Evolution of localization methods for non-palpable breast lesions: a literature review from a translational medicine perspective.

Background and Objective: With an increasing number of non-palpable breast lesions detected due to improved screening, accurate localization of these lesions for surgery is crucial. This literature review explores the evolution of localization methods for non-palpable breast lesions, highlighting the translational journey from concept to clinical practice. Methods: A comprehensive search of PubMed, Embase, and Scopus databases until September 2023 was conducted. Key Content and Findings: Multiple methods have been developed throughout the past few decades. (I) Wire-guided localization (WGL) introduced in 1966, has become a reliable method for localization. Its simplicity and cost-effectiveness are its key advantages, but challenges include logistical constraints, patient discomfort, and potential wire migration. (II) Intraoperative ultrasound localization (IOUS) has shown promise in ensuring complete lesion removal with higher negative margin rates. However, its utility is limited to lesions visible on ultrasound (US) imaging. (III) Breast biopsy marker localization: the use of markers has improved the precision of localization without the need for wire. However, marker visibility remains a challenge despite improvements in their design. (IV) Radioactive techniques: radio-guided occult lesion localization (ROLL) and radioactive seed localization (RSL) offer flexibility in scheduling and improved patient comfort. However, they require close multidisciplinary collaboration and specific equipment due to radioactive concerns. (V) Other wireless non-radioactive techniques: wireless non-radioactive techniques have been developed in recent three decades to provide flexible and patient-friendly alternatives. It includes magnetic seed localization, radar techniques, and radiofrequency techniques. Their usage has been gaining popularity due to their safety profile and allowance of more flexible scheduling. However, their high cost and need for additional training remain a barrier to a wider adoption. Conclusions: The evolution of breast lesion localization methods has progressed to more patient-friendly techniques, each with its unique advantages and limitations. Future research on patient-reported outcomes, cosmetic outcomes, breast biopsy markers and integration of augmented reality with breast lesion localization are needed.

Pregnancy-related breast cancer: 14-year experience in a tertiary institution in Hong Kong.

BACKGROUND: The incidence of pregnancy-associated breast cancer (PABC) is increasing. Its tumor characteristics and overall survival compared with those in nonpregnant patients remain controversial. While there have been suggestions that PABC patients have a 40 % increase in the risk of death compared to non-pregnant patients, other studies suggested similar disease outcomes. This study aims to review our local experience with PABC. METHODS: Twenty-eight patients diagnosed with PABC and twenty-eight patients diagnosed at premenopausal age randomly selected by a computer-generated system during the same period were recruited. Background characteristics, tumor features, and survival were compared. RESULTS: Among the twenty-eight pregnant patients, seventeen were diagnosed during pregnancy, and eleven were diagnosed in the postpartum period. Compared to the non-pregnant breast cancer patients, they presented with less progesterone receptor-positive tumor (35.7 % vs. 64.2 %, p = 0.03). Although there was no statistically significant difference in tumor size (p = 0.44) and nodal status (p = 0.16), the tumor tended to be larger in size (2.94 +/- 1.82 vs 2.40 +/- 1.69 cm) and with more nodal involvement (35.7 % vs 25.0 %). There was also a trend of delayed presentation to medical attention, with a mean duration of 13.1 weeks in the PABC group and 8.6 weeks in the control group. However, the overall survival did not differ (p = 0.63). CONCLUSION: PABC is increasing in incidence. They tend to have more aggressive features, but overall survival remains similar. A multidisciplinary approach is beneficial for providing the most appropriate care.

ChatGPT versus human in generating medical graduate exam multiple choice questions-A multinational prospective study (Hong Kong S.A.R., Singapore, Ireland, and the United Kingdom).

INTRODUCTION: Large language models, in particular ChatGPT, have showcased remarkable language processing capabilities. Given the substantial workload of university medical staff, this study aims to assess the quality of multiple-choice questions (MCQs) produced by ChatGPT for use in graduate medical examinations, compared to questions written by university professoriate staffs based on standard medical textbooks. METHODS: 50 MCQs were generated by ChatGPT with reference to two standard undergraduate medical textbooks (Harrison's, and Bailey & Love's). Another 50 MCQs were drafted by two university professoriate staff using the same medical textbooks. All 100 MCQ were individually numbered, randomized and sent to five independent international assessors for MCQ quality assessment using a standardized assessment score on five assessment domains, namely, appropriateness of the question, clarity and specificity, relevance, discriminative power of alternatives, and suitability for medical graduate examination. RESULTS: The total time required for ChatGPT to create the 50 questions was 20 minutes 25 seconds, while it took two human examiners a total of 211 minutes 33 seconds to draft the 50 questions. When a comparison of the mean score was made between the questions constructed by A.I. with those drafted by humans, only in the relevance domain that the A.I. was inferior to humans (A.I.: 7.56 +/- 0.94 vs human: 7.88 +/- 0.52; p = 0.04). There was no significant difference in question quality between questions drafted by A.I. versus humans, in the total assessment score as well as in other domains. Questions generated by A.I. yielded a wider range of scores, while those created by humans were consistent and within a narrower range. CONCLUSION: ChatGPT has the potential to generate comparable-quality MCQs for medical graduate examinations within a significantly shorter time.