Author response to Cunha et al

Rivka R. Colen, Christian Rolfo, Murat Ak, Mira Ayoub, Sara Ahmed, Nabil Elshafeey, Priyadarshini Mamindla, Pascal O. Zinn, Chaan Ng, Raghu Vikram, Spyridon Bakas, Christine B. Peterson, Jordi Rodon Ahnert, Vivek Subbiah, Daniel D. Karp, Bettzy Stephen, Joud Hajjar, Aung Naing

Research output: Contribution to journalReview articlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere003299
JournalJournal for immunotherapy of cancer
Volume9
Issue number7
DOIs
StatePublished - Jul 27 2021

Keywords

  • immunotherapy

ASJC Scopus subject areas

  • Immunology and Allergy
  • Immunology
  • Molecular Medicine
  • Oncology
  • Pharmacology
  • Cancer Research

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