Oncotarget

Research Papers:

A predictive nomogram improved diagnostic accuracy and interobserver agreement of perirectal lymph nodes metastases in rectal cancer

Yongfeng Liu, Renjie Wang, Ying Ding, Shanshan Tu, Yi Liu, Youcun Qian, Linghui Xu, Tong Tong, Sanjun Cai and Junjie Peng _

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Oncotarget. 2016; 7:14755-14764. https://doi.org/10.18632/oncotarget.7548

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Abstract

Yongfeng Liu1,*, Renjie Wang2,3,*, Ying Ding4, Shanshan Tu4, Yi Liu5, Youcun Qian1, Linghui Xu3,6, Tong Tong3,6, Sanjun Cai2,3, Junjie Peng2,3

1Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, China

2Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China

3Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

4Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA

5Department of Statistics, Ohio State University, Columbus, OH, USA

6Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China

*These authors contributed equally to this work

Correspondence to:

Junjie Peng, e-mail: [email protected]

Keywords: nomogram, rectal cancer, lymph node, metastases, MRI

Received: October 07, 2015     Accepted: January 29, 2016     Published: February 21, 2016

ABSTRACT

Objective: To develop a predictive nomogram to improve the diagnostic accuracy and interobserver agreement of pre-therapeutic lymph nodes metastases in patients with rectal cancer.

Materials and Methods: An institutional database of 411 patients with rectal cancer was used to develop a nomogram to predict perirectal lymph nodes metastases. Patients’ clinicopathological and MRI-assessed imaging variables were included in the multivariate logistic regression analysis. The model was externally validated and the performance was assessed by area under curve (AUC) of the receiver operator characteristics (ROC) curves. The interobserver agreement was measured between two independent radiologists.

Results: The diagnostic accuracy of the conventional MRI-assessed cN stage was 68%; 14.2% of the patients were over-staged and 17.8% of the patients were under-staged. A total of 35.1% of the patients had disagreed diagnosis for the cN stage between the two radiologists, with a kappa value of 0.295. A nomogram for predicting pathological lymph nodes metastases was successfully developed, with an AUC of 0.78 on the training data and 0.71 on the validation data. The predictors included in the nomogram were MRI cT stage, CRM involvement, preoperative CEA, tumor grade and lymph node size category. This nomogram yielded improved prediction in cN stage than the conventional MRI-based assessment.

Conclusions: By incorporating clinicopathological and MRI imaging features, we established a nomogram that improved the diagnostic accuracy and remarkably minimized the interobserver disagreement in predicting lymph nodes metastases in rectal cancers.


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