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ePoster Display

1357P - A deep radiomics approach to assess PD-L1 expression and clinical outcomes in patients with advanced non-small cell lung cancer treated with immune checkpoint inhibitors: A multicentric study

Date

16 Sep 2021

Session

ePoster Display

Topics

Staging and Imaging;  Immunotherapy;  Translational Research

Tumour Site

Non-Small Cell Lung Cancer

Presenters

Marion Tonneau

Citation

Annals of Oncology (2021) 32 (suppl_5): S949-S1039. 10.1016/annonc/annonc729

Authors

M. Tonneau1, K. Phan2, S. Kazandjian3, A. Elkrief3, J. Panasci3, C. Richard1, A. Nolin-Lapalme1, R. El Sayed1, L. Ding2, T. Nair2, J. Malo1, F. Chandelier2, K. Kafi2, J. O'Brien4, L. Di Jorio2, T. Muanza3, B. Routy1

Author affiliations

  • 1 Onco-microbiome, CRCHUM - Centre de recherche du CHUM, H2X 0A9 - Montréal/CA
  • 2 Imagia, IMAGIA, Montréal/CA
  • 3 Department Of Oncology, McGill University and Jewish General Hospital Segal Cancer Center, H3T 1E2 - Montréal/CA
  • 4 Dept. Of Diagnostic Radiology, Jewish General Hospital, H3T 1E2 - Montréal/CA

Resources

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Abstract 1357P

Background

Recent studies suggest imaging-based signatures can be a promising non-invasive approach to predict response to immune checkpoint inhibitors (ICI). In this multi-centric study, we explored the use of radiomics models to characterize the relationship between PD-L1 expression, imaging features, and clinical outcomes.

Methods

Primary lung lesion on pre-ICI CT scans were semi-automatically annotated from 299 advanced NSCLC patients from two institutions (n=171 CHUM and n=128 JGH). Multivariate models incorporating clinical variables (e.g., ECOG, 1st line vs 2nd line, stage, smoking status), PD-L1 expression, and PyRadiomic (PR) or DeepRadiomics (DR) features were developed in a discovery cohort (80%) to predict ORR, PFS at 6 months, OS at 1 year and were evaluated in a validation cohort (20%).

Results

Baseline characteristic and clinical outcomes were well balanced, moreover, principal component analysis did not reveal difference in radiomics features between both centers. The ORR was 55.1% and the median PFS and OS were 5.4 and 14.3 months, respectively. DR features were significantly superior to predict PD-L1 ≥ 50% compared to PR in the discovery cohort (AUC 0.81 [95%CI 0.76-0.87] vs 0.72 [95%CI 0.64-078]). Their performance in the validation cohort were 0.63 [95%CI 0.51-0.79] and 0.61 [95%CI 0.47-0.75], respectively. Incorporation of radiomic features improved the discrimination of ORR, PFS, and OS beyond clinical variables and PD-L1 prediction potential in the discovery cohort. For ORR and PFS neither PR nor DR improved prediction on validation cohort. However, in the validation cohort the AUC for OS of clinical variables and PD-L1 was AUC 0.68 (95%CI 0.54-0.80) compared to 0.59 (95%CI 0.46-0.73) and 0.66 (95%CI 0.51-0.83) with PR and DR, respectively.

Conclusions

These results show the added potential of deep radiomics methods in predicting PD-L1 expression as well as ICI response in addition to clinical variables while demonstrating the challenges of their generalizability. Expansion of the study is underway to improve sample size and explore novel feature engineering methods better suited for generalization.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

B. Routy.

Funding

FRQS - Concours Onco-Tech.

Disclosure

K. Phan: Other, Personal, Full or part-time Employment: Imagia. L. Ding: Financial Interests, Personal, Full or part-time Employment: Imagia. T. Nair: Financial Interests, Personal, Full or part-time Employment: Imagia. F. Chandelier: Financial Interests, Personal, Full or part-time Employment: Imagia. K. Kafi: Financial Interests, Personal, Full or part-time Employment: Imagia. L. Di Jorio: Financial Interests, Personal, Full or part-time Employment: Imagia. All other authors have declared no conflicts of interest.

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