Novel head and neck cancer survival analysis approach: Random survival forests versus cox proportional hazards regression

Datema, Frank R.; Moyá, Ana; Krause, Peter; Bäck, Thomas; Willmes, Lars; Langeveld, Ton P. M.; Jong, Robert J. Baatenburg de; Blom, H. M.

doi:10.1002/hed.21698

Cited by 53 publications

(53 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These findings are in line with other studies that used support vector machines for analyzing survival [3]–[6]. On the other hand, our findings also support the results of previous studies that relied on Cox regression modeling to predict the five year mortality and the overall mortality of newly diagnosed patients with HNSCC [15]–[17].…”

Section: Discussionsupporting

confidence: 92%

“…Predictors in this model included Tumor location, Age at diagnosis, Gender, T-N-M classification (T = the extent of the primary tumor, N = the absence or presence and extent of regional lymph node metastasis, M = the absence or presence of distant metastasis) and Prior malignancies. In 2010, Datema et al [16], [17] published an updated model including comorbidity according to the Adult Comorbidity Evaluation, based on a 27-item comorbidity index (ACE27) [18]. In our study, we excluded patients for whom comorbidity was unknown, resulting in a total of 1282 patients .…”

Section: Methodsmentioning

confidence: 99%

“…To compare the performance, we applied these techniques and conventional regression analysis in the prediction of survival of 1282 Dutch patients with Head and Neck Squamous Cell Carcinoma (HNSCC), using predictors as described in earlier studies [12]–[14]. The survival of this particular population of newly diagnosed patients with HNSCC has already been studied by applying conventional Kaplan-Meier analysis, Cox regression and random survival forests (RSF) to 60-month survival and overall survival [15]–[17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Survival with Alternative Modeling Techniques Using Pseudo Values

2014

View full text Add to dashboard Cite

BackgroundThe use of alternative modeling techniques for predicting patient survival is complicated by the fact that some alternative techniques cannot readily deal with censoring, which is essential for analyzing survival data. In the current study, we aimed to demonstrate that pseudo values enable statistically appropriate analyses of survival outcomes when used in seven alternative modeling techniques.MethodsIn this case study, we analyzed survival of 1282 Dutch patients with newly diagnosed Head and Neck Squamous Cell Carcinoma (HNSCC) with conventional Kaplan-Meier and Cox regression analysis. We subsequently calculated pseudo values to reflect the individual survival patterns. We used these pseudo values to compare recursive partitioning (RPART), neural nets (NNET), logistic regression (LR) general linear models (GLM) and three variants of support vector machines (SVM) with respect to dichotomous 60-month survival, and continuous pseudo values at 60 months or estimated survival time. We used the area under the ROC curve (AUC) and the root of the mean squared error (RMSE) to compare the performance of these models using bootstrap validation.ResultsOf a total of 1282 patients, 986 patients died during a median follow-up of 66 months (60-month survival: 52% [95% CI: 50%−55%]). The LR model had the highest optimism corrected AUC (0.791) to predict 60-month survival, followed by the SVM model with a linear kernel (AUC 0.787). The GLM model had the smallest optimism corrected RMSE when continuous pseudo values were considered for 60-month survival or the estimated survival time followed by SVM models with a linear kernel. The estimated importance of predictors varied substantially by the specific aspect of survival studied and modeling technique used.ConclusionsThe use of pseudo values makes it readily possible to apply alternative modeling techniques to survival problems, to compare their performance and to search further for promising alternative modeling techniques to analyze survival time.

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Survival with Alternative Modeling Techniques Using Pseudo Values

2014

View full text Add to dashboard Cite

show abstract

“…In spite of mentioned studies in the context of learning algorithms, there are also comparisons between RSF and Cox, as the most widely used method for modeling the censored data (Hothorn, Bühlmann, Dudoit, Molinaro, & Van Der Laan, 2006). Through some of these comparative studies Cox has shown, not only the same (Hsich et al, 2011), but also better performance than all diversity of forests (Datema et al, 2012). Additionally, the supremacy of Cox has been confirmed by other simulation studies (Kurt Omurlu et al, 2009).…”

Section: Discussionmentioning

confidence: 77%

“…For instance comparative studies, using both of RSF and Cox proportional hazard, for modeling the survival of patients with different cancers as breast (Kurt Omurlu, Ture, & Tokatli, 2009), prostate (Gerds, Kattan, Schumacher, & Yu, 2013), head and neck (Datema et al, 2012), as well as patients with systolic heart failure (Hsich, Gorodeski, Blackstone, Ishwaran, & Lauer, 2011). Forests were also compared with variety of learning techniques (Mirmohammadi, Shishehgar, & Ghapanchi, 2014;Pang, Datta, & Zhao, 2010) and survival trees, as the forest elements (Yosefian, Mosa Farkhani, & Baneshi, 2015); but as far as we know, the RSF has never been compared with MoBRP.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran

Safe¹,

Mahjub²,

Faradmal³

2016

GJHS

View full text Add to dashboard Cite

Background: Breast cancer is the main cause of women cancer mortality. Therefore, precise prediction of patients' risk level is the major concern in therapeutic strategies. Although statistical learning algorithms are high quality risk prediction methods, but usually their better prediction quality leads to more loss of interpretability. Therefore, the aim of this study is to compare 'Model-Based Recursive Partitioning' and 'Random Survival Forest'; whether the partitioning, as the more interpretable learning technique, could be a suitable successor for forests.

show abstract

Prognostic risk factor of major salivary gland carcinomas and survival prediction model based on random survival forests

Chen

Jiang

et al. 2023

Cancer Medicine

View full text Add to dashboard Cite

Salivary gland malignancies are rare and are often acompanied by poor prognoses. So, identifying the populations with risk factors and timely intervention to avoid disease progression is significant. This study provides an effective prediction model to screen the target patients and is helpful to construct a cost‐effective follow‐up strategy. We enrolled 249 patients diagnosed with salivary gland tumors and analyzed prognostic risk factors using Cox proportional hazard univariable and multivariable regression models. The patients' data were split into training and validation sets on a 7:3 ratio, and the random survival forest (RSF) model was established using the training sets and validated using the validation sets. The maximally selected rank statistics method was used to determine a cut point value corresponding to the most significant relation with survival. Univariable Cox regression suggested age, smoking, alcohol consumption, untreated, neural invasion, capsular invasion, skin invasion, tumors larger than 4 cm, advanced T and N stage, distant metastasis, and non‐mucous cell carcinoma were risk factors for poor prognosis, and multivariable analysis suggested that female, aging, smoking, untreated, and non‐mucous cell carcinoma were risk factors. The time‐dependent ROC curve showed the AUC of the RSF prediction model on 1‐, 2‐, and 3‐year survival were 0.696, 0.779, and 0.765 respectively in the validation sets. Log‐rank tests suggested that the cut point 7.42 risk score calculated from the RSF was most effective in dividing patients with significantly different prognoses. The prediction model based on the RSF could effectively screen patients with poor prognoses.

show abstract

Novel head and neck cancer survival analysis approach: Random survival forests versus cox proportional hazards regression

Cited by 53 publications

References 16 publications

Prediction of Survival with Alternative Modeling Techniques Using Pseudo Values

Prediction of Survival with Alternative Modeling Techniques Using Pseudo Values

A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran

Prognostic risk factor of major salivary gland carcinomas and survival prediction model based on random survival forests

Contact Info

Product

Resources

About