Bone Marrow Radiomics of T1-Weighted Lumber Spinal MRI to Identify Diffuse Hematologic Marrow Diseases: Comparison With Human Readings
Bone Marrow Radiomics of T1-Weighted Lumber Spinal MRI to Identify Diffuse Hematologic Marrow Diseases: Comparison With Human Readings
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DOI：
10.1109/access.2020.3010006
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Jung, Joon-Yong
中科院分区：
文献类型：
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作者：
Hwang, Eo-Jin;Kim, Sanghee;Jung, Joon-Yong
我们开发了一个放射组学模型来区分血液学骨髓疾病，并将其表现与放射科医生的读数和定量测量进行了比较。回顾性分析患病（n = 254）和对照组（n = 230）的患者。腰椎矢状面t1加权MR图像经椎间盘归一化，骨髓分割。提取100个特征，并利用主成分分析（PCA）和最小绝对收缩选择算子（LASSO）选择最终特征。最后，训练随机森林（RF）和逻辑回归（LR）模型。两位具有不同经验水平的放射科医生独立分析了骨髓疾病存在的图像。评估受试者工作特征曲线（AUC）和决策曲线分析（DCA）下的面积。其中，363例被分配为训练集，121例被分配为验证集。LASSO与RF联合使用效果最好。验证集的灵敏度（SE）为87.3%，特异度（SP）为86.2%，AUC为0.928 （p < 0.05）。我们选择Firstorder -Maximum作为鉴别病变骨髓的最佳特征，SE为75.0%，AUC为0.787 （p < 0.05）。11年经验的读者SE为86.5%，AUC为0.861 （p < 0.05）。第二位有1年经验的读者SE为75.0%，AUC为0.767 （p < 0.05）。我们展示了骨髓放射组学优于传统的放射科医生的读数和定量测量诊断方法。
We developed a radiomics model to differentiate hematologic marrow diseases and compared the performance with radiologists' readings and a quantitative measurement. Patients were retrospectively analyzed from the diseased (n = 254) and control groups (n = 230). Asagittal T1-weighted lumbar spinal MR image was normalized by an intervertebral disk, and bone marrow was segmented. A hundred features were extracted, and final features were selected using Principle Component Analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Finally, Random forest (RF) and logistic regression (LR) models were trained. Two radiologists with different levels of experience analyzed the images for the presence of bone marrow diseases, independently. The area under the receiver operating characteristic curves (AUC) and decision curve analysis (DCA) was evaluated. Among the subjects, 363 cases were assigned as a training set and 121 as a validation set. The combination of LASSO and RF produced the best results. With the validation set, the sensitivity (SE) was 87.3%, specificity (SP) was 86.2% and AUC was 0.928 (p < 0.05). We selected Firstorder -Maximum as the best feature to identify diseased marrows, which achieved SE of 75.0% and AUC of 0.787 (p < 0.05). The reader with 11 years of experience yielded SE of 86.5% and AUC of 0.861 (p < 0.05). The second reader with 1 year of experience yielded SE of 75.0% and AUC of 0.767 (p < 0.05). We demonstrated the advantage of bone marrow radiomics over conventional methods of diagnosing with radiologists' readings and quantitative measurements.