Dataset Information

Assessment of Cervical Cancer with a Parameter-Free Intravoxel Incoherent Motion Imaging Algorithm.

ABSTRACT:

Objective

To evaluate the feasibility of a parameter-free intravoxel incoherent motion (IVIM) approach in cervical cancer, to assess the optimal b-value threshold, and to preliminarily examine differences in the derived perfusion and diffusion parameters for different histological cancer types.

Materials and methods

After Institutional Review Board approval, 19 female patients (mean age, 54 years; age range, 37-78 years) gave consent and were enrolled in this prospective magnetic resonance imaging study. Clinical staging and biopsy results were obtained. Echo-planar diffusion weighted sequences at 13 b-values were acquired at 3 tesla field strength. Single-sliced region-of-interest IVIM analysis with adaptive b-value thresholds was applied to each tumor, yielding the optimal fit and the optimal parameters for pseudodiffusion (D^*), perfusion fraction (F_p) and diffusion coefficient (D). Monoexponential apparent diffusion coefficient (ADC) was calculated for comparison with D.

Results

Biopsy revealed squamous cell carcinoma in 10 patients and adenocarcinoma in 9. The b-value threshold (median [interquartile range]) depended on the histological type and was 35 (22.5-50) s/mm² in squamous cell carcinoma and 150 (100-150) s/mm² in adenocarcinoma (p < 0.05). Comparing squamous cell vs. adenocarcinoma, D^* (45.1 [25.1-60.4] × 10^-3 mm²/s vs. 12.4 [10.5-21.2] × 10^-3 mm²/s) and F_p (7.5% [7.0-9.0%] vs. 9.9% [9.0-11.4%]) differed significantly between the subtypes (p < 0.02), whereas D did not (0.89 [0.75-0.94] × 10^-3 mm²/s vs. 0.90 [0.82-0.97] × 10^-3 mm²/s, p = 0.27). The residuals did not differ (0.74 [0.60-0.92] vs. 0.94 [0.67-1.01], p = 0.32). The ADC systematically underestimated the magnitude of diffusion restriction compared to D (p < 0.001).

Conclusion

The parameter-free IVIM approach is feasible in cervical cancer. The b-value threshold and perfusion-related parameters depend on the tumor histology type.

SUBMITTER: Becker AS

PROVIDER: S-EPMC5390620 | biostudies-literature | 2017 May-Jun

REPOSITORIES: biostudies-literature

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Publications

Assessment of Cervical Cancer with a Parameter-Free Intravoxel Incoherent Motion Imaging Algorithm.

Becker Anton S AS Perucho Jose A JA Wurnig Moritz C MC Boss Andreas A Ghafoor Soleen S Khong Pek-Lan PL Lee Elaine Y P EYP

Korean journal of radiology 20170403 3

<h4>Objective</h4>To evaluate the feasibility of a parameter-free intravoxel incoherent motion (IVIM) approach in cervical cancer, to assess the optimal b-value threshold, and to preliminarily examine differences in the derived perfusion and diffusion parameters for different histological cancer types.<h4>Materials and methods</h4>After Institutional Review Board approval, 19 female patients (mean age, 54 years; age range, 37-78 years) gave consent and were enrolled in this prospective magnetic ...[more]

PMID: 28458603

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Project description:Applicability of intravoxel incoherent motion (IVIM) imaging in the clinical setting is hampered by the limited reliability in particular of the perfusion-related parameter estimates. To alleviate this problem, various advanced postprocessing methods have been introduced. However, the underlying algorithms are not readily available and generally suffer from an increased computational burden. Contrary, several computationally fast image denoising methods have recently been proposed which are accessible online and may improve reliability of IVIM parameter estimates. The objective of the present work is to investigate the impact of image denoising on accuracy and precision of IVIM parameter estimates using comprehensive in-silico and in-vivo experiments. Image denoising is performed with four different algorithms that work on magnitude data: two algorithms which are based on nonlocal means (NLM) filtering, one algorithm that relies on local principal component analysis (LPCA) of the diffusion-weighted images, and another algorithms that exploits joint rank and edge constraints (JREC). Accuracy and precision of IVIM parameter estimates is investigated in an in-silico brain phantom and an in-vivo ground truth as a function of the signal-to-noise ratio for spatially homogenous and inhomogenous levels of Rician noise. Moreover, precision is evaluated using bootstrap analysis of in-vivo measurements. In the experiments, IVIM parameters are computed a) by using a segmented fit method and b) by performing a biexponential fit of the entire attenuation curve based on nonlinear least squares estimates. Irrespective of the fit method, the results demonstrate that reliability of IVIM parameter estimates is substantially improved by image denoising. The experiments show that the LPCA and the JREC algorithms perform in a similar manner and outperform the NLM-related methods. Relative to noisy data, accuracy of the IVIM parameters in the in-silico phantom improves after image denoising by 76-79%, 79-81%, 84-99% and precision by 74-80%, 80-83%, 84-95% for the perfusion fraction, the diffusion coefficient, and the pseudodiffusion coefficient, respectively, when the segmented fit method is used. Beyond that, the simulations reveal that denoising performance is not impeded by spatially inhomogeneous levels of Rician noise in the image. Since all investigated algorithms are freely available and work on magnitude data they can be readily applied in the clinical setting which may foster transition of IVIM imaging into clinical practice.

Project description:BackgroundTo explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the early assessment of colorectal cancer liver metastases (CRLM).MethodsA total of 34 patients with pathologically confirmed unresectable CRLM were enrolled. All participants uniformly received capecitabine and oxaliplatin (CAPOX) plus bevacizumab chemotherapy as standard first-line treatment for advanced colorectal cancer (CRC). Participants underwent 1.5-T conventional magnetic resonance imaging (MRI) and IVIM-DWI sequence scans with 9 b values (0 to 1,000 s/mm2) before treatment and at 3 weeks of treatment, and conventional MRI scans were performed at 6 and 12 weeks after the initial treatment. The IVIM-DWI parameters in the tumor target area were extracted using image post-processing software, including perfusion fraction (f), true diffusion coefficient (D), and false diffusion coefficient (D*). The response assessment was based on the Response Evaluation Criteria in Solid Tumors (RECIST) v. 1.1 by measuring the longest tumor diameter on dynamic contrast-enhanced (DCE) T1-weighted images.ResultsAccording to the RECIST v. 1.1 criteria, the 34 participants were divided into a response group (n=16) and a non-response group (n=18). In the response group, the f value was significantly lowered (P=0.001) and the D value was significantly increased after treatment (P=0.002). In the non-response group, the D value was increased slightly after treatment (P=0.039), and there was no significant difference in the f value and the D* value. In addition, the f value at baseline was significantly greater in the response group than in the non-response group (0.221±0.033 vs. 0.175±0.040; P=0.001). The receiver operating characteristic (ROC) curve analysis showed that the percentage change of the f value obtained the largest area under the curve (AUC =0.797), and the AUC obtained by the Fisher's linear discriminant analysis (FLDA) method (Δf & ΔD combination) was 0.819.ConclusionsThe IVIM-DWI parameters (f values and D values) provided effective assessment of the therapeutic effect of CAPOX combined with bevacizumab in patients with CRLM at an early stage, and the f value of the pre-treatment tumor area was shown to be useful for predicting the treatment response of patients.

Dataset Information

Assessment of Cervical Cancer with a Parameter-Free Intravoxel Incoherent Motion Imaging Algorithm.

Objective

Materials and methods

Results

Conclusion

Publications

Assessment of Cervical Cancer with a Parameter-Free Intravoxel Incoherent Motion Imaging Algorithm.

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