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    P3.16 - Treatment of Early Stage/Localized Disease (Not CME Accredited Session) (ID 982)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/26/2018, 12:00 - 13:30, Exhibit Hall
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      P3.16-10 - Radiomic Features on CT are Prognostic of Recurrence as well as Predictive of Added Benefit of Adjuvant Chemotherapy in ES-NSCLC (ID 14270)

      12:00 - 13:30  |  Author(s): Pingfu Fu

      • Abstract
      • Slides

      Background

      Early-Stage non-small cell lung cancer (ES-NSCLC) accounts for approximately 40% of NSCLC cases, with 5-year survival rates varying between 31-49%. The decision to offer adjuvant chemotherapy for these patients is primarily dependent on several clinical and visual radiographic factors as there is a lack of biomarkers which can accurately stratify and predict disease risk

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Retrospective chart review between 2005-14 yielded 315 ES-NSCLC patients who underwent surgery with the primary tumor having relapsed in 75 cases. From the entire cohort, 74 underwent adjuvant chemotherapy. This cohort was randomly divided into a training(N=60) and validation(N=255). A total of 248 intratumoral(IT) and peritumoral(PT) radiomic textural features were extracted for every patient. The most stable, significant and uncorrelated features were selected from training cohort using LASSO Cox-regression model. Performance of imaging features was evaluated using hazard ratio(HR) and concordance index(CI). Linear Discriminant Classifier(LDA) was trained using top imaging features and performance of predicted labels was assessed using Kaplan-Meier survival curves and log-rank test.

      4c3880bb027f159e801041b1021e88e8 Result

      Top nine radiomic textural features (from the Haralick, Collage, Laws, Gabor texture families) included a combination of four IT and five PT from 0-12mm distance outside the nodule. The features were prognostic of recurrence (N=255, CI=0.66, HR =1.8, p<0.05). To evaluate the predictive model, subset analysis was performed on the test set. The imaging feature based classifier was able to identify low and high risk groups in the surgery alone setting (N=181, CI=0.73, HR=4.4, p<0.005), potentially identifying patients who might have benefitted from adjuvant chemotherapy. Meanwhile, in the group of patients who received adjuvant chemotherapy following surgery, the classifier did not identify any difference between high and low risk groups (N=74, CI=0.69, HR=1, p>0.05).

      8eea62084ca7e541d918e823422bd82e Conclusion

      We identified radiomic features from within and outside lung nodule that were prognostic of recurrence and also predictive of added benefit of adjuvant chemotherapy in ES-NSCLC.

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