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Integrating pretreatment diffusion weighted MRI into a multivariable prognostic model for head and neck squamous cell
Maarten Lambrecht1, Ben Van Calster2, Vincent Vandecaveye3
1Laboratory of Experimental Radiotherapy, Department of Oncology, KU Leuven, Belgium.
Introduction:
In head and neck squamous cell carcinoma (HNSCC) the ability to anticipate an individual patient's outcome is very valuable. With this study we wanted to assess the prognostic value of pretreatment apparent diffusion coefficient (ADC) in a large patient population and integrate it into a multivariable prognostic model.
Methods:
From 2004 to 2010 175 patients with pathology proven HNSCC were included in this study. All patients underwent a pretreatment MRI with diffusion weighted imaging (DWI) using six b-values. For each tumor, three ADC values were calculated using different b-value combinations: ADC(low) (b 0-50-100 s/mm(2)), ADChigh (b 500-750-1000 s/mm(2)) and ADC(avg) (all b-values). The clinical and radiological variables included: tumor and nodal volume, tumor location and age. Disease recurrence was analyzed using competing risk regression. A prognostic model for disease recurrence was developed, and internal validation was performed using bootstrapping and by dividing patients in three equal sized groups based on prognosis.
Results:
One hundred and sixty-one patients were eligible for analysis. Median follow-up was 50 months (range 4-86). A total of 67 patients experienced disease recurrence during follow-up (42%). ADC(high) was a prognostic factor for disease recurrence (adjusted hazard ratio: 1.14 per 10(-4) mm(2)/s, 95% CI 1.04-1.25). Harrell's c-index of the multivariable prognostic model was 0.62 (95% CI 0.56-0.70) after internal validation. The validated 3-year disease recurrence rates for the groups with worst, intermediate, and best prognosis were 56%, 33% and 31% respectively.
Conclusion:
Pretreatment ADC value derived from high b-values is an independent prognostic factor in HNSCC and increases the performance of a multivariable prognostic model in addition to known clinical and radiological variables. Integration of other biomarkers and external validation is necessary to ensure its clinical applicability.
