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Michael Lanuti



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    MA18 - Modelling, Decision-Making and Population-Based Outcomes (ID 920)

    • Event: WCLC 2018
    • Type: Mini Oral Abstract Session
    • Track: Treatment in the Real World - Support, Survivorship, Systems Research
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/25/2018, 13:30 - 15:00, Room 201 F
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      MA18.01 - Non-Small Cell Lung Cancer Risk Assessment with Artificial Neural Networks (ID 13532)

      13:30 - 13:35  |  Author(s): Michael Lanuti

      • Abstract
      • Presentation
      • Slides

      Background

      Lung cancer is a heterogeneous disease with many clinically important subtypes. Given the complexity of classification, there is room for innovative risk assessment tools to help ascertain prognosis and management. In this work we tested an Artificial Neural Network (ANN) to stratify patients into clinically significant low and high risk categories.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      CT imaging, survival, and cancer staging data was extracted for a sample of 311 patients with Stage-I (n = 186) and Stage-II (n = 125) non-small cell lung cancer (NSCLC) from the comprehensive Boston Lung Cancer Survival (BLCS) cohort. Median follow-up from time of diagnosis was 3.5 years, with 86% 2-year survival. A deep convolutional neural network pretrained on ImageNet was used, with fine-tuning of the last convolutional layers, dense layers, and softmax for stratification. Inputs of this model were 50 x 50 mm2 image patches. Training was performed on 182 labeled CT scans (112 Stage-I and 70 Stage-II). 46 cases were used for initial cross-validation, with an independent test set of 83 cases. The median prediction probability from the ANN was used as a cutoff to divide patients into low and high risk groups.

      4c3880bb027f159e801041b1021e88e8 Result

      The model was able to perform classification of cancer stage on the heterogeneous test set (AUC = 0.73, p< 0.0005). The test set was split evenly into low risk (n = 42) and high risk (n= 41) groups based on model predictions. There was statistically significant separation in the Kaplan Meier-estimates for survivorship in the two stratified groups (p < 0.02).

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      8eea62084ca7e541d918e823422bd82e Conclusion

      ANNs can be effective tools for quantitative risk stratification in NSCLC. In addition to the potential for real-time clinical decision support, ANNs may also help create new paradigms in lung cancer risk assessment. The models have the capacity to perform suprahuman computations, which can help meet future demands of clinical practice, given expanding digital-imaging volumes.

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    P1.12 - Small Cell Lung Cancer/NET (Not CME Accredited Session) (ID 944)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/24/2018, 16:45 - 18:00, Exhibit Hall
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      P1.12-17 - Overall Survival and Recurrence After Surgical Resection of Pure and Mixed Large Cell Neuroendocrine Tumors. (ID 13051)

      16:45 - 18:00  |  Author(s): Michael Lanuti

      • Abstract
      • Slides

      Background

      Large-cell neuroendocrine carcinoma (LCNC) is an aggressive tumor with poor prognosis and undefined treatment. We performed a retrospective analysis on the outcomes of surgical resection and adjuvant therapy to assess the effectiveness of treatment.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Retrospective review of patients with LCNC who underwent surgical resection at a single-center tertiary care facility from 2002-2017. Survival times were assessed from day of surgery until death. A Kaplan-Meier method for overall survival (OS) and for recurrence was used and compared across prognostic factors using log-rank analysis and a Cox proportional hazard model.

      4c3880bb027f159e801041b1021e88e8 Result

      Sixty-two patients were identified with a median follow up of 3.4 years. Of these, 26 (41.9%) were male and 56 (90.3%) were current or former smokers. The majority of patients underwent a lobectomy/segmentectomy (72.6%), while a smaller percentage (14.5%) underwent wedge resection, and the remainder pneumonectomy or bi-lobectomy (4.8%). Pathologically, 31 (54.4%) were stage I, 20 (35.1%) stage II, and 6 (10.5%) stage III-IV. Additionally, 35 (56.4%) represented pure LCNC while the remaining 27 (43.6%) had a mixed histology. Median OS for resected stage I disease was 11.3 years, decreased to 4.4 years in stage II disease, and was 0.8 years in stage III-IV disease (p = 0.01) (Figure 1). For those that recurred, median time to recurrence was 1.20 years for stage I and 1.15 years for stage II disease. Adjuvant therapy, type of resection, and tumor histology (pure vs. mixed) had no significant impact on OS on unadjusted or adjusted analysis.

      figure1.jpg

      8eea62084ca7e541d918e823422bd82e Conclusion

      LCNC is associated with early recurrence after surgical resection and poor survival for patients with stage III and IV disease. In patients with mixed histology survival and recurrence remain similar to those with pure LCNC tumors.

      6f8b794f3246b0c1e1780bb4d4d5dc53

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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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      P3.16-01 - A Multi-Omic Study Reveals BTG2 as a Reliable Prognostic Marker for Early-Stage Non-Small Cell Lung Cancer (ID 13586)

      12:00 - 13:30  |  Author(s): Michael Lanuti

      • Abstract
      • Slides

      Background

      Background: B-cell translocation gene 2 (BTG2), which functions as a tumor suppress gene, has been reported to be involved in several cancers. However, no study has focused on its role in lung cancer progression or prognosis. We aimed to investigate the role of BTG2 in early-stage non-small cell lung cancer (NSCLC) survival

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Patients and Methods: This study included 1,230 early-stage (I, II) surgically treated NSCLC patients with methylation and expression data from five international cohorts. We built a prognostic model based on BTG2 methylation. Then, we explored BTG2 expression and NSCLC survival in 3,038 cases, including the above-mentioned cohorts as well as 17 extended public datasets by meta-analysis. Further, we integrated the clinical information, expression, and methylation to build an integration model and evaluated its prediction ability using C-index.

      4c3880bb027f159e801041b1021e88e8 Result

      Results: Three risk CpG probes (cg01798157, cg06373167, cg23371584) were associated with overall survival. The prognostic model based on methylation could distinguish patient survival in the four cohorts [hazard ratio (HR) range, 1.51 to 2.21] and the independent validation set (HR = 1.85). In the expression analysis, BTG2 acted as a tumor-suppress gene in each cohort (HR range, 0.28 to 0.68). A meta-analysis showed high BTG2 expression was associated with better survival (HR = 0.61, 95%CI: 0.54-0.68). The three CpG probes were all negatively correlated with BTG2 expression. Further, the integration model based on BTG2 methylation, expression and clinical information showed a better prediction ability in the training set and validation set.

      8eea62084ca7e541d918e823422bd82e Conclusion

      Conclusions: The methylation and integrated prognostic signatures based on BTG2 are stable and reliable biomarkers for early-stage NSCLC. They may have new applications for appropriate clinical adjuvant trials and personalized treatments in the future.

      6f8b794f3246b0c1e1780bb4d4d5dc53

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