progression, consistently outperforming traditional linear models and their derived nomograms. Future research should incorporate more comprehensive treatment features, emphasize disease progression outcomes, and establish model generalizability through external validations and the use of multicenter datasets.

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Competing risk models versus traditional Cox models for prognostic factors’ prediction and care recommendation in patients with advanced laryngeal squamous carcinoma: a population-based study

Song

Chen

Sun

et al. 2023

Eur Arch Otorhinolaryngol

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A deep learning-based model predicts survival for patients with laryngeal squamous cell carcinoma: a large population-based study

Cited by 5 publications

References 22 publications

Deep-learning-based survival prediction of patients with cutaneous malignant melanoma

Deep-learning-based survival prediction of patients with cutaneous malignant melanoma

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

Competing risk models versus traditional Cox models for prognostic factors’ prediction and care recommendation in patients with advanced laryngeal squamous carcinoma: a population-based study

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