179P - Prognostic prediction models for patients with stage IV colorectal cancer after concurrent curative resection

Date 19 December 2015
Event ESMO Asia 2015 Congress
Session Poster presentation 1
Topics Colon Cancer
Rectal Cancer
Surgery and/or Radiotherapy of Cancer
Presenter Norikatsu Miyoshi
Citation Annals of Oncology (2015) 26 (suppl_9): 42-70. 10.1093/annonc/mdv523
Authors N. Miyoshi, M. Ohue, M. Yasui, K. Sugumira, A. Tomokuni, H. Akita, K. Demura, S. Kobayashi, H. Takahashi, T. Omori, Y. Fujiwara, M. Yano
  • Surgery, Osaka Medical Center for Cancer and Cardiovascular Dideases, 5378511 - Osaka/JP

Abstract

Aim/Background

We developed a prediction tool for recurrence and survival in patients with stage IV colorectal cancer (CRC) following surgically curative resection.

Methods

From January 1983 to December 2010, 97 patients with CRC and synchronous liver and/or lung metastatic CRC were investigated at the Osaka Medical Center for Cancer and Cardiovascular Diseases. All patients underwent curative resection of both primary and metastatic lesions. A Cox proportional hazards model was used to develop prediction models for 3-year relapse-free survival (RFS) and cancer-specific survival (CSS).

Results

Univariate analysis of clinicopathological factors showed that the following factors were significantly correlated with RFS and CSS: preoperative serum carcinoembryonic antigen level, tumor location, pathologically defined tumor invasion, and synchronous metastatic lesions. Using these variables, novel prediction models predicting RFS and CSS were constructed using the logistic regression model with AUC of 0.787 and 0.853 for RFS and CSS, respectively. The prediction models were validated by external datasets in an independent patient group.

Conclusions

We developed novel and reliable personalized prognostic models, integrating not only TNM factors but also the preoperative serum carcinoembryonic antigen level, tumor location, and metastatic lesions, to predict patients' prognosis following surgically curative resection. These individualized prediction models may help clinicians in the treatment of postoperative stage IV CRC following surgically curative resection.

Clinical trial identification

Disclosure

All authors have declared no conflicts of interest.