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 lesio...

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Vydáno v:Cancer imaging Ročník 20; číslo 1; s. 6 - 10
Hlavní autoři: Almeida, Sílvia D., Santinha, João, Oliveira, Francisco P. M., Ip, Joana, Lisitskaya, Maria, Lourenço, João, Uysal, Aycan, Matos, Celso, João, Cristina, Papanikolaou, Nikolaos
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 13.01.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Adult
Age
Aged
Algorithms
Atlas-based segmentation
Biomarkers
Bone marrow
Cancer Research
Correlation coefficients
Datasets
Diffusion coefficient
Diffusion Magnetic Resonance Imaging - methods
Diffusion weighted imaging
Driving while intoxicated
Female
Gold standard
Histograms
Humans
Image Processing, Computer-Assisted - methods
Image segmentation
Imaging
Lesions
Lymphoma
Magnetic resonance imaging
Male
Medicine
Medicine & Public Health
Middle Aged
Multiple myeloma
Multiple Myeloma - diagnostic imaging
Multiple Myeloma - pathology
Muscles
Nuclear Medicine
Oncology
Outliers (statistics)
Patients
Performance evaluation
Radiology
Registration
Research Article
Semi-automatic segmentation
Sensitivity analysis
Total lesion burden
Tumor Burden
Tumors
Semi-automatic segmentation
Atlas-based segmentation
Total lesion burden
Diffusion weighted imaging
Multiple myeloma
ISSN:1470-7330, 1740-5025, 1470-7330
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Shrnutí: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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ISSN:1470-7330
1740-5025
1470-7330
DOI:10.1186/s40644-020-0286-5