Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient-specific normal features and pathologies. A key distinction from previous models lies in its ability to produce enriched image descriptors solely from binary information, eliminating the need for costly and difficult to obtain tumor segmentation. The proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision-making processes. This generalization makes possible for future research in different medical diagnosis areas with almost not any change in the system. We conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain scans, achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. Our findings highlight the significant potential for improving the efficiency and accuracy of comparative diagnostics and the treatment of tumoral pathologies.

翻译：在脑肿瘤评估中进行比较诊断，使得在评估新患者时能够利用医疗中心现有信息来对比相似病例。通过利用人工智能模型，所提出的系统能够针对给定查询检索出最相似的脑肿瘤病例。其主要目标是通过生成更准确的医学影像表征来增强诊断过程，尤其关注患者特定的正常特征与病理特征。与先前模型的一个关键区别在于，该系统能够仅从二值信息中生成丰富的图像描述符，从而无需成本高昂且难以获取的肿瘤分割。该模型利用人工智能检测患者特征，从数据库中推荐最相似的病例。系统不仅推荐相似病例，还通过设计平衡了健康与异常特征的表征。这不仅促进了其应用的泛化性，也辅助了临床医生的决策过程。这种泛化能力使得未来在不同医学诊断领域的研究几乎无需对系统进行任何修改。我们针对类似研究进行了方法比较分析。所提出的架构在患者的肿瘤区域和健康区域均获得了0.474的Dice系数，优于先前文献结果。我们的模型擅长从脑部扫描中提取并整合解剖与病理特征，在依赖成本更低的标签信息的同时实现了最先进的效果，从而大幅降低了训练过程的总体成本。研究结果凸显了其在提升比较诊断效率与准确性以及肿瘤病理治疗方面的巨大潜力。