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Beatriz Núñez-García



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    EP1.16 - Treatment in the Real World - Support, Survivorship, Systems Research (ID 206)

    • Event: WCLC 2019
    • Type: E-Poster Viewing in the Exhibit Hall
    • Track: Treatment in the Real World - Support, Survivorship, Systems Research
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/08/2019, 08:00 - 18:00, Exhibit Hall
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      EP1.16-28 - ALK Translocated Patients: Survival in an Unselected Population (ID 1426)

      08:00 - 18:00  |  Presenting Author(s): Beatriz Núñez-García

      • Abstract
      • Slides

      Background

      Lung cancer is the leading cause of death from cancer in our environment. About 5% have ALK translocation, which is more frequent in young, Asian women and non-smoking patients. In the last years, multiple treatments have been developed for patients with ALK translocation, improving prognosis and reaching overall survivals (OS) of more than two years.

      Method

      A cohort of 34 patients diagnosed of non-small cell lung cancer with ALK translocation were retrospectively analyzed in our center between 2008-2018. Baseline demographics characteristics were described. OS was calculated as the main objective.

      Result

      Patients were followed a median of 47 months (IQR 30-203). Median age was 59 years (IQR 36-83), being 47% male and 53% female. 44% were never smokers and 58% had any comorbidities. At diagnosis, 83% were symptomatic and the most frequent metastases were bone ones (32%).

      Complete baseline characteristics are shown in Table 1.

      Median OS was 32 months (IQR 15-78). 1-year, 2-year and 3- year survival was 75%, 69% 49.9% respectively. Kaplan-Meier curve is shown in Figure 1.

      Conclusion

      The ALK translocation and targeted treatments have led to a dramatic improvement in overall survival in clinical trials confirmed in our series.

      table 1.png

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    MA16 - Prioritizing Use of Technology to Improve Survival of Lung Cancer Subgroups and Outcomes with Chemotherapy and Surgery (ID 142)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Treatment in the Real World - Support, Survivorship, Systems Research
    • Presentations: 1
    • Now Available
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      MA16.03 - Big Data Analysis for Personalized Medicine in Lung Cancer Patients (Now Available) (ID 2532)

      15:45 - 17:15  |  Author(s): Beatriz Núñez-García

      • Abstract
      • Presentation
      • Slides

      Background

      The use of Big Data in healthcare is in its early days, and most of the potential for value creation remains unclaimed.

      Electronic Health Records (EHR) contain a large amount of information about the patient's condition, which can potentially revolutionize the clinical practice, such information is seldom considered due to the complexity of its extraction and analysis. We report on a first integration of an NLP framework for the analysis of clinical records of lung cancer in Puerta de Hierro University Hospital (HUPHM).

      Method

      A cohort of 1000 patients diagnosed of non-small cell lung cancer (NSCLC) from 2009 to 2018 at HUPHM were included in this observational study. Unstructured clinical data were obtained from the EHR. The semantic indexing and the information analysis was performed by the Politecnica University of Madrid, using Big Data and machine learning techniques. Clinical notes were converted into usable data, and combined with genomic data, images and bibliography, such as PubMed or Drugbank.

      Result

      A total of 251.730 documents were analyzed (240.851 notes and 10.879 reports). These heterogeneous sources of information were analyzed and integrated in an interactive user interface (Figure 1). As a result, all this large amounts of data turns into actionable and exploitable information for clinicians and authorities for planning public health policies and also create new clinical trials.

      The interactive platform will allow the clinician obtain immediate and personalized information of each patient and will elaborate predictive models for long survivors, identify risk patients, reduce overtreatments, etc.

      Conclusion

      By using Big Data we will be able to exploit large amounts of clinical information and combine them with multiple databases developing interactive user interface, increasing lung cancer knowledge and directing medicine towards a more personalized one.

      This work was supported by the EU H2020 programme, under grant agreement Nº 727658 ( Project iASiS).

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    P2.16 - Treatment in the Real World - Support, Survivorship, Systems Research (ID 187)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Treatment in the Real World - Support, Survivorship, Systems Research
    • Presentations: 2
    • Now Available
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.16-20 - Big Data and Survival Predictors in Lung Cancer Patients (ID 1943)

      10:15 - 18:15  |  Presenting Author(s): Beatriz Núñez-García

      • Abstract
      • Slides

      Background

      Lung cancer is the most common and fatal one (18% of all cancer deaths). Parameters which imply better survival are still unknown.

      The objective of this project is to turn the large amount of data from each patient into exploitable information.

      Method

      Between 2008-2019, 935 non-small cell lung cancer patients from our hospital were enrolled in an observational study.

      Unstructured data was obtained from the patient Electronic Health Records.

      Politecnica University from Madrid made the information analysis using Big Data and machine learning techniques.

      Result

      A total of 251.730 documents have been analyzed from 935 patients, 54% in stage IV.

      EGFR/ALK mutation was found in 9%, showing better OS than non-mutated (23.5 months vs 12 months, log-rank p=0.016). Survival curves are shown in figure 1.

      In a multivariate analysis (table 1), independent predictors of mortality were male sex, squamous histology and PS status. Additionally, independent predictors of survival were receiving immunotherapy, surgery treatment or developing endocrine toxicities.

      Conclusion

      Big data is a very useful tool to exploit a large amount of lung cancer data, increasing knowledge about these disease and allowing the development of survival predictive models.


      This work was supported by the EU H2020 programme, under grant agreement Nº 727658 (Project iASiS).

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      P2.16-34 - Is Prophylactic Cranial Irradiation Useful in Real World? (Now Available) (ID 1398)

      10:15 - 18:15  |  Presenting Author(s): Beatriz Núñez-García

      • Abstract
      • Slides

      Background

      Small cell lung cancer (SCLC) is the most aggressive lung cancer subtype. Just one third of patients are diagnosed as limited stage (LS), in which the goal is to perform a radical treatment. However, the majority will develop metastasis, being in central nervous system (CNS) one of the most frequent. In patients with LS, after systemic treatment, prophylactic cranial irradiation (PCI) should be considered. Nevertheless, the effectiveness of PCI has been a controversial issue in terms of overall survival (OS).

      Method

      A cohort of 81 patients diagnosed of localized SCLC were retrospectively analyzed in our center over a 10-year period (January 2008-December 2017). Brain imagen was done before chemo-radiotherapy (CRT) and repeated before PCI. Baseline demographics characteristics and brain metastases rate incidence were described.

      Result

      From 81 patients, 48 received PCI and 33 did not. Complete baseline characteristics from both groups are shown in table 1. No differences were found in performance status at diagnosis between groups . From those who did not receive PCI, 8 (26%) had developed brain metastases after CRT and before PCI. Brain metastases incidence rate in PCI subgroup was 9/100 people per year vs 35/100 people per year in those who did not receive PCI, in whom 54.5% had brain or systemic progression before PCI planning. Progression free survival in both subgroups was 13.5 months and OS was 21.2 months.

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      Conclusion

      In our series, PCI had a significant effect in decreasing brain metastases. This study also confirms the requirement of brain imaging to confirm lack of brain metastases after initial CRT and before PCI.

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