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Automated real-world data integration improves cancer outcome prediction.

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Harnessing unstructured health data with natural language processing (NLP) and genomic information significantly improves cancer outcome prediction models. This approach enhances understanding of clinicogenomic relationships for better patient care.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Medical Informatics

Background:

  • Digitized health records and tumor DNA sequencing offer rich data for cancer outcome research.
  • Patient data often exists in unstructured text and siloed datasets, limiting comprehensive analysis.
  • Integrating diverse data sources is crucial for advancing precision oncology.

Purpose of the Study:

  • To create a harmonized clinicogenomic real-world dataset (MSK-CHORD) by combining NLP annotations with structured clinical and genomic data.
  • To leverage this dataset for discovering novel clinicogenomic relationships.
  • To develop and validate machine learning models for predicting patient outcomes, including overall survival and metastasis.

Main Methods:

  • Combined natural language processing (NLP) annotations with structured data (medication, demographics, tumor registry, genomics) from 24,950 patients.
  • Developed the Memorial Sloan Kettering-Cancer Research Commons (MSK-CHORD) dataset, including data for lung, breast, colorectal, prostate, and pancreatic cancers.
  • Trained machine learning models to predict overall survival and metastatic potential using features derived from NLP and genomic data.

Main Results:

  • Machine learning models incorporating NLP-derived features (e.g., sites of disease) outperformed models based solely on genomic data or cancer stage for predicting overall survival.
  • The MSK-CHORD dataset enabled the discovery of clinicogenomic relationships not apparent in smaller datasets.
  • Identified predictors of metastasis to specific organ sites, including a validated association between SETD2 mutation and reduced metastatic potential in lung adenocarcinoma.

Conclusions:

  • Automated annotation of unstructured clinical notes using NLP is feasible and valuable for predicting patient outcomes.
  • The integrated clinicogenomic dataset (MSK-CHORD) significantly enhances the ability to uncover complex cancer determinants.
  • The MSK-CHORD dataset is released as a public resource to facilitate real-world oncologic research.