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Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI

BACKGROUND: Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesi...

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Autores principales: Almeida, Sílvia D., Santinha, João, Oliveira, Francisco P. M., Ip, Joana, Lisitskaya, Maria, Lourenço, João, Uysal, Aycan, Matos, Celso, João, Cristina, Papanikolaou, Nikolaos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6958755/
https://www.ncbi.nlm.nih.gov/pubmed/31931880
http://dx.doi.org/10.1186/s40644-020-0286-5
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author Almeida, Sílvia D.
Santinha, João
Oliveira, Francisco P. M.
Ip, Joana
Lisitskaya, Maria
Lourenço, João
Uysal, Aycan
Matos, Celso
João, Cristina
Papanikolaou, Nikolaos
author_facet Almeida, Sílvia D.
Santinha, João
Oliveira, Francisco P. M.
Ip, Joana
Lisitskaya, Maria
Lourenço, João
Uysal, Aycan
Matos, Celso
João, Cristina
Papanikolaou, Nikolaos
author_sort Almeida, Sílvia D.
collection PubMed
description BACKGROUND: Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance by comparing it to the manual segmentations performed by radiologists. METHODS: An atlas-based segmentation was developed to remove the high-signal intensity normal tissues on WB-DWI and to restrict the lesion area to the skeleton. Then, an outlier threshold-based segmentation was applied to WB-DWI images, and the segmented area’s signal intensity was compared to the average signal intensity of a low-fat muscle on T1-weighted images. This method was validated in 22 whole-body DWI images of patients diagnosed with MM. Dice similarity coefficient (DSC), sensitivity and positive predictive value (PPV) were computed to evaluate the SA performance against the gold standard (GS) and to compare with the radiologists. A non-parametric Wilcoxon test was also performed. Apparent diffusion coefficient (ADC) histogram metrics and lesion volume were extracted for the GS segmentation and for the correctly identified lesions by SA and their correlation was assessed. RESULTS: The mean inter-radiologists DSC was 0.323 ± 0.268. The SA vs GS achieved a DSC of 0.274 ± 0.227, sensitivity of 0.764 ± 0.276 and PPV 0.217 ± 0.207. Its distribution was not significantly different from the mean DSC of inter-radiologist segmentation (p = 0.108, Wilcoxon test). ADC and lesion volume intraclass correlation coefficient (ICC) of the GS and of the correctly identified lesions by the SA was 0.996 for the median and 0.894 for the lesion volume (p < 0.001). The duration of the lesion volume segmentation by the SA was, on average, 10.22 ± 0.86 min, per patient. CONCLUSIONS: The SA provides equally reproducible segmentation results when compared to the manual segmentation of radiologists. Thus, the proposed method offers robust and efficient segmentation of MM lesions on WB-DWI. This method may aid accurate assessment of tumor burden and therefore provide insights to treatment response assessment.
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spelling pubmed-69587552020-01-17 Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI Almeida, Sílvia D. Santinha, João Oliveira, Francisco P. M. Ip, Joana Lisitskaya, Maria Lourenço, João Uysal, Aycan Matos, Celso João, Cristina Papanikolaou, Nikolaos Cancer Imaging Research Article BACKGROUND: Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance by comparing it to the manual segmentations performed by radiologists. METHODS: An atlas-based segmentation was developed to remove the high-signal intensity normal tissues on WB-DWI and to restrict the lesion area to the skeleton. Then, an outlier threshold-based segmentation was applied to WB-DWI images, and the segmented area’s signal intensity was compared to the average signal intensity of a low-fat muscle on T1-weighted images. This method was validated in 22 whole-body DWI images of patients diagnosed with MM. Dice similarity coefficient (DSC), sensitivity and positive predictive value (PPV) were computed to evaluate the SA performance against the gold standard (GS) and to compare with the radiologists. A non-parametric Wilcoxon test was also performed. Apparent diffusion coefficient (ADC) histogram metrics and lesion volume were extracted for the GS segmentation and for the correctly identified lesions by SA and their correlation was assessed. RESULTS: The mean inter-radiologists DSC was 0.323 ± 0.268. The SA vs GS achieved a DSC of 0.274 ± 0.227, sensitivity of 0.764 ± 0.276 and PPV 0.217 ± 0.207. Its distribution was not significantly different from the mean DSC of inter-radiologist segmentation (p = 0.108, Wilcoxon test). ADC and lesion volume intraclass correlation coefficient (ICC) of the GS and of the correctly identified lesions by the SA was 0.996 for the median and 0.894 for the lesion volume (p < 0.001). The duration of the lesion volume segmentation by the SA was, on average, 10.22 ± 0.86 min, per patient. CONCLUSIONS: The SA provides equally reproducible segmentation results when compared to the manual segmentation of radiologists. Thus, the proposed method offers robust and efficient segmentation of MM lesions on WB-DWI. This method may aid accurate assessment of tumor burden and therefore provide insights to treatment response assessment. BioMed Central 2020-01-13 /pmc/articles/PMC6958755/ /pubmed/31931880 http://dx.doi.org/10.1186/s40644-020-0286-5 Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Almeida, Sílvia D.
Santinha, João
Oliveira, Francisco P. M.
Ip, Joana
Lisitskaya, Maria
Lourenço, João
Uysal, Aycan
Matos, Celso
João, Cristina
Papanikolaou, Nikolaos
Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI
title Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI
title_full Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI
title_fullStr Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI
title_full_unstemmed Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI
title_short Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI
title_sort quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of wb-dwi
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6958755/
https://www.ncbi.nlm.nih.gov/pubmed/31931880
http://dx.doi.org/10.1186/s40644-020-0286-5
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