Yenamandra Ashwini K, Hughes Caitlin, Maris Alexander S
Department Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
J Pathol Inform. 2021 Sep 16;12:35. doi: 10.4103/jpi.jpi_26_21. eCollection 2021.
Plasma cell neoplasm and/or plasma cell myeloma (PCM) is a mature B-cell lymphoproliferative neoplasm of plasma cells that secrete a single homogeneous immunoglobulin called paraprotein or M-protein. Plasma cells accumulate in the bone marrow (BM) leading to bone destruction and BM failure. Diagnosis of PCM is based on clinical, radiologic, and pathological characteristics. The percent of plasma cells by manual differential (bone marrow morphology), the white blood cell (WBC) count, cytogenetics, fluorescence hybridization (FISH), microarray, and next-generation sequencing of BM are used in the risk stratification of newly diagnosed PCM patients. The genetics of PCM is highly complex and heterogeneous with several genetic subtypes that have different clinical outcomes. National Comprehensive Cancer Network guidelines recommend targeted FISH analysis of plasma cells with specific DNA probes to detect genetic abnormalities for the staging of PCM (4.2021). Recognition of risk categories through training software for classification of high-risk PCM and a novel way of addressing the current approaches through bioinformatics will be a significant step toward automation of PCM analysis.
A new artificial neural network (ANN) classification model was developed and tested in Python programming language with a first data set of 301 cases and a second data set of 176 cases for a total of 477 cases of PCM at diagnosis. Classification model was also developed with support vector machines (SVM) algorithm in R studio and interactive data visuals using Tableau.
The resulting ANN algorithm had 94% accuracy for the first and second data sets with a classification summary of precision (PPV): 0.97, recall (sensitivity): 0.76, f1 score: 0.83, and accuracy of logistic regression of 1.0. SVM of plasma cells versus TP53 revealed a 95% accuracy level.
A novel classification model based only on specific morphological and genetic variables was developed using a machine learning algorithm, the ANN. ANN identified an association of WBC and BM plasma cell percentage with two of the high-risk genetic categories in the diagnostic cases of PCM. With further training and testing of additional data sets that include morphologic and additional genetic rearrangements, the newly developed ANN model has the potential to develop an accurate classification of high-risk categories of PCM.
浆细胞肿瘤和/或浆细胞骨髓瘤（PCM）是一种成熟的B细胞浆细胞淋巴增殖性肿瘤，可分泌一种称为副蛋白或M蛋白的单一均质免疫球蛋白。浆细胞在骨髓（BM）中积聚，导致骨质破坏和BM衰竭。PCM的诊断基于临床、放射学和病理学特征。通过手工分类（骨髓形态学）得出的浆细胞百分比、白细胞（WBC）计数、细胞遗传学、荧光杂交（FISH）、微阵列以及BM的下一代测序被用于新诊断PCM患者的风险分层。PCM的遗传学高度复杂且具有异质性，存在几种具有不同临床结果的基因亚型。美国国立综合癌症网络指南推荐使用特定DNA探针进行浆细胞的靶向FISH分析，以检测PCM分期的基因异常（2021年4月）。通过用于高危PCM分类的训练软件识别风险类别，并通过生物信息学采用新方法处理当前方法，将是迈向PCM分析自动化的重要一步。
开发了一种新的人工神经网络（ANN）分类模型，并在Python编程语言中进行测试，使用了301例的第一个数据集和176例的第二个数据集，共477例诊断时的PCM病例。还在R studio中使用支持向量机（SVM）算法和使用Tableau的交互式数据可视化开发了分类模型。
所得的ANN算法对第一个和第二个数据集的准确率为94%，分类汇总的精确率（PPV）：0.97，召回率（灵敏度）：0.76，F1分数：0.83，逻辑回归准确率为1.0。浆细胞与TP53的SVM显示准确率为95%。
使用机器学习算法ANN开发了一种仅基于特定形态学和基因变量的新型分类模型。ANN在PCM诊断病例中确定了WBC和BM浆细胞百分比与两种高危基因类别之间的关联。随着对包括形态学和其他基因重排的更多数据集进行进一步训练和测试，新开发的ANN模型有可能对PCM的高危类别进行准确分类。
