Nomogram Development and External Validation for Predicting the Risk of Lymph Node Metastasis in T1 Colorectal Cancer
- Author:
Jung Ryul OH
1
;
Boram PARK
;
Seongdae LEE
;
Kyung Su HAN
;
Eui Gon YOUK
;
Doo Han LEE
;
Do Sun KIM
;
Doo Seok LEE
;
Chang Won HONG
;
Byung Chang KIM
;
Bun KIM
;
Min Jung KIM
;
Sung Chan PARK
;
Dae Kyung SOHN
;
Hee Jin CHANG
;
Jae Hwan OH
Author Information
- Publication Type:Original Article
- Keywords: Colorectal neoplasms; Lymph nodes; Neoplasm metastasis; Nomograms
- MeSH: Adenoma; Area Under Curve; Calibration; Colorectal Neoplasms; Decision Making; Discrimination (Psychology); Humans; Logistic Models; Lymph Nodes; Neoplasm Metastasis; Nomograms; Risk Factors; ROC Curve
- From:Cancer Research and Treatment 2019;51(4):1275-1284
- CountryRepublic of Korea
- Language:English
- Abstract: PURPOSE: Predicting lymph node metastasis (LNM) risk is crucial in determining further treatment strategies following endoscopic resection of T1 colorectal cancer (CRC). This study aimed to establish a new prediction model for the risk of LNM in T1 CRC patients. MATERIALS AND METHODS: The development set included 833 patients with T1 CRC who had undergone endoscopic (n=154) or surgical (n=679) resection at the National Cancer Center. The validation set included 722 T1 CRC patients who had undergone endoscopic (n=249) or surgical (n=473) resection at Daehang Hospital. A logistic regression model was used to construct the prediction model. To assess the performance of prediction model, discrimination was evaluated using the receiver operating characteristic (ROC) curves with area under the ROC curve (AUC), and calibration was assessed using the Hosmer-Lemeshow (HL) goodness-of-fit test. RESULTS: Five independent risk factors were determined in the multivariable model, including vascular invasion, high-grade histology, submucosal invasion, budding, and background adenoma. In final prediction model, the performance of the model was good that the AUC was 0.812 (95% confidence interval [CI], 0.770 to 0.855) and the HL chi-squared test statistic was 1.266 (p=0.737). In external validation, the performance was still good that the AUC was 0.771 (95% CI, 0.708 to 0.834) and the p-value of the HL chi-squared test was 0.040. We constructed the nomogram with the final prediction model. CONCLUSION: We presented an externally validated new prediction model for LNM risk in T1 CRC patients, guiding decision making in determining whether additional surgery is required after endoscopic resection of T1 CRC.
