Prediction of High-Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods.
classifier
cytogenetics
magnetic resonance imaging
multiple myeloma
radiomics
Journal
Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850
Informations de publication
Date de publication:
10 2021
10 2021
Historique:
revised:
28
03
2021
received:
12
01
2021
accepted:
30
03
2021
pubmed:
13
5
2021
medline:
30
9
2021
entrez:
12
5
2021
Statut:
ppublish
Résumé
Radiomics has shown promising results in the diagnosis, efficacy, and prognostic assessments of multiple myeloma (MM). However, little evidence exists on the utility of radiomics in predicting a high-risk cytogenetic (HRC) status in MM. To develop and test a magnetic resonance imaging (MRI)-based radiomics model for predicting an HRC status in MM patients. Retrospective. Eighty-nine MM patients (HRC [n: 37] and non-HRC [n: 52]). A 3.0 T; fast spin-echo (FSE): T1-weighted image (T1WI) and fat-suppression T2WI (FS-T2WI). Overall, 1409 radiomics features were extracted from each volume of interest drawn by radiologists. Three sequential feature selection steps-variance threshold, SelectKBest, and least absolute shrinkage selection operator-were repeated 10 times with 5-fold cross-validation. Radiomics models were constructed with the top three frequency features of T Mann-Whitney U-test, Chi-squared test, Z test, and DeLong method. The LR classifier performed better than the other classifiers based on different data (AUC: 0.65-0.82; P < 0.05). The two-sequence MRI models performed better than the other data models using different classifiers (AUC: 0.68-0.82; P < 0.05). Thus, the LR two-sequence model yielded the best performance (AUC: 0.82 ± 0.02; sensitivity: 84.1%; specificity: 68.1%; accuracy: 74.7%; P < 0.05). The LR-based machine learning method appears superior to other classifier methods for assessing HRC in MM. Radiomics features based on two-sequence MRI showed good performance in differentiating HRC and non-HRC statuses in MM. 3 TECHNICAL EFFICACY: Stage 2.
Sections du résumé
BACKGROUND
Radiomics has shown promising results in the diagnosis, efficacy, and prognostic assessments of multiple myeloma (MM). However, little evidence exists on the utility of radiomics in predicting a high-risk cytogenetic (HRC) status in MM.
PURPOSE
To develop and test a magnetic resonance imaging (MRI)-based radiomics model for predicting an HRC status in MM patients.
STUDY TYPE
Retrospective.
POPULATION
Eighty-nine MM patients (HRC [n: 37] and non-HRC [n: 52]).
FIELD STRENGTH/SEQUENCE
A 3.0 T; fast spin-echo (FSE): T1-weighted image (T1WI) and fat-suppression T2WI (FS-T2WI).
ASSESSMENT
Overall, 1409 radiomics features were extracted from each volume of interest drawn by radiologists. Three sequential feature selection steps-variance threshold, SelectKBest, and least absolute shrinkage selection operator-were repeated 10 times with 5-fold cross-validation. Radiomics models were constructed with the top three frequency features of T
STATISTICAL TESTS
Mann-Whitney U-test, Chi-squared test, Z test, and DeLong method.
RESULTS
The LR classifier performed better than the other classifiers based on different data (AUC: 0.65-0.82; P < 0.05). The two-sequence MRI models performed better than the other data models using different classifiers (AUC: 0.68-0.82; P < 0.05). Thus, the LR two-sequence model yielded the best performance (AUC: 0.82 ± 0.02; sensitivity: 84.1%; specificity: 68.1%; accuracy: 74.7%; P < 0.05).
CONCLUSION
The LR-based machine learning method appears superior to other classifier methods for assessing HRC in MM. Radiomics features based on two-sequence MRI showed good performance in differentiating HRC and non-HRC statuses in MM.
EVIDENCE LEVEL
3 TECHNICAL EFFICACY: Stage 2.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1303-1311Informations de copyright
© 2021 International Society for Magnetic Resonance in Medicine.
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