A Simple Scoring System Based on Clinical Features to Predict Locally Advanced Rectal Cancers
Department of Colorectal Surgery, Cancer Hospital, Fudan University, 270 Dong An Road, Shanghai, 200032, People's Republic of China. Journal of Gastrointestinal Surgery
(Impact Factor: 2.8).
04/2009; 13(7):1299-305. DOI: 10.1007/s11605-009-0892-9
The purpose of this study was to identify clinical risk factors and establish a prediction scoring system for locally advanced rectal cancer.
Retrospective univariate and multivariate logistic analyses were conducted for 413 curable rectal cancer patients. Clinical factors found to be significantly related with tumor stages were incorporated into a scoring system to predict locally advanced stages, which was validated in an independent cohort of 279 rectal cancer patients.
In the training set, tumor size, differentiation, and serum carcinoembryonic antigen (CEA) level (P < 0.01) were significant predictors of locally advanced rectal cancer in both univariate and multivariate analyses, which were incorporated into a proposed scoring system to predict locally advanced stages. The area under the receiver operating characteristic curve (AUROC) of this scoring system was 0.751 and the prediction accuracy was 78.2%. Patients were categorized into three subsets according to the total score. The low-risk group (score 0) had a smaller chance (18.2%) to have locally advanced rectal cancer, compared to mean 49.2% for the intermediate-risk group (score 1) and mean 83.0% for the high-risk group (score of 2-4; P < 0.05). In the validation set, the AUROC of the scoring system was 0.756 and the prediction accuracy was 75.3%.
Tumor size more than 2 cm, poor differentiation, and elevated serum CEA level are high-risk factors of locally advanced rectal cancer. A simple scoring system based on these three factors may be valuable to predict locally advanced rectal cancer.
Available from: Alessandro Lugli
- "Wolmark and colleagues found that among patients with Dukes C colorectal tumors, depth of penetration was related to tumor size and number of positive lymph nodes . In a comprehensive analysis, Cai and colleagues, reported an independent predictive effect of tumor size along with pre-operative CEA level and tumor differentiation for identifying locally advanced rectal cancer . In a series of 265 pT1 and pT2 colorectal cancer patients including 164 rectal cancers, Chock and Law showed that tumor size was associated with pT stage but not lymph node status . "
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ABSTRACT: Clinical management of rectal cancer patients relies on pre-operative staging. Studies however continue to report moderate degrees of over/understaging as well as inter-observer variability. The aim of this study was to determine the sensitivity, specificity and accuracy of tumor size for predicting T and N stages in pre-operatively untreated rectal cancers.
We examined a test cohort of 418 well-documented patients with pre-operatively untreated rectal cancer admitted to the University Hospital of Basel between 1987 and 1996. Classification and regression tree (CART) and logistic regression analysis were carried out to determine the ability of tumor size to discriminate between early (pT1-2) and late (pT3-4) T stages and between node-negative (pN0) and node-positive (pN1-2) patients. Results were validated by an external patient cohort (n = 28).
A tumor diameter threshold of 34 mm was identified from the test cohort resulting in a sensitivity and specificity for late T stage of 76.3%, and 67.4%, respectively and an odds ratio (OR) of 6.67 (95%CI:3.4-12.9). At a threshold value of 29 mm, sensitivity and specificity for node-positive disease were 94% and 15.5%, respectively with an OR of 3.02 (95%CI:1.5-6.1). Applying these threshold values to the validation cohort, sensitivity and specificity for T stage were 73.7% and 77.8% and for N stage 50% and 75%, respectively.
Tumor size at a threshold value of 34 mm is a reproducible predictive factor for late T stage in rectal cancers. Tumor size may help to complement clinical staging and further optimize the pre-operative management of patients with rectal cancer.
BMC Gastroenterology 06/2010; 10(1):61. DOI:10.1186/1471-230X-10-61 · 2.37 Impact Factor
Available from: Aung Ko Win
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ABSTRACT: Risk prediction models are important to identify individuals at high risk of developing the disease who can then be offered individually tailored clinical management, targeted screening and interventions to reduce the burden of disease. They are also useful for research purposes when attempting to identify new risk factors for the disease. In this article, we review the risk prediction models that have been developed for colorectal cancer and appraise their applicability, strengths, and weaknesses. We also discuss the factors to be considered for future development and improvement of models for colorectal cancer risk prediction. We conclude that there is no model that sufficiently covers the known risk factors for colorectal cancer that is suitable for assessment of people from across the full range of risk and that a new comprehensive model is needed.
Cancer Epidemiology Biomarkers & Prevention 12/2011; 21(3):398-410. DOI:10.1158/1055-9965.EPI-11-0771 · 4.13 Impact Factor
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ABSTRACT: The aim of this study is to certify a rationality of Pathological Prognostic Score (PPS) in determining the prognosis of patients with colorectal carcinoma.
Three hundred and thirty-one patients with colorectal carcinoma, which had been treated by surgical resection, were enrolled. One point was added for each element among four tumor-related pathological factors of depth of tumor, lymph node metastasis, venous invasion, and lymphatic invasion. PPS was determined by an aggregate of the points.
There existed a significant difference both between survivals of patients with PPS 0 or 1 and 2 or 3 (P = 0.0005) and between survivals of patients with PPS 2 or 3 and 4 (P < 0.0001).
PPS could be an easy and useful criteria to stratify prognosis of patients with colorectal carcinoma.
Journal of Surgical Oncology 09/2012; 106(3):243-7. DOI:10.1002/jso.23024 · 3.24 Impact Factor
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