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Michael Yang



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    MA15 - Usage of Computer and Molecular Analysis in Treatment Selection and Disease Prognostication (ID 141)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Pathology
    • Presentations: 1
    • Now Available
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      MA15.05 - Computerized Measurements of Cellular Diversity on H&E Tissue Are Prognostic of OS and Associated with Mutational Status in NSCLC (Now Available) (ID 1975)

      15:45 - 17:15  |  Author(s): Michael Yang

      • Abstract
      • Presentation
      • Slides

      Background

      Tumor heterogeneity is known to be implicated in chemotherapeutic resistance and poor prognosis for non-small cell lung cancer (NSCLC). In this study we sought to evaluate the role of computer extracted features reflecting the intrinsic cellular morphological diversity (ICMD) of tumors from digitized H&E stained images of early-stage NSCLC patients. Additionally, we sought to evaluate the association of these ICMD features in adenocarcinomas with the ALK and EGFR mutational status.

      Method

      Two cohorts, D1 and D2, of digitized H&E stained tissue microarray images (TMA) of NSCLC, n=395 and n=91, respectively, were used for modeling the ICMD predictor. A pretrained deep learning model was used for segmentation of nuclei, and clusters of proximally located nuclei were identified. The ICMD features were then extracted as the variations in shape, size, and texture measurements of nuclei within the clusters. A Cox proportional hazard model using the ICMD features was then trained for lung adenocarcinomas (LUAD, n=270), and squamous cell carcinomas (LUSC, n=216), separately, and was validated on independent cohort from (D3) The Cancer Genome Atlas (TCGA) (n=473) to predict Overall Survival (OS). Univariate and multivariate analyses were performed on (D3).

      Result

      In (D3), high risk patients predicted by the ICMD features had significantly poorer survival (HR (95% CI) = 1.48 (1.06-2.06), p=0.021 for LUSC, HR (95% CI) = 1.59 (1.11-2.29), p=0.006 for LUAD) in univariate analysis. In multivariate analysis, controlling for major clinical variables, ICMD was independently associated with 5-year OS (p<0.016). (See Table 1) We also found that ICMD features were associated with driver mutations ALK (p=0.0204) and EGFR (p=0.0017) in LUAD.

      Table 1| Multivariate analysis for overall survival on the validation set D3.

      Multivariate Cox Proportional Hazard Model Analysis Controlling for Other Variables

      TCGA-LUSC

      TCGA-LUAD

      Variable

      HR (95% CI)

      p value

      HR (95% CI)

      p value

      Age (>65 vs <=65)

      1.14(0.81-1.61)

      0.451

      0.89(0.63-1.28)

      0.540

      Smoking status

      1.36(0.83-2.23)

      0.221

      1.14(0.64-2.01)

      0.661

      Overall Stage (Stage II vs I)

      1.13(0.66-1.94)

      0.651

      1.86(1.04-3.32)

      0.037

      T-Stage (T2,3 vs T1)

      1.26(0.85-1.87)

      0.244

      1.25(0.85-1.85)

      0.263

      N-Stage (N1 vs N0)

      1.36(0.77-2.41)

      0.292

      3.11(1.55-6.23)

      0.001

      Developed Model

      High risk vs. Low risk

      1.52(1.08-2.13)

      0.016

      1.55(1.09-2.22)

      0.015

      CI = 95% confidence interval; HR = Mantel-Haenszel Hazard ratio. Values in bold are statistically significant, p<=0.05.

      Conclusion

      Computer extracted image features of cellular diversity were able to predict OS in NSCLC and were also associated with the ALK and EGFR mutational status. Future work will entail evaluating ICMD features in predicting added benefit of adjuvant therapy in early stage NSCLCs as well as correlating with gene expression data.

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