Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, China; The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
Department of Radiology, Xuecheng District People's Hospital, Shandong, China.
Clin Radiol. 2023 Jun;78(6):e463-e468. doi: 10.1016/j.crad.2023.02.012. Epub 2023 Mar 6.
To determine the prospect of using machine learning with magnetic resonance imaging (MRI) to identify aplastic anaemia (AA) and myelodysplastic syndromes (MDS).
This retrospective study included patients diagnosed with AA or MDS by pathological bone marrow biopsy, who underwent pelvic MRI with the iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) between December 2016 and August 2020. Based on values of right ilium fat fraction (FF) and radiomic features extracted from T1-weighted (T1W) and IDEAL-IQ images, three machine learning algorithms including linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM) were used to identify AA and MDS.
A total of 77 patients were included in the study, including 37 men and 40 women, aged 20-84 years (median age 47 years). There were 21 patients with MDS (nine men and 12 women, aged 38-84 years, median age 55 years) and 56 patients with AA (28 men and 28 women, aged 20-69 years, median age 41 years). The ilium FF of patients with AA (mean ± standard deviation [SD]: 79.23 ± 15.04%) was determined to be significantly greater compared to MDS patients (mean ± SD: 42.78 ± 30.09%, p<0.001). Selecting from the machine learning models based on ilium FF, T1W imaging and IDEAL-IQ, the IDEAL-IQ-based SVM classifier model had the best predictive ability.
The combination of machine learning and IDEAL-IQ technology may enable non-invasive and accurate identification of AA and MDS.
探讨机器学习联合磁共振成像（MRI）识别再生障碍性贫血（AA）和骨髓增生异常综合征（MDS）的前景。
本回顾性研究纳入 2016 年 12 月至 2020 年 8 月期间经病理骨髓活检诊断为 AA 或 MDS 的患者，这些患者均接受了盆腔 MRI 检查，包括迭代水脂分解和最小二乘估计定量（IDEAL-IQ）技术。基于右侧髂骨脂肪分数（FF）值和从 T1 加权（T1W）及 IDEAL-IQ 图像提取的放射组学特征，采用线性判别分析（LDA）、逻辑回归（LR）和支持向量机（SVM）三种机器学习算法来识别 AA 和 MDS。
本研究共纳入 77 例患者，其中男 37 例，女 40 例，年龄 20-84 岁（中位年龄 47 岁）。21 例患者患有 MDS（男 9 例，女 12 例，年龄 38-84 岁，中位年龄 55 岁），56 例患者患有 AA（男 28 例，女 28 例，年龄 20-69 岁，中位年龄 41 岁）。AA 患者的髂骨 FF（均值±标准差[SD]：79.23±15.04%）明显高于 MDS 患者（均值±SD：42.78±30.09%，p<0.001）。在基于髂骨 FF、T1W 成像和 IDEAL-IQ 的机器学习模型中，基于 IDEAL-IQ 的 SVM 分类器模型具有最佳的预测能力。
机器学习与 IDEAL-IQ 技术的结合可能实现 AA 和 MDS 的无创、准确识别。
