RT Journal Article
SR Electronic
T1 Author response to Cunha <em>et al</em>
JF Journal for ImmunoTherapy of Cancer
JO J Immunother Cancer
FD BMJ Publishing Group Ltd
SP e003299
DO 10.1136/jitc-2021-003299
VO 9
IS 7
A1 Colen, Rivka R
A1 Rolfo, Christian
A1 Ak, Murat
A1 Ayoub, Mira
A1 Ahmed, Sara
A1 Elshafeey, Nabil
A1 Mamindla, Priyadarshini
A1 Zinn, Pascal O
A1 Ng, Chaan
A1 Vikram, Raghu
A1 Bakas, Spyridon
A1 Peterson, Christine B
A1 Rodon Ahnert, Jordi
A1 Subbiah, Vivek
A1 Karp, Daniel D
A1 Stephen, Bettzy
A1 Hajjar, Joud
A1 Naing, Aung
YR 2021
UL http://jitc.bmj.com/content/9/7/e003299.abstract
AB The need to identify biomarkers to predict immunotherapy response for rare cancers has been long overdue. We aimed to study this in our paper, ‘Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers’. In this response to the Letter to the Editor by Cunha et al, we explain and discuss the reasons behind choosing LASSO (Least Absolute Shrinkage and Selection Operator) and XGBoost (eXtreme Gradient Boosting) with LOOCV (Leave-One-Out Cross-Validation) as the feature selection and classifier method, respectively for our radiomics models. Also, we highlight what care was taken to avoid any overfitting on the models. Further, we checked for the multicollinearity of the features. Additionally, we performed 10-fold cross-validation instead of LOOCV to see the predictive performance of our radiomics models.