Objective To investigate the value of multimodal model integrating digital mammography(MG)and MRI radiomics features for preoperative prediction of lymphovascular invasion(LVI)status in breast cancer.Methods The clinical and imaging data from 336 patients with pathologically confirmed invasive breast cancer were retrospectively analyzed and randomly divided into a training group(235 cases)and a test group(101 cases)according to the ratio of 7∶3.Feature dimensionality reduction was carried out by Pearson correlation analysis followed by least absolute shrinkage and selection operator(LASSO)regression.Radiomics models were constructed based on MG craniocaudal(CC),dynamic contrast enhancement(DCE),T2 WI,and integrated MRI sequences;a multimodal model was further developed by incorporating clinical high-risk factors.The predictive efficiency of each model was evaluated by plotting receiver operating characteristic(ROC)curve.Results The ROC curve analysis showed that the multimodal model performed the best predictive efficiency,with area under the curve(AUC)of 0.989 and 0.861,accuracy of 0.949 and 0.782,sensitivity of 0.923 and 0.828,and specificity of 0.962 and 0.764 in the training group and test group respectively.Conclusion The multimodal model,integrating MG and MRI radiomics features,show optimal performance and can be served as a preoperative prediction of LVI status in breast cancer.

Objective To investigate the value of multimodal model integrating digital mammography(MG)and MRI radiomics features for preoperative prediction of lymphovascular invasion(LVI)status in breast cancer.Methods The clinical and imaging data from 336 patients with pathologically confirmed invasive breast cancer were retrospectively analyzed and randomly divided into a training group(235 cases)and a test group(101 cases)according to the ratio of 7∶3.Feature dimensionality reduction was carried out by Pearson correlation analysis followed by least absolute shrinkage and selection operator(LASSO)regression.Radiomics models were constructed based on MG craniocaudal(CC),dynamic contrast enhancement(DCE),T2 WI,and integrated MRI sequences;a multimodal model was further developed by incorporating clinical high-risk factors.The predictive efficiency of each model was evaluated by plotting receiver operating characteristic(ROC)curve.Results The ROC curve analysis showed that the multimodal model performed the best predictive efficiency,with area under the curve(AUC)of 0.989 and 0.861,accuracy of 0.949 and 0.782,sensitivity of 0.923 and 0.828,and specificity of 0.962 and 0.764 in the training group and test group respectively.Conclusion The multimodal model,integrating MG and MRI radiomics features,show optimal performance and can be served as a preoperative prediction of LVI status in breast cancer.

As a low-load physiological monitoring technology, wearable devices can provide new methods for monitoring, evaluating and managing chronic diseases, which is a direction for the future development of monitoring technology. However, as a new type of monitoring technology, its clinical application mode and value are still unclear and need to be further explored. In this study, a central monitoring system based on wearable devices was built in the general ward (non-ICU ward) of PLA General Hospital, the value points of clinical application of wearable physiological monitoring technology were analyzed, and the system was combined with the treatment process and applied to clinical monitoring. The system is able to effectively collect data such as electrocardiogram, respiration, blood oxygen, pulse rate, and body position/movement to achieve real-time monitoring, prediction and early warning, and condition assessment. And since its operation from March 2018, 1 268 people (657 patients) have undergone wearable continuous physiological monitoring until January 2020, with data from a total of 1 198 people (632 cases) screened for signals through signal quality algorithms and manual interpretation were available for analysis, accounting for 94.48 % (96.19%) of the total. Through continuous physiological data analysis and manual correction, sleep apnea event, nocturnal hypoxemia, tachycardia, and ventricular premature beats were detected in 232 (36.65%), 58 (9.16%), 30 (4.74%), and 42 (6.64%) of the total patients, while the number of these abnormal events recorded in the archives was 4 (0.63%), 0 (0.00%), 24 (3.80%), and 15 (2.37%) cases. The statistical analysis of sleep apnea event outcomes revealed that patients with chronic diseases were more likely to have sleep apnea events than healthy individuals, and the incidence was higher in men (62.93%) than in women (37.07%). The results indicate that wearable physiological monitoring technology can provide a new monitoring mode for inpatients, capturing more abnormal events and provide richer information for clinical diagnosis and treatment through continuous physiological parameter analysis, and can be effectively integrated into existing medical processes. We will continue to explore the applicability of this new monitoring mode in different clinical scenarios to further enrich the clinical application of wearable technology and provide richer tools and methods for the monitoring, evaluation and management of chronic diseases.
Heart Rate ; Humans ; Monitoring, Physiologic ; Movement ; Sleep Apnea Syndromes ; Wearable Electronic Devices