Jose R. Teruel

Dynamic contrast‐enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

The aim of this study was to investigate the potential of texture analysis, applied to dynamic contrast‐enhanced MRI (DCE‐MRI), to predict the clinical and pathological response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) before NAC is started. Fifty‐eight patients with LABC were classified on the basis of their clinical response according to the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines after four cycles of NAC, and according to their pathological response after surgery. T1‐weighted DCE‐MRI with a temporal resolution of 1 min was acquired on a 3‐T Siemens Trio scanner using a dedicated four‐channel breast coil before the onset of treatment. Each lesion was segmented semi‐automatically using the 2‐min post‐contrast subtracted image. Sixteen texture features were obtained at each non‐subtracted post‐contrast time point using a gray level co‐occurrence matrix. Appropriate statistical analyses were performed and false discovery rate‐based q values were reported to correct for multiple comparisons. Statistically significant results were found at 1–3 min post‐contrast for various texture features for the prediction of both the clinical and pathological response. In particular, eight texture features were found to be statistically significant at 2 min post‐contrast, the most significant feature yielding an area under the curve (AUC) of 0.77 for response prediction for stable disease versus complete responders after four cycles of NAC. In addition, four texture features were found to be significant at the same time point, with an AUC of 0.69 for response prediction using the most significant feature for classification based on the pathological response. Our results suggest that texture analysis could provide clinicians with additional information to increase the accuracy of prediction of an individual response before NAC is started. Copyright

T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results

AbstractPurposeTo evaluate the diagnostic relevance of T2-weighted (T2W) MRI-derived textural features relative to quantitative physiological parameters derived from diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI in Gleason score (GS) 3+4 and 4+3 prostate cancers.Materials and Methods3T multiparametric-MRI was performed on 23 prostate cancer patients prior to prostatectomy. Textural features [angular second moment (ASM), contrast, correlation, entropy], apparent diffusion coefficient (ADC), and DCE pharmacokinetic parameters (Ktrans and Ve) were calculated from index tumours delineated on the T2W, DW, and DCE images, respectively. The association between the textural features and prostatectomy GS and the MRI-derived parameters, and the utility of the parameters in differentiating between GS 3+4 and 4+3 prostate cancers were assessed statistically.ResultsASM and entropy correlated significantly (p < 0.05) with both GS and median ADC. Contrast correlated moderately with median ADC. The textural features correlated insignificantly with Ktrans and Ve. GS 4+3 cancers had significantly lower ASM and higher entropy than 3+4 cancers, but insignificant differences in median ADC, Ktrans, and Ve. The combined texture-MRI parameters yielded higher classification accuracy (91%) than the individual parameter sets.ConclusionT2W MRI-derived textural features could serve as potential diagnostic markers, sensitive to the pathological differences in prostate cancers.Key Points• T2W MRI-derived textural features correlate significantly with Gleason score and ADC. • T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers. • T2W image textural features could augment tumour characterization.

Inhomogeneous static magnetic field-induced distortion correction applied to diffusion weighted MRI of the breast at 3T.

To evaluate the performance of an advanced method for correction of inhomogeneous static magnetic field induced distortion in echo‐planar imaging (EPI), applied to diffusion‐weighted MRI (DWI) of the breast.

Diffusion weighted imaging for the differentiation of breast tumors: From apparent diffusion coefficient to high order diffusion tensor imaging.

To compare “standard” diffusion weighted imaging, and diffusion tensor imaging (DTI) of 2nd and 4th‐order for the differentiation of malignant and benign breast lesions.

Stimulated echo diffusion tensor imaging (STEAM‐DTI) with varying diffusion times as a probe of breast tissue

To explore the application of diffusion tensor imaging (DTI) for breast tissue and breast pathologies using a stimulated‐echo acquisition mode (STEAM) with variable diffusion times.

Diffusion‐weighted MRI for early detection and characterization of prostate cancer in the transgenic adenocarcinoma of the mouse prostate model

To improve early diagnosis of prostate cancer to aid clinical decision‐making. Diffusion‐weighted magnetic resonance imaging (DW‐MRI) is sensitive to water diffusion throughout tissues, which correlates with Gleason score, a histological measure of prostate cancer aggressiveness. In this study the ability of DW‐MRI to detect prostate cancer onset and development was evaluated in transgenic adenocarcinoma of the mouse prostate (TRAMP) mice.

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study

Diffusion‐weighted MRI (DWI) is currently one of the fastest developing MRI‐based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning.

Geometric distortion correction in prostate diffusion‐weighted MRI and its effect on quantitative apparent diffusion coefficient analysis

To evaluate the effect of correction for B0 inhomogeneity‐induced geometric distortion in echo‐planar diffusion‐weighted imaging on quantitative apparent diffusion coefficient (ADC) analysis in multiparametric prostate MRI.

High temporal resolution motion estimation using a self-navigated simultaneous multi-slice echo planar imaging acquisition: Motion Estimation Using Self-Navigated SMS-EPI Acquisition

Subject motion is known to produce spurious covariance among time‐series in functional connectivity that has been reported to induce distance‐dependent spurious correlations.

Relationship between kurtosis and bi-exponential characterization of high b-value diffusion-weighted imaging: application to prostate cancer.

Background High b-value diffusion-weighted imaging has application in the detection of cancerous tissue across multiple body sites. Diffusional kurtosis and bi-exponential modeling are two popular model-based techniques, whose performance in relation to each other has yet to be fully explored. Purpose To determine the relationship between excess kurtosis and signal fractions derived from bi-exponential modeling in the detection of suspicious prostate lesions. Material and Methods This retrospective study analyzed patients with normal prostate tissue (n = 12) or suspicious lesions (n = 13, one lesion per patient), as determined by a radiologist whose clinical care included a high b-value diffusion series. The observed signal intensity was modeled using a bi-exponential decay, from which the signal fraction of the slow-moving component was derived (SFs). In addition, the excess kurtosis was calculated using the signal fractions and ADCs of the two exponentials (KCOMP). As a comparison, the kurtosis was also calculated using the cumulant expansion for the diffusion signal (KCE). Results Both K and KCE were found to increase with SFs within the range of SFs commonly found within the prostate. Voxel-wise receiver operating characteristic performance of SFs, KCE, and KCOMP in discriminating between suspicious lesions and normal prostate tissue was 0.86 (95% confidence interval [CI] = 0.85 – 0.87), 0.69 (95% CI = 0.68–0.70), and 0.86 (95% CI = 0.86–0.87), respectively. Conclusion In a two-component diffusion environment, KCOMP is a scaled value of SFs and is thus able to discriminate suspicious lesions with equal precision. KCE provides a computationally inexpensive approximation of kurtosis but does not provide the same discriminatory abilities as SFs and KCOMP.