Deep neural network provides personalized treatment recommendations for de novo metastatic breast cancer patients

Background: It has long been controversial whether surgery should be performed for de novo metastatic breast cancer (dnMBC). The choice and timing of the primary tumor resection for dnMBC patients need to be individualized, but there was no tool to assist clinicians in decision-making. Methods: A 1:1:2 propensity score matching (PSM) was applied to examine the prognosis of dnMBC patients who underwent neoadjuvant systemic therapy followed by surgery (NS), surgery followed by chemotherapy (SC), and chemotherapy without surgery (CW). Then, two deep feed-forward neural network models were constructed to conduct personalized treatment recommendations. Results: The PSM-adjusted data showed that not all the dnMBC patients could benefit from surgery, and the advantages of NS and SC were different among various subgroups. Patients with stage T1-2, and pathological grade II tumors can be operated on directly, whereas those with stage T3-4, pathological grade III/IV diseases require NS. However, patients with grade I diseases, over 80 years of age, or with brain metastases could not benefit from surgery, regardless of whether they received neoadjuvant systemic therapy. Our deep neural network models exhibited high accuracy on both the train and test sets, one model can assist in deciding whether surgery is requested for dnMBC patient, if the surgery is necessary, another model can determine whether neoadjuvant systemic therapy is needed. Conclusion: This study investigated the prognosis of dnMBC patients, and two artificial intelligence (AI) assisted surgery decision-making models were developed to assist clinicians in delivering precision medicine while improving the survival of dnMBC patients.