Background: Anal adenocarcinoma is a rare malignancy with a poor prognosis, and no randomized data are available to guide management. Prior retrospective analyses offer differing conclusions on the benefit of surgical resection after chemoradiotherapy (CRT) in these patients. We used the National Cancer Database (NCDB) to analyze survival outcomes in patients undergoing CRT with and without subsequent surgical resection. Methods: Patients with adenocarcinoma of the anus diagnosed in 2004 through 2015 were identified using the NCDB. Patients with metastatic disease and survival <90 days were excluded. We analyzed patients receiving CRT and stratified by receipt of surgical resection. Logistic regression was used to evaluate predictors of use of surgery and to form a propensity score–matched cohort. Overall survival (OS) was compared between treatment strategies using Cox proportional hazards regression. Results: We identified 1,747 patients with anal adenocarcinoma receiving CRT, of whom 1,005 (58%) received surgery. Predictors of increased receipt of surgery included age <65 years, private insurance, overlapping involvement of the anus and rectum, N0 disease, and external-beam radiation dose ≥4,000 cGy. With a median follow-up of 3.5 years, 5-year OS was 61.1% in patients receiving CRT plus surgery compared with 39.8% in patients receiving CRT alone (log-rank P<.001). In multivariate analysis, surgery was associated with significantly improved OS (hazard ratio, −0.59; 95% CI, 0.50–0.68; P<.001). This survival benefit persisted in a propensity score–matched cohort (log-rank P<.001). Conclusions: In the largest series of anal adenocarcinoma cases to date, treatment with CRT followed by surgery was associated with a significant survival benefit compared with CRT alone in propensity score–matching analysis. Our findings support national guideline recommendations of neoadjuvant CRT followed by resection for patients with anal adenocarcinoma.
Submitted February 11, 2019; accepted for publication April 5, 2019.
Author contributions:Study conception and design: Li, Amini. Data collection and analysis: Li, Amini. Manuscript writing and editing: All authors. Study supervision: Amini.
Disclosures: The authors have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.
Correspondence: Arya Amini, MD, Department of Radiation Oncology, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010. Email: email@example.com
FranklinRA, GiriS, ValasareddyP,
. Comparative survival of patients with anal adenocarcinoma, squamous cell carcinoma of the anus, and rectal adenocarcinoma. Clin Colorectal Cancer2016;15:47–53.
FranklinRA, GiriS, ValasareddyP, . Comparative survival of patients with anal adenocarcinoma, squamous cell carcinoma of the anus, and rectal adenocarcinoma. Clin Colorectal Cancer 2016;15:47–53.10.1016/j.clcc.2015.07.00726362848)| false
BelkacémiY, BergerC, PoortmansP, . Management of primary anal canal adenocarcinoma: a large retrospective study from the Rare Cancer Network. Int J Radiat Oncol Biol Phys 2003;56:1274–1283.1287367110.1016/S0360-3016(03)00277-3)| false
BilimoriaKY, StewartAK, WinchesterDP, . The National Cancer Data Base: a powerful initiative to improve cancer care in the United States. Ann Surg Oncol 2008;15:683–690.10.1245/s10434-007-9747-318183467)| false
American College of Surgeons. National Cancer Database: participant user file data dictionary. Available at: ncdbpuf.facs.org. Accessed November 13, 2017.
International Classification of Diseases for Oncology; World Health Organization. International Classification of Diseases for Oncology: ICD-O-3 online. Available at: http://codes.iarc.fr/. Accessed November 13, 2017.
BelleraCA, MacGroganG, DebledM,
. Variables with time-varying effects and the Cox model: some statistical concepts illustrated with a prognostic factor study in breast cancer. BMC Med Res Methodol2010;10:20.
BelleraCA, MacGroganG, DebledM, . Variables with time-varying effects and the Cox model: some statistical concepts illustrated with a prognostic factor study in breast cancer. BMC Med Res Methodol 2010;10:20.2023343510.1186/1471-2288-10-20)| false