We have developed and validated a nomogram that predicts the development of postoperative liver metastasis in early breast cancer patients. The nomogram included three items, including tumor size, lymph node metastasis, and HER2 status, and showed good agreement between the predicted and actual probabilities in the derived and validated cohorts.
Liver metastasis is a growing problem in the treatment of breast cancer. Liver metastasis severely affects patients' life quality and prognosis. Therefore, predicting higher risk liver metastasis breast cancer patients will enrich the population who may treated more specifically and thereby improve clinical outcomes in these patients.
Based on this nomogram, assuming a breast cancer patient with T3-4, lymph node metastasis and HER2-positive tumors, her total score was 205, as shown in Figure 1. Using a nomogram, the patient is expected to have a 20% possibility to develop liver metastasis. Therefore, patients with the above characteristics are expected to benefit from liver metastasis screening.
In contrast, an assuming patient with a T1-2 tumor, no lymph node metastasis, and the HER2 negative status had a total score of 0, as shown in Figure 1. Using a nomogram, the predicted chance for this patient to get liver metastasis is relatively low (less than 5%).
There is currently no specific preventive treatment to reduce the incidence of liver metastasis in breast cancer. But due to the local liver treatment (surgery, intrahepatic local chemotherapy, etc.), strengthen surveillance may bring benefits for high-risk metastatic breast cancer patients. We are not the only one trying to establish a nomogram on breast cancer liver metastasis. Lin and his colleagues constructed a nomogram using variables such as sex, histology type, N stage, grade, age, ER, PR, HER2 status. The problem with their nomogram is that the patients they enrolled are de novo liver metastasis, which means that the diagnosis of liver metastasis and the diagnosis of breast cancer are simultaneous and thus it didn't have enough predictive value. The patients included in this study are those who have liver recurrence after early breast cancer treatment. Thus, our nomogram has more superior predictive value than theirs.
It is worth noting that this study also has some limitations. Ideally, external verification should be done using a separate data set from another organization. The current validation set came from the same center, which can lead to over-fitting of the model. Besides, the nomogram was based only on Chinese patients and may not be suitable for the western population. Moreover, this nomogram was constructed using retrospective data, so more prospective studies should be performed for further validation.
In summary, we developed a nomogram, which is a powerful tool for predicting subsequent liver metastasis in early breast cancer patients. Our model will allow the identification of patient populations at high risk of liver metastasis, helping to design preventive trials for affected populations. Further research is needed to determine whether it can be applied to other populations.