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Robert Foerster



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    Lunch & Poster Display session (ID 58)

    • Event: ELCC 2019
    • Type: Poster Display session
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 4/11/2019, 12:30 - 13:00, Hall 1
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      56P - CT image standardization is superior to larger but heterogeneous data for robust radiomic models (ID 433)

      12:30 - 13:00  |  Author(s): Robert Foerster

      • Abstract

      Background

      Radiomics is a promising tool for identification of new prognostic biomarkers. Radiomic models are often based on single-institution data however multi-centric data, highly heterogeneous due to different scanning protocols, reflect better the clinical reality. Robustness studies are crucial to find features robust to e.g. scanner settings. We study if a CT radiomics overall survival (OS) model trained on multi-centric data with robust feature pre-selection can achieve a similar performance as a model on standardized data.

      a9ded1e5ce5d75814730bb4caaf49419 Methods

      Pre-treatment CT data from 121 IIIA/N2 NSCLC patients from a prospective multi-centric randomized trial (SAKK 16/00, neoadj. chemo- or radiochemotherapy prior to surgery) were used to calculate 1404 radiomic features on the primary tumor. Two OS radiomic models were trained on (1) a sub-cohort with standardized imaging protocol (native CT, standard kernel, n = 84) and on (2) the entire heterogeneous cohort but with robust radiomic feature pre-selection. Robust features were extracted from four robustness studies (contrast, convolution kernel, motion, delineation) using the intra-class correlation coefficient (> 0.9 considered stable). Seperately for each model, principal component (PC) analysis was performed and PCs describing in total 95% data variance were selected. The feature with highest correlation to the PCs were used for the multivariate Cox model with backward selection. Model performances were quantified using Concordance Index (CI), verified with 10-fold cross-validation and compared using bootstrap with resampling.

      20c51b5f4e9aeb5334c90ff072e6f928 Results

      Robustness studies revealed 113 stable features. The convolution kernel was the largest influence on the feature stability. Final OS model on the entire heterogeneous data consisted of four and on standardized data of six features (all identified as unstable). The model on standardized data showed significant better prognostic performance compared to the model with robust feature pre-selection based on the entire heterogeneous data (CI = 0.64 and 0.61, p < 0.05, resp.).

      fd69c5cf902969e6fb71d043085ddee6 Conclusions

      For our prognostic NSCLC radiomic models image protocol standardization appears superior to using larger but heterogeneous imaging data combined with robust feature pre-selection.

      b651e8a99c4375feb982b7c2cad376e9 Legal entity responsible for the study

      The authors.

      213f68309caaa4ccc14d5f99789640ad Funding

      Swiss National Science Foundation (SNSF) Swiss Group for Clinical Cancer Research SAKK.

      682889d0a1d3b50267a69346a750433d Disclosure

      M. Guckenberger: Board member: European Society for Therapeutic Radiation Oncology (ESTRO). All other authors have declared no conflicts of interest.

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