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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144690" target="_blank" >RIV/00216305:26220/22:PU144690 - isvavai.cz</a>

Result on the web

<a href="https://journals.lww.com/investigativeradiology/Abstract/2022/11000/Combining_Deep_Learning_and_Radiomics_for.6.aspx" target="_blank" >https://journals.lww.com/investigativeradiology/Abstract/2022/11000/Combining_Deep_Learning_and_Radiomics_for.6.aspx</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1097/RLI.0000000000000891" target="_blank" >10.1097/RLI.0000000000000891</a>

Result language

angličtina

Original language name

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

Original language description

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

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30204 - Oncology

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

INVESTIGATIVE RADIOLOGY

ISSN

0020-9996

e-ISSN

1536-0210

Volume of the periodical

57

Issue of the periodical within the volume

11

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

752-763

UT code for WoS article

000863907000006

EID of the result in the Scopus database

2-s2.0-85138615686