In recent years, the issue of conflicts of interest in the transformation of scientific and technological achievements in medical institutions in China has become prominent, mainly manifested as personal and institutional conflicts of interest, with the characteristics of complexity and sustainability. At present, there were some problems in the conflict management of scientific and technological achievements transformation of medical institutions, such as insufficient support of relevant laws, regulations and policies, insufficient supervision of medical institutions, and the lack of industry management atmosphere. The author suggestted that government departments should strengthen the formulation of relevant policies and regulations, medical institutions should establish an interest conflict management system and an independent management department, and industry associations should give full play to their role in assisting, so as to provide reference for promoting medical institutions to effectively manage interest conflicts in the transformation of scientific and technological achievements.

Objective:To explore the values of ultrasound, pathology combined with inflammatory indicators in predicting high nodal burden (HNB) in patients with early breast cancer and to construct a nomogram to provide reference for individualized diagnosis and treatment.Methods:The ultrasonographic, pathological features and preoperative inflammatory indicators of 378 female patients diagnosed with early breast cancer confirmed by pathology in the South Hospital of the Sixth People′s Hospital Affiliated to Shanghai Jiaotong University from January 2014 to July 2022 were retrospectively analyzed. They were randomly divided into training set ( n=302) and test set ( n=76) in a ratio of 8∶2, and the baseline data of the two groups were compared. The optimal cutoff values of neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR) and lymphocyte to monocyte ratio (LMR) were obtained by ROC curve. In the training set, with axillary high lymph node load (≥3 metastatic lymph nodes) as the dependent variable, independent influencing factors of HNB were identified by univariate and multivariate Logistic regression analyses, and the nomogram was established. The test set data were used to verify the model. The discrimination, calibration and clinical applicability of the model were assessed by the area under the ROC curve (AUC), C-index, the calibration curve, Brier score and the decision curve analysis, respectively. Results:There were no significant differences in all variables between the training set and the test set (all P>0.05). ROC curve analysis results showed that AUCs of NLR, PLR and LMR were 0.578, 0.547 and 0.516, respectively, and the optimal cut-off values were 2.184, 150 and 3.042, respectively. Univariate Logistic regression analysis showed that age, pathological type, histological grade, Ki-67, lymphovascular invasion, NLR, PLR, ultrasonic characteristics (maximum diameter of primary tumor, shape, long/short diameter of lymph node, cortical thickness, cortical and medullary boundary, lymph node hilum, lymph node blood flow pattern) were correlated with HNB of early breast cancer (all P<0.05). Multivariate Logistic regression analysis showed that ultrasonic characteristics (maximum diameter of primary tumor >2 cm, effacement of lymph node hilum, non-lymphatic portal blood flow), lymphovascular invasion, Ki-67>14% and NLR>2.184 were independent risk factors for HNB in early breast cancer ( OR=7.258, 8.784, 6.120, 8.031, 3.394 and 3.767, respectively; all P<0.05) and were used to construct the nomogram model. The AUC of the training set was 0.914 (95% CI=0.878-0.949), C-index was 0.914; The AUC of the test set was 0.871 (95% CI=0.769-0.973), C-index was 0.871, indicating good discrimination. Calibration curve and Brier score were 0.090, indicating high calibration degree of the model. The clinical decision curve indicated good clinical benefit. Conclusions:The nomogram based on ultrasonic characteristics (maximum diameter of primary tumor, lymph node hilum, lymph node blood flow pattern), lymphovascular invasion, Ki-67 and NLR can effectively predict the risk of HNB in patients with early breast cancer, and provide a reference for precision diagnosis and treatment to avoid excessive or insufficient treatment.