Update and external validation of a head and neck cancer prognostic model

Datema, Frank R.; Ferrier, Marciano B.; Vergouwe, Yvonne; Moyá, Ana; Molenaar, Jan Willem; Piccirillo, Jay F.; Jong, Robert J. Baatenburg de

doi:10.1002/hed.23117

Cited by 48 publications

(68 citation statements)

References 66 publications

(52 reference statements)

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“…The Leiden calculator was externally validated on 598 patients (17,18). They found a C-index of 0.73 with their data and 0.69 when they performed external validation.…”

Section: Discussionmentioning

confidence: 99%

“…For the LifeMath calculator the authors used all head and neck cancer sites in the SEER population (18). As there is significant variation in the behavior and response of squamous cell cancer from different subsites in the head and neck, these differences have to be accounted for, which the authors attempted to do by including a term for cancer site in their equations.…”

Section: Discussionmentioning

confidence: 99%

“…Emerick et al, did externally validate their results with a Massachusetts General Hospital data set. Their metric for evaluation was based on a correlation that is not directly comparable to the AUC or the C-index (18). We provide an external validation from our data and find a C-index of 0.665 and an AUC of 0.681.…”

Section: Discussionmentioning

confidence: 99%

“…The calculators developed by Knight, LifeMath, and MyCancerJourney all used data from the SEER database (15, 18). It is important to remember that the accuracy of these prediction tools is dependent upon the limitations of SEER data.…”

Section: Discussionmentioning

confidence: 99%

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Individualized risk prediction of outcomes for oral cavity cancer patients

et al. 2016

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Background Optimal management of oral cancer relies upon accurate and individualized risk prediction of relevant clinical outcomes. Individualized prognostic calculators have been developed to guide patient–physician communication and treatment-related decision-making. However it is critical to scrutinize their accuracy prior to integrating into clinical care. Aim To compare and evaluate oral cavity cancer prognostic calculators using an independent dataset. Methods: Five prognostic calculators incorporating patient and tumor characteristics were identified that evaluated five-year overall survival. A total of 505 patients with previously untreated oral cancer diagnosed between 2003 and 2014 were analyzed. Calculators were applied to each patient to generate individual predicted survival probabilities. Predictions were compared among prognostic tools and with observed outcomes using Kaplan-Meier plots, ROC curves and calibration plots. Results Correlation between the five calculators varied from 0.59 to 0.86. There were considerable differences between individual predictions from pairs of calculators, with as many as 64% of patients having predictions that differed by more than 10%. Four of five calculators were well calibrated. For all calculators the predictions were associated with survival outcomes. The area under the ROC curve ranged from 0.65 to 0.71, with C-indices ranging from 0.63 to 0.67. An average of the 5 predictions had slightly better performance than any individual calculator. Conclusion Five prognostic calculators designed to predict individual outcomes of oral cancer differed significantly in their assessments of risk. Most were well calibrated and had modest discriminatory ability. Given the increasing importance of individualized risk prediction, more robust models are needed.

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“…The Leiden calculator was externally validated on 598 patients (17,18). They found a C-index of 0.73 with their data and 0.69 when they performed external validation.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Individualized risk prediction of outcomes for oral cavity cancer patients

et al. 2016

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“…However, we consider only the limited size of the training set has possibly contributed to the decrease in performance. Another possible explanation could be the lack of accounting for other, unknown prognostic covariates such as biological or genetic risk factors [30]. In addition, correct representation of the neutropenic potential of combination chemotherapy regimens remains a challenge.…”

Section: Ad Hoc Analysis Of Baseline Albuminmentioning

confidence: 99%

External validation of a risk model of febrile neutropenia occurrence in patients with non-Hodgkin lymphoma

Schwenkglenks

Bendall²,

Pfeil

et al. 2013

Leukemia & Lymphoma

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Febrile neutropenia (FN) is a common and serious complication of chemotherapy treatment. Clinical risk models may help to identify patients at high risk of FN but must undergo external validation before implementation in medical practice. Therefore, this study externally validated previously published clinical models of FN occurrence during chemotherapy in 240 patients with non-Hodgkin lymphoma by using an independent observational dataset (n = 1829). The models demonstrated predictive ability, and validation criteria for predicting any cycle of FN were partially met but a larger than expected decrease in performance was noted (area under the receiver operating characteristic curve was 0.71 in the validation dataset and 0.83 in the training dataset). Age, weight, baseline white blood cell count and planned chemotherapy parameters were confirmed to predict FN risk. Chemotherapy dose reductions, dose delays and colony-stimulating factor use were confirmed as risk modifiers during treatment. Further work is needed to improve the predictive ability of FN risk models. non-Hodgkin lymphoma patients by using an independent observational dataset (N=1829).The models demonstrated predictive ability, and validation criteria for predicting any cycle FN were partially met but a larger than expected decrease in performance was noted (area under the receiver operating characteristic curve was 0.71 in the validation dataset and 0.83 in the training dataset). Age, weight, baseline white blood cell counts and planned chemotherapy parameters were confirmed to predict FN risk. Chemotherapy dose reductions, dose delays and colony-stimulating factor use were confirmed as risk modifiers during treatment. Further work is needed to improve the predictive ability of FN risk models.3

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Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer

et al. 2020

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Key Points Question Can a machine learning model provide survival risk stratification for patients with advanced oral cancer who have comprehensive clinicopathologic and genetic data? Findings In this 15-year cohort study of 334 patients, a risk stratification model using comprehensive clinicopathologic and genetic data accurately differentiated the high-risk group from the low-risk group in postoperative cancer-specific and locoregional recurrence–free survival for patients with advanced oral cancer. Meaning The proposed model demonstrated good discrimination in stratifying patients with different risks of survival by using comprehensive clinicopathologic and genetic data, which can provide additional personalized information for postoperative management of patients with advanced oral squamous cancer.

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Update and external validation of a head and neck cancer prognostic model

Abstract: We used recent follow-up information to update the Leiden prognostic model for newly diagnosed patients with head and neck cancer. The model showed acceptably good calibration and discrimination results in internal and external validation procedures.

Cited by 48 publications

References 66 publications

Individualized risk prediction of outcomes for oral cavity cancer patients

Individualized risk prediction of outcomes for oral cavity cancer patients

External validation of a risk model of febrile neutropenia occurrence in patients with non-Hodgkin lymphoma

Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer

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