Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148184" target="_blank" >RIV/00216305:26220/23:PU148184 - isvavai.cz</a>

Result on the web

<a href="https://journals.lww.com/investigativeradiology/fulltext/2023/10000/prediction_of_bone_marrow_biopsy_results_from_mri.7.aspx" target="_blank" >https://journals.lww.com/investigativeradiology/fulltext/2023/10000/prediction_of_bone_marrow_biopsy_results_from_mri.7.aspx</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1097/RLI.0000000000000986" target="_blank" >10.1097/RLI.0000000000000986</a>

Result language

angličtina

Original language name

Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics

Original language description

ObjectivesIn multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated framework to predict local BM biopsy results from magnetic resonance imaging (MRI).Materials and MethodsThis retrospective multicentric study used data from center 1 for algorithm training and internal testing, and data from center 2 to 8 for external testing. An nnU-Net was trained for automated segmentation of pelvic BM from T1-weighted whole-body MRI. Radiomics features were extracted from these segmentations, and random forest models were trained to predict PCI and the presence or absence of cytogenetic aberrations. Pearson correlation coefficient and the area under the receiver operating characteristic were used to evaluate the prediction performance for PCI and cytogenetic aberrations, respectively.ResultsA total of 672 MRIs from 512 patients (median age, 61 years; interquartile range, 53-67 years; 307 men) from 8 centers and 370 corresponding BM biopsies were included. The predicted PCI from the best model was significantly correlated (P & LE; 0.01) to the actual PCI from biopsy in all internal and external test sets (internal test set: r = 0.71 [0.51, 0.83]; center 2, high-quality test set: r = 0.45 [0.12, 0.69]; center 2, other test set: r = 0.30 [0.07, 0.49]; multicenter test set: r = 0.57 [0.30, 0.76]). The areas under the receiver operating characteristic of the prediction models for the different cytogenetic aberrations ranged from 0.57 to 0.76 for the internal test set, but no model generalized well to all 3 external test sets.ConclusionsThe automated image analysis framework established in this study allows for noninvasive prediction of a surrogate parameter for P

Czech name

—

Czech description

—

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

2023

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

58

Issue of the periodical within the volume

10

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

754-765

UT code for WoS article

001071299400007

EID of the result in the Scopus database

2-s2.0-85173591927