Initial Clinical Findings of a Mathematical Model to Predict Survival of Head and Neck Cancer

Emerick, Kevin S.; Leavitt, Erica; Michaelson, James; Diephuis, Bradford; Clark, John R.; Deschler, Daniel G.

doi:10.1177/0194599813495178

Cited by 20 publications

(25 citation statements)

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“…Each calculator was derived from a different dataset, with a different set of factors available and different choices made about which factors to include. This variation and the general uncertainty regarding which patient and tumor factors are most important to prognosis in OCSCC may account for the differences in input variables and outcomes across cancer site–specific calculators (19). …”

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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Section: Discussionmentioning

confidence: 99%

“…Inclusion criteria stipulated applicability to OCSCC, and provision of 5 year overall survival prediction estimates. Five prognostic calculators were identified (15-19). Each calculator was reviewed for content and format, and formulas for the models were either requested from the respective authors or derived.…”

Section: Methodsmentioning

confidence: 99%

Individualized risk prediction of outcomes for oral cavity cancer patients

et al. 2016

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“…Inclusion criteria mandated that the calculator used clinical data to predict 5‐year overall survival for squamous cell carcinoma of the larynx. Four prognostic calculators were identified (MAASTRO, LifeMath, Leiden, and MyCancerJourney), and each model was reviewed for content and format . The calculators' mathematical formulas were acquired from the original publication, supplementary online materials, or computational derivation.…”

Section: Methodsmentioning

confidence: 99%

“…The calculators were modeled from patient data contained in the Surveillance, Epidemiology, and End Results (SEER) registry; regional study cohorts; or a combination of 2 patient populations. The 4 study cohorts included patients who received treatment with curative intent between 1973 and 2009 …”

Section: Methodsmentioning

confidence: 99%

Individualized outcome prognostication for patients with laryngeal cancer

Hoban

Beesley

Bellile

et al. 2017

Cancer

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Suboptimal reliability and accuracy limit the integration of existing individualized prediction tools into routine clinical decision making. The calculators predicted significantly worse than observed survival among patients who received induction chemotherapy and primary surgery, suggesting a need for updated consideration of modern treatment modalities. Further development of individualized prognostic calculators may improve risk prediction, treatment planning, and counseling for patients with laryngeal cancer. Cancer 2018;124:706-16. © 2017 American Cancer Society.

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“…Oral cancer's five year survival rate has slightly increased over the past four decades to 65% in 2009; however, unfortunately its increase has not improved as much as other cancers over the same period [3]. This is because clinicians face considerable challenges in visually identifying oral neoplasia at an early stage, leading to many diagnoses occurring late in neoplasia progression [3], [4]…”

Section: Introductionmentioning

confidence: 99%

Molecular Imaging of Oral Premalignant and Malignant Lesions Using Fluorescently Labeled Lectins

Baeten¹,

Suresh

Johnson³

et al. 2014

Translational Oncology

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Aberrant glycosylation during carcinogenesis results in altered glycan expression on oral cancer cells. The objective of this study was to detect this atypical glycosylation via imaging of fluorophore-conjugated lectins. Paired normal and tumor tissue from seven patients with oral squamous cell carcinoma were investigated for sialic acid expression via the legume protein wheat germ agglutinin (WGA). Fluorophore (Alexa Fluor 350 and Alexa Fluor 647) conjugated WGA was topically applied to the tissue samples and imaged using a custom wide-field fluorescence imaging system. All seven patients had histologically confirmed disease with 6/7 exhibiting squamous cell carcinoma and 1/7 exhibiting dysplasia. Fluorescent data collected from all patients demonstrated that fluorophore conjugated WGA could distinguish between pathologically normal and diseased tissue with the average signal-to-noise ratio (SNR) among all patients being 5.88 (P = .00046). This SNR was statistically significantly higher than the SNR from differences in tissue autofluorescence (P = .0049). A lectin inhibitory experiment confirmed that lectin binding is molecularly specific to overexpressed tumor glycans and that fluorescence is not due to tissue optical properties or tissue diffusion differences. These results illustrate that changes in tumor glycan content of oral neoplasms can be detected with optical imaging using topically applied fluorescently labeled WGA. Lectin targeting of oral lesions using optical imaging may provide a new avenue for the early detection of oral cancers.

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Initial Clinical Findings of a Mathematical Model to Predict Survival of Head and Neck Cancer

Cited by 20 publications

References 10 publications

Individualized risk prediction of outcomes for oral cavity cancer patients

Individualized risk prediction of outcomes for oral cavity cancer patients

Individualized outcome prognostication for patients with laryngeal cancer

Molecular Imaging of Oral Premalignant and Malignant Lesions Using Fluorescently Labeled Lectins

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