Wilson Mitchell P, Haidey Jordan, Murad Mohammad H, Sept Logan, Low Gavin
Department of Radiology and Diagnostic Imaging, University of Alberta, 2B2.41 WMC, 8440-112 Street NW, Edmonton, AB, T6G 2B7, Canada.
Evidence-Based Practice Center, Mayo Clinic, Room 2-54, 2053Rd Ave SW, Rochester, MN, 55905, USA.
Eur Radiol. 2023 Dec;33(12):8605-8616. doi: 10.1007/s00330-023-09916-2. Epub 2023 Jul 13.
This systematic review and meta-analysis evaluated the diagnostic accuracy of CT and MRI for differentiating atypical lipomatous tumors and malignant liposarcomas from benign lipomatous lesions.
MEDLINE, EMBASE, Scopus, the Cochrane Library, and the gray literature from inception to January 2022 were systematically evaluated. Original studies with > 5 patients evaluating the accuracy of CT and/or MRI for detecting liposarcomas with a histopathological reference standard were included. Meta-analysis was performed using a bivariate mixed-effects regression model. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). This study is registered on PROSPERO, number CRD42022306479.
Twenty-six studies with a total of 2613 patients were included. Mean/median reported patient ages ranged between 50 and 63 years. The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. Deep depth to fascia, thickened septations, enhancing components, and lesion size (≥ 10 cm) all demonstrated sensitivities ≥ 85%. Other imaging characteristics including heterogenous/amorphous signal intensity, irregular tumor margin, and nodules present demonstrated lower sensitivities ranging from 43 to 65%. Inter-reader reliability for radiologist gestalt within studies ranged from fair to substantial (k = 0.23-0.7). Risk of bias was predominantly mixed for patient selection, low for index test and reference standard, and unclear for flow and timing.
Higher sensitivities for detecting liposarcomas were achieved with radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size. Combined clinical and imaging scoring and/or radiomics both show promise for optimal performance, though require further analysis with prospective study designs.
This pooled analysis evaluates the accuracy of CT and MRI for detecting atypical lipomatous tumors and malignant liposarcomas. Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large size demonstrate the highest overall sensitivities.
• The summary sensitivity and specificity of radiologist gestalt for detecting liposarcomas was 85% (79-90% 95% CI) and 63% (52-72%), respectively. • Radiologist gestalt, deep depth to fascia, thickened septations, enhancing components, and large tumor size (≥ 10 cm) showed the highest sensitivities for detecting atypical lipomatous tumors/well-differentiated liposarcomas and malignant liposarcomas. • A combined clinical and imaging scoring system and/or radiomics is likely to provide the best overall diagnostic accuracy, although currently proposed scoring systems and radiomic feature analysis require further study with prospective study designs.
本系统评价和荟萃分析评估了CT和MRI在鉴别非典型脂肪瘤性肿瘤及恶性脂肪肉瘤与良性脂肪瘤性病变方面的诊断准确性。
系统评价了从创刊至2022年1月的MEDLINE、EMBASE、Scopus、Cochrane图书馆及灰色文献。纳入了对5例以上患者进行研究的原始研究,这些研究采用组织病理学参考标准评估CT和/或MRI检测脂肪肉瘤的准确性。使用双变量混合效应回归模型进行荟萃分析。采用诊断准确性研究的质量评估2(QUADAS-2)评估偏倚风险。本研究已在国际前瞻性系统评价注册库(PROSPERO)注册,注册号为CRD42022306479。
纳入了26项研究,共2613例患者。报告的患者平均/中位年龄在50至63岁之间。放射科医生凭经验判断检测脂肪肉瘤的汇总敏感性和特异性分别为85%(95%CI:79-90%)和63%(95%CI:52-72%)。至筋膜的深度较深、分隔增厚、强化成分及病变大小(≥10cm)的敏感性均≥85%。其他影像学特征,包括信号强度不均匀/无定形、肿瘤边缘不规则及存在结节,敏感性较低,范围为43%至65%。研究中放射科医生凭经验判断的读者间可靠性从中度到高度一致(k=0.23-0.7)。患者选择方面的偏倚风险主要为混杂,指标检测和参考标准方面的偏倚风险较低,流程和时间方面的偏倚风险不明确。
放射科医生凭经验判断、至筋膜的深度较深、分隔增厚、强化成分及大尺寸在检测脂肪肉瘤方面具有较高的敏感性。临床和影像评分及/或放射组学联合应用均有望实现最佳性能,不过需要通过前瞻性研究设计进行进一步分析。
本汇总分析评估了CT和MRI检测非典型脂肪瘤性肿瘤及恶性脂肪肉瘤的准确性。放射科医生凭经验判断、至筋膜的深度较深、分隔增厚、强化成分及大尺寸显示出最高的总体敏感性。
• 放射科医生凭经验判断检测脂肪肉瘤的汇总敏感性和特异性分别为85%(95%CI:79-90%)和63%(95%CI:52-72%)。• 放射科医生凭经验判断、至筋膜的深度较深、分隔增厚、强化成分及大肿瘤尺寸(≥10cm)在检测非典型脂肪瘤性肿瘤/高分化脂肪肉瘤及恶性脂肪肉瘤方面显示出最高的敏感性。• 临床和影像评分系统及/或放射组学联合应用可能会提供最佳的总体诊断准确性,尽管目前提出的评分系统和放射组学特征分析需要通过前瞻性研究设计进行进一步研究。