Diagnostic performance of diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) MRI for the differentiation of benign from malignant soft-tissue tumors

2019 ◽  
Vol 50 (3) ◽  
pp. 798-809 ◽  
Author(s):  
Young Jin Choi ◽  
In Sook Lee ◽  
You Seon Song ◽  
Jeung Il Kim ◽  
Kyung-Un Choi ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Diagnostic Performance ◽  
Soft Tissue Tumors ◽  
Dce Mri ◽  
Malignant Soft Tissue Tumors ◽  
Dynamic Contrast Enhanced ◽  
Diffusion Weighted ◽  
Contrast Enhanced ◽  
Malignant Soft Tissue

scholarly journals Benign or Malignant Characterization of Soft-Tissue Tumors by Using Semiquantitative and Quantitative Parameters of Dynamic Contrast-Enhanced Magnetic Resonance Imaging

2020 ◽  
Vol 71 (1) ◽  
pp. 92-99
Author(s):  
Yu Zhang ◽  
Bin Yue ◽  
Xiaodan Zhao ◽  
Haisong Chen ◽  
Lingling Sun ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Soft Tissue ◽  
Soft Tissue Tumors ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Malignant Soft Tissue Tumors ◽  
Dynamic Contrast Enhanced ◽  
Quantitative Parameters ◽  
Contrast Enhanced ◽  
Malignant Soft Tissue

Purpose: To evaluate the efficacy of the semiquantitative and quantitative parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating between benign and malignant soft-tissue tumors. Methods: A total of 45 patients with pathologically confirmed soft-tissue tumors (15 benign and 30 malignant tumors) underwent DCE-MRI. The semiquantitative parameters assessed were as follows: time to peak (TTP), maximum concentration (MAX Conc), area under the curve of time-concentration curve (AUC-TC), and maximum rise slope (MAX Slope). Quantitative DCE-MRI was analyzed with the extended Tofts-Kety model to assess the following quantitative parameters: volume transfer constant (Ktrans), microvascular permeability reflux constant (Kep), and distribute volume per unit tissue volume (Ve). Data were evaluated using the independent t test or Mann-Whitney U test and receiver operating characteristic (ROC) curves. Results: The TTP ( P = .0035), MAX Conc ( P = .0018), AUC-TC ( P = .0018), MAX Slope ( P = .0018), Ktrans ( P = .0018), and Kep ( P = .0035) were significantly different between the benign and malignant soft-tissue tumors. The AUC of the ROC curve demonstrated the diagnostic potential of TTP (0.778), MAX Conc (0.849), AUC-TC (0.831), MAX Slope (0.847), Ktrans (0.836), Kep (0.778), and Ve (0.638). Conclusions: The use of semiquantitative and quantitative parameters of DCE-MRI enabled differentiation between benign and malignant soft-tissue tumors. The values of TTP were lower, while those of MAX Conc, AUC-TC, MAX Slope, Ktrans, and Kep were higher in malignant than in benign tumors.

scholarly journals Multiparametric quantitative analysis of tumor perfusion and diffusion with 3T MRI: differentiation between benign and malignant soft tissue tumors

2020 ◽  
Vol 93 (1115) ◽  
pp. 20191035
Author(s):  
Seul Ki Lee ◽  
Won-Hee Jee ◽  
Chan Kwon Jung ◽  
Yang-Guk Chung
Keyword(s):  
Soft Tissue ◽  
Subgroup Analysis ◽  
Malignant Tumors ◽  
Soft Tissue Tumors ◽  
Multiparametric Mri ◽  
Time Signal ◽  
Dce Mri ◽  
Malignant Soft Tissue Tumors ◽  
Benign Soft Tissue Tumors ◽  
Malignant Soft Tissue

Objective: To evaluate multiparametric MRI for differentiating benign and malignant soft tissue tumors. Methods: This retrospective study included 67 patients (mean age, 55 years; 18–82 years) with 35 benign and 32 malignant soft tissue tumors. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI)-derived parameters (D, D*, f), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE)-MRI parameters (Ktrans, Kep, Ve, iAUC) were calculated. Myxoid and non-myxoid soft tissue tumors were divided for subgroup analysis. The parameters were compared between benign and malignant tumors. Results: ADC and D were significantly lower in malignant than benign soft tissue tumors (1170 ± 488 vs 1472 ± 349 µm2/s; 1132 ± 500 vs 1415 ± 374 µm2/s; p < 0.05). Ktrans, Kep, Ve, and iAUC were significantly different between malignant and benign soft tissue tumors (0.209 ± 0.160 vs 0.092 ± 0.067 min−1; 0.737 ± 0.488 vs 0.311 ± 0.230 min−1; 0.32 ± 0.17 vs 0.44 ± 0.28; 0.23 ± 0.14 vs 0.12 ± 0.09, p < 0.05, respectively). ADC (0.752), D (0.742), and Kep (0.817) had high AUCs. Subgroup analysis showed that only Ktrans, and iAUC were significantly different in myxoid tumors, while, ADC, D, Ktrans, Kep, and iAUC were significantly different in non-myxoid tumor for differentiating benign and malignant tumors. D, Kep, and iAUC were the most significant parameters predicting malignant soft tissue tumors. Conclusion: Multiparametric MRI can be useful to differentiate benign and malignant soft tissue tumors using IVIM-DWI and DCE-MRI. Advances in knowledge: 1. Pure tissue diffusion (D), transfer constant (Ktrans), rate constant (Kep), and initial area under time–signal intensity curve (iAUC) can be used to differentiate benign malignant soft tissue tumors. 2. Ktrans and iAUC enable differentiation of benign and malignant myxoid soft tissue tumors.

scholarly journals Radiomics of diffusion-weighted MRI compared to conventional measurement of apparent diffusion-coefficient for differentiation between benign and malignant soft tissue tumors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seung Eun Lee ◽  
Joon-Yong Jung ◽  
Yoonho Nam ◽  
So-Yeon Lee ◽  
Hyerim Park ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Malignant Tumors ◽  
Diagnostic Value ◽  
Soft Tissue Tumors ◽  
Imaging Features ◽  
Malignant Soft Tissue Tumors ◽  
Diffusion Weighted ◽  
Significant Difference ◽  
Apparent Diffusion ◽  
Malignant Soft Tissue

AbstractDiffusion-weighted imaging (DWI) is proven useful to differentiate benign and malignant soft tissue tumors (STTs). Radiomics utilizing a vast array of extracted imaging features has a potential to uncover disease characteristics. We aim to assess radiomics using DWI can outperform the conventional DWI for STT differentiation. In 151 patients with 80 benign and 71 malignant tumors, ADCmean and ADCmin were measured on solid portion within the mass by two different readers. For radiomics approach, tumors were segmented and 100 original radiomic features were extracted on ADC map. Eight radiomics models were built with training set (n = 105), using combinations of 2 different algorithms—multivariate logistic regression (MLR) and random forest (RF)—and 4 different inputs: radiomics features (R), R + ADCmin (I), R + ADCmean (E), R + ADCmin and ADCmean (A). All models were validated with test set (n = 46), and AUCs of ADCmean, ADCmin, MLR-R, RF-R, MLR-I, RF-I, MLR-E, RF-E, MLR-A and RF-A models were 0.729, 0.753 0.698, 0.700, 0.773, 0.807, 0.762, 0.744, 0.773 and 0.807, respectively, without statistically significant difference. In conclusion, radiomics approach did not add diagnostic value to conventional ADC measurement for differentiating benign and malignant STTs.

Usefulness of diffusion-weighted imaging for differentiating between desmoid tumors and malignant soft tissue tumors

2010 ◽  
Vol 33 (1) ◽  
pp. 189-193 ◽  
Author(s):  
Kiyoshi Oka ◽  
Toshitake Yakushiji ◽  
Hiro Sato ◽  
Toru Fujimoto ◽  
Toshinori Hirai ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Diffusion Weighted Imaging ◽  
Soft Tissue Tumors ◽  
Desmoid Tumors ◽  
Malignant Soft Tissue Tumors ◽  
Diffusion Weighted ◽  
Malignant Soft Tissue

Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors

2021 ◽  
Author(s):  
Brandon K. K. Fields ◽  
Natalie L. Demirjian ◽  
Darryl H. Hwang ◽  
Bino A. Varghese ◽  
Steven Y. Cen ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Tumors ◽  
Volumetric Mri ◽  
Malignant Soft Tissue Tumors ◽  
Malignant Soft Tissue
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