Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI: A Multicentric Feasibility Study

článek v časopise ve Web of Science, Jimp

angličtina

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.

deep learning; segmentation; nnU-Net; radiomics; whole-body; MRI; bone marrow; smoldering multiple myeloma; multiple myeloma; multicenter

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.

27. 5. 2022

Wolters Kluwer Health, Inc.

0020-9996

INVESTIGATIVE RADIOLOGY

57

11

Spojené státy americké

752

763

12

https://journals.lww.com/investigativeradiology/Abstract/2022/11000/Combining_Deep_Learning_and_Radiomics_for.6.aspx