多模态MRI探索宫颈癌侵袭性及同步放化疗疗效评估的应用研究

项目名称： 多模态MRI探索宫颈癌侵袭性及同步放化疗疗效评估的应用研究

项目编号： No.81501443

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 医药、卫生

项目作者： 刘颖

作者单位： 天津医科大学

项目金额： 18万元

中文摘要： 磁共振扩散加权成像（DWI）和动态增强MRI（DCE-MRI）两项功能成像技术在评价宫颈癌组织学类型、病理级别和淋巴结转移状态，以及评估中晚期宫颈癌患者同步放化疗敏感性方面发挥重要作用。然而常规图像分析方法多选取病灶最大层面设置兴趣区，使得研究结果可重复性较差，更无法全面而准确地反映肿瘤组织的异质性，而后者可能是导致同样病期和同一病理级别宫颈癌患者放化疗疗效产生明显差异的重要原因之一。此外，量化评价患者对放化疗敏感性，特别是确定早期评估疗效的时间窗，以便合理制订和及时调整治疗方案，也是妇科肿瘤领域一直尚未解决的重大问题。本项目在前期相关研究的基础之上，运用多模态MRI技术并借助3D Slicer这一免费、开源的可视化图像分析软件平台，获得可靠的图像分割，从整体上深入挖掘图像信息，获取高通量参数，多维度评估肿瘤异质性，进而获取宫颈癌侵袭性和同步放化疗敏感性的早期影像标志物，以便临床推广应用。

中文关键词： 医学影像；宫颈癌；侵袭性；疗效评价；功能磁共振成像

英文摘要： Diffusion-weighted MR imaging (DWI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) play an important role in the identification of histological characteristics for uterine cervical cancer which include histological subtype, grade of differentiation and lymphatic metastases, and in the evaluation of therapeutic response to concurrent chemoradiotherapy (CCRT) for locally advanced cervical cancer as well. Previously noted conventional approach to these images, however, only placed a discrete region of interest (ROI) encompassing a portion of a lesion in the largest lesion slice. Some of the limitations of such ROI-based methods include interobserver variability in ROI placement, difficulty with reproducibility, and failing to take into account tumor heterogeneity which might be one of the reasons that not all tumors of the same type will respond equally to a specific treatment. Furthermore, one of the greatest challenges in cancer management is to develop a method of rapidly and objectively measuring tumor response to therapy, especially select the optimal time window to make proper and effective observation. A reliable and early marker of response would have immense clinical value to avoid unnecessary toxicities and costs, and to personalize treatment strategy. The overall goal of this work is to investigate the combined role of DWI and DCE-MRI (multimodal MRI) in assessing tumor invasiveness and providing early and reproducible response predictors in cervical cancer patients by using 3D Slicer, which is a free, open source software package for image analysis and scientific visualization. 3D slicer generally uses a volumetric approach, incorporating all voxels within the lesion on all slices. Such an analysis could provide multi- metrics from different functional imaging techniques; this goes beyond what can be achieved by using any single functional technique and will provides a more comprehensive evaluation of the entirety of the lesion.

英文关键词： medical imaging;uterine cervical cancer;aggressiveness;treatment response ;functional MRI