Department of Diagnostic and Interventional Radiology, University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany.
System Technologies and Image Exploitation IOSB, Fraunhofer Institute of Optronics, Fraunhoferstraße 1, 76131, Karlsruhe, Germany.
Eur Radiol. 2022 May;32(5):2901-2911. doi: 10.1007/s00330-021-08419-2. Epub 2021 Dec 18.
To demonstrate the feasibility of an automated, non-invasive approach to estimate bone marrow (BM) infiltration of multiple myeloma (MM) by dual-energy computed tomography (DECT) after virtual non-calcium (VNCa) post-processing.
Individuals with MM and monoclonal gammopathy of unknown significance (MGUS) with concurrent DECT and BM biopsy between May 2018 and July 2020 were included in this retrospective observational study. Two pathologists and three radiologists reported BM infiltration and presence of osteolytic bone lesions, respectively. Bone mineral density (BMD) was quantified CT-based by a CE-certified software. Automated spine segmentation was implemented by a pre-trained convolutional neural network. The non-fatty portion of BM was defined as voxels > 0 HU in VNCa. For statistical assessment, multivariate regression and receiver operating characteristic (ROC) were conducted.
Thirty-five patients (mean age 65 ± 12 years; 18 female) were evaluated. The non-fatty portion of BM significantly predicted BM infiltration after adjusting for the covariable BMD (p = 0.007, r = 0.46). A non-fatty portion of BM > 0.93% could anticipate osteolytic lesions and the clinical diagnosis of MM with an area under the ROC curve of 0.70 [0.49-0.90] and 0.71 [0.54-0.89], respectively. Our approach identified MM-patients without osteolytic lesions on conventional CT with a sensitivity and specificity of 0.63 and 0.71, respectively.
Automated, AI-supported attenuation assessment of the spine in DECT VNCa is feasible to predict BM infiltration in MM. Further, the proposed method might allow for pre-selecting patients with higher pre-test probability of osteolytic bone lesions and support the clinical diagnosis of MM without pathognomonic lesions on conventional CT.
• The retrospective study provides an automated approach for quantification of the non-fatty portion of bone marrow, based on AI-supported spine segmentation and virtual non-calcium dual-energy CT data. • An increasing non-fatty portion of bone marrow is associated with a higher infiltration determined by invasive biopsy after adjusting for bone mineral density as a control variable (p = 0.007, r = 0.46). • The non-fatty portion of bone marrow might support the clinical diagnosis of multiple myeloma when conventional CT images are negative (sensitivity 0.63, specificity 0.71).
通过虚拟非钙（VNCa）后处理，展示一种用于通过双能 CT（DECT）自动、无创评估多发性骨髓瘤（MM）骨髓浸润的方法的可行性。
本回顾性观察研究纳入了 2018 年 5 月至 2020 年 7 月期间同时接受 DECT 和骨髓活检的 MM 和意义未明单克隆丙种球蛋白血症（MGUS）患者。两名病理学家和三名放射科医生分别报告骨髓浸润和溶骨性骨病变的存在。通过经过 CE 认证的软件对基于 CT 的骨密度（BMD）进行定量评估。通过预训练的卷积神经网络实现脊柱自动分段。在 VNCa 中，将 BM 的非脂肪部分定义为 HU 值大于 0 的体素。为了进行统计评估，进行了多元回归和受试者工作特征（ROC）分析。
共评估了 35 名患者（平均年龄 65±12 岁；18 名女性）。调整了协变量 BMD 后，BM 的非脂肪部分与骨髓浸润显著相关（p=0.007，r=0.46）。BM 的非脂肪部分大于 0.93%可以预测溶骨性病变和 MM 的临床诊断，ROC 曲线下面积分别为 0.70 [0.49-0.90] 和 0.71 [0.54-0.89]。我们的方法能够识别出常规 CT 上没有溶骨性病变的 MM 患者，其灵敏度和特异性分别为 0.63 和 0.71。
DECT VNCa 中基于 AI 支持的脊柱衰减评估的自动、人工智能支持的方法可用于预测 MM 中的骨髓浸润。此外，该方法可以预先选择具有更高溶骨性骨病变阳性预测值的患者，并在常规 CT 上没有特征性病变的情况下支持 MM 的临床诊断。
本回顾性研究提供了一种基于 AI 支持的脊柱分段和虚拟非钙双能 CT 数据的骨髓非脂肪部分的自动定量方法。
在调整骨矿物质密度作为控制变量后（p=0.007，r=0.46），骨髓中非脂肪部分的增加与浸润程度更高相关。
当常规 CT 图像为阴性时，骨髓中非脂肪部分可能有助于支持多发性骨髓瘤的临床诊断（灵敏度 0.63，特异性 0.71）。
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