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头颈部癌肿瘤体积变化的建模：磁共振成像对接受诱导化疗患者的贡献。

Modelling tumour volume variations in head and neck cancer: contribution of magnetic resonance imaging for patients undergoing induction chemotherapy.

作者信息

Dinapoli N, Tartaglione T, Bussu F, Autorino R, Miccichè F, Sciandra M, Visconti E, Colosimo C, Paludetti G, Valentini V

机构信息

Institute of Radiotherapy, Università Cattolica del Sacro Cuore, Rome, Italy.

Institute of Radiology, Università Cattolica del Sacro Cuore, Rome, Italy.

出版信息

Acta Otorhinolaryngol Ital. 2017 Feb;37(1):9-16. doi: 10.14639/0392-100X-906.

DOI:10.14639/0392-100X-906
PMID:27897274
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5384316/
Abstract

Primary tumour volume evaluation has predictive value for estimating survival outcomes. Using volumetric data acquired by MRI in patients undergoing induction chemotherapy (IC) these outcomes were estimated before the radiotherapy course in head and neck cancer (HNC) patients. MRI performed before and after IC in 36 locally advanced HNC patients were analysed to measure primary tumour volume. The two volumes were correlated using the linear-log ratio (LLR) between the volume in the first MRI and the volume in the second. Cox's proportional hazards models (CPHM) were defined for loco-regional control (LRC), disease-free survival (DFS) and overall survival (OS). Strict evaluation of the influence of volume delineation uncertainties on prediction of final outcomes has been defined. LLR showed good predictive value for all survival outcomes in CPHM. Predictive models for LRC and DFS at 24 months showed optimal discrimination and prediction capability. Evaluation of primary tumour volume variations in HNC after IC provides an example of modelling that can be easily used even for other adaptive treatment approaches. A complete assessment of uncertainties in covariates required for running models is a prerequisite to create reliable clinically models.

摘要

原发肿瘤体积评估对估计生存结果具有预测价值。利用接受诱导化疗（IC）患者的MRI获取的体积数据，对头颈部癌（HNC）患者在放疗疗程前估计这些结果。分析了36例局部晚期HNC患者在IC前后进行的MRI，以测量原发肿瘤体积。使用第一次MRI中的体积与第二次MRI中的体积之间的线性对数比（LLR）对这两个体积进行关联。为局部区域控制（LRC）、无病生存（DFS）和总生存（OS）定义了Cox比例风险模型（CPHM）。已定义对体积勾画不确定性对最终结果预测影响的严格评估。LLR在CPHM中对所有生存结果均显示出良好的预测价值。24个月时LRC和DFS的预测模型显示出最佳的区分和预测能力。IC后HNC中原发肿瘤体积变化的评估提供了一个建模示例，该示例甚至可轻松用于其他适应性治疗方法。运行模型所需的协变量不确定性的完整评估是创建可靠临床模型的先决条件。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cfe/5384316/1bb899edbc18/0392-100X-37-9-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cfe/5384316/048ec7deffc3/0392-100X-37-9-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cfe/5384316/73d7f8943988/0392-100X-37-9-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cfe/5384316/e0f146562b55/0392-100X-37-9-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cfe/5384316/1bb899edbc18/0392-100X-37-9-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cfe/5384316/048ec7deffc3/0392-100X-37-9-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cfe/5384316/73d7f8943988/0392-100X-37-9-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cfe/5384316/e0f146562b55/0392-100X-37-9-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cfe/5384316/1bb899edbc18/0392-100X-37-9-g004.jpg

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本文引用的文献

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Pretreatment perfusion CT and CT volumetry in squamous cell carcinoma of the head and neck region.头颈部鳞状细胞癌的预处理灌注CT和CT容积测量
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