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用于软组织肿瘤细胞密度评估和基质特征分析的扩散加权成像。

Diffusion-weighted imaging for the cellularity assessment and matrix characterization of soft tissue tumour.

作者信息

Robba Tiziana, Chianca Vito, Albano Domenico, Clementi Valeria, Piana Raimondo, Linari Alessandra, Comandone Alessandro, Regis Guido, Stratta Maurizio, Faletti Carlo, Borrè Alda

机构信息

Dipartimento di Radiologia, Azienda Ospedaliera Città della Salute e della Scienza, Centro Traumatologico Ortopedico, Via Zuretti 29, 10126, Torino, Italy.

Dipartimento di Scienze Biomediche Avanzate, Università degli studi Federico II, Via Pansini 5, 80131, Napoli, Italy.

出版信息

Radiol Med. 2017 Nov;122(11):871-879. doi: 10.1007/s11547-017-0787-x. Epub 2017 Jul 8.

Abstract

PURPOSE

To evaluate whether apparent diffusion coefficient (ADC) of diffusion-weighted imaging (DWI) is able to investigate the histological features of soft tissue tumours.

METHODS

We reviewed MRIs of soft tissue tumours performed from 2012 to 2015 to calculate the average ADCs. We included 46 patients (27 male; mean age: 57 years, range 12-85 years) with histologically proven soft tissue tumours (10 benign, 2 intermediate 34 malignant) grouped into eight tumour type classes. An experienced pathologist assigned a semi-quantitative cellularity score (very high, high, medium and low) and tumour grading. The t test, ANOVA and linear regression were used to correlate ADC with clinicopathological data. Approximate receiver operating characteristic curves were created to predict possible uses of ADC to differentiate benign from malignant tumours.

RESULTS

There was a significant difference (p < 0.01) in ADCs between these three groups excluding myxoid sarcomas. A significant difference was also evident between the tumour type classes (p < 0.001), grade II and III myxoid lesions (p < 0.05), tumour grading classes (p < 0.001) and cellularity scores classes (p < 0.001), with the lowest ADCs in the very high cellularity. While the linear regression analysis showed a significant relationship between ADC and tumour cellularity (r = 0.590, p ≤ 0.05) and grading (r = 0.437, p ≤ 0.05), no significant relationship was found with age, gender, tumour size and histological subtype. An optimal cut-off ADC value of 1.45 × 10 mm/s with 76.8% accuracy was found to differentiate benign from malignant tumours.

CONCLUSIONS

DWI may offer adjunctive information about soft tissue tumours, but its clinical role is still to be defined.

摘要

目的

评估扩散加权成像(DWI)的表观扩散系数(ADC)是否能够研究软组织肿瘤的组织学特征。

方法

我们回顾了2012年至2015年期间进行的软组织肿瘤MRI,以计算平均ADC值。我们纳入了46例经组织学证实的软组织肿瘤患者(27例男性;平均年龄:57岁,范围12 - 85岁),这些肿瘤分为8种肿瘤类型,包括10例良性、2例中间型和34例恶性。一位经验丰富的病理学家给出了半定量的细胞密度评分(非常高、高、中、低)和肿瘤分级。采用t检验、方差分析和线性回归分析将ADC与临床病理数据进行关联。绘制近似的受试者操作特征曲线,以预测ADC用于鉴别良性和恶性肿瘤的可能用途。

结果

排除黏液样肉瘤后,这三组之间的ADC值存在显著差异(p < 0.01)。肿瘤类型组之间(p < 0.001)、II级和III级黏液样病变之间(p < 0.05)、肿瘤分级组之间(p < 0.001)以及细胞密度评分组之间(p < 0.001)也存在显著差异,细胞密度非常高的肿瘤ADC值最低。虽然线性回归分析显示ADC与肿瘤细胞密度(r = 0.590,p ≤ 0.05)和分级(r = 0.437,p ≤ 0.05)之间存在显著关系,但未发现与年龄、性别、肿瘤大小和组织学亚型有显著关系。发现区分良性和恶性肿瘤的最佳ADC临界值为1.45×10⁻³ mm²/s,准确率为76.8%。

结论

DWI可能为软组织肿瘤提供辅助信息,但其临床作用仍有待确定。

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