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ObjectiveThis study aims at establishing a teaching catalog and content for breast rare diseases and developing the syllabus for the breast rare disease using integrated multimodality of case-/problem-/resource-based learning (CBL+PBL+RBL).MethodsBy conducting bibliometrics co-occurrence analysis, we collected 6291 articles on breast rare disease published from January, 1975 to June, 2024. Additionally, we retrieved the Textbook on Rare Diseases, the Catalog of Chinese Rare Disease, and Second Batch of Rare Disease Catalog and then decided the teaching content.ResultsFrom 16, 387 keywords, 1000 (6.1%) keywords were identified through co-occurrence analysis, including 50(0.3%) candidate diseases. These were classified into three categories: rare primary breast diseases, rare genetic mutation-related diseases associated with breast cancer, and rare systemic multi-system diseases involving the breast. From the candidate list, 20(0.1%) rare primary breast diseases were further selected for their notable clinical teaching significance, and significant multi-systemic diseases affecting the breast, whether related to gene mutations or not. Teaching plans were drafted using a diversified parallel teaching approaches, taking into account the characteristics of different diseases and the focus of different teaching methods.ConclusionsThis study initiated the development of the teaching content for breast rare diseases and developed the teaching syllabus using the CBL+PBL+RBL integrated multi teaching model and targeting each rare breast disease for the critical point for teaching.

Objective To investigate the impact of dynamic adaptive teaching model on surgical education. Methods Due to the COVID-19 pandemic in 2020, we adopted dynamic adaptive teaching model in the Department of Breast Surgery, Peking Union Medical College Hospital, which divided the whole curriculum into several individual modules and recombined different modules to accommodate to student's levels and schedules. Meanwhile, adaptive strategy also increased the proportion of online teaching and fully utilized electronic medical resources. The present study included quantitative teaching score (QTS) recorded from January 2020 to June 2020, and used the corresponding data from 2019 as control. The main endpoint was to explore the impact of dynamic adaptive teaching model on overall QTS and its interaction effect with trainer's experience and student category. Results Totally, 20 trainers and 181 trainees were enrolled in the present study. With implementation of dynamic adaptive strategy, the overall QTS decreased dramatically (1.76±0.84 vs. 4.91±1.15, t=4.85, P=0.005). The impact was consistent irrespective of trainers' experience (high experience trainers: 0.85±0.40 vs. 2.12±0.44, t=4.98, P=0.004; medium experience trainers: 0.85±0.29 vs. 2.06±0.53, t=4.51, P=0.006; and low experience trainers: 0.10±0.16 vs. 0.44±0.22, t=2.62, P=0.047). For resident (including graduate) and undergraduate student teaching, both QTS was lower with dynamic strategy (residents: 0.18±0.34 vs. 0.97±0.14, t=4.35, P=0.007; undergraduate students 1.57±0.55 vs. 3.77±1.24, t=3.62, P=0.015), but dynamic strategy was effective for post-doc student subgroup and reached comparable QTS as traditional model (0.00±0.00 vs. 0.17±0.41, t=1.00, P=0.363). Conclusions Dynamic adaptive teaching strategy could be a useful alternative to traditional teaching model for post-doc students. It could be a novel effective solution for saving teaching resources and providing individualized surgical teaching modality.

Breast cancer has become the most prevalent malignant tumor worldwide. Screening improves the rate of early detection and is important for the treatment and prognosis of breast cancer. Breast ultrasound is a common method for screening breast cancer, which can be used as a complementary screening tool to mammography to improve the rate of detection, or used for screening breast cancer alone. In recent years, the use of new techniques of ultrasound in screening breast cancer such as shear wave elastography and contrast-enhanced ultrasound, are rapidly developing. This article reviewed the research progress of clinical application of breast ultrasound in screening breast cancer.

Background: Breast cancer has become a critical international healthcare issue. Specifically, among the different subtypes, breast cancer marked by human epidermal growth factor receptor 2 (HER2)-overexpression usually correlates with low survival and a poor prognosis and poses challenges in treatment, thus leading to high mortality., Case Description: A 54-year-old female patient was diagnosed with a large T4cN2aM0 stage IIIB breast tumor with HER2 overexpression. The tumor size was large, and there was a lack of opportunity for surgery. However, after neoadjuvant chemotherapy (NACT), the size of the tumor continuously shrank, and the patient successfully underwent a modified radical mastectomy. Even though a certain amount of mass remained and she did not complete six courses of NACT, our patient's postoperative pathological result still revealed that a pathological complete response (pCR) was achieved. The appropriate time window for choosing surgical intervention should be determined based on the patient's general condition instead of complying with the treatment guidelines. Also, imaging findings may be misleading in patients who have undergone NACT. Moreover, the regimen should be chosen flexibly., Conclusions: Patients with locally advanced breast cancer can still achieve a radical surgical resection following appropriate comprehensive treatment. Hopefully, this case can provide new ideas for surgeons when they face similar conditions., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-24-92/coif). The authors have no conflicts of interest to declare., (2024 Gland Surgery. All rights reserved.)

Background: Given the limited access to breast cancer (BC) screening, the authors developed and validated a mobile phone-artificial intelligence-based infrared thermography (AI-IRT) system for BC screening., Materials and Methods: This large prospective clinical trial assessed the diagnostic performance of the AI-IRT system. The authors constructed two datasets and two models, performed internal and external validation, and compared the diagnostic accuracy of the AI models and clinicians. Dataset A included 2100 patients recruited from 19 medical centres in nine regions of China. Dataset B was used for independent external validation and included 102 patients recruited from Langfang People's Hospital., Results: The area under the receiver operating characteristic curve of the binary model for identifying low-risk and intermediate/high-risk patients was 0.9487 (95% CI: 0.9231-0.9744) internally and 0.9120 (95% CI: 0.8460-0.9790) externally. The accuracy of the binary model was higher than that of human readers (0.8627 vs. 0.8088, respectively). In addition, the binary model was better than the multinomial model and used different diagnostic thresholds based on BC risk to achieve specific goals., Conclusions: The accuracy of AI-IRT was high across populations with different demographic characteristics and less reliant on manual interpretations, demonstrating that this model can improve pre-clinical screening and increase screening rates., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)