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基于影像数据的多模态融合技术在前列腺癌诊疗中的研究现状
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Abstract:
前列腺癌(PCa)是全球男性中最常见的恶性肿瘤之一，早期准确诊断和个性化治疗是降低其死亡率的重要策略。传统的单一模态诊断方法难以全面评估PCa的复杂性，而多模态数据融合(MDF)技术通过整合影像资料、临床信息、生化指标、病理数据和基因组数据等多种信息源，为PCa的诊断、分级、预后评估及治疗方案制定提供了新的思路。本文综述了基于影像数据的MDF技术在PCa诊疗中的研究现状，包括数据预处理与标准化、特征提取与表示、融合策略以及融合后数据的分析与决策。探讨了MDF在影像数据间的融合、影像与临床信息、生化指标、病理和基因数据融合中的具体应用。尽管MDF展现出巨大的应用潜力，但在数据异质性、模型可解释性和标准化方面仍面临诸多挑战。未来研究应着重开发更加高效且可解释的融合算法，推动数据标准化进程，进一步提高PCa诊疗的精准性和个性化水平，为患者提供更优质的医疗服务。
Prostate cancer (PCa) is one of the most common malignant tumors in men worldwide. Early and accurate diagnosis, along with personalized treatment, are essential strategies for reducing its mortality rate. Traditional single-modality diagnostic methods often fall short in comprehensively assessing the complexity of PCa. Multimodal data fusion (MDF) technology, which integrates diverse information sources such as imaging data, clinical information, biochemical markers, pathology data, and genomic data, offers a novel approach for the diagnosis, grading, prognosis assessment, and treatment planning of PCa. This review summarizes the current research status of MDF technology based on imaging data in PCa diagnosis and treatment, including aspects such as data preprocessing and standardization, feature extraction and representation, fusion strategies, and post-fusion data analysis and decision-making. It explores the specific applications of MDF in integrating imaging data with clinical information, biochemical markers, pathology, and genetic data. Despite the significant potential of MDF, challenges remain in terms of data heterogeneity, model interpretability, and standardization. Future research should focus on developing more efficient and interpretable fusion algorithms, promoting data standardization, and further enhancing the precision and personalization of PCa diagnosis and treatment to provide better healthcare services for patients.
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