PMID- 34760332
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220729
IS  - 2229-5089 (Print)
IS  - 2153-3539 (Electronic)
VI  - 12
DP  - 2021
TI  - Artificial Intelligence in Plasma Cell Myeloma: Neural Networks and Support 
      Vector Machines in the Classification of Plasma Cell Myeloma Data at Diagnosis.
PG  - 35
LID - 10.4103/jpi.jpi_26_21 [doi]
LID - 35
AB  - BACKGROUND: 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 in situ 
      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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
CI  - Copyright: (c) 2021 Journal of Pathology Informatics.
FAU - Yenamandra, Ashwini K
AU  - Yenamandra AK
AD  - Department Pathology, Microbiology and Immunology, Vanderbilt University Medical 
      Center, Nashville, TN, USA.
FAU - Hughes, Caitlin
AU  - Hughes C
AD  - Department Pathology, Microbiology and Immunology, Vanderbilt University Medical 
      Center, Nashville, TN, USA.
FAU - Maris, Alexander S
AU  - Maris AS
AD  - Department Pathology, Microbiology and Immunology, Vanderbilt University Medical 
      Center, Nashville, TN, USA.
LA  - eng
PT  - Journal Article
DEP - 20210916
PL  - United States
TA  - J Pathol Inform
JT  - Journal of pathology informatics
JID - 101528849
PMC - PMC8529344
OTO - NOTNLM
OT  - Artificial neural network
OT  - National Comprehensive Cancer Network
OT  - Next Generation Sequencing
OT  - cytogenetics
OT  - fluorescence in situ hybridization
OT  - machine learning
OT  - microarray
OT  - plasma cell myeloma
OT  - support vector machines kernel trick
COIS- There are no conflicts of interest.
EDAT- 2021/11/12 06:00
MHDA- 2021/11/12 06:01
PMCR- 2021/09/16
CRDT- 2021/11/11 06:57
PHST- 2021/04/09 00:00 [received]
PHST- 2021/05/23 00:00 [revised]
PHST- 2021/05/28 00:00 [accepted]
PHST- 2021/11/11 06:57 [entrez]
PHST- 2021/11/12 06:00 [pubmed]
PHST- 2021/11/12 06:01 [medline]
PHST- 2021/09/16 00:00 [pmc-release]
AID - S2153-3539(22)00157-2 [pii]
AID - JPI-12-35 [pii]
AID - 10.4103/jpi.jpi_26_21 [doi]
PST - epublish
SO  - J Pathol Inform. 2021 Sep 16;12:35. doi: 10.4103/jpi.jpi_26_21. eCollection 2021.