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Automated identification and assignment of colonoscopy surveillance recommendations for individuals with colorectal polyps

  • Author Footnotes
    ∗ Ms Peterson and Dr May contributed equally to this article.
    Emma Peterson
    Footnotes
    ∗ Ms Peterson and Dr May contributed equally to this article.
    Affiliations
    Department of Radiological Sciences, Data Integration, Architecture, and Analytics Group, University of California Los Angeles, Los Angeles, California, USA
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  • Author Footnotes
    ∗ Ms Peterson and Dr May contributed equally to this article.
    Folasade P. May
    Footnotes
    ∗ Ms Peterson and Dr May contributed equally to this article.
    Affiliations
    Department of Medicine, Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA

    UCLA Center for Cancer Prevention and Control Research, UCLA Kaiser Permanente Center for Health Equity and Department of Health Policy and Management, Fielding School of Public Health and Jonsson Comprehensive Cancer Center, Los Angeles, California, USA

    Division of Gastroenterology, Department of Medicine, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California, USA
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  • Odet Kachikian
    Affiliations
    Department of Radiological Sciences, Data Integration, Architecture, and Analytics Group, University of California Los Angeles, Los Angeles, California, USA
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  • Camille Soroudi
    Affiliations
    Department of Medicine, Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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  • Bita Naini
    Affiliations
    Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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  • Yuna Kang
    Affiliations
    Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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  • Anthony Myint
    Affiliations
    Department of Medicine, Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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  • Gordon Guyant
    Affiliations
    Department of Radiological Sciences, Data Integration, Architecture, and Analytics Group, University of California Los Angeles, Los Angeles, California, USA
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  • Joann Elmore
    Affiliations
    Department of Medicine, Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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  • Roshan Bastani
    Affiliations
    UCLA Center for Cancer Prevention and Control Research, UCLA Kaiser Permanente Center for Health Equity and Department of Health Policy and Management, Fielding School of Public Health and Jonsson Comprehensive Cancer Center, Los Angeles, California, USA
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  • Cleo Maehara
    Affiliations
    Department of Radiological Sciences, Data Integration, Architecture, and Analytics Group, University of California Los Angeles, Los Angeles, California, USA
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  • William Hsu
    Correspondence
    Reprint requests: William Hsu, PhD, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd, Ste 420, Los Angeles, CA 90024.
    Affiliations
    Department of Radiological Sciences, Data Integration, Architecture, and Analytics Group, University of California Los Angeles, Los Angeles, California, USA
    Search for articles by this author
  • Author Footnotes
    ∗ Ms Peterson and Dr May contributed equally to this article.

      Background and Aims

      Determining surveillance intervals for patients with colorectal polyps is critical but time-consuming and challenging to do reliably. We present the development and assessment of a pipeline that leverages natural language processing techniques to automatically extract and analyze relevant polyp findings from free-text colonoscopy and pathology reports. Using this information, we categorized individual patients into 6 postcolonoscopy surveillance intervals defined by the U.S. Multi-Society Task Force on Colorectal Cancer.

      Methods

      Using a set of 546 randomly selected colonoscopy and pathology reports from 324 patients in a single health system, we used a combination of statistical classifiers and rule-based methods to extract polyp properties from each report type, associate properties with unique polyps, and classify a patient into 1 of 6 risk categories by integrating information from both report types. We then assessed the pipeline’s performance by determining the positive predictive value (PPV), sensitivity, and F-score of the algorithm, compared with the determination of surveillance intervals by a gastroenterologist.

      Results

      The pipeline was developed using 346 reports (224 colonoscopy and 122 pathology) from 224 patients and evaluated on an independent test set of 200 reports (100 colonoscopy and 100 pathology) from 100 patients. We achieved an average PPV, sensitivity, and F-score of .92, .95, and .93, respectively, across targeted entities for colonoscopy. Pathology extraction achieved a PPV, sensitivity, and F-score of .95, .97, and .96. The system achieved an overall accuracy of 92% in assigning the recommended interval for surveillance colonoscopy.

      Conclusions

      This study demonstrates the feasibility of using machine learning to automatically extract findings and classify patients to appropriate risk categories and corresponding surveillance intervals. Incorporating this system can facilitate proactive and timely follow-up after screening colonoscopy and enable real-time quality assessment of prevention programs and providers.

      Abbreviations:

      CRC (colorectal cancer), EHR (electronic health record), NLP (natural language processing), PPV (positive predictive value)
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