Abstract
The Editor’s Award of the Canadian Association of Radiologists Journal (CARJ) recognizes original research articles that represent novelty, quality, importance, and which have potential high scientific and clinical impact. The award winner is determined by the Selection Committee, which is comprised of members of the CARJ Standing Committee. This year, original research articles which were published between January 1, 2020, and December 31, 2021, were included for consideration.
In 2023, the CARJ Editor’s Award is given to the manuscript entitled “Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer” by Jay Kumar Raghavan Nair, Umar Abid Saeed, Connor C McDougall, Ali Sabri, Bojan Kovacina, B V S Raidu, Riaz Ahmed Khokhar, Stephan Probst, Vera Hirsh, Jeffrey Chankowsky, Léon C Van Kempen, and Jana Taylor. 1
This original research article describes the creation of radiogenomic models from texture signatures derived from computed tomography (CT) and 18F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor (EGFR) mutations. The authors demonstrated that logistic regression models evaluating texture features derived from FDG PET and from CT were able to differentiate EGFR mutant from wild type NSCLC and discriminate between mutations in EGFR exon 19 and 21. These imaging signatures could help in therapeutic decision-making and prognostication in the future.
Congratulations to the authors on their achievement and on their important contribution to radiology research and applied data science!
