Simplified, standardized methods to assess the accuracy of clinical cancer staging. (2020)

• Record Type: Journal Article
• Authors: Wu, Dolly Y.; Spangler, Ann E.; Vo, Dat T.; de Hoyos, Alberto; Seiler, Stephen J.
• Is Part Of: Cancer treatment and research communications. Number 25(2020)
• Publication Date: 2020
• Subjects: Cancer staging -- Accuracy -- Monitoring methods -- Quality -- Lung cancer -- Registry data -- SEER
• DOI: 10.1016/j.ctarc.2020.100253
• Languages: English
• ISSNs: 2468-2942

Abstract

Highlights: Introduce simple, intuitive methods to assess and monitor accuracy of clinical cancer staging to help hospitals improve staging and standardize reporting of discordance with pathological staging. Provide new, unambiguous definitions of agreement between clinical and pathological staging. Quantify clinical and pathological errors due to biases in measuring tumor length and staging categorization. Apply methods to over 9600 surgically treated non-small cell lung cancer cases from the U.S. National Cancer Institute's SEER program. Provide quality numerical results so that hospitals may use them to benchmark their own results. Explain and demonstrate how to implement the new methods. Abstract: Background: Hospitals lack intuitive methods to monitor their accuracy of clinical cancer staging, which is critical to treatment planning, prognosis, refinements, and registering quality data. Methods: We introduce a tabulation framework to compare clinical staging with the reference-standard pathological staging, and quantify systematic errors. As an example, we analyzed 9, 644 2016 U.S. National Cancer Institute SEER surgically-treated non-small cell lung cancer (NSCLC) cases, and computed concordance with different denominators to compare with incompatible past results. Results: The concordance for clinical versus pathological lymph node N-stage is very good, 83.4 ± 1.0%, but the tumor length-location T-stage is only 58.1 ± 0.9%. There are intuitive insights to the causes of discordance. Approximately 29% of the cases are pathological T-stage greater than clinical T-stage, and 12% lower than the clinical T-stage, which is due partly to the fact that surgically-treated NSCLC are typically lower-stage cancer cases, which results in a bounded higher probability for pathological upstaging. Individual T-stage categories Tis, T1a, T1b, T2a, T2b, T3, T4 invariant percent-concordances are 85.2 ± 9.7 + 10.3%; 72.7 ± 1.6 + 11.3%; 46.6 ± 1.8 + 10.9%; 54.6 ± 1.6 – 20.5%; 41.6 ± 3.3 – 0.1%; 54.7 ± 2.8 – 24.1%; 55.2 ± 4.7 + 2.6%, respectively. Each percent-concordance is referenced to an averaged number of pathological and clinical cases. The first error number quantifies statistical fluctuations; the second quantifies clinical and pathological staging biases. Lastly, comparison of over and under staging versus clinical characteristics provides further insights. Conclusions: Clinical NSCLC staging accuracy and concordance with pathological values can improve. As a first step, the framework enables standardizing comparing staging results and detecting possible problem areas. Cancer hospitals and registries can implement the efficient framework to monitor staging accuracy.