A failure-type specific risk prediction tool for selection of head-and-neck cancer patients for experimental treatments
| Autor: | Gregers Brünnich Rasmussen, Jeppe Friborg, Søren M. Bentzen, K. Håkansson, Jacob H. Rasmussen, Barbara M. Fischer, Lena Specht, Ivan R. Vogelius, Thomas A. Gerds, Flemming L. Andersen |
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| Rok vydání: | 2017 |
| Předmět: |
Male
Risk Oncology Cancer Research medicine.medical_specialty Specific risk Disease 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Internal medicine medicine Humans Neoplasm Metastasis Aged Proportional Hazards Models Retrospective Studies Performance status Squamous Cell Carcinoma of Head and Neck business.industry Proportional hazards model Patient Selection Head and neck cancer Middle Aged Nomogram Decision Support Systems Clinical Prognosis medicine.disease Surgery Clinical trial Head and Neck Neoplasms 030220 oncology & carcinogenesis Carcinoma Squamous Cell T-stage Female Oral Surgery business |
| Zdroj: | Oral Oncology. 74:77-82 |
| ISSN: | 1368-8375 |
| Popis: | 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, 8th edition (AUCLRF = 72.7% vs 64.2%, p 20% risk of tumor relapse. Conversely, 9 of the 15 dose escalation trial participants had LRF risks 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. |
| Databáze: | OpenAIRE |
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