Prognosis and local treatment strategies of breast cancer patients with different numbers of micrometastatic lymph nodes

Preoperative plasma fibrinogen level is a risk factor for the long-term survival of postmenopausal women after surgery for breast cancer

BACKGROUND

Studies have indicated an association between fibrinogen levels and the prognosis of breast cancer patients. However, fibrinogen levels are notably susceptible to fluctuations due to the menstrual cycle. This study explored the relationship between preoperative plasma fibrinogen levels and the prognosis of postmenopausal breast cancer women after surgery.

METHOD

855 patients with postmenopausal breast cancer were monitored for 10 years. Cox proportional hazards regression models were used to perform univariate and multivariate analyses to identify factors that are of substantial prognostic value.

RESULTS

The median follow-up was 77 months (51-105 months), and the maximum 142 months. Over the follow-up period, 65 deaths (7.6 %) were recorded. Multivariate Cox regression results show that preoperative plasma fibrinogen level (hazard ratio [HR] =1.615, 95 % confidence interval [CI]: 1.233-2.115) and age (HR = 1.626, 95%CI: 1.250-2.116) were independent risk factors for 10-year overall survival after surgery in postmenopausal breast cancer patients, while endocrine therapy (HR = 0.414, 95%CI: 0.202-0.846) was an independent protective factor. Multivariate Cox regression results also show preoperative plasma fibrinogen level was an independent risk factor for 10-year disease-free survival (HR = 1.398, 95 % CI: 1.137-1.719) and 10-year distant metastasis-free survival (HR = 1.436, 95%CI: 1.153-1.787).

CONCLUSION

Elevated pretreatment plasma fibrinogen levels are associated with a poorer long-term prognosis in postmenopausal breast cancer patients following surgical treatment.

Enhancements in Radiological Detection of Metastatic Lymph Nodes Utilizing AI-Assisted Ultrasound Imaging Data and the Lymph Node Reporting and Data System Scale

The paper presents a novel approach for the automatic detection of neoplastic lesions in lymph nodes (LNs). It leverages the latest advances in machine learning (ML) with the LN Reporting and Data System (LN-RADS) scale. By integrating diverse datasets and network structures, the research investigates the effectiveness of ML algorithms in improving diagnostic accuracy and automation potential. Both Multinominal Logistic Regression (MLR)-integrated and fully connected neuron layers are included in the analysis. The methods were trained using three variants of combinations of histopathological data and LN-RADS scale labels to assess their utility. The findings demonstrate that the LN-RADS scale improves prediction accuracy. MLR integration is shown to achieve higher accuracy, while the fully connected neuron approach excels in AUC performance. All of the above suggests a possibility for significant improvement in the early detection and prognosis of cancer using AI techniques. The study underlines the importance of further exploration into combined datasets and network architectures, which could potentially lead to even greater improvements in the diagnostic process.

Update on Sentinel Lymph Node Methods and Pathology in Breast Cancer

Breast cancer stands out as the most commonly diagnosed cancer among women globally. Precise lymph node staging holds critical significance for both predicting outcomes in early-stage disease and formulating effective treatment strategies to control regional disease progression in breast cancer patients. No imaging technique possesses sufficient accuracy to identify lymph node metastases in the early stages (I or II) of primary breast cancer. However, the sentinel node procedure emerges as a valuable approach for identifying metastatic axillary nodes. The sentinel lymph node is the hypothetical first lymph node or group of nodes draining a cancer. In case of established cancerous dissemination, it is postulated that the sentinel lymph nodes are the target organs primarily reached by metastasizing cancer cells from the tumor. The utilization of the sentinel node technique has brought about changes in the assessment of lymph nodes. It involves evaluating the sentinel node during surgery, enabling prompt lymph node dissection when the sentinel node procedure is positive. Additionally, histological ultra-stratification is employed to uncover occult metastases. This review aims to provide an update of this valuable technique, with focus on the practical aspects of the procedure and the different histological protocols of sentinel node evaluation in breast cancer.