Giannakaki Aikaterini-Gavriela, Papachatzopoulou Eftychia, Papapanagiotou Ioannis, Koura Sophia, Baroutis Dimitris, Marinopoulos Spyridon, Daskalakis George, Dimitrakakis Constantine
First Department of Obstetrics and Gynecology, Alexandra University Hospital, National and Kapodistrian University of Athens, 11528 Athens, Greece.
J Clin Med. 2025 Jun 24;14(13):4468. doi: 10.3390/jcm14134468.
Autologous fat grafting (AFT) has become a widely used technique in breast reconstruction, offering natural aesthetics, tissue integration, and patient satisfaction. However, its clinical outcomes require comparison with implant-based reconstruction (IBR), the most common method in clinical practice. While AFT provides a more natural appearance and avoids foreign body-related complications, issues such as fat resorption, procedural variability, and oncological concerns necessitate further investigation. Additionally, artificial intelligence (AI) has been increasingly integrated into breast imaging and reconstructive planning, improving diagnostic accuracy, procedural optimization, and complication prevention. This study aims to compare AFT and IBR while exploring AI's role in enhancing breast reconstruction outcomes. A comprehensive review of clinical studies was conducted to evaluate the advantages, limitations, and oncological implications of AFT versus IBR. AI-driven applications in breast imaging and reconstructive planning were examined for their potential in predicting fat graft retention and optimizing implant selection. Data from systematic reviews and meta-analyses were incorporated to refine reconstruction strategies. AFT offers superior aesthetic outcomes with better tissue integration but presents variability in fat resorption. IBR remains the preferred approach due to its predictability but carries risks of implant-related complications. AI technologies contribute to improved reconstruction planning, enhancing surgical precision and long-term patient outcomes. Optimized patient selection and long-term follow-up are essential for improving breast reconstruction techniques. AI-driven approaches provide valuable tools for enhancing procedural predictability and personalized treatment strategies. Future research should focus on refining AI algorithms and establishing standardized protocols for reconstructive decision-making.
自体脂肪移植（AFT）已成为乳房重建中广泛应用的技术，具有自然美观、组织整合性好和患者满意度高等优点。然而，其临床效果需要与临床实践中最常用的基于植入物的重建（IBR）方法进行比较。虽然AFT能提供更自然的外观并避免与异物相关的并发症，但诸如脂肪吸收、手术变异性和肿瘤学问题等仍需进一步研究。此外，人工智能（AI）已越来越多地融入乳房成像和重建规划中，提高了诊断准确性、手术优化和并发症预防能力。本研究旨在比较AFT和IBR，同时探讨AI在改善乳房重建效果中的作用。我们对临床研究进行了全面综述，以评估AFT与IBR的优势、局限性和肿瘤学意义。研究了AI在乳房成像和重建规划中的应用，以评估其在预测脂肪移植保留和优化植入物选择方面的潜力。纳入了系统评价和荟萃分析的数据以完善重建策略。AFT具有更好的美学效果和组织整合性，但脂肪吸收存在变异性。IBR因其可预测性仍是首选方法，但存在与植入物相关的并发症风险。AI技术有助于改善重建规划，提高手术精度和患者长期效果。优化患者选择和长期随访对于改进乳房重建技术至关重要。AI驱动的方法为提高手术可预测性和个性化治疗策略提供了有价值的工具。未来的研究应专注于完善AI算法，并建立重建决策的标准化方案。
