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Hao-Jen Wang

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    OA03 - Advances in Lung Cancer Pathology (ID 897)

    • Event: WCLC 2018
    • Type: Oral Abstract Session
    • Track: Pathology
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/24/2018, 10:30 - 12:00, Room 205 BD
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      OA03.06 - Extraction of Radiomic Values from Lung Adenocarcinoma with Near-Pure Histological Subtypes (ID 13840)

      11:25 - 11:35  |  Author(s): Hao-Jen Wang

      • Abstract
      • Presentation
      • Slides


      Histological subtypes of lung adenocarcinomas classified by the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) system have been investigated using radiomic approaches. However, the results have had limitations since of invasive lung adenocarcinomas may be heterogeneous, with two or more subtypes. To reduce the influence of heterogeneity during radiomic analysis, computed tomography (CT) images of lung adenocarcinomas with near-pure adenocarcinoma subtypes were analyzed to extract representative radiomic features of different subtypes.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      We enrolled 95 patients who underwent complete resection for lung adenocarcinoma and a pathological diagnosis of a “near-pure” (≥70%) IASLC/ATS/ERS histological subtype. Conventional histogram/morphological features and complex radiomic features (grey-level-based statistical features and component variance-based features) of thin-cut CT data of tumor regions were analyzed. A prediction model based on leave-one-out cross-validation (LOOCV) and logistic regression (LR) was used to classify all five subtypes and three pathologic grades (lepidic, acinar/papillary, micropapillary/solid) of adenocarcinomas. The validation was performed using 36 near-pure adenocarcinomas in a later cohort.

      4c3880bb027f159e801041b1021e88e8 Result

      A total of 31 lepidic, 14 papillary, 32 acinar, 10 micropapillary, and 8 solid adenocarcinomas were analyzed. With 21 conventional and complex radiomic features, for 5 subtypes and 3 pathological grades, the prediction models achieved accuracy rates of 84.2% (80/95) and 91.6% (87/95), respectively, while accuracy was 71.6% and 85.3%, respectively, if only conventional features were used. The accuracy rate for the validation set (n=36) was 83.3% (30/36) and 94.4% (34/36) in 5 subtypes and 3 pathological grades, respectively, using conventional and complex features, while it was 66.7% and 77.8% only using conventional features, respectively.

      8eea62084ca7e541d918e823422bd82e Conclusion

      Lung adenocarcinoma with high purity histological subtypes demonstrates strong stratification of radiomic values, which provide basic information for accurate pathological subtyping and image parcellation of tumor sub-regions.

      figure for wclc 2018.png


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