Department of Computer Sciences, LISAC Laboratory, Sidi Mohammed Ben Abdellah University, Fez, Morocco.
USPN, La Maison Des Sciences Numériques, Paris, France.
BMC Cancer. 2024 Oct 21;24(1):1300. doi: 10.1186/s12885-024-13049-0.
Despite suffering from the same disease, each patient exhibits a distinct microbiological profile and variable reactivity to prescribed treatments. Most doctors typically use a standardized treatment approach for all patients suffering from a specific disease. Consequently, the challenge lies in the effectiveness of this standardized treatment and in adapting it to each individual patient. Personalized medicine is an emerging field in which doctors use diagnostic tests to identify the most effective medical treatments for each patient. Prognosis, disease monitoring, and treatment planning rely on manual, error-prone methods. Artificial intelligence (AI) uses predictive techniques capable of automating prognostic and monitoring processes, thus reducing the error rate associated with conventional methods.
This paper conducts an analysis of current literature, encompassing the period from January 2015 to 2023, based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).
In assessing 25 pertinent studies concerning predicting neoadjuvant treatment (NAT) response in breast cancer (BC) patients, the studies explored various imaging modalities (Magnetic Resonance Imaging, Ultrasound, etc.), evaluating results based on accuracy, sensitivity, and area under the curve. Additionally, the technologies employed, such as machine learning (ML), deep learning (DL), statistics, and hybrid models, were scrutinized. The presentation of datasets used for predicting complete pathological response (PCR) was also considered.
This paper seeks to unveil crucial insights into the application of AI techniques in personalized oncology, particularly in the monitoring and prediction of responses to NAT for BC patients. Finally, the authors suggest avenues for future research into AI-based monitoring systems.
尽管患有相同的疾病，每个患者表现出不同的微生物特征和对规定治疗的不同反应。大多数医生通常对所有患有特定疾病的患者采用标准化治疗方法。因此，挑战在于这种标准化治疗的有效性以及如何将其适应每个个体患者。个性化医学是一个新兴领域，医生使用诊断测试来确定每位患者最有效的医疗方法。预后、疾病监测和治疗计划依赖于手动、容易出错的方法。人工智能 (AI) 使用预测技术，能够自动化预后和监测过程，从而降低传统方法的错误率。
本文基于系统评价和荟萃分析的首选报告项目 (PRISMA)，对 2015 年 1 月至 2023 年期间的现有文献进行了分析。
在评估了 25 项关于预测乳腺癌 (BC) 患者新辅助治疗 (NAT) 反应的相关研究后，这些研究探索了各种成像方式（磁共振成像、超声等），并根据准确性、敏感性和曲线下面积来评估结果。此外，还研究了所使用的技术，如机器学习 (ML)、深度学习 (DL)、统计学和混合模型。还考虑了用于预测完全病理缓解 (PCR) 的数据集的表示。
本文旨在揭示人工智能技术在个性化肿瘤学中的应用的重要见解，特别是在监测和预测 BC 患者对 NAT 的反应方面。最后，作者为基于人工智能的监测系统的未来研究提出了一些建议。
