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Annals of Surgical Oncology
Published in: Annals of Surgical Oncology 6/2011

01-06-2011 | Colorectal Cancer

An Interactive Tool for Individualized Estimation of Conditional Survival in Rectal Cancer

Authors: Samuel J. Wang, MD, PhD, Amanda R. Wissel, BS, Join Y. Luh, MD, C. David Fuller, MD, Jayashree Kalpathy-Cramer, PhD, Charles R. Thomas Jr, MD

Published in: Annals of Surgical Oncology | Issue 6/2011

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Abstract

Background

For rectal cancer patients who have already survived a period of time after diagnosis, survival probability changes and is more accurately depicted by conditional survival. The specific aim of this study was to develop an interactive tool for individualized estimation of changing prognosis for rectal cancer patients.

Methods

A multivariate Cox proportional hazards (CPH) survival model was constructed using data from rectal cancer patients diagnosed from 1994 to 2003 from the Surveillance, Epidemiology, and End Results (SEER) database. Age, race, sex, and stage were used as covariates in the survival prediction model. The primary outcome variable was overall survival conditional on having survived up to 5 years from diagnosis.

Results

Data from 42,830 rectal cancer patients met the inclusion criteria. The multivariate CPH model showed age, race, sex, and stage as significant independent predictors of survival. The survival prediction model demonstrated good calibration and discrimination, with a bootstrap-corrected concordance index of 0.75. A web-based prediction tool was built from this regression model that can compute individualized estimates of changing prognosis over time.

Conclusions

An interactive prediction modeling tool can estimate prognosis for rectal cancer patients who have already survived a period of time after diagnosis and treatment. Having more accurate prognostic information can empower both patients and clinicians to be able to make more appropriate decisions regarding follow-up, surveillance testing, and future treatment.
Literature
1.
2.
Gloeckler Ries LA, Reichman ME, Lewis DR, Hankey BF, Edwards BK. Cancer survival and incidence from the Surveillance, Epidemiology, and End Results (SEER) program. Oncologist. 2003;8:541–52.PubMedCrossRef
3.
Merrill RM, Hunter BD. Conditional survival among cancer patients in the United States. Oncologist. 2010;15:873–82.PubMedCrossRef
4.
Henson DE, Ries LA, Carriaga MT. Conditional survival of 56,268 patients with breast cancer. Cancer. 1995;76:237–42.PubMedCrossRef
5.
Merrill RM, Henson DE, Ries LA. Conditional survival estimates in 34,963 patients with invasive carcinoma of the colon. Dis Colon Rectum. 1998;41:1097–106.PubMedCrossRef
6.
Chang GJ, Hu CY, Eng C, Skibber JM, Rodriguez-Bigas MA. Practical application of a calculator for conditional survival in colon cancer. J Clin Oncol. 2009;27:5938–43.PubMedCrossRef
7.
Nathan H, de Jong MC, Pulitano C, Ribero D, Strub J, Mentha G, et al. Conditional survival after surgical resection of colorectal liver metastasis: an international multi-institutional analysis of 949 patients. J Am Coll Surg. 2010;210:755–64, 764–6.
8.
Davis FG, McCarthy BJ, Freels S, Kupelian V, Bondy ML. The conditional probability of survival of patients with primary malignant brain tumors: surveillance, epidemiology, and end results (SEER) data. Cancer. 1999;85:485–91.PubMedCrossRef
9.
Hwang SL, Yang YH, Lieu AS, Chuang MC, Chang SJ, Chang YY, et al. The conditional survival statistics for survivors with primary supratentorial astrocytic tumors. J Neurooncol. 2000;50:257–64.PubMedCrossRef
10.
Lin CL, Lieu AS, Lee KS, Yang YH, Kuo TH, Hung MH, et al. The conditional probabilities of survival in patients with anaplastic astrocytoma or glioblastoma multiforme. Surg Neurol. 2003;60:402–6; discussion 406.PubMedCrossRef
11.
Merrill RM, Henson DE, Barnes M. Conditional survival among patients with carcinoma of the lung. Chest. 1999;116:697–703.PubMedCrossRef
12.
Skuladottir H, Olsen JH. Conditional survival of patients with the four major histologic subgroups of lung cancer in Denmark. J Clin Oncol. 2003;21:3035–40.PubMedCrossRef
13.
Yang YH, Liu SH, Ho PS, Huang IY, Chen CH, Shieh TY. Conditional survival rates of buccal and tongue cancer patients: how far does the benefit go? Oral Oncol. 2009;45:177–83.PubMedCrossRef
14.
Kato I, Severson RK, Schwartz AG. Conditional median survival of patients with advanced carcinoma: surveillance, epidemiology, and end results data. Cancer. 2001;92:2211–9.PubMedCrossRef
15.
Janssen-Heijnen ML, Houterman S, Lemmens VE, Brenner H, Steyerberg EW, Coebergh JW. Prognosis for long-term survivors of cancer. Ann Oncol. 2007;18:1408–13.PubMedCrossRef
16.
Janssen-Heijnen ML, Gondos A, Bray F, Hakulinen T, Brewster DH, Brenner H, et al. Clinical relevance of conditional survival of cancer patients in europe: age-specific analyses of 13 cancers. J Clin Oncol. 2010;28:2520–8.PubMedCrossRef
17.
Bowles TL, Xing Y, Hu CY, Mungovan KS, Askew RL, Chang GJ, et al. Conditional survival estimates improve over 5 years for melanoma survivors with node-positive disease. Ann Surg Oncol. 2010;17:2015–23.PubMedCrossRef
18.
Xing Y, Chang GJ, Hu CY, Askew RL, Ross MI, Gershenwald JE, et al. Conditional survival estimates improve over time for patients with advanced melanoma: results from a population-based analysis. Cancer. 2010;116:2234–41.PubMed
19.
Rueth NM, Groth SS, Tuttle TM, Virnig BA, Al-Refaie WB, Habermann EB. Conditional survival after surgical treatment of melanoma: an analysis of the Surveillance, Epidemiology, and End Results database. Ann Surg Oncol. 2010;17:1662–8.PubMedCrossRef
20.
Wang SJ, Luh JY, Thomas CR. Conditional survival of breast cancer patients In: American society of breast diseases, 29th annual symposium. Las Vegas, NV.
21.
Wang SJ, Luh JY, Fuller CD, Thomas CR. Impact of ethnicity on conditional survival of breast cancer patients: analysis from the SEER database. In: 28th annual San Antonio breast cancer symposium. San Antonio, TX.
22.
Luh JY, Wang SJ, Fuller CD, Thomas CR. A SEER database analysis of conditional survival for prostate cancer patients. In: American society of clinical oncology, 42nd annual meeting. Atlanta, GA.
23.
Wang SJ, Fuller CD, Luh JY, Thomas CR, Bleyer WA. Older adolescents and young adults with cancer: Conditional survival deficit. In: American society for therapeutic radiology and oncology, 48th annual meeting. Philadelphia, PA.
24.
Fuller CD, Wang SJ, Thomas CR, Hoffman HT, Weber RS, Rosenthal DI. Conditional survival in head and neck squamous cell carcinoma: Results from the SEER Dataset 1973–1998. Cancer. 2007;109:1331–43.PubMedCrossRef
25.
Wang SJ, Fuller CD, Emery R, Thomas CR. Conditional survival in rectal cancer: a SEER database analysis. Gastrointest Cancer Res. 2007;1:84–9.PubMed
26.
Wang SJ, Fuller CD, Sittig DF, Holland JM, Thomas CR. A regression model for predicting conditional survival for head & neck cancer patients: a SEER analysis. In: ASCO annual meeting. Chicago, IL.
27.
Wang SJ, Emery R, Fuller CD, Kim JS, Sittig DF, Thomas CR. Conditional survival in gastric cancer: a SEER database analysis. Gastric Cancer. 2007;10:153–8.PubMedCrossRef
28.
Wang SJ, Fuller CD, Thomas CR, Jr. Ethnic disparities in conditional survival of patients with non-small cell lung cancer. J Thorac Oncol. 2007;2:180–90.PubMedCrossRef
29.
Choi M, Fuller CD, Thomas CR, Jr., Wang SJ. Conditional survival in ovarian cancer: results from the SEER dataset 1988–2001. Gynecol Oncol. 2008;109:203–9.PubMedCrossRef
30.
Bleyer A, Choi M, Fuller CD, Thomas CR, Jr., Wang SJ. Relative lack of conditional survival improvement in young adults with cancer. Semin Oncol. 2009;36:460–7.PubMedCrossRef
31.
Zamboni BA, Yothers G, Choi M, Fuller CD, Dignam JJ, Raich PC, et al. Conditional survival and the choice of conditioning set for patients with colon cancer: an analysis of NSABP trials C-03 through C-07. J Clin Oncol. 2010;28:2544–8.PubMedCrossRef
32.
Kattan MW, Zelefsky MJ, Kupelian PA, Cho D, Scardino PT, Fuks Z, et al. Pretreatment nomogram for predicting the outcome of three-dimensional conformal radiotherapy in prostate cancer. J Clin Oncol. 2000;18:3352–9.PubMed
33.
Kattan MW. When and how to use informatics tools in caring for urologic patients. Nat Clin Pract Urol. 2005;2:183–90.PubMedCrossRef
34.
Ravdin PM. A computer based program to assist in adjuvant therapy decisions for individual breast cancer patients. Bull Cancer. 1995;82 Suppl 5:561 s–4 s.PubMed
35.
Rouzier R, Pusztai L, Delaloge S, Gonzalez-Angulo AM, Andre F, Hess KR, et al. Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer. J Clin Oncol. 2005;23:8331–9.PubMedCrossRef
36.
Wang SJ, Fuller CD, Kim JS, Sittig DF, Thomas CR Jr, Ravdin PM. Prediction model for estimating the survival benefit of adjuvant radiotherapy for gallbladder cancer. J Clin Oncol. 2008;26:2112–7.PubMedCrossRef
37.
Kattan MW, Karpeh MS, Mazumdar M, Brennan MF. Postoperative nomogram for disease-specific survival after an R0 resection for gastric carcinoma. J Clin Oncol. 2003;21:3647–50.PubMedCrossRef
38.
Gross ND, Patel SG, Carvalho AL, Chu PY, Kowalski LP, Boyle JO, et al. Nomogram for deciding adjuvant treatment after surgery for oral cavity squamous cell carcinoma. Head Neck. 2008;30:1352–60.PubMedCrossRef
39.
Abu-Rustum NR, Zhou Q, Gomez JD, Alektiar KM, Hensley ML, Soslow RA, et al. A nomogram for predicting overall survival of women with endometrial cancer following primary therapy: toward improving individualized cancer care. Gynecol Oncol. 2010;116:399–403.PubMedCrossRef
40.
Surveillance, Epidemiology, and End Results (SEER) Program (www.​seer.​cancer.​gov) SEER*Stat Database: Incidence—SEER 17 Regs Limited-Use + Hurricane Katrina Impacted Louisiana Cases, Nov 2008 Sub (1973–2006 varying)—Linked to County Attributes—Total U.S., 1969–2006 Counties, National Cancer Institute, DCCPS, Surveillance Research Program, Cancer Statistics Branch, released April 2009, based on the November 2008 submission.
41.
42.
Harrell FE. Regression modeling strategies. New York: Springer-Verlag; 2001.
Metadata
Title
An Interactive Tool for Individualized Estimation of Conditional Survival in Rectal Cancer
Authors
Samuel J. Wang, MD, PhD
Amanda R. Wissel, BS
Join Y. Luh, MD
C. David Fuller, MD
Jayashree Kalpathy-Cramer, PhD
Charles R. Thomas Jr, MD
Publication date
01-06-2011
Publisher
Springer-Verlag
Published in
Annals of Surgical Oncology / Issue 6/2011
Print ISSN: 1068-9265
Electronic ISSN: 1534-4681
DOI
https://doi.org/10.1245/s10434-010-1512-3

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