Virtual Library

Start Your Search

Ho Yun Lee



Author of

  • +

    OA14 - Update of Phase 3 Trials and the Role of HPD (ID 148)

    • Event: WCLC 2019
    • Type: Oral Session
    • Track: Immuno-oncology
    • Presentations: 1
    • Now Available
    • +

      OA14.07 - Clinical and Genetic Characterization of Hyperprogression Based on Volumetry in Advanced NSCLC Treated with Immunotherapy (Now Available) (ID 1067)

      11:30 - 13:00  |  Author(s): Ho Yun Lee

      • Abstract
      • Presentation
      • Slides

      Background

      Hyperprogressive disease (HPD), characterized by accelerated tumor progression, has been proposed as a new pattern of progression following immune checkpoint inhibitor (ICI) treatment. The aim of this study was to describe the characteristics of HPD and investigate its predictive markers.

      Method

      Clinical and radiological findings of 335 advanced non-small cell lung cancer (NSCLC) patients treated with ICI monotherapy were retrospectively analyzed. Radiological data were quantitatively and longitudinally analyzed for tumor size and volume by comparing baseline and follow-up computerized tomography results. The findings were matched to individual genomic profiles generated by deep sequencing of 380 genes.

      Result

      Among 135 patients with progressive disease (PD), as assessed by RECIST, 48 (14·3% of total and 35·6% among PD) and 44 (13·1% of total and 32·6% among PD) were found to have HPD by volumetric (HPDV) and one-dimensional (HPDR) analysis, respectively. HPDV patients were associated with significantly inferior overall survival (OS) compared with non-HPDV PD patients (median OS (months), 4·7 [95% confidence interval (CI), 3·5–11·9)] vs. 7·9 [95% CI, 6·0–13·5], p=0·004); OS did not differ between HPDR and non-HPDR patients. HPDV status was an independent OS factor. Derived neutrophil-to-lymphocyte ratio (dNLR) greater than 4 and lactate dehydrogenase (LDH) greater than the upper normal limit were significantly associated with HPDV. Moreover, we identified coinciding KRAS and STK11 mutations in the HPDV cohort (3/16), while none were found in the non-HPDV cohort (0/28).

      Conclusion

      Defining HPD treated with ICI based on volumetric measurement is more precise than that based on one-dimensional analysis. Pre-ICI dNLR, LDH, and concurrence of STK11 and KRAS mutations could, thus, be used as potential biomarkers for HPD prediction.

      Only Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login, select "Add to Cart" and proceed to checkout. If you would like to become a member of IASLC, please click here.

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.

  • +

    P1.04 - Immuno-oncology (ID 164)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Immuno-oncology
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
    • +

      P1.04-48 - Prediction of Tumor Doubling Time of Lung Adenocarcinoma Using Radiomics Margin Characteristics (Now Available) (ID 443)

      09:45 - 18:00  |  Author(s): Ho Yun Lee

      • Abstract
      • Slides

      Background

      Because shape or irregularity along the tumor perimeter can result from interactions between the tumor and the surrounding parenchyma, there could be a difference in tumor growth rate according to tumor margin or shape. However, no attempt has been made to evaluate the correlation between margin or shape features and tumor growth. Thus, the purpose of our study was to identify the tumor doubling time (DT) of lung adenocarcinomas (ADCs) through margin-related radiomic features.

      Method

      We evaluated 52 lung ADC patients who had at least two computed tomographic (CT) examinations before curative resection. Volume-based DTs were calculated based on CT scans, and patients were divided into two groups according to the growth pattern of their ADCs (gradually growing tumors [GP I] vs. growing tumors with a temporary decrease in DT [GP II]). CT radiomic features reflecting margin characteristics were extracted, and radiomic features reflective of tumor DT were selected.

      Result

      Among the 52 patients, 41 (78.8%) were assigned to GP I and 11 (21.2%) to GP II. Of the 94 radiomic features extracted, eccentricity, surface-to-volume ratio, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5) were ultimately selected for tumor DT prediction. Selected radiomic features in GP I were surface-to-volume ratio, contrast, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5), similar to those for total subjects, whereas the radiomic features in GP II were solidity, energy, and busyness.

      Conclusion

      This study demonstrated the potential of margin-related radiomic features to predict tumor DT in lung ADCs. The results of this study may help predict tumor aggressiveness and behavior in patients with lung ADC and contribute to the development of treatment strategies

      Only Active Members that have purchased this event or have registered via an access code will be able to view this content. To view this presentation, please login or select "Add to Cart" and proceed to checkout.