AI and Radiomics in Non-Small Cell Lung Cancer

AI and Radiomics in Non-Small Cell Lung Cancer

For this project, we were approached by the client to write a high-level review paper on the use of artificial intelligence and radiomics—a field of medicine that has been referred to as “the bridge between medical imaging and personalized medicine”1—to predict clinical outcomes in non-small cell lung cancer for publication in Nature Precision Oncology. As lung cancer is the second leading cause of cancer-related deaths worldwide,2 early detection, diagnosis, and interventions are paramount to patient outcomes3. We developed this review* based on several bulleted headings and a suggested title provided by the client. When we embarked on this project, it was necessary to rapidly familiarize ourselves with the field. Although there was a lot to unpack, we were able to turn around a complete draft for the client in five weeks. As the topic was so extensive and the word limit for Nature Precision Oncology relatively low, we were able to provide more technical detail on radiomics, deep radiomics, delta radiomics, and explainability in the Supplemental Material. The paper was quickly accepted with accolades regarding the quality of the writing and relatively few comments to address. We were able to rapidly address the reviewer comments, and the paper was subsequently published in the target journal. It is our honor to help our clients publish high quality papers that advance the field of oncology.

*Note: We did not develop the Title or Figures for this paper.

References   

1 Lambin, P. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology 14, 749-762 (2017). https://doi.org/10.1038/nrclinonc.2017.141

2 Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71, 209-249 (2021). https://doi.org/10.3322/caac.21660

3 Blandin Knight, S. et al. Progress and prospects of early detection in lung cancer. Open Biology 7, 170070 (2017). https://doi.org/10.1098/rsob.170070

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