TY  - JOUR
AU  - Wennmann, Markus
AU  - Kächele, Jessica
AU  - von Salomon, Arvin
AU  - Nonnenmacher, Tobias
AU  - Bujotzek, Markus
AU  - Xiao, Shuhan
AU  - Martinez Mora, Andres
AU  - Hielscher, Thomas
AU  - Hajiyianni, Marina
AU  - Menis, Ekaterina
AU  - Grözinger, Martin
AU  - Bauer, Fabian
AU  - Riebl, Veronika
AU  - Rotkopf, Lukas Thomas
AU  - Zhang, Kevin Sun
AU  - Afat, Saif
AU  - Besemer, Britta
AU  - Hoffmann, Martin
AU  - Ringelstein, Adrian
AU  - Graeven, Ullrich
AU  - Fedders, Dieter
AU  - Hänel, Mathias
AU  - Antoch, Gerald
AU  - Fenk, Roland
AU  - Mahnken, Andreas H
AU  - Mann, Christoph
AU  - Mokry, Theresa
AU  - Raab, Marc-Steffen
AU  - Weinhold, Niels
AU  - Mai, Elias Karl
AU  - Goldschmidt, Hartmut
AU  - Weber, Tim Frederik
AU  - Delorme, Stefan
AU  - Neher, Peter
AU  - Schlemmer, Heinz-Peter
AU  - Maier-Hein, Klaus
TI  - Automated Detection of Focal Bone Marrow Lesions From MRI: A Multi-center Feasibility Study in Patients with Monoclonal Plasma Cell Disorders.
JO  - Academic radiology
VL  - nn
SN  - 1076-6332
CY  - Philadelphia, PA [u.a.]
PB  - Elsevier
M1  - DKFZ-2025-01372
SP  - nn
PY  - 2025
N1  - #EA:E010#LA:E010#LA:E230# / epub
AB  - To train and test an AI-based algorithm for automated detection of focal bone marrow lesions (FL) from MRI.This retrospective feasibility study included 444 patients with monoclonal plasma cell disorders. For this feasibility study, only FLs in the left pelvis were included. Using the nnDetection framework, the algorithm was trained based on 334 patients with 494 FLs from center 1, and was tested on an internal test set (36 patients, 89 FLs, center 1) and a multicentric external test set (74 patients, 262 FLs, centers 2-11). Mean average precision (mAP), F1-score, sensitivity, positive predictive value (PPV), and Spearman correlation coefficient between automatically determined and actual number of FLs were calculated.On the internal/external test set, the algorithm achieved a mAP of 0.44/0.34, F1-Score of 0.54/0.44, sensitivity of 0.49/0.34, and a PPV of 0.61/0.61, respectively. In two subsets of the external multicentric test set with high imaging quality, the performance nearly matched that of the internal test set, with mAP of 0.45/0.41, F1-Score of 0.50/0.53, sensitivity of 0.44/0.43, and a PPV of 0.60/0.71, respectively. There was a significant correlation between the automatically determined and actual number of FLs on both the internal (r=0.51, p=0.001) and external multicentric test set (r=0.59, p<0.001).This study demonstrates that the automated detection of FLs from MRI, and thereby the automated assessment of the number of FLs, is feasible.
KW  - AI (Other)
KW  - Detection (Other)
KW  - Focal lesions (Other)
KW  - Monoclonal plasma cell disorders (Other)
KW  - Multicenter (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:40640054
DO  - DOI:10.1016/j.acra.2025.06.034
UR  - https://inrepo02.dkfz.de/record/302832
ER  -