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000182096 1001_ $$0P:(DE-He78)e7c860fe438c12cbe5f071b3f86d5738$$aWennmann, Markus$$b0$$eFirst author$$udkfz
000182096 245__ $$aCombining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI: A Multicentric Feasibility Study.
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000182096 520__ $$aDisseminated 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).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.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).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.
000182096 536__ $$0G:(DE-HGF)POF4-315$$a315 - Bildgebung und Radioonkologie (POF4-315)$$cPOF4-315$$fPOF IV$$x0
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000182096 650_2 $$2MeSH$$aBone Marrow: diagnostic imaging
000182096 650_2 $$2MeSH$$aDeep Learning
000182096 650_2 $$2MeSH$$aFeasibility Studies
000182096 650_2 $$2MeSH$$aHumans
000182096 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000182096 650_2 $$2MeSH$$aNeoplasms
000182096 650_2 $$2MeSH$$aPilot Projects
000182096 650_2 $$2MeSH$$aPrecision Medicine
000182096 650_2 $$2MeSH$$aRetrospective Studies
000182096 650_2 $$2MeSH$$aWhole Body Imaging
000182096 7001_ $$0P:(DE-He78)c7e087ffc0e1f319d0a00fca36012845$$aKlein, André$$b1$$eFirst author$$udkfz
000182096 7001_ $$0P:(DE-He78)adc25b1dbf85abdffe5d2300d1265031$$aBauer, Fabian$$b2$$udkfz
000182096 7001_ $$0P:(DE-He78)f077a58da75628246c446610ef17dcb9$$aChmelik, Jiri$$b3
000182096 7001_ $$0P:(DE-He78)cf4656ab05919cc784af4e9812f5a9fa$$aGrözinger, Martin$$b4$$udkfz
000182096 7001_ $$0P:(DE-He78)5b981b1ba485b2e221430d51102a1546$$aUhlenbrock, Charlotte$$b5$$udkfz
000182096 7001_ $$0P:(DE-He78)08730c69aeee4474df9b41511469d637$$aLochner, Jakob$$b6
000182096 7001_ $$aNonnenmacher, Tobias$$b7
000182096 7001_ $$0P:(DE-He78)d7135c1486ffd923f71735d40a3d7a0c$$aRotkopf, Lukas Thomas$$b8$$udkfz
000182096 7001_ $$aSauer, Sandra$$b9
000182096 7001_ $$0P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f$$aHielscher, Thomas$$b10$$udkfz
000182096 7001_ $$0P:(DE-He78)abd768f879e71d08068d48fabb7e96cf$$aGötz, Michael$$b11$$udkfz
000182096 7001_ $$0P:(DE-He78)f0ab09cfecf353f363bab4cc983de95d$$aFloca, Ralf Omar$$b12$$udkfz
000182096 7001_ $$0P:(DE-He78)64313331bb3bdc0902ff88697f402c92$$aNeher, Peter$$b13$$udkfz
000182096 7001_ $$0P:(DE-He78)ea098e4d78abeb63afaf8c25ec6d6d93$$aBonekamp, David$$b14$$udkfz
000182096 7001_ $$aHillengass, Jens$$b15
000182096 7001_ $$aKleesiek, Jens$$b16
000182096 7001_ $$aWeinhold, Niels$$b17
000182096 7001_ $$aWeber, Tim Frederik$$b18
000182096 7001_ $$0P:(DE-He78)a1aa959d47e3e026abe157a8adf24b96$$aGoldschmidt, Hartmut$$b19$$udkfz
000182096 7001_ $$0P:(DE-He78)3e76653311420a51a5faeb80363bd73e$$aDelorme, Stefan$$b20$$udkfz
000182096 7001_ $$0P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3$$aMaier-Hein, Klaus$$b21$$eLast author$$udkfz
000182096 7001_ $$0P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec$$aSchlemmer, Heinz-Peter$$b22$$eLast author$$udkfz
000182096 773__ $$0PERI:(DE-600)2041543-6$$a10.1097/RLI.0000000000000891$$gVol. 57, no. 11, p. 752 - 763$$n11$$p752 - 763$$tInvestigative radiology$$v57$$x0020-9996$$y2022
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