Predicting cytogenetic risk in multiple myeloma using conventional whole-body MRI, spinal dynamic contrast-enhanced MRI, and spinal diffusion-weighted imaging
Abstract Objectives Cytogenetic abnormalities are predictors of poor prognosis in multiple myeloma (MM). This paper aims to build and validate a multiparametric conventional and functional whole-body MRI-based prediction model for cytogenetic risk classification in newly diagnosed MM. Methods Patien...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-04-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-024-01672-1 |
| _version_ | 1797209262747811840 |
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| author | Thomas Van Den Berghe Bert Verberckmoes Nicolas Kint Steven Wallaert Nicolas De Vos Chloé Algoet Maxim Behaeghe Julie Dutoit Nadine Van Roy Philip Vlummens Amélie Dendooven Jo Van Dorpe Fritz Offner Koenraad Verstraete |
| author_facet | Thomas Van Den Berghe Bert Verberckmoes Nicolas Kint Steven Wallaert Nicolas De Vos Chloé Algoet Maxim Behaeghe Julie Dutoit Nadine Van Roy Philip Vlummens Amélie Dendooven Jo Van Dorpe Fritz Offner Koenraad Verstraete |
| author_sort | Thomas Van Den Berghe |
| collection | DOAJ |
| description | Abstract Objectives Cytogenetic abnormalities are predictors of poor prognosis in multiple myeloma (MM). This paper aims to build and validate a multiparametric conventional and functional whole-body MRI-based prediction model for cytogenetic risk classification in newly diagnosed MM. Methods Patients with newly diagnosed MM who underwent multiparametric conventional whole-body MRI, spinal dynamic contrast-enhanced (DCE-)MRI, spinal diffusion-weighted MRI (DWI) and had genetic analysis were retrospectively included (2011–2020/Ghent University Hospital/Belgium). Patients were stratified into standard versus intermediate/high cytogenetic risk groups. After segmentation, 303 MRI features were extracted. Univariate and model-based methods were evaluated for feature and model selection. Testing was performed using receiver operating characteristic (ROC) and precision-recall curves. Models comparing the performance for genetic risk classification of the entire MRI protocol and of all MRI sequences separately were evaluated, including all features. Four final models, including only the top three most predictive features, were evaluated. Results Thirty-one patients were enrolled (mean age 66 ± 7 years, 15 men, 13 intermediate-/high-risk genetics). None of the univariate models and none of the models with all features included achieved good performance. The best performing model with only the three most predictive features and including all MRI sequences reached a ROC-area-under-the-curve of 0.80 and precision-recall-area-under-the-curve of 0.79. The highest statistical performance was reached when all three MRI sequences were combined (conventional whole-body MRI + DCE-MRI + DWI). Conventional MRI always outperformed the other sequences. DCE-MRI always outperformed DWI, except for specificity. Conclusions A multiparametric MRI-based model has a better performance in the noninvasive prediction of high-risk cytogenetics in newly diagnosed MM than conventional MRI alone. Critical relevance statement An elaborate multiparametric MRI-based model performs better than conventional MRI alone for the noninvasive prediction of high-risk cytogenetics in newly diagnosed multiple myeloma; this opens opportunities to assess genetic heterogeneity thus overcoming sampling bias. Key points • Standard genetic techniques in multiple myeloma patients suffer from sampling bias due to tumoral heterogeneity. • Multiparametric MRI noninvasively predicts genetic risk in multiple myeloma. • Combined conventional anatomical MRI, DCE-MRI, and DWI had the highest statistical performance to predict genetic risk. • Conventional MRI alone always outperformed DCE-MRI and DWI separately to predict genetic risk. DCE-MRI alone always outperformed DWI separately, except for the parameter specificity to predict genetic risk. • This multiparametric MRI-based genetic risk prediction model opens opportunities to noninvasively assess genetic heterogeneity thereby overcoming sampling bias in predicting genetic risk in multiple myeloma. Graphical Abstract |
| first_indexed | 2024-04-24T09:51:55Z |
| format | Article |
| id | doaj.art-25a0ca9573a84621a80961b477933727 |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-04-24T09:51:55Z |
| publishDate | 2024-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-25a0ca9573a84621a80961b4779337272024-04-14T11:18:29ZengSpringerOpenInsights into Imaging1869-41012024-04-0115111910.1186/s13244-024-01672-1Predicting cytogenetic risk in multiple myeloma using conventional whole-body MRI, spinal dynamic contrast-enhanced MRI, and spinal diffusion-weighted imagingThomas Van Den Berghe0Bert Verberckmoes1Nicolas Kint2Steven Wallaert3Nicolas De Vos4Chloé Algoet5Maxim Behaeghe6Julie Dutoit7Nadine Van Roy8Philip Vlummens9Amélie Dendooven10Jo Van Dorpe11Fritz Offner12Koenraad Verstraete13Department of Radiology and Medical Imaging, Ghent University HospitalDepartment of Radiology and Medical Imaging, Ghent University HospitalDepartment of Clinical Hematology, Ghent University HospitalDepartment of Biostatistics, Ghent University HospitalDepartment of Radiology and Medical Imaging, Ghent University HospitalDepartment of Radiology and Medical Imaging, Ghent University HospitalDepartment of Radiology and Medical Imaging, Ghent University HospitalDepartment of Radiology and Medical Imaging, Ghent University HospitalCenter for Medical Genetics, Ghent University HospitalDepartment of Clinical Hematology, Ghent University HospitalDepartment of Pathology, Ghent University HospitalDepartment of Pathology, Ghent University HospitalDepartment of Clinical Hematology, Ghent University HospitalDepartment of Radiology and Medical Imaging, Ghent University HospitalAbstract Objectives Cytogenetic abnormalities are predictors of poor prognosis in multiple myeloma (MM). This paper aims to build and validate a multiparametric conventional and functional whole-body MRI-based prediction model for cytogenetic risk classification in newly diagnosed MM. Methods Patients with newly diagnosed MM who underwent multiparametric conventional whole-body MRI, spinal dynamic contrast-enhanced (DCE-)MRI, spinal diffusion-weighted MRI (DWI) and had genetic analysis were retrospectively included (2011–2020/Ghent University Hospital/Belgium). Patients were stratified into standard versus intermediate/high cytogenetic risk groups. After segmentation, 303 MRI features were extracted. Univariate and model-based methods were evaluated for feature and model selection. Testing was performed using receiver operating characteristic (ROC) and precision-recall curves. Models comparing the performance for genetic risk classification of the entire MRI protocol and of all MRI sequences separately were evaluated, including all features. Four final models, including only the top three most predictive features, were evaluated. Results Thirty-one patients were enrolled (mean age 66 ± 7 years, 15 men, 13 intermediate-/high-risk genetics). None of the univariate models and none of the models with all features included achieved good performance. The best performing model with only the three most predictive features and including all MRI sequences reached a ROC-area-under-the-curve of 0.80 and precision-recall-area-under-the-curve of 0.79. The highest statistical performance was reached when all three MRI sequences were combined (conventional whole-body MRI + DCE-MRI + DWI). Conventional MRI always outperformed the other sequences. DCE-MRI always outperformed DWI, except for specificity. Conclusions A multiparametric MRI-based model has a better performance in the noninvasive prediction of high-risk cytogenetics in newly diagnosed MM than conventional MRI alone. Critical relevance statement An elaborate multiparametric MRI-based model performs better than conventional MRI alone for the noninvasive prediction of high-risk cytogenetics in newly diagnosed multiple myeloma; this opens opportunities to assess genetic heterogeneity thus overcoming sampling bias. Key points • Standard genetic techniques in multiple myeloma patients suffer from sampling bias due to tumoral heterogeneity. • Multiparametric MRI noninvasively predicts genetic risk in multiple myeloma. • Combined conventional anatomical MRI, DCE-MRI, and DWI had the highest statistical performance to predict genetic risk. • Conventional MRI alone always outperformed DCE-MRI and DWI separately to predict genetic risk. DCE-MRI alone always outperformed DWI separately, except for the parameter specificity to predict genetic risk. • This multiparametric MRI-based genetic risk prediction model opens opportunities to noninvasively assess genetic heterogeneity thereby overcoming sampling bias in predicting genetic risk in multiple myeloma. Graphical Abstracthttps://doi.org/10.1186/s13244-024-01672-1Diffusion magnetic resonance imagingGeneticsMagnetic resonance imagingMultiparametric magnetic resonance imagingMultiple myeloma |
| spellingShingle | Thomas Van Den Berghe Bert Verberckmoes Nicolas Kint Steven Wallaert Nicolas De Vos Chloé Algoet Maxim Behaeghe Julie Dutoit Nadine Van Roy Philip Vlummens Amélie Dendooven Jo Van Dorpe Fritz Offner Koenraad Verstraete Predicting cytogenetic risk in multiple myeloma using conventional whole-body MRI, spinal dynamic contrast-enhanced MRI, and spinal diffusion-weighted imaging Insights into Imaging Diffusion magnetic resonance imaging Genetics Magnetic resonance imaging Multiparametric magnetic resonance imaging Multiple myeloma |
| title | Predicting cytogenetic risk in multiple myeloma using conventional whole-body MRI, spinal dynamic contrast-enhanced MRI, and spinal diffusion-weighted imaging |
| title_full | Predicting cytogenetic risk in multiple myeloma using conventional whole-body MRI, spinal dynamic contrast-enhanced MRI, and spinal diffusion-weighted imaging |
| title_fullStr | Predicting cytogenetic risk in multiple myeloma using conventional whole-body MRI, spinal dynamic contrast-enhanced MRI, and spinal diffusion-weighted imaging |
| title_full_unstemmed | Predicting cytogenetic risk in multiple myeloma using conventional whole-body MRI, spinal dynamic contrast-enhanced MRI, and spinal diffusion-weighted imaging |
| title_short | Predicting cytogenetic risk in multiple myeloma using conventional whole-body MRI, spinal dynamic contrast-enhanced MRI, and spinal diffusion-weighted imaging |
| title_sort | predicting cytogenetic risk in multiple myeloma using conventional whole body mri spinal dynamic contrast enhanced mri and spinal diffusion weighted imaging |
| topic | Diffusion magnetic resonance imaging Genetics Magnetic resonance imaging Multiparametric magnetic resonance imaging Multiple myeloma |
| url | https://doi.org/10.1186/s13244-024-01672-1 |
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