TY - JOUR
T1 - Development, external validation and clinical usefulness of a practical prediction model for radiation-induced dysphagia in lung cancer patients
AU - Dehing-Oberije, Cary
AU - De Ruysscher, Dirk
AU - Petit, Steven
AU - Van Meerbeeck, Jan
AU - Vandecasteele, Katrien
AU - De Neve, Wilfried
AU - Dingemans, Anne Marie C.
AU - El Naqa, Issam
AU - Deasy, Joseph
AU - Bradley, Jeff
AU - Huang, Ellen
AU - Lambin, Philippe
N1 - Funding Information:
The acquisition of the data of Washington University in St. Louis, Department of Radiation Oncology, St. Louis, USA was partially supported by US NIH Grant R01 85181 (J.O.D) and K25 (I.E.N.).
PY - 2010/12
Y1 - 2010/12
N2 - Introduction: Acute dysphagia is a distressing dose-limiting toxicity occurring frequently during concurrent chemo-radiation or high-dose radiotherapy for lung cancer. It can lead to treatment interruptions and thus jeopardize survival. Although a number of predictive factors have been identified, it is still not clear how these could offer assistance for treatment decision making in daily clinical practice. Therefore, we have developed and validated a nomogram to predict this side-effect. In addition, clinical usefulness was assessed by comparing model predictions to physicians' predictions. Materials and methods: Clinical data from 469 inoperable lung cancer patients, treated with curative intent, were collected prospectively. A prediction model for acute radiation-induced dysphagia was developed. Model performance was evaluated by the c-statistic and assessed using bootstrapping as well as two external datasets. In addition, a prospective study was conducted comparing model to physicians' predictions in 138 patients. Results: The final multivariate model consisted of age, gender, WHO performance status, mean esophageal dose (MED), maximum esophageal dose (MAXED) and overall treatment time (OTT). The c-statistic, assessed by bootstrapping, was 0.77. External validation yielded an AUC of 0.94 on the Ghent data and 0.77 on the Washington University St. Louis data for dysphagia ≥ grade 3. Comparing model predictions to the physicians' predictions resulted in an AUC of 0.75 versus 0.53, respectively. Conclusions: The proposed model performed well was successfully validated and demonstrated the ability to predict acute severe dysphagia remarkably better than the physicians. Therefore, this model could be used in clinical practice to identify patients at high or low risk.
AB - Introduction: Acute dysphagia is a distressing dose-limiting toxicity occurring frequently during concurrent chemo-radiation or high-dose radiotherapy for lung cancer. It can lead to treatment interruptions and thus jeopardize survival. Although a number of predictive factors have been identified, it is still not clear how these could offer assistance for treatment decision making in daily clinical practice. Therefore, we have developed and validated a nomogram to predict this side-effect. In addition, clinical usefulness was assessed by comparing model predictions to physicians' predictions. Materials and methods: Clinical data from 469 inoperable lung cancer patients, treated with curative intent, were collected prospectively. A prediction model for acute radiation-induced dysphagia was developed. Model performance was evaluated by the c-statistic and assessed using bootstrapping as well as two external datasets. In addition, a prospective study was conducted comparing model to physicians' predictions in 138 patients. Results: The final multivariate model consisted of age, gender, WHO performance status, mean esophageal dose (MED), maximum esophageal dose (MAXED) and overall treatment time (OTT). The c-statistic, assessed by bootstrapping, was 0.77. External validation yielded an AUC of 0.94 on the Ghent data and 0.77 on the Washington University St. Louis data for dysphagia ≥ grade 3. Comparing model predictions to the physicians' predictions resulted in an AUC of 0.75 versus 0.53, respectively. Conclusions: The proposed model performed well was successfully validated and demonstrated the ability to predict acute severe dysphagia remarkably better than the physicians. Therefore, this model could be used in clinical practice to identify patients at high or low risk.
KW - Dysphagia
KW - Lung cancer
KW - Nomogram
KW - Prediction model
UR - http://www.scopus.com/inward/record.url?scp=78651478579&partnerID=8YFLogxK
U2 - 10.1016/j.radonc.2010.09.028
DO - 10.1016/j.radonc.2010.09.028
M3 - Article
C2 - 21084125
AN - SCOPUS:78651478579
SN - 0167-8140
VL - 97
SP - 455
EP - 461
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
IS - 3
ER -