Cancer Res Treat.  2019 Oct;51(4):1275-1284. 10.4143/crt.2018.569.

Nomogram Development and External Validation for Predicting the Risk of Lymph Node Metastasis in T1 Colorectal Cancer

Affiliations
  • 1Center for Colorectal Cancer, Research Institute and Hospital, National Cancer Center, Goyang, Korea. kshan@ncc.re.kr
  • 2Biometrics Research Branch, Research Institute and Hospital, National Cancer Center, Goyang, Korea.
  • 3Department of Surgery, Daehang Hospital, Seoul, Korea.

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.

Keyword

Colorectal neoplasms; Lymph nodes; Neoplasm metastasis; Nomograms

MeSH Terms

Adenoma
Area Under Curve
Calibration
Colorectal Neoplasms*
Decision Making
Discrimination (Psychology)
Humans
Logistic Models
Lymph Nodes*
Neoplasm Metastasis*
Nomograms*
Risk Factors
ROC Curve

Figure

  • Fig. 1. Consort diagram of development set. CRC, colorectal cancer; LNM, lymph node metastasis.

  • Fig. 2. Consort diagram of validation set. CRC, colorectal cancer; LNM, lymph node metastasis.

  • Fig. 3. Receiver-operator characteristic curve (A) and calibration plots (B) of the prediction model in the development and validation sets. AUC, area under the receiver operating characteristics curve; CI, confidence interval; LNM, lymph node metastasis.

  • Fig. 4. Nomogram.


Cited by  1 articles

LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer
Jeonghyun Kang, Yoon Jung Choi, Im-kyung Kim, Hye Sun Lee, Hogeun Kim, Seung Hyuk Baik, Nam Kyu Kim, Kang Young Lee
Cancer Res Treat. 2021;53(3):773-783.    doi: 10.4143/crt.2020.974.


Reference

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