Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU145368" target="_blank" >RIV/00216305:26220/23:PU145368 - isvavai.cz</a>

Výsledek na webu

<a href="https://journals.lww.com/investigativeradiology/Abstract/9900/Deep_Learning_for_Automatic_Bone_Marrow_Apparent.64.aspx" target="_blank" >https://journals.lww.com/investigativeradiology/Abstract/9900/Deep_Learning_for_Automatic_Bone_Marrow_Apparent.64.aspx</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1097/RLI.0000000000000932" target="_blank" >10.1097/RLI.0000000000000932</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study

Popis výsledku v původním jazyce

Objectives: Diffusion-weighted magnetic resonance imaging plays an increasing role in patients with multiple myeloma. The objective of this study was to develop and test an algorithm for automatic pelvic bone marrow analysis from whole-body apparent diffusion coefficient maps in patients with multiple myeloma, by automatically segmentation of pelvic bones and subsequent extraction of objective, representative ADC measurements from each bone. Material and Methods: This retrospective multicentric study used 180 MRIs from 54 patients for developing an nnU-Net for automatic, individual segmentation of the right hip bone, the left hip bone, and the sacral bone. Precision of the automatic segmentation was tested on 15 wb-MRIs from 3 centers using the dice score. In three independent test-sets from three centers, which comprised a total of 312 whole-body MRIs, agreement between automatically extracted mean ADC values from the nnU-Net segmentation were compared to manual ADC-measurements by two radiologists. Bland-Altman plots were constructed, and absolute bias, relative bias to mean, limits of agreement, and coefficients of variation were calculated. In 56 patients with newly diagnosed multiple myeloma who had undergone bone marrow biopsy, ADC-values were correlated with biopsy results using Spearman correlation. Results: The ADC-nnU-Net achieved automatic segmentations with mean dice scores of 0.92, 0.93, and 0.85 for the right pelvis, the left pelvis, and the sacral bone, while the interrater experiment gave mean dice scores of 0.86, 0.86 and 0.77, respectively. The agreement between radiologists’ manual ADC measurements and automatic ADC measurements was as follows: the bias between the first rater and the automatic approach was 49 x10-6 mm2/s, 7 x10-6 mm2/s and -58 x10-6 mm2/s, and the bias between the second rater and the automatic approach was 12 x10-6 mm2/s, 2 x10-6 mm2/s and -66 x10-6 mm2/s for the right pelvis, the left pelvis, and the sacral bone. The bias betw

Název v anglickém jazyce

Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study

Popis výsledku anglicky

Objectives: Diffusion-weighted magnetic resonance imaging plays an increasing role in patients with multiple myeloma. The objective of this study was to develop and test an algorithm for automatic pelvic bone marrow analysis from whole-body apparent diffusion coefficient maps in patients with multiple myeloma, by automatically segmentation of pelvic bones and subsequent extraction of objective, representative ADC measurements from each bone. Material and Methods: This retrospective multicentric study used 180 MRIs from 54 patients for developing an nnU-Net for automatic, individual segmentation of the right hip bone, the left hip bone, and the sacral bone. Precision of the automatic segmentation was tested on 15 wb-MRIs from 3 centers using the dice score. In three independent test-sets from three centers, which comprised a total of 312 whole-body MRIs, agreement between automatically extracted mean ADC values from the nnU-Net segmentation were compared to manual ADC-measurements by two radiologists. Bland-Altman plots were constructed, and absolute bias, relative bias to mean, limits of agreement, and coefficients of variation were calculated. In 56 patients with newly diagnosed multiple myeloma who had undergone bone marrow biopsy, ADC-values were correlated with biopsy results using Spearman correlation. Results: The ADC-nnU-Net achieved automatic segmentations with mean dice scores of 0.92, 0.93, and 0.85 for the right pelvis, the left pelvis, and the sacral bone, while the interrater experiment gave mean dice scores of 0.86, 0.86 and 0.77, respectively. The agreement between radiologists’ manual ADC measurements and automatic ADC measurements was as follows: the bias between the first rater and the automatic approach was 49 x10-6 mm2/s, 7 x10-6 mm2/s and -58 x10-6 mm2/s, and the bias between the second rater and the automatic approach was 12 x10-6 mm2/s, 2 x10-6 mm2/s and -66 x10-6 mm2/s for the right pelvis, the left pelvis, and the sacral bone. The bias betw

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30204 - Oncology

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

INVESTIGATIVE RADIOLOGY

ISSN

0020-9996

e-ISSN

1536-0210

Svazek periodika

58

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

273-282

Kód UT WoS článku

000958267800004

EID výsledku v databázi Scopus

2-s2.0-85150040717