Dataset Information

Dual-Phase ADC Modelling of Breast Masses in Diffusion-Weighted Imaging: Comparison with Histopathologic Findings.

ABSTRACT: To investigate the diagnostic value of dual-phase apparent diffusion coefficient (ADC) compared to traditional ADC values in quantitative diffusion-weighted imaging (DWI) for differentiating between benign and malignant breast masses.Diffusion-weighted images of pathologically confirmed 88 benign and 85 malignant lesions acquired using a 3.0T MR scanner were analyzed. Small region-of-interests focusing on the highest signal intensity of lesions were used. Lesion ADC estimates were obtained separately from all b-value images (ADC; b=50, 400 and 800s/mm2), lower b-value images (ADClow; b=50 and 400s/mm2) and higher b-value images (ADChigh; b=400 and 800s/mm2). A set of dual-phase ADC (dpADC) models were constructed using ADClow, ADChigh and a perfusion influence factor ranging from 0 to 1.Strong positive correlation is observable between ADC and all dpADCs (ρ=0.80-1.00). Differences in ADC and dpADCs between the benign and the malignant lesions are all significant (p<0.05). In detecting malignancy, traditional lesion ADC provides a good performance (AUC=89.9%) however dpADC0.5 (dpADC with a factor of 0.5) accomplishes a better performance (AUC=90.8%). At optimal thresholds, ADC achieves 94.1% sensitivity, 72.7% specificity and 83.2% accuracy while dpADC0.5 leads to 92.9% sensitivity, 79.5% specificity and 86.1% accuracy.Dual-phase ADC modelling may improve the accuracy in breast cancer diagnosis using DWI. Further prospective studies are needed to justify its benefit in clinical setting.

SUBMITTER: Ertas G

PROVIDER: S-EPMC5939984 | biostudies-other | 2018 Apr

REPOSITORIES: biostudies-other

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Dual-Phase ADC Modelling of Breast Masses in Diffusion-Weighted Imaging: Comparison with Histopathologic Findings.

Ertaş Gökhan G Onaygil Can C Buğdaycı Onur O Arıbal Erkin E

European journal of breast health 20180401 2

<h4>Objective</h4>To investigate the diagnostic value of dual-phase apparent diffusion coefficient (ADC) compared to traditional ADC values in quantitative diffusion-weighted imaging (DWI) for differentiating between benign and malignant breast masses.<h4>Materials and methods</h4>Diffusion-weighted images of pathologically confirmed 88 benign and 85 malignant lesions acquired using a 3.0T MR scanner were analyzed. Small region-of-interests focusing on the highest signal intensity of lesions wer ...[more]

PMID: 29774316

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Project description:PURPOSE:To compare diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and tri-exponential models of the diffusion magnetic resonance imaging (MRI) signal for the characterization of renal lesions in relationship to histopathological findings. METHODS:Sixteen patients planned to undergo nephrectomy for kidney tumour were scanned before surgery at 3?T magnetic resonance imaging (MRI), with T2-weighted imaging, DTI and diffusion weighted imaging (DWI) using ten b-values. DTI parameters (mean diffusivity [MD] and fractional anisotropy [FA]) were obtained by iterative weighted linear least squared fitting of the DTI data and bi-, and tri-exponential fit parameters (Dbi, fstar,and Dtri, ffast,finterm) using a nonlinear fit of the multiple b-value DWI data. Average parameters were calculated for regions of interest, selecting the lesions and healthy kidney tissue. Tumour type and specificities were determined after surgery by histological examination. Mean parameter values of healthy tissue and solid lesions were compared using a Wilcoxon-signed ranked test and MANOVA. RESULTS:Thirteen solid lesions (nine clear cell carcinomas, two papillary renal cell carcinoma, one haemangioma and one oncocytoma) and four cysts were included. The mean MD of solid lesions are significantly (p?<?0.05) lower than healthy cortex and medulla, (1.94?±?0.32*10-?3?mm2/s versus 2.16?±?0.12*10-?3?mm2/s and 2.21?±?0.14*10-?3?mm2/s, respectively) whereas ffast is significantly higher (7.30?±?3.29% versus 4.14?±?1.92% and 4.57?±?1.74%) and finterm is significantly lower (18.7?±?5.02% versus 28.8?±?5.09% and 26.4?±?6.65%). Diffusion coefficients were high (?2.0*10-?3?mm2/s for MD, 1.90*10-?3?mm2/s for Dbi and 1.6*10-?3?mm2/s for Dtri) in cc-RCCs with cystic structures and/or haemorrhaging and low (?1.80*10-?3?mm2/s for MD, 1.40*10-?3?mm2/s for Dbi and 1.05*10-?3?mm2/s for Dtri) in tumours with necrosis or sarcomatoid differentiation. CONCLUSION:Parameters derived from a two- or three-component fit of the diffusion signal are sensitive to histopathological features of kidney lesions.

Project description:To assess the value of arterial spin-labeling (ASL) perfusion magnetic resonance (MR) imaging in the characterization of solid renal masses by using histopathologic findings as the standard of reference.This prospective study was compliant with HIPAA and approved by the institutional review board. Informed consent was obtained from all patients before imaging. Forty-two consecutive patients suspected of having renal masses underwent ASL MR imaging before their routine 1.5-T clinical MR examination. Mean and peak tumor perfusion levels were obtained by one radiologist, who was blinded to the final histologic diagnosis, by using region of interest analysis. Perfusion values were correlated with histopathologic findings by using analysis of variance. A linear correlation model was used to evaluate the relationship between tumor size and perfusion in clear cell renal cell carcinoma (RCC). P < .05 was considered indicative of a statistically significant difference.Histopathologic findings were available in 34 patients (28 men, six women; mean age ± standard deviation, 60.4 years ± 11.7). The mean perfusion of papillary RCC (27.0 mL/min/100 g ± 15.1) was lower than that of clear cell RCC (171.6 mL/min/100 g ± 61.2, P = .001), chromophobe RCC (152.9 mL/min/100 g ± 80.7, P = .04), unclassified RCC (208.0 mL/min/100 g ± 41.1, P = .001), and oncocytoma (373.9 mL/min/100 g ± 99.2, P < .001). The mean and peak perfusion levels of oncocytoma (373.9 mL/min/100 g ± 99.2 and 512.3 mL/min/100 g ± 146.0, respectively) were higher than those of papillary RCC (27.0 mL/min/100 g ± 15.1 and 78.2 mL/min/100 g ± 39.7, P < .001 for both), chromophobe RCC (152.9 mL/min/100 g ± 80.7 and 260.9 mL/min/100 g ± 61.9; P < .001 and P = .02, respectively), and unclassified RCC (208.0 mL/min/100 g ± 41.1 and 273.3 mL/min/100 g ± 83.4; P = .01 and P = .03, respectively). The mean tumor perfusion of oncocytoma was higher than that of clear cell RCC (P < .001).ASL MR imaging enables distinction among different histopathologic diagnoses in renal masses on the basis of their perfusion level. Oncocytomas demonstrate higher perfusion levels than RCCs, and papillary RCCs exhibit lower perfusion levels than other RCC subtypes.

Project description:PurposeDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used for breast cancer diagnosis as supplementary to conventional imaging techniques. Combining of diffusion-weighted imaging (DWI) of morphology and kinetic features from DCE-MRI to improve the discrimination power of malignant from benign breast masses is rarely reported.Materials and methodsThe study comprised of 234 female patients with 85 benign and 149 malignant lesions. Four distinct groups of features, coupling with pathological tests, were estimated to comprehensively characterize the pictorial properties of each lesion, which was obtained by a semi-automated segmentation method. Classical machine learning scheme including feature subset selection and various classification schemes were employed to build prognostic model, which served as a foundation for evaluating the combined effects of the multi-sided features for predicting of the types of lesions. Various measurements including cross validation and receiver operating characteristics were used to quantify the diagnostic performances of each feature as well as their combination.ResultsSeven features were all found to be statistically different between the malignant and the benign groups and their combination has achieved the highest classification accuracy. The seven features include one pathological variable of age, one morphological variable of slope, three texture features of entropy, inverse difference and information correlation, one kinetic feature of SER and one DWI feature of apparent diffusion coefficient (ADC). Together with the selected diagnostic features, various classical classification schemes were used to test their discrimination power through cross validation scheme. The averaged measurements of sensitivity, specificity, AUC and accuracy are 0.85, 0.89, 90.9% and 0.93, respectively.ConclusionMulti-sided variables which characterize the morphological, kinetic, pathological properties and DWI measurement of ADC can dramatically improve the discriminatory power of breast lesions.

Dataset Information

Dual-Phase ADC Modelling of Breast Masses in Diffusion-Weighted Imaging: Comparison with Histopathologic Findings.

Publications

Dual-Phase ADC Modelling of Breast Masses in Diffusion-Weighted Imaging: Comparison with Histopathologic Findings.

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