9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Validation of the 8th Edition American Joint Commission on Cancer (AJCC) Gallbladder Cancer Staging System: Prognostic Discrimination and Identification of Key Predictive Factors

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Simple Summary

          Despite implementing numerous changes in the American Joint Committee on Cancer (AJCC) staging system for gallbladder cancer (GBC), the ability to accurately prognosticate survival in these patients has not been vigorously evaluated. The purpose of our study was to compare the prognostic ability of AJCC 7th and 8th edition, investigate the effect of AJCC 8th edition nodal status on the survival, and identify risk factors associated with the survival after N reclassification in GBC patients. We used the largest cancer database in the United States and determined that the updated AJCC 8th edition GBC staging system was comparable to the 7th edition, with no major improvements in survival discrimination. The recently implemented changes in N classification do not appear to improve the prognostic performance of the AJCC cancer staging system with regard to survival in GBC patients.

          Abstract

          The scope of our study was to compare the predictive ability of American Joint Committee on Cancer (AJCC) 7th and 8th edition in gallbladder carcinoma (GBC) patients, investigate the effect of AJCC 8th nodal status on the survival, and identify risk factors associated with the survival after N reclassification using the National Cancer Database (NCDB) in the period 2005–2015. The cohort consisted of 7743 patients diagnosed with GBC; 202 patients met the criteria for reclassification and were denoted as stage ≥III by AJCC 7th and 8th edition criteria. Overall survival concordance indices were similar for patients when classified by AJCC 8th (OS c-index: 0.665) versus AJCC 7th edition (OS c-index: 0.663). Relative mortality was higher within strata of T1, T2, and T3 patients with N2 compared with N1 stage (T1 HR: 2.258, p < 0.001; T2 HR: 1.607, p < 0.001; Τ3 HR: 1.306, p < 0.001). The risk of death was higher in T1–T3 patients with Nx compared with N1 stage (T1 HR: 1.281, p = 0.043, T2 HR: 2.221, p < 0.001, T3 HR: 2.194, p < 0.001). In patients with AJCC 8th edition stage ≥IIIB GBC and an available grade, univariate analysis showed that higher stage, Charlson–Deyo score ≥ 2, higher tumor grade, and unknown nodal status were associated with an increased risk of death, while year of diagnosis after 2013, academic center, chemotherapy. and radiation therapy were associated with decreased risk of death. Chemotherapy and radiation therapy were associated with decreased risk of death in patients with T3–T4 and T2–T4 GBC, respectively. In conclusion, the updated AJCC 8th GBC staging system was comparable to the 7th edition, with the recently implemented changes in N classification assessment failing to improve the prognostic performance of the staging system. Further prospective studies are needed to validate the T2 stage subclassification as well as to clarify the association, if any is actually present, between advanced N staging and increased risk of death in patients of the same T stage.

          Related collections

          Most cited references54

          • Record: found
          • Abstract: found
          • Article: not found

          Cancer statistics, 2020

          Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging.

            The American Joint Committee on Cancer (AJCC) staging manual has become the benchmark for classifying patients with cancer, defining prognosis, and determining the best treatment approaches. Many view the primary role of the tumor, lymph node, metastasis (TNM) system as that of a standardized classification system for evaluating cancer at a population level in terms of the extent of disease, both at initial presentation and after surgical treatment, and the overall impact of improvements in cancer treatment. The rapid evolution of knowledge in cancer biology and the discovery and validation of biologic factors that predict cancer outcome and response to treatment with better accuracy have led some cancer experts to question the utility of a TNM-based approach in clinical care at an individualized patient level. In the Eighth Edition of the AJCC Cancer Staging Manual, the goal of including relevant, nonanatomic (including molecular) factors has been foremost, although changes are made only when there is strong evidence for inclusion. The editorial board viewed this iteration as a proactive effort to continue to build the important bridge from a "population-based" to a more "personalized" approach to patient classification, one that forms the conceptual framework and foundation of cancer staging in the era of precision molecular oncology. The AJCC promulgates best staging practices through each new edition in an effort to provide cancer care providers with a powerful, knowledge-based resource for the battle against cancer. In this commentary, the authors highlight the overall organizational and structural changes as well as "what's new" in the Eighth Edition. It is hoped that this information will provide the reader with a better understanding of the rationale behind the aggregate proposed changes and the exciting developments in the upcoming edition. CA Cancer J Clin 2017;67:93-99. © 2017 American Cancer Society.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

              Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
                Bookmark

                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Cancers (Basel)
                Cancers (Basel)
                cancers
                Cancers
                MDPI
                2072-6694
                01 February 2021
                February 2021
                : 13
                : 3
                : 547
                Affiliations
                [1 ]Institute of Health Innovations and Outcomes Research, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, USA; dimitrisgiannhs@ 123456gmail.com
                [2 ]Department of Surgery, Duke University Medical Center, Durham, NC 27710, USA; marcelo.cerullo@ 123456duke.edu (M.C.); kevin.n.shah@ 123456duke.edu (K.N.S.); garth.herbert@ 123456duke.edu (G.H.); sabino.zani@ 123456duke.edu (S.Z.); trey.blazer@ 123456duke.edu (D.G.B.3rd); peter.allen@ 123456duke.edu (P.J.A.); michael.lidsky@ 123456duke.edu (M.E.L.)
                Author notes
                [* ]Correspondence: dimitrios.moris@ 123456duke.edu ; Tel.: +1-2165716614
                Author information
                https://orcid.org/0000-0002-5276-0699
                https://orcid.org/0000-0003-0500-2016
                Article
                cancers-13-00547
                10.3390/cancers13030547
                7867111
                33535552
                85a44ec5-e28f-429f-9f4e-e98c0618f3d5
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 03 November 2020
                : 26 January 2021
                Categories
                Article

                gallbladder cancer,ajcc,national cancer database,staging

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content311

                Cited by16

                Most referenced authors1,565