| Autor: |
Vivek Subbiah, Sara Ahmed, Christian Rolfo, Aung Naing, Joud Hajjar, Bettzy Stephen, Jordi Rodon Ahnert, Daniel D Karp, Rivka R Colen, Murat Ak, Mira Ayoub, Nabil Elshafeey, Priyadarshini Mamindla, Pascal O Zinn, Chaan Ng, Raghu Vikram, Spyridon Bakas, Christine B Peterson |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Journal for ImmunoTherapy of Cancer, Vol 9, Iss 7 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2051-1426 |
| DOI: |
10.1136/jitc-2021-003299 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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