MRI Signatures of Parotid Tumours Impacting Management Decisions: A Retrospective Study With Radiology and Pathology Correlation

IF 1.4 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Nivedita Chakrabarty, Prathamesh Pai, Arpita Sahu, Oindrila Roy Chowdhury, Pashmina Kandalgaonkar, Tapish Dadlani, Munita Menon, Suman Kumar Ankathi
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引用次数: 0

Abstract

Introduction

Fine needle aspiration (FNA) from parotid tumour is inadequate and nondiagnostic in 8% and FNA/biopsy from deep lobe is technically challenging; hence, our first objective was to evaluate MRI findings which best predict the benign and malignant nature of parotid tumour. Our second objective was to develop MRI signatures for parotid tumour histopathologies including grades of carcinoma, to help in decision making regarding elective neck dissection.

Methods

Two head and neck radiologists retrospectively evaluated and developed signatures of common benign and malignant parotid tumours using morphology and signal intensity–related variables for 98 patients on MRI available in PACS from 01 January 2016 to 26 December 2022. T1 weighted image (WI), T2WI, short tau inversion recovery, diffusion WI/apparent diffusion coefficient and postcontrast T1WI sequences were evaluated. The developed MRI signatures were then validated by a blinded third radiologist.

Results

Sensitivity, specificity, accuracy, positive and negative predictive values using MRI signatures were 92.31%, 100%, 94.23%, 100% and 81.25%, respectively, for benign and malignant nature of parotid tumours with a highly significant p-value (< 1e-04). Developed MRI signatures also showed high statistical performance and significant p-value for parotid tumour histopathologies and grades of mucoepidermoid carcinoma (MEC). T2 signal intensity and enhancement patterns can help identify low-grade MEC, impacting management decisions regarding elective neck dissection.

Conclusions

MRI can predict the benign and malignant nature, parotid tumour histopathologies and grades of MEC when typical signatures are present, impacting management decisions.

Abstract Image

腮腺肿瘤的MRI特征影响治疗决策:一项具有放射学和病理学相关性的回顾性研究。
导言:腮腺肿瘤的细针穿刺(FNA)不充分,8%无法诊断,深叶的FNA/活检在技术上具有挑战性;因此,我们的第一个目标是评估最能预测腮腺肿瘤良恶性性质的MRI结果。我们的第二个目标是开发腮腺肿瘤组织病理学的MRI特征,包括癌的等级,以帮助决定是否选择颈部清扫。方法:2016年1月1日至2022年12月26日,两名头颈部放射科医生回顾性评估并制定了98例PACS MRI患者常见良恶性腮腺肿瘤的形态学和信号强度相关变量。评估T1加权图像(WI)、T2WI、短tau反转恢复、弥散WI/表观弥散系数和对比后T1WI序列。开发的核磁共振成像特征然后由盲的第三放射科医生验证。结果:MRI特征对腮腺肿瘤良恶性的敏感性、特异性、准确性、阳性预测值和阴性预测值分别为92.31%、100%、94.23%、100%和81.25%,p值高度显著(结论:当典型特征存在时,MRI可以预测腮腺肿瘤的良恶性、组织病理学和MEC的分级,影响管理决策)。
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来源期刊
CiteScore
3.30
自引率
6.20%
发文量
133
审稿时长
6-12 weeks
期刊介绍: Journal of Medical Imaging and Radiation Oncology (formerly Australasian Radiology) is the official journal of The Royal Australian and New Zealand College of Radiologists, publishing articles of scientific excellence in radiology and radiation oncology. Manuscripts are judged on the basis of their contribution of original data and ideas or interpretation. All articles are peer reviewed.
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