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Poster display session

1102 - Evaluation of a Predictive Radiomics Signature for Response to Immune Checkpoint Inhibitors (ICIs)

Date

11 Sep 2017

Session

Poster display session

Presenters

Amy Prawira

Citation

Annals of Oncology (2017) 28 (suppl_5): v22-v42. 10.1093/annonc/mdx363

Authors

A. Prawira1, A.B. Stundzia2, P. Dufort3, J. Halankar3, D.M. Paravasthu3, A. Spreafico1, A.R. Hansen1, A.R. Abdul Razak1, P.L. Bedard1, M. Butler1, S. Lheureux1, A.M. Oza1, R.W. Jang1, K.M. Suta1, S. Boross-Harmer1, J. Cipollone1, H. Chow1, U. Metser1, L.L. Siu1

Author affiliations

  • 1 Division Of Medical Oncology And Hematology, Princess Margaret Cancer Centre, M5G 1Z5 - Toronto/CA
  • 2 Medical Image Analysis, Tomographix IP, M5E 1W7M5E - Toronto/CA
  • 3 Joint Department Of Medical Imaging, University Health Network, M5G 2M9 - Toronto/CA
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Resources

Abstract 1102

Background

Radiomics (RAD) uses advanced image processing techniques to extract a large set of quantitative texture and geometric features from tumor regions of interest, and subject these to a supervised machine learning protocol to train a classifier, which we exploit to develop a predictive signature of response to ICIs. We previously developed a lesion-based predictive RAD classifier of response for recurrent/metastatic squamous cell carcinoma of the head and neck (RM SCCHN) pts to ICIs based on RAD features extracted from their CT images (Prawira, ESMO 2016).

Methods

INSPIRE (NCT02644369) is an investigator-initiated phase II study evaluating biomarkers for pembrolizumab (anti-PD1 monoclonal antibody) in multiple cohorts of pts with advanced solid tumors. The primary endpoint of this project is to validate the previously developed RAD classifier from RM SCCHN pts, with pts from INSPIRE. Texture feature algorithm generation and accuracy determination were as previously described. Cross validation accuracy values were generated for combinations of 3 parameters: fraction, cost, and gamma, yielding a 3 dimensional (3D) accuracy space.

Results

Eighty lesions from 23 pts were available for analysis: median age 59, 22% males. Best response: 12 progressive disease, 3 partial response, 8 stable disease (median duration 18 weeks). Primary site: SCCHN/2, triple negative breast cancer/4, high-grade serous ovarian cancer/11, malignant melanoma/2, other advanced cancers/4. Twentyseven lesions were excluded as RECIST 1.1 responses were not yet available. Fiftythree target lesions were contoured. Per lesion RECIST 1.1 radiological outcome: 17 R, 36 NR. Cross validation in the 3D space yielded a set of ROC curves with an accuracy of 71.4% (AUC 0.41, p = 0.7) with 11.2% sensitivity and 99.9% specificity, where specificity corresponds to the proportion of NR tumors classified correctly, and sensitivity to the proportion of R tumors classified correctly.

Conclusions

Heterogeneous histologies and low pt numbers may account for the negative result in this study, suggesting that RAD may be histology-specific. Further validation in a large independent cohort of RM SCCHN pts treated with pembrolizumab is planned.

Clinical trial identification

INSPIRE (NCT02644369)

Legal entity responsible for the study

Princess Margaret Cancer Centre, Drug Development Program

Funding

Merck

Disclosure

All authors have declared no conflicts of interest.

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