Laurent Dercle, MD, PhD, Receives Two 2023 RSNA Research Awards

Second-year radiology resident Laurent Dercle, MD, PhD, has been awarded two grants from the Radiological Society of North America (RSNA). He is the recipient of a 2023 RSNA Research and Education Foundation Resident/Fellow Research Grant as well as a 2023 RSNA Roentgen Resident/Fellow Research Award.

Dercle will receive a total of $50,000 for his project, "Prospective deployment of artificial intelligence software using standard-of-care CT scans to predict the efficacy of cancer treatments." His research is focused on artificial intelligence (AI)-driven extraction of information from medical images to guide clinical decision-making in cancer patients who are being treated with systemic therapies such as immunotherapy.

By training AI on routine standard-of-care medical images, Dercle's signatures have consistently outperformed historical standard-of-care tumor size-based imaging biomarkers. Dercle has identified critical barriers to translation into clinical practice, which he plans to address in the next phase of his research.

Dercle joined the Department of Radiology in 2016 as an associate research scientist with proficiency in radiology, nuclear medicine, oncology, engineering, and artificial intelligence. He is currently completing a radiology residency in the department. Dercle's extensive research has focused on applying AI to enhance the clinical care of patients diagnosed with cancer, resulting in publications in prestigious scientific journals and numerous grants and awards. Trained at Gustave Roussy, Europe's leading cancer center, Dercle is dedicated to advancing AI-guided precision medicine to improve drug development and clinical care in oncology.


Selected Publications

  1. Emerging and Evolving Concepts in Cancer Immunotherapy Imaging, Radiology, 2023.
  2. Early readout on overall survival of patients with melanoma treated with immunotherapy using a novel imaging analysis, JAMA oncology, 2022.
  3. High serum LDH and liver metastases are the dominant predictors of primary cancer resistance to anti-PD (L) 1 immunotherapy, European Journal of Cancer, 2022.
  4. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy, Journal for Immunotherapy of Cancer, 2022.
  5. Toward generalizability in the deployment of artificial intelligence in radiology: role of computation stress testing to overcome underspecification, Radiology: Artificial Intelligence, 2021.
  6. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules, European radiology, 2020.
  7. Radiomics response signature for identification of metastatic colorectal cancer sensitive to therapies targeting EGFR pathway, JNCI: Journal of the National Cancer Institute, 2020.
  8. Identification of Non–Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using RadiomicsRadiomics Signatures in NSCLC, Clinical Cancer Research, 2020.
  9. 18F-FDG PET and CT scans detect new imaging patterns of response and progression in patients with Hodgkin lymphoma treated by anti–programmed death 1 immune checkpoint inhibitor, Journal of Nuclear Medicine, 2018.
  10. Vol-PACT: a foundation for the NIH public-private partnership that supports sharing of clinical trial data for the development of improved imaging biomarkers in oncology, JCO clinical cancer informatics, 2018.
  11. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study, The Lancet Oncology, 2018.
  12. Hyperprogressive Disease Is a New Pattern of Progression in Cancer Patients Treated by Anti-PD-1/PD-L1Hyperprogressive Disease with Anti-PD-1/PD-L1 Therapy, Clinical Cancer Research, 2017.
  13. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology, Annals of Oncology, 2017.