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WENNMANN, M. MING, W. BAUER, F. CHMELIK, J. KLEIN, A. UHLENBROCK, C. GROZINGER, M. KIM-CELINE, K. NONNENMACHER, T. DEBIC, M. HIELSCHER, T. THIERJUNG, H. ROTKOPF, L. STANZCYK, N. SAUER, S. JAUCH, A. GOTZ, M. KURZ, F. SCHLAMP, K. HORGER, M. AFAT, S. BESEMER, B. HOFFMANN, M. HOFFEND, J. KRAEMER, D. GRAEVEN, U. RINGELSTEIN, A. BONEKAMP, D. KLEESIEK, J. FLOCA, R. HILLENGASS, J. MAI, E. WEINHOLD, N. WEBER, T. GOLDSCHMIDT, H. SCHLEMMER, H. MAIER-HEIN, K. DELORME, S. NEHER, P.
Original Title
Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics
Type
journal article in Web of Science
Language
English
Original Abstract
ObjectivesIn 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 MethodsThis 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.ResultsA 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 & LE; 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.ConclusionsThe 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.
Keywords
deep learning; segmentation; radiomics; MRI; bone marrow; biopsy; plasma cell infiltration; cytogenetic aberrations; multiple myeloma; multicenter
Authors
WENNMANN, M.; MING, W.; BAUER, F.; CHMELIK, J.; KLEIN, A.; UHLENBROCK, C.; GROZINGER, M.; KIM-CELINE, K.; NONNENMACHER, T.; DEBIC, M.; HIELSCHER, T.; THIERJUNG, H.; ROTKOPF, L.; STANZCYK, N.; SAUER, S.; JAUCH, A.; GOTZ, M.; KURZ, F.; SCHLAMP, K.; HORGER, M.; AFAT, S.; BESEMER, B.; HOFFMANN, M.; HOFFEND, J.; KRAEMER, D.; GRAEVEN, U.; RINGELSTEIN, A.; BONEKAMP, D.; KLEESIEK, J.; FLOCA, R.; HILLENGASS, J.; MAI, E.; WEINHOLD, N.; WEBER, T.; GOLDSCHMIDT, H.; SCHLEMMER, H.; MAIER-HEIN, K.; DELORME, S.; NEHER, P.
Released
8. 9. 2023
Publisher
Wolters Kluwer Health, Inc.
ISBN
0020-9996
Periodical
INVESTIGATIVE RADIOLOGY
Year of study
58
Number
10
State
United States of America
Pages from
754
Pages to
765
Pages count
12
URL
https://journals.lww.com/investigativeradiology/fulltext/2023/10000/prediction_of_bone_marrow_biopsy_results_from_mri.7.aspx
BibTex
@article{BUT183581, author="Markus {Wennmann} and Wenlong {Ming} and Fabian {Bauer} and Jiří {Chmelík} and André {Klein} and Charlotte {Uhlenbrock} and Martin {Grözinger} and Kahl {Kim-Celine} and Tobias {Nonnenmacher} and Manuel {Debic} and Thomas {Hielscher} and Heidi {Thierjung} and Lukas {Rotkopf} and Nikolas {Stanzcyk} and Sandra {Sauer} and Anna {Jauch} and Michael {Gotz} and Felix {Kurz} and Kai {Schlamp} and Marius {Horger} and Saif {Afat} and Britta {Besemer} and Martin {Hoffmann} and Johanes {Hoffend} and Doris {Kraemer} and Ullrich {Graeven} and Adrian {Ringelstein} and David {Bonekamp} and Jens {Kleesiek} and Ralf {Floca} and Jens {Hillengass} and Elias {Mai} and Niels {Weinhold} and Tim {Weber} and Hartmut {Goldschmidt} and Heinz-Peter {Schlemmer} and Klaus {Maier-Hein} and Stefan {Delorme} and Peter {Neher}", title="Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics", journal="INVESTIGATIVE RADIOLOGY", year="2023", volume="58", number="10", pages="754--765", doi="10.1097/RLI.0000000000000986", issn="0020-9996", url="https://journals.lww.com/investigativeradiology/fulltext/2023/10000/prediction_of_bone_marrow_biopsy_results_from_mri.7.aspx" }