A failure-type specific risk prediction tool for selection of head-and-neck cancer patients for experimental treatments. (November 2017)

• Record Type: Journal Article
• Authors: Håkansson, Katrin; Rasmussen, Jacob H.; Rasmussen, Gregers B.; Friborg, Jeppe; Gerds, Thomas A.; Fischer, Barbara Malene; Andersen, Flemming L.; Bentzen, Søren M.; Specht, Lena; Vogelius, Ivan R.
• Is Part Of: Oral oncology. Volume 74(2017)
• Page Start: 77
• Page End: 82
• Publication Date: 2017-11
• Subjects: AUC Area under the ROC curve -- Death NED Death with no evidence of disease -- DM Distant metastasis -- CI Confidence interval -- FDG 18F-fluorodeoxyglucose -- HNSCC Head and neck squamous cell carcinoma -- HPV Human papilloma virus -- IMRT intensity-modulated radiotherapy -- LRF Loco-regional failure -- NSUVmax Maximum standard uptake value in nodes -- OS Overall survival -- PSF Point-spread-function -- SUV Standard uptake value -- TSUVmax Maximum standard uptake value in primary tumor; Head and neck neoplasms -- Decision support systems, clinical -- Risk -- Patient selection -- Nomograms; Mouth -- Cancer -- Periodicals; Mouth -- Tumors -- Periodicals; Mouth Diseases -- Periodicals; Mouth Neoplasms -- Periodicals; Bouche -- Cancer -- Périodiques; Bouche -- Tumeurs -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9943105
• DOI: 10.1016/j.oraloncology.2017.09.018
• Languages: English
• ISSNs: 1368-8375

Abstract

Highlights: A prognostic model was developed to provide decision support for clinicians. The model provides absolute risk estimates for loco-regional and distant failure. The predictive performance of the model was superior to the UICC staging (8th ed.). The model is published as an on-line interactive tool. Abstract: Objectives: The objective of this work was to develop a tool for decision support, providing simultaneous predictions of the risk of loco-regional failure (LRF) and distant metastasis (DM) after definitive treatment for head-and-neck squamous cell carcinoma (HNSCC). Materials and Methods: Retrospective data for 560 HNSCC patients were used to generate a multi-endpoint model, combining three cause-specific Cox models (LRF, DM and death with no evidence of disease (death NED)). The model was used to generate risk profiles of patients eligible for/included in a de-intensification study (RTOG 1016) and a dose escalation study (CONTRAST), respectively, to illustrate model predictions versus classic inclusion/exclusion criteria for clinical trials. The model is published as an on-line interactive tool (https://katrin.shinyapps.io/HNSCCmodel/ ) . Results: The final model included pre-selected clinical variables (tumor subsite, T stage, N stage, smoking status, age and performance status) and one additional variable (tumor volume). The treatment failure discrimination ability of the developed model was superior of that of UICC staging, 8 th edition (AUCLRF = 72.7% vs 64.2%, p < 0.001 and AUCDM = 70.7% vs 58.8%, p < 0.001). Using the model for trial inclusion simulation, it was found that 14% of patients eligible for the de-intensification study had >20% risk of tumor relapse. Conversely, 9 of the 15 dose escalation trial participants had LRF risks <20%. Conclusion: A multi-endpoint model was generated and published as an on-line interactive tool. Its potential in decision support was illustrated by generating risk profiles for patients eligible for/included in clinical trials for HNSCC.