Pathological-Features-Modified TNM Staging System Improves Prognostic Accuracy for Rectal Cancer.

BACKGROUND: Variations in survival outcomes are observed in the American Joint Committee on Cancer's 8th edition TNM staging system.

OBJECTIVE: Machine learning ensemble methods were used to develop and evaluate the effectiveness of a pathological-features-modified tumor node metastasis staging system in predicting survival for patients with rectal cancer by using commonly reported pathological features, such as histological grade, tumor deposits, and perineural invasion, to improve the prognostic accuracy.

DESIGN: This was a retrospective population-based study.

SETTINGS: Data were assessed from the database of the Surveillance, Epidemiology, and End Results Program.

PATIENTS: The study cohort comprised 14,468 rectal cancer patients diagnosed between 2010 and 2015. The development cohort included those who underwent surgery as the primary treatment while patients who received neoadjuvant therapy were assigned to the validation cohort.

MAIN OUTCOME MEASURES: The primary outcome measures included cumulative rectal cancer survival, adjusted hazard ratios, and both calibration and discrimination statistics to evaluate model performance and internal validation.

RESULTS: Multivariable Cox regression analysis identified all three pathological features as prognostic factors, following which patients were categorized into four pathological groups based on the number of pathological features (i.e., 0, 1, 2, and 3). Distinct survival differences were observed among the groups, especially with stage III patients. The proposed pathological-features-modified tumor-node-metastasis staging outperformed the TNM staging in both the development and validation cohorts.

LIMITATIONS: Retrospective in design and lack of external validation.

CONCLUSIONS: The proposed pathological-features-modified tumor-node-metastasis staging could complement the current TNM staging by improving the accuracy of rectal cancer patients' survival estimation. See Video Abstract.