TY - JOUR
T1 - A failure-type specific risk prediction tool for selection of head-and-neck cancer patients for experimental treatments
AU - Håkansson, Katrin
AU - Rasmussen, Jacob H.
AU - Rasmussen, Gregers B.
AU - Friborg, Jeppe
AU - Gerds, Thomas A.
AU - Fischer, Barbara Malene
AU - L. Andersen, Flemming
AU - M. Bentzen, Søren
AU - Specht, Lena
AU - R. Vogelius, Ivan
N1 - Copyright © 2017 Elsevier Ltd. All rights reserved.
PY - 2017/11
Y1 - 2017/11
N2 - 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 < 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.
AB - 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 < 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.
KW - Journal Article
U2 - 10.1016/j.oraloncology.2017.09.018
DO - 10.1016/j.oraloncology.2017.09.018
M3 - Journal article
C2 - 29103755
SN - 1741-9409
VL - 74
SP - 77
EP - 82
JO - Oral Oncology Extra
JF - Oral Oncology Extra
ER -
