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开发一种风险评估模型,以在磁共振成像上鉴别肌肉骨骼软组织肿块的良恶性。

Development of a risk assessment model to differentiate malignant and benign musculoskeletal soft-tissue masses on magnetic resonance imaging.

作者信息

Obaid Haron, Vassos Nicholas, Adams Scott J, Bryce Rhonda, Donuru Achala, Sinclair Nicolette

机构信息

Department of Medical Imaging, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.

Clinical Research Support Unit, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.

出版信息

J Med Imaging Radiat Oncol. 2020 Feb;64(1):9-17. doi: 10.1111/1754-9485.12981. Epub 2019 Dec 2.

Abstract

INTRODUCTION

This study aimed to develop a risk stratification model to differentiate benign and malignant MRI-imaged musculoskeletal soft-tissue tumours, informing decisions surrounding biopsy and follow-up imaging.

METHODS

Imaging of patients who underwent MRI and subsequent biopsy to evaluate a soft-tissue mass was retrospectively reviewed. Features analysed included patient age; tumour size; shape; margins; enhancement pattern; signal intensity pattern; deep fascia, neurovascular bundle, bone and joint involvement; and the presence of necrosis, haemorrhage, oedema and intralesional fat. Univariate comparisons, by final histopathological status, employed t-tests and chi-square tests, followed by simple and multiple logistic regressions. Variables included in the final multiple regression model were used to define a three-level risk stratification strategy.

RESULTS

One-hundred and ten patients were included in the analysis. Univariate relationships were identified between malignancy and age, tumour size, deep fascia involvement, neurovascular involvement, necrosis, haemorrhage, oedema and heterogeneous enhancement (all P < 0.01). Final multiple regression modelling included size, enhancement and oedema. Thirty of 40 (75%) tumours >5 cm with surrounding oedema ('high risk') were malignant, 13 of 47 (28%) tumours with one or more of tumour size >5 cm, surrounding oedema or heterogeneous enhancement ('moderate risk') were malignant, and none of the 16 tumours ≤5 cm with the absence of surrounding oedema and heterogeneous enhancement ('low risk') were malignant.

CONCLUSIONS

A model including tumour size, enhancement and oedema has potential to stratify soft-tissue tumours into high-, intermediate- and low-risk categories; this may inform decisions surrounding biopsy and follow-up imaging.

摘要

引言

本研究旨在开发一种风险分层模型,以区分MRI成像的肌肉骨骼软组织肿瘤的良恶性,为活检和后续成像相关决策提供依据。

方法

对接受MRI检查并随后进行活检以评估软组织肿块的患者的影像进行回顾性分析。分析的特征包括患者年龄、肿瘤大小、形状、边缘、强化模式、信号强度模式、深筋膜、神经血管束、骨和关节受累情况,以及坏死、出血、水肿和瘤内脂肪的存在情况。根据最终组织病理学状态进行单因素比较,采用t检验和卡方检验,随后进行简单和多元逻辑回归。最终多元回归模型中纳入的变量用于定义三级风险分层策略。

结果

110例患者纳入分析。在恶性肿瘤与年龄、肿瘤大小、深筋膜受累、神经血管受累、坏死、出血、水肿和不均匀强化之间发现了单因素关系(所有P<0.01)。最终的多元回归模型包括大小、强化和水肿。40例肿瘤>5 cm且伴有周围水肿(“高风险”)的患者中有30例(75%)为恶性,47例具有肿瘤大小>5 cm、周围水肿或不均匀强化中的一项或多项(“中度风险”)的患者中有13例(28%)为恶性,16例肿瘤≤5 cm且无周围水肿和不均匀强化(“低风险”)的患者中无一例为恶性。

结论

一个包括肿瘤大小、强化和水肿的模型有可能将软组织肿瘤分为高、中、低风险类别;这可能为活检和后续成像相关决策提供依据。

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