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. 2018 May 25;9(12):2098-2106.
doi: 10.7150/jca.24445. eCollection 2018.

A Nomogram for Distinction and Potential Prediction of Liver Metastasis in Breast Cancer Patients

Zhenhai Lin  1 , Shican Yan  2 , Jieyun Zhang  3 , Qi Pan  1
Affiliations

A Nomogram for Distinction and Potential Prediction of Liver Metastasis in Breast Cancer Patients

Zhenhai Lin et al. J Cancer. .

Abstract

Liver metastasis from breast cancer has poor prognosis. We aimed at developing a reliable tool for making a distinction and prediction for liver metastasis in breast cancer patients, thus helping clinical diagnosis and treatment. In this study, totally 6238 patients from SEER database with known distant metastasis status and clinicopathologic variables were enrolled and divided randomly into training and validating groups. Logistic regression was used to screen variables and a nomogram was constructed. After multivariate logistic regression, sex, histology type, N stage, grade, age, ER, PR, HER2 status as significant variables for constructing the nomogram. The nomogram for distinguishing and predicting liver metastasis in breast cancer passed the calibration and validation steps and the areas under the receiver operating characteristic curve of the training set and the validation set were 0.6602 and 0.6511 respectively. Our nomogram is a reliable and robust tool for the distinction and prediction of liver metastasis in breast cancer patients, thus helping better choose medical examinations and optimize therapeutic regimen under the cooperation among medical oncologists and surgeons.

Keywords: SEER; breast cancer; liver metastasis; nomogram.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Fig 1
Fig 1
Kaplan-Meier curves for metastatic patients with or without liver metastasis (P < 0.001)
Fig 2
Fig 2
A nomogram for distinction and prediction of liver metastasis for breast cancer patients. Instructions for use of the nomogram: First, assign the points of each characteristic of the patient by drawing a vertical line from that variable to the points scale. Then, sum all the points and draw a vertical line from the total points scale to liver metastasis axis to obtain the probability.
Fig 3
Fig 3
Internal calibration curves for probability of liver metastasis nomogram construction (Bootstrap = 1000 repetitions).
Fig 4
Fig 4
ROC curves in training (A) and validating groups (B) for validating nomogram model. In the training set, the AUC was 0.6602 (95%CI = 0.6385-0.6819) and in the validation set the AUC was 0.6511 (95%CI = 0.6286-0.6736).
Fig 5
Fig 5
Kaplan-Meier curves for all breast cancer patients with predicted liver metastasis possibility above or below mean (P < 0.001)
See this image and copyright information in PMC

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