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WENNMANN, M. KLEIN, A. BAUER, F. CHMELÍK, J. GRÖZINGER, M. UHLENBROCK, C. LOCHNER, J. NONNENMACHER, T. ROTKOPF, L. SAUER, S. HIELSCHER, T. GOTZ, M. FLOCA, R. NEHER, P. BONEKAMP, D. HILLENGASS, J. KLEESIEK, J. WEINHOLD, N. WEBER, T. GOLDSCHMIDT, H. MAIER-HEIN, K. SCHLEMMER, H.
Original Title
Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI: A Multicentric Feasibility Study
Type
journal article in Web of Science
Language
English
Original Abstract
Objectives: Disseminated bone marrow (BM) involvement is frequent in multiple myeloma (MM). Whole-body magnetic resonance imaging (wb-MRI) enables to evaluate the whole BM. Reading of such whole-body scans is time-consuming, and yet radiologists can transfer only a small fraction of the information of the imaging data set to the report. This limits the influence that imaging can have on clinical decision-making and in research toward precision oncology. The objective of this feasibility study was to implement a concept for automatic, comprehensive characterization of the BM from wb-MRI, by automatic BM segmentation and subsequent radiomics analysis of 30 different BM spaces (BMS). Materials and Methods: This retrospective multicentric pilot study used a total of 106 wb-MRI from 102 patients with (smoldering) MM from 8 centers. Fifty wb-MRI from center 1 were used for training of segmentation algorithms (nnU-Nets) and radiomics algorithms. Fifty-six wb-MRI from 8 centers, acquired with a variety of different MRI scanners and protocols, were used for independent testing. Manual segmentations of 2700 BMS from 90 wb-MRI were performed for training and testing of the segmentation algorithms. For each BMS, 296 radiomics features were calculated individually. Dice score was used to assess similarity between automatic segmentations and manual reference segmentations. Results: The “multilabel nnU-Net” segmentation algorithm, which performs segmentation of 30 BMS and labels them individually, reached mean dice scores of 0.88 ± 0.06/0.87 ± 0.06/0.83 ± 0.11 in independent test sets from center 1/center 2/center 3–8 (interrater variability between radiologists, 0.88 ± 0.01). The subset from the multicenter, multivendor test set (center 3–8) that was of high imaging quality was segmented with high precision (mean dice score, 0.87), comparable to the internal test data from center 1. The radiomic BM phenotype consisting of 8880 descriptive parameters per patient, which result from calculation of 296 radiomics features for each of the 30 BMS, was calculated for all patients. Exemplary cases demonstrated connections between typical BM patterns in MM and radiomic signatures of the respective BMS. In plausibility tests, predicted size and weight based on radiomics models of the radiomic BM phenotype significantly correlated with patients' actual size and weight (P = 0.002 and P = 0.003, respectively). Conclusions: This pilot study demonstrates the feasibility of automatic, objective, comprehensive BM characterization from wb-MRI in multicentric data sets. This concept allows the extraction of high-dimensional phenotypes to capture the complexity of disseminated BM disorders from imaging. Further studies need to assess the clinical potential of this method for automatic staging, therapy response assessment, or prediction of biopsy results.
Keywords
deep learning; segmentation; nnU-Net; radiomics; whole-body; MRI; bone marrow; smoldering multiple myeloma; multiple myeloma; multicenter
Authors
WENNMANN, M.; KLEIN, A.; BAUER, F.; CHMELÍK, J.; GRÖZINGER, M.; UHLENBROCK, C.; LOCHNER, J.; NONNENMACHER, T.; ROTKOPF, L.; SAUER, S.; HIELSCHER, T.; GOTZ, M.; FLOCA, R.; NEHER, P.; BONEKAMP, D.; HILLENGASS, J.; KLEESIEK, J.; WEINHOLD, N.; WEBER, T.; GOLDSCHMIDT, H.; MAIER-HEIN, K.; SCHLEMMER, H.
Released
27. 5. 2022
Publisher
Wolters Kluwer Health, Inc.
ISBN
0020-9996
Periodical
INVESTIGATIVE RADIOLOGY
Year of study
57
Number
11
State
United States of America
Pages from
752
Pages to
763
Pages count
12
URL
https://journals.lww.com/investigativeradiology/Abstract/2022/11000/Combining_Deep_Learning_and_Radiomics_for.6.aspx
BibTex
@article{BUT178120, author="Markus {Wennmann} and André {Klein} and Fabian {Bauer} and Jiří {Chmelík} and Martin {Grözinger} and Charlotte {Uhlenbrock} and Jakob {Lochner} and Tobias {Nonnenmacher} and Lukas {Rotkopf} and Sandra {Sauer} and Thomas {Hielscher} and Michael {Gotz} and Ralf {Floca} and Peter {Neher} and David {Bonekamp} and Jens {Hillengass} and Jens {Kleesiek} and Niels {Weinhold} and Tim {Weber} and Hartmut {Goldschmidt} and Klaus {Maier-Hein} and Heinz-Peter {Schlemmer}", title="Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI: A Multicentric Feasibility Study", journal="INVESTIGATIVE RADIOLOGY", year="2022", volume="57", number="11", pages="752--763", doi="10.1097/RLI.0000000000000891", issn="0020-9996", url="https://journals.lww.com/investigativeradiology/Abstract/2022/11000/Combining_Deep_Learning_and_Radiomics_for.6.aspx" }