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Cancer Survival Analysis01:21

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Refined Algorithm for Identifying Recurrence Among Patients with Non-Metastatic Colorectal Cancer Based on Danish

Mikail Gögenur1, Karoline Bendix Bräuner1, Lea Löffler1

  • 1Center for Surgical Science, Department of Surgery, Zealand University Hospital, Koge, Denmark.

Clinical Epidemiology
|December 18, 2025
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Summary

A refined algorithm improves colorectal cancer (CRC) recurrence detection in national health registries. This enhanced method increases patient inclusion and provides better population subgroup representation for accurate CRC recurrence surveillance.

Keywords:
colorectal cancerrecurrenceregistry-based algorithm

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Area of Science:

  • Oncology
  • Public Health
  • Health Informatics

Background:

  • Colorectal cancer (CRC) recurrence is not routinely registered in national health registries.
  • Existing algorithms for CRC recurrence detection in Denmark have selection biases due to exclusion criteria.
  • There is a need for a more generalizable and refined algorithm for identifying CRC recurrence.

Purpose of the Study:

  • To refine an existing registry-based algorithm for detecting colorectal cancer recurrence.
  • To increase the generalizability and reduce selection bias of the algorithm.
  • To improve the accuracy of CRC recurrence identification in national health data.

Main Methods:

  • Utilized data from 5077 patients with non-metastatic CRC from 2008-2019.
  • Linked electronic health records with multiple Danish national health registries.
  • Refined the algorithm by adding targeted/radiation therapy codes, including patients who died within 180 days, revising pathology codes, and removing preoperative exclusions.

Main Results:

  • The refined algorithm included more patients (4388 vs 3684) compared to the conventional algorithm.
  • Demonstrated marginally improved sensitivity (0.92 vs 0.90) and specificity (0.97 vs 0.96).
  • Corrected a significant difference in cumulative incidence of recurrence for UICC stage I previously detected by the conventional algorithm.

Conclusions:

  • The refined algorithm enhances the identification of colorectal cancer recurrence in national datasets.
  • The improved algorithm allows for broader patient inclusion and better representation of diverse population subgroups.
  • This advancement supports more accurate population-level surveillance of CRC recurrence.