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Maria Torrente



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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
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      MA16.03 - Big Data Analysis for Personalized Medicine in Lung Cancer Patients (Now Available) (ID 2532)

      15:45 - 17:15  |  Presenting Author(s): Maria Torrente

      • 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: 1
    • 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  |  Author(s): Maria Torrente

      • 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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