Background: Axillary lymph node (ALN) is the most common metastasis path for breast cancer, and ALN dissection directly affects the postoperative staging and prognosis of breast cancer patients. Therefore, additional research is needed to accurately predict ALN metastasis before surgery and construct predictive models to assist in surgical decision-making and optimize patient care.
Methods: We retrospectively analyzed the clinical data, radiomics, and pathomics of the patients diagnosed with breast cancer in the Breast Cancer Center of Hubei Cancer Hospital from January 2017 to December 2022. The study participants were randomly assigned to either the training queue (70%) or the validation queue (30%). Logistic regression (ie generalized linear regression model [GLRM]) and random forest model (RFM) were used to construct an ALN prediction model in the training queue, and the discriminant power of the model was evaluated using area under curve (AUC) and decision curve analysis (DCA). Meanwhile, the validation queue was used to evaluate the ALN prediction performance of the constructed model.
Results: Out of the 422 patients encompassed in the study, 18.7% were diagnosed with ALN by postoperative pathology. The logical model included shear wave elastography (SWE) related to maximum, minimum, centre, ratio 1, pathomics (Feature 1, Feature 3, and Feature 5) and a nomogram of the GLRM was drawn. The AUC of GLRM was 0.818 (95% CI: 0.757~0.879), significantly lower than that of RFM's AUC 0.893 (95% CI: 0.836~0.950).
Conclusion: The prediction models based on machine learning (ML) algorithms and multiomics have shown good performance in predicting ALN metastasis, and RFM shows greater advantages compared to traditional GLRM. The findings of this study can help clinicians identify patients with higher risk of ALN metastasis and provide personalized perioperative management to assist preoperative decision-making and improve patient prognosis.
Keywords: axillary lymph node metastasis; breast cancer; machine learning; nomogram; pathomics; radiomics; random forest.
© 2024 Ke et al.