文章：

软组织肉瘤的放射组学：迈向肿瘤学精准影像

Radiomics in Soft Tissue Sarcoma: Toward Precision Imaging in Oncology

原文发布日期：14 November 2025

DOI: 10.3390/cancers17223661

类型: Article

开放获取: 是

英文摘要：

Radiomics entails a data-driven approach to imaging with a wide array of potential uses in characterizing soft tissue sarcomas, enabling extraction of quantitative features from routine clinical CT and MRI examinations. These features—encompassing descriptors of size, shape, and internal heterogeneity—can improve diagnostic accuracy, tumor grading, and treatment response assessment. Radiomics has shown promise in distinguishing benign from malignant lesions, subtyping sarcomas, and predicting metastatic potential. In particular, models integrating radiomic data with clinical variables have demonstrated performance comparable to expert radiologists in challenging diagnostic scenarios. Machine learning enhances radiomics by automating feature selection and improving predictive modeling. Despite its potential, challenges remain in standardizing imaging protocols, ensuring reproducibility, and integrating radiomics into clinical workflows. Multi-institutional collaboration is essential for broader model validation and clinical integration. By leveraging specific radiomics features as novel quantitative imaging biomarkers, radiomics can drive precision oncology in sarcoma, supporting tailored therapies and improving prognostic accuracy.

摘要翻译： 

影像组学是一种数据驱动的影像分析方法，在软组织肉瘤的特征描述方面具有广泛的应用潜力，能够从常规临床CT和MRI检查中提取定量特征。这些特征——包括大小、形状及内部异质性等描述符——可提高诊断准确性、肿瘤分级和治疗反应评估的效能。影像组学在区分良恶性病变、肉瘤亚型分类及预测转移潜力方面展现出良好前景。特别是将影像组学数据与临床变量整合的模型，在复杂诊断场景中已表现出与放射科专家相当的诊断性能。机器学习通过自动化特征选择和优化预测模型，进一步增强了影像组学的应用价值。尽管潜力巨大，影像组学在标准化成像协议、确保结果可重复性以及融入临床工作流程方面仍面临挑战。多机构合作对于扩大模型验证和临床整合至关重要。通过利用特定的影像组学特征作为新型定量影像生物标志物，影像组学可推动肉瘤精准肿瘤学的发展，支持个体化治疗并提高预后判断的准确性。

原文链接：

Radiomics in Soft Tissue Sarcoma: Toward Precision Imaging in Oncology