Prediction of bone marrow biopsy results from MRI in multiple myeloma patients using deep learning and radiomics
Objectives - In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to ass...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
October 2023
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| In: |
Investigative radiology
Year: 2023, Volume: 58, Issue: 10, Pages: 754-765 |
| ISSN: | 1536-0210 |
| DOI: | 10.1097/RLI.0000000000000986 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1097/RLI.0000000000000986 Verlag, lizenzpflichtig, Volltext: https://journals.lww.com/investigativeradiology/abstract/2023/10000/prediction_of_bone_marrow_biopsy_results_from_mri.7.aspx 
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| Author Notes: | Markus Wennmann, Wenlong Ming, Fabian Bauer, Jiri Chmelik, André Klein, Charlotte Uhlenbrock, Martin Grözinger, Kim-Celine Kahl, Tobias Nonnenmacher, Manuel Debic, Thomas Hielscher, Heidi Thierjung, Lukas T. Rotkopf, Nikolas Stanczyk, Sandra Sauer, Anna Jauch, Michael Götz, Felix T. Kurz, Kai Schlamp, Marius Horger, Saif Afat, Britta Besemer, Martin Hoffmann, Johannes Hoffend, Doris Kraemer, Ullrich Graeven, Adrian Ringelstein, David Bonekamp, Jens Kleesiek, Ralf O. Floca, Jens Hillengass, Elias K. Mai, Niels Weinhold, Tim F. Weber, Hartmut Goldschmidt, Heinz-Peter Schlemmer, Klaus Maier-Hein, Stefan Delorme, Peter Neher |
| Summary: | Objectives - In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated framework to predict local BM biopsy results from magnetic resonance imaging (MRI). - Materials and Methods - This retrospective multicentric study used data from center 1 for algorithm training and internal testing, and data from center 2 to 8 for external testing. An nnU-Net was trained for automated segmentation of pelvic BM from T1-weighted whole-body MRI. Radiomics features were extracted from these segmentations, and random forest models were trained to predict PCI and the presence or absence of cytogenetic aberrations. Pearson correlation coefficient and the area under the receiver operating characteristic were used to evaluate the prediction performance for PCI and cytogenetic aberrations, respectively. - Results - A total of 672 MRIs from 512 patients (median age, 61 years; interquartile range, 53-67 years; 307 men) from 8 centers and 370 corresponding BM biopsies were included. The predicted PCI from the best model was significantly correlated (P ≤ 0.01) to the actual PCI from biopsy in all internal and external test sets (internal test set: r = 0.71 [0.51, 0.83]; center 2, high-quality test set: r = 0.45 [0.12, 0.69]; center 2, other test set: r = 0.30 [0.07, 0.49]; multicenter test set: r = 0.57 [0.30, 0.76]). The areas under the receiver operating characteristic of the prediction models for the different cytogenetic aberrations ranged from 0.57 to 0.76 for the internal test set, but no model generalized well to all 3 external test sets. - Conclusions - The automated image analysis framework established in this study allows for noninvasive prediction of a surrogate parameter for PCI, which is significantly correlated to the actual PCI from BM biopsy. |
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| Item Description: | Gesehen am 08.04.2024 |
| Physical Description: | Online Resource |
| ISSN: | 1536-0210 |
| DOI: | 10.1097/RLI.0000000000000986 |