Clinical Value of Ultrasound-Guided Minimally Invasive Biopsy in the Diagnosis or Treatment of Breast Nodules

Comparison of the diagnostic efficacy of mathematical models in distinguishing ultrasound imaging of breast nodules

This study compared the diagnostic efficiency of benign and malignant breast nodules using ultrasonographic characteristics coupled with several machine-learning models, including logistic regression (Logistics), partial least squares discriminant analysis (PLS-DA), linear support vector machine (Linear SVM), linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN) and random forest (RF). The clinical information and ultrasonographic characteristics of 926 female patients undergoing breast nodule surgery were collected and their relationships were analyzed using Pearson's correlation. The stepwise regression method was used for variable selection and the Monte Carlo cross-validation method was used to randomly divide these nodule cases into training and prediction sets. Our results showed that six independent variables could be used for building models, including age, background echotexture, shape, calcification, resistance index, and axillary lymph node. In the prediction set, Linear SVM had the highest diagnosis rate of benign nodules (0.881), and Logistics, ANN and LDA had the highest diagnosis rate of malignant nodules (0.910~0.912). The area under the ROC curve (AUC) of Linear SVM was the highest (0.890), followed by ANN (0.883), LDA (0.880), Logistics (0.878), RF (0.874), PLS-DA (0.866), and KNN (0.855), all of which were better than that of individual variances. On the whole, the diagnostic efficacy of Linear SVM was better than other methods.

Step-by-Step Method Combined with Simulation-Enhanced Learner Outcomes in Minimally Invasive Breast Surgery Education

PURPOSE

Traditional standardized training in ultrasound-guided minimally invasive breast surgery (UMIBS) focused on lecture-based learning (LBL) resulted in students' insufficient analysis, limited spatial visual conversion ability, and poor practical application. This study examined the effects of the step-by-step (SBS) method combined with a simulation model in UMIBS education.

SUBJECTS AND METHODS

A total of 84 residents participated in this study. The residents were divided into the SBS group (experience group, n=42) and the LBL group (control group, n=42), and the same teacher taught the two groups to ensure a comparable result. Based on the pork simulation model, two experts evaluated student performance scores, and the total time taken by each student was also counted. The participants were surveyed with 7 questions after the training, and each answer was assigned a score of 1, 2 or 3 to compare the participants' satisfaction.

RESULTS

The average value of the surgical skills for SBS group were significantly higher than LBS group, which was 82.8±4.4 and 72.7±4.0 (t=4.27, P<0.001), the time spend of neoplasm localization by the experience group was significantly less than the control group, which was 17.9±1.6 and 20.9±1.2 secs, (t=1.58, P<0.001), and there were significant differences in puncture accuracy and excision integrity between the two groups (P<0.05). In addition, the results of the questionnaire survey showed that learning interest, surgical ability and satisfaction were better in the SBS group than in the LBS group (P<0.05), and there were no significant differences in clinical thinking and learning pressure between the two groups.

CONCLUSION

The SBS teaching method may help to improve the surgical skills and learning interest, as well as reduce adverse reactions and cultivate clinical thinking of the students in UMIBS training. Future studies could consider multicenter clinical research to further confirm the practicality of this teaching method and reduce the risk of deviation.

Case Report: Upper limb dysfunction may be caused by chest wall mass excision: An enlightenment from a special case

Of all the thoracic surgical procedures, chest wall surgery is probably the lowest-risk type. In fact, it is not so. Clinical work also often has the trap of chest wall surgery. An operation to remove a mass in the axilla may result in upper limb disability on the affected side. Here, we report the case of a 47-year-old female patient with a left chest wall adjacent axillary mass, which was considered an abnormal structural lymph node on color ultrasound examination and chest CT. Otherwise, she felt no discomfort. The left upper limb moved freely without being affected by the mass. A routine resection of the tumor was performed after the preoperative examination was completed. After the operation, the incision recovered well. However, the day after the surgery, she developed numbness and pain in her left little finger and ring finger, pain that often kept her from sleeping. The mass was confirmed to be a schwannoma with cystic degeneration by pathology slicing after the operation. By this time, doctors were alerted to the fact that the removal of the chest wall mass had nearly disabled the left upper limb of the patient, which was a great warning to the thoracic surgeon. In this case report, we hope that all surgeons will be cautious and careful and will not trust the imaging diagnosis too much. It is also hoped that the patient understands that some procedures may lead to unexpected complications.