TY - JOUR
T1 - Nomogram
predicting response after chemoradiotherapy in rectal cancer using sequential
PETCT imaging: A multicentric prospective study with external validation.
AU - Van Stiphout, Ruud G.P.M.
AU - Valentini, Vincenzo
AU - Buijsen, Jeroen
AU - Lammering, Guido
AU - Meldolesi, Elisa
AU - Van Soest, Johan
AU - Leccisotti, Lucia
AU - Giordano, Alessandro
AU - Gambacorta, Maria Antonietta
AU - Dekker, Andre
AU - Lambin, Philippe
PY - 2014
Y1 - 2014
N2 - PURPOSE:
To develop and externally validate a predictive model for pathologic complete response (pCR) for locally advanced rectal cancer (LARC) based on clinical features and early sequential (18)F-FDG PETCT imaging.
MATERIALS AND METHODS:
Prospective data (i.a. THUNDER trial) were used to train (N=112, MAASTRO Clinic) and validate (N=78, Università Cattolica del S. Cuore) the model for pCR (ypT0N0). All patients received long-course chemoradiotherapy (CRT) and surgery. Clinical parameters were age, gender, clinical tumour (cT) stage and clinical nodal (cN) stage. PET parameters were SUVmax, SUVmean, metabolic tumour volume (MTV) and maximal tumour diameter, for which response indices between pre-treatment and intermediate scan were calculated. Using multivariate logistic regression, three probability groups for pCR were defined.
RESULTS:
The pCR rates were 21.4% (training) and 23.1% (validation). The selected predictive features for pCR were cT-stage, cN-stage, response index of SUVmean and maximal tumour diameter during treatment. The models' performances (AUC) were 0.78 (training) and 0.70 (validation). The high probability group for pCR resulted in 100% correct predictions for training and 67% for validation. The model is available on the website www.predictcancer.org.
CONCLUSIONS:
The developed predictive model for pCR is accurate and externally validated. This model may assist in treatment decisions during CRT to select complete responders for a wait-and-see policy, good responders for extra RT boost and bad responders for additional chemotherapy.
Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.
KEYWORDS:
(18)F-FDG PET imaging; External validation; Outcome prediction; Prospective study; Rectal cancer; Tumour response
AB - PURPOSE:
To develop and externally validate a predictive model for pathologic complete response (pCR) for locally advanced rectal cancer (LARC) based on clinical features and early sequential (18)F-FDG PETCT imaging.
MATERIALS AND METHODS:
Prospective data (i.a. THUNDER trial) were used to train (N=112, MAASTRO Clinic) and validate (N=78, Università Cattolica del S. Cuore) the model for pCR (ypT0N0). All patients received long-course chemoradiotherapy (CRT) and surgery. Clinical parameters were age, gender, clinical tumour (cT) stage and clinical nodal (cN) stage. PET parameters were SUVmax, SUVmean, metabolic tumour volume (MTV) and maximal tumour diameter, for which response indices between pre-treatment and intermediate scan were calculated. Using multivariate logistic regression, three probability groups for pCR were defined.
RESULTS:
The pCR rates were 21.4% (training) and 23.1% (validation). The selected predictive features for pCR were cT-stage, cN-stage, response index of SUVmean and maximal tumour diameter during treatment. The models' performances (AUC) were 0.78 (training) and 0.70 (validation). The high probability group for pCR resulted in 100% correct predictions for training and 67% for validation. The model is available on the website www.predictcancer.org.
CONCLUSIONS:
The developed predictive model for pCR is accurate and externally validated. This model may assist in treatment decisions during CRT to select complete responders for a wait-and-see policy, good responders for extra RT boost and bad responders for additional chemotherapy.
Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.
KEYWORDS:
(18)F-FDG PET imaging; External validation; Outcome prediction; Prospective study; Rectal cancer; Tumour response
KW - rectal cancer
KW - rectal cancer
UR - http://hdl.handle.net/10807/62120
U2 - 10.1016/j.radonc.2014.11.002
DO - 10.1016/j.radonc.2014.11.002
M3 - Article
SN - 0167-8140
VL - 113
SP - 215
EP - 222
JO - Radiotherapy and Oncology
JF - Radiotherapy and Oncology
ER -
