结直肠癌
医学
肿瘤科
内科学
阶段(地层学)
癌症
生物
古生物学
作者
Simon Lundström,Erik Agger,Marie‐Louise Lydrup,Fredrik Jörgren,Pamela Buchwald
出处
期刊:Ejso
[Elsevier BV]
日期:2025-08-29
卷期号:51 (11): 110420-110420
被引量:2
标识
DOI:10.1016/j.ejso.2025.110420
摘要
INTRODUCTION: Tumor deposits are an important negative prognostic factor for long-term oncological outcomes in colorectal cancer patients, independent of lymph node status. Several novel models have been proposed to further integrate tumor deposits into the TNM-staging system, but their comparative performance remains unclear. The aim of this study was to identify, compare and validate novel prognostic models incorporating tumor deposits for N-stage classification. METHODS: A scoping literature review identified novel prognostic models that incorporated tumor deposits or tumor deposit count into N-staging. The identified models were validated using patient data from the Swedish Colorectal Cancer Registry, assessing overall survival, distant metastasis, and local recurrence. Prognostic performance was compared to the TNM N-staging using Kaplan-Meier curves for visual analysis, Harrell's C-index for discriminative ability, and Bayesian information criterion for model fit. RESULTS: Of 792 articles, seventeen met the inclusion criteria, resulting in ten unique models in addition to TNM. For the patient cohort, 26,970 patients remained after exclusion, of whom 3,312 (12 %) had tumor deposits. All models were superior to TNM with two models standing out; an integrated model combining lymph node and tumor deposit count, and a ratio model considering number of tumor deposits, positive lymph nodes, and total number of extracted nodal structures. All models provided prognostic value, but differences were modest. CONCLUSION: This study demonstrated that although all models outperformed TNM, prognostic differences between the models were small. While tumor deposits provide valuable prognostic information for high-risk patients, additional risk factors are required to further enhance the staging system.
科研通智能强力驱动
Strongly Powered by AbleSci AI