Farswan Akanksha, Gupta Anubha, Gupta Ritu, Hazra Saswati, Khan Sadaf, Kumar Lalit, Sharma Atul
SBILab, Department of ECE, Indraprastha Institute of Information Technology-Delhi, New Delhi 110020, India.
SBILab, Department of ECE, Indraprastha Institute of Information Technology-Delhi, New Delhi 110020, India.
Transl Oncol. 2021 Sep;14(9):101157. doi: 10.1016/j.tranon.2021.101157. Epub 2021 Jul 8.
An efficient readily employable risk prognostication method is desirable for MM in settings where genomics tests cannot be performed owing to geographical/economical constraints. In this work, a new Modified Risk Staging (MRS) has been proposed for newly diagnosed Multiple Myeloma (NDMM) that exploits six easy-to-acquire clinical parameters i.e. age, albumin, β2-microglobulin (β2M), calcium, estimated glomerular filtration rate (eGFR) and hemoglobin.
MRS was designed using a training cohort of 716 NDMM patients of our inhouse MM Indian (MMIn) cohort and validated on MMIn (n=354) cohort and MMRF (n=900) cohort. K-adaptive partitioning (KAP) was used to find new thresholds for the parameters. Risk staging rules, obtained via training a J48 classifier, were used to build MRS.
New thresholds were identified for albumin (3.6 g/dL), β2M (4.8 mg/L), calcium (11.13 mg/dL), eGFR (48.1 mL/min), and hemoglobin (12.3 g/dL) using KAP on the MMIn dataset. On the MMIn dataset, MRS outperformed ISS for OS prediction in terms of C-index, hazard ratios, and its corresponding p-values, but performs comparable in prediction of PFS. On both MMIn and MMRF datasets, MRS performed better than RISS in terms of C-index and p-values. A simple online tool was also designed to allow automated calculation of MRS based on the values of the parameters.
Our proposed ML-derived yet simple staging system, MRS, although does not employ genetic features, outperforms RISS as confirmed by better separability in KM survival curves and higher values of C-index on both MMIn and MMRF datasets.
Grant: BT/MED/30/SP11006/2015 (Department of Biotechnology, Govt. of India), Grant: DST/ICPS/CPS-Individual/2018/279(G) (Department of Science and Technology, Govt. of India), UGC-Senior Research Fellowship.
在因地理/经济限制无法进行基因组检测的情况下，骨髓瘤（MM）需要一种高效且易于应用的风险预测方法。在这项研究中，针对新诊断的多发性骨髓瘤（NDMM）提出了一种新的改良风险分期（MRS）方法，该方法利用了六个易于获取的临床参数，即年龄、白蛋白、β2-微球蛋白（β2M）、钙、估计肾小球滤过率（eGFR）和血红蛋白。
MRS是使用我们内部的印度骨髓瘤（MMIn）队列中的716例NDMM患者的训练队列设计的，并在MMIn队列（n = 354）和MMRF队列（n = 900）中进行了验证。采用K自适应划分（KAP）来确定参数的新阈值。通过训练J48分类器获得的风险分期规则用于构建MRS。
在MMIn数据集上使用KAP确定了白蛋白（3.6 g/dL）、β2M（4.8 mg/L）、钙（11.13 mg/dL）、eGFR（48.1 mL/min）和血红蛋白（12.3 g/dL）的新阈值。在MMIn数据集上，就C指数、风险比及其相应的p值而言，MRS在总生存期（OS）预测方面优于国际分期系统（ISS），但在无进展生存期（PFS）预测方面表现相当。在MMIn和MMRF数据集上，MRS在C指数和p值方面均优于修订国际分期系统（RISS）。还设计了一个简单的在线工具，以便根据参数值自动计算MRS。
我们提出的基于机器学习的简单分期系统MRS，虽然未采用基因特征，但在MMIn和MMRF数据集上，通过KM生存曲线中更好的可分离性以及更高的C指数值证实，其性能优于RISS。
资助：BT/MED/30/SP11006/2015（印度政府生物技术部），资助：DST/ICPS/CPS-Individual/2018/279(G)（印度政府科学技术部），大学教育资助委员会高级研究奖学金。
