Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients

Purpose: Lymph node metastasis is a multifactorial event. Several scholars have developed nomograph models to predict the sentinel lymph nodes (SLN) metastasis before operation. According to the clinical and pathological characteristics of breast cancer patients, we use the new method to establish a more comprehensive model and add some new factors which have never been analyzed in the world and explored the prospect of its clinical application.Materials and methods: The clinicopathological data of 633 patients with breast cancer who underwent SLN examination from January 2011 to December 2014 were retrospectively analyzed. Because of the imbalance in data, we used smote algorithm to oversample the data to increase the balanced amount of data. Our study for the first time included the shape of the tumor and breast gland content. The location of the tumor was analyzed by the vector combining quadrant method, at the same time we use the method of simply using quadrant or vector for comparing. We also compared the predictive ability of building models through logistic regression and Bagged-Tree algorithm. The Bagged-Tree algorithm was used to categorize samples. The SMOTE-Bagged Tree algorithm and 5-fold cross-validation was used to established the prediction model. The clinical application value of the model in early breast cancer patients was evaluated by confusion matrix and the area under receiver operating characteristic (ROC) curve (AUC).Results: Our predictive model included 12 variables as follows: age, body mass index (BMI), quadrant, clock direction, the distance of tumor from the nipple, morphology of tumor molybdenum target, glandular content, tumor size, ER, PR, HER2, and Ki-67.Finally, our model obtained the AUC value of 0.801 and the accuracy of 70.3%.We used logistic regression to established the model, in the modeling and validation groups, the area under the curve (AUC) were 0.660 and 0.580.We used the vector combining quadrant method to analyze the original location of the tumor, which is more precise than simply using vector or quadrant (AUC 0.801 vs. 0.791 vs. 0.701, Accuracy 70.3 vs. 70.3 vs. 63.6%).Conclusions: Our model is more reliable and stable to assist doctors predict the SLN metastasis in breast cancer patients before operation

Unveiling the Electro‐Chemo‐Mechanical Failure Mechanism of Sodium Metal Anodes in Sodium–Oxygen Batteries by Synchrotron X‐Ray Computed Tomography

Rechargeable sodium–oxygen batteries (NaOBs) are receiving extensive research interests because of their advantages such as ultrahigh energy density and cost efficiency. However, the severe failure of Na metal anodes has impeded the commercial development of NaOBs. Herein, combining in situ synchrotron X-ray computed tomography (SXCT) and other complementary characterizations, a novel electro-chemo-mechanical failure mechanism of sodium metal anode in NaOBs is elucidated. It is visually showcased that the Na metal anodes involve a three-stage decay evolution of a porous Na reactive interphase layer (NRIL): from the initially dot-shaped voids evolved into the spindle-shaped voids and the eventually-developed ruptured cracks. The initiation of this three-stage evolution begins with chemical-resting and is exacerbated by further electrochemical cycling. From corrosion science and fracture mechanics, theoretical simulations suggest that the evolution of porous NRIL is driven by the concentrated stress at crack tips. The findings illustrate the importance of preventing electro-chemo-mechanical degradation of Na anodes in practically rechargeable NaOBs