Impact of ROI Positioning and Lesion Morphology on Apparent Diffusion Coefficient Analysis for the Differentiation Between Benign and Malignant Nonfatty Soft-Tissue Lesions

Bonarelli, Chloé; Teixeira, Pedro Augusto Gondim; Hossu, Gabriela; Meyer, J.-B.; Chen, Bailiang; Blum, Alain

doi:10.2214/ajr.14.13865

Cited by 21 publications

(22 citation statements)

References 29 publications

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“…We believe one of the possible reasons for this is the difference in ROI selection. 29 In our study, the ROIs covered the whole tumor, including heterogeneous cellularity, calcification, hemorrhage, or necrosis; consequently, the DDC min may not well represent the components densely packed with malignant cells. Another possible reason is that the minimums are highly sensitive to noise in the maps, whereas 10 th percentiles are not.…”

Section: Discussionmentioning

confidence: 82%

Breast lesion characterization using whole‐lesion histogram analysis with stretched‐exponential diffusion model

Liu

Wang

et al. 2017

Magnetic Resonance Imaging

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Section: Discussionmentioning

confidence: 82%

Breast lesion characterization using whole‐lesion histogram analysis with stretched‐exponential diffusion model

Liu

Wang

et al. 2017

Magnetic Resonance Imaging

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“…Quantitative assessment of nodules on DWI relies on ADC measurement. It has been shown that the ADC min values are more successful than mean ADC (ADC mean ) values for the malignant and benign tumor differentiation . Park et al showed that the use of ADC min may allow better performance than ADC mean in predicting of Gleason score in prostate cancer.…”

Section: Discussionmentioning

confidence: 99%

Diffusion‐weighted MRI of pulmonary lesions: Comparison of apparent diffusion coefficient and lesion‐to‐spinal cord signal intensity ratio in lesion characterization

Çakmak

Ufuk

Karabulut

2016

Magnetic Resonance Imaging

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“…DWI is new method used to differentiate benign from malignant soft tissue lesions based on ADC values; quantitative DWI are useful for differentiating between malignant and benign superficial masses [12][13][14] . In this study, 500s/mm 2 of b value was performed and mean ADC value of solid GCT-ST lesions was 2.19×10 −3 mm 2 /s, which was higher compared to the surrounding normal soft tissue.…”

Section: Discussionmentioning

confidence: 99%

A Pilot Evaluation of Magnetic Resonance Imaging Characteristics Seen With Primary Soft Tissue Giant Cell Tumor in 2 patients

Zhang

Kang

Zhang

et al. 2019

Preprint

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Background Primary soft tissue giant cell tumor (GCT-ST) is a rare tumor with low malignant potential. Here we reported two cases of patients with soft tissue giant cell tumor in the limb, including their clinical and imaging findings (conventional Magnetic resonance imaging (MRI) and Diffusion weighted imaging (DWI)).Methods This retrospective study included two pathology-confirmed GCT-ST patients. Plain MRI, dynamic contrast enhancement MRI (DCE-MRI), and DWI were performed with a 3.0T whole-body MR scanner before surgery. The following characteristics of lesion were recorded: signal intensity on T 1 FSPGR and T 2 WI, morphology, maximum lesion size, time intensity curve (TIC) on DCE-MRI, and apparent diffusion coefficient (ADC) value from DWI.Results The maximum lesion size ranged from 4.0 cm to 6.0 cm. Signal intensities of all lesions were heterogeneous on T 1 FSPGR and T 2 WI. Nodular enhancements were observed for all lesions with either oval or irregular shapes on MRI. All lesion margins were blurred, and internal enhancements were heterogeneous on DCE-MRI. TIC appeared with a slow increase type. Lesions on DWI (b=500s/mm 2 ) were hyperintense with an higher mean ADC value of 2.19×10 −3 mm 2 /s compared to surrounding normal soft tissue (1.03×10 −3 mm 2 /s).Conclusions DWI may be a useful tool for differentiating benign soft tissue mass from giant cell-rich soft tissue neoplasms or malignant tumors.

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Impact of ROI Positioning and Lesion Morphology on Apparent Diffusion Coefficient Analysis for the Differentiation Between Benign and Malignant Nonfatty Soft-Tissue Lesions

Abstract: The ADCmin values obtained with manual ROI positioning offered the best diagnostic performance for tumor characterization. The semiautomatic method yielded similar specificity values. For solid masses, the ADCavg values were better correlated with tumor histology than the ADCmin values.

Cited by 21 publications

References 29 publications

Breast lesion characterization using whole‐lesion histogram analysis with stretched‐exponential diffusion model

Breast lesion characterization using whole‐lesion histogram analysis with stretched‐exponential diffusion model

Diffusion‐weighted MRI of pulmonary lesions: Comparison of apparent diffusion coefficient and lesion‐to‐spinal cord signal intensity ratio in lesion characterization

A Pilot Evaluation of Magnetic Resonance Imaging Characteristics Seen With Primary Soft Tissue Giant Cell Tumor in 2 patients

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