Computational Bio-Medicine Laboratory (CBML), Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH), Nikolaou Plastira 100, Vassilika Vouton, GR-70013, Heraklion, Crete, Greece.
Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece.
Eur Radiol Exp. 2020 Aug 3;4(1):45. doi: 10.1186/s41747-020-00175-0.
We investigated a recently proposed multiexponential (Mexp) fitting method applied to T2 relaxometry magnetic resonance imaging (MRI) data of benign and malignant adipocytic tumours and healthy subcutaneous fat. We studied the T2 distributions of the different tissue types and calculated statistical metrics to differentiate benign and malignant tumours.
Twenty-four patients with primary benign and malignant adipocytic tumours prospectively underwent 1.5-T MRI with a single-slice T2 relaxometry (Carr-Purcell-Meiboom-Gill sequence, 25 echoes) prior to surgical excision and histopathological assessment. The proposed method adaptively chooses a monoexponential or biexponential model on a voxel basis based on the adjusted R goodness of fit criterion. Linear regression was applied on the statistical metrics derived from the T2 distributions for the classification.
Healthy subcutaneous fat and benign lipoma were better described by biexponential fitting with a monoexponential and biexponential prevalence of 0.0/100% and 0.2/99.8% respectively. Well-differentiated liposarcomas exhibit 17.6% monoexponential and 82.4% biexponential behaviour, while more aggressive liposarcomas show larger degree of monoexponential behaviour. The monoexponential/biexponential prevalence was 47.6/52.4% for myxoid tumours, 52.8/47.2% for poorly differentiated parts of dedifferentiated liposarcomas, and 24.9/75.1% pleomorphic liposarcomas. The percentage monoexponential or biexponential model prevalence per patient was the best classifier distinguishing between malignant and benign adipocytic tumours with a 0.81 sensitivity and a 1.00 specificity.
Healthy adipose tissue and benign lipomas showed a pure biexponential behaviour with similar T2 distributions, while decreased adipocytic cell differentiation characterising aggressive neoplasms was associated with an increased rate of monoexponential decay curves, opening a perspective adipocytic tumour classification.
我们研究了一种新提出的多指数（Mexp）拟合方法，应用于良性和恶性脂肪肿瘤及健康皮下脂肪的 T2 弛豫时间磁共振成像（MRI）数据。我们研究了不同组织类型的 T2 分布，并计算了统计指标来区分良性和恶性肿瘤。
24 例原发性良性和恶性脂肪肿瘤患者前瞻性地在 1.5-T MRI 上进行单次 T2 弛豫时间测量（Carr-Purcell-Meiboom-Gill 序列，25 个回波），然后进行手术切除和组织病理学评估。该方法基于调整的 R 拟合优度标准，自适应地选择单指数或双指数模型进行体素拟合。线性回归应用于从 T2 分布得出的统计指标进行分类。
健康的皮下脂肪和良性脂肪瘤用双指数拟合更好，单指数和双指数的流行率分别为 0.0/100%和 0.2/99.8%。高分化脂肪肉瘤表现出 17.6%的单指数和 82.4%的双指数行为，而更具侵袭性的脂肪肉瘤表现出更大程度的单指数行为。粘液性肿瘤的单指数/双指数流行率分别为 47.6/52.4%，去分化脂肪肉瘤的低分化部分为 52.8/47.2%，多形性脂肪肉瘤为 24.9/75.1%。每个患者的单指数或双指数模型流行率百分比是区分恶性和良性脂肪肿瘤的最佳分类器，具有 0.81 的敏感性和 1.00 的特异性。
健康的脂肪组织和良性脂肪瘤表现出纯双指数行为，具有相似的 T2 分布，而具有侵袭性的肿瘤中脂肪细胞分化程度降低与单指数衰减曲线的比例增加有关，为脂肪肿瘤的分类开辟了新的前景。
