Differences in morphological features and minimum apparent diffusion coefficient values among breast cancer subtypes using 3-tesla MRI

Kato, Fumiki; Kudo, Kohsuke; Yamashita, Hiroko; Wang, Jeff; Hosoda, Mitsuchika; Hatanaka, Kanako C.; Mimura, Rie; Oyama‐Manabe, Noriko; Shirato, Hiroki

doi:10.1016/j.ejrad.2015.10.018

Cited by 33 publications

(29 citation statements)

References 19 publications

(27 reference statements)

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“…Notably, HER2-positive cancer had significantly higher ADC 0 and sADC values than luminal B (HER2-negative) cancer. This difference was not observed in other group (19). There was a trend of smaller ADC 0 , sADC, and ADC values in luminal B (HER2-negative) cancer than in luminal A cancer, which was in agreement with the literature (19), although our observed trend did not reach the statistical difference after Bonferroni correction.…”

Section: Relationship Of Non-gaussian Dw Imaging and Ivim Parameters supporting

confidence: 91%

Intravoxel Incoherent Motion and Quantitative Non-Gaussian Diffusion MR Imaging: Evaluation of the Diagnostic and Prognostic Value of Several Markers of Malignant and Benign Breast Lesions

et al. 2018

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Purpose To investigate the performance of integrated approaches that combined intravoxel incoherent motion (IVIM) and non-Gaussian diffusion parameters compared with the Breast Imaging and Reporting Data System (BI-RADS) to establish multiparameter thresholds scores or probabilities by using Bayesian analysis to distinguish malignant from benign breast lesions and their correlation with molecular prognostic factors. Materials and Methods Between May 2013 and March 2015, 411 patients were prospectively enrolled and 199 patients (allocated to training [n = 99] and validation [n = 100] sets) were included in this study. IVIM parameters (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion parameters (theoretical apparent diffusion coefficient [ADC] at b value of 0 sec/mm [ADC] and kurtosis [K]) by using IVIM and kurtosis models were estimated from diffusion-weighted image series (16 b values up to 2500 sec/mm), as well as a synthetic ADC (sADC) calculated by using b values of 200 and 1500 (sADC) and a standard ADC calculated by using b values of 0 and 800 sec/mm (ADC). The performance of two diagnostic approaches (combined parameter thresholds and Bayesian analysis) combining IVIM and diffusion parameters was evaluated and compared with BI-RADS performance. The Mann-Whitney U test and a nonparametric multiple comparison test were used to compare their performance to determine benignity or malignancy and as molecular prognostic biomarkers and subtypes of breast cancer. Results Significant differences were found between malignant and benign breast lesions for IVIM and non-Gaussian diffusion parameters (ADC, K, fIVIM, fIVIM · D*, sADC and ADC; P < .05). Sensitivity and specificity for the validation set by radiologists A and B were as follows: sensitivity, 94.7% and 89.5%, and specificity, 75.0% and 79.2% for sADC, respectively; sensitivity, 94.7% and 96.1%, and specificity, 75.0% and 66.7%, for the combined thresholds approach, respectively; sensitivity, 92.1% and 92.1%, and specificity, 83.3% and 66.7%, for Bayesian analysis, respectively; and sensitivity and specificity, 100% and 79.2%, for BI-RADS, respectively. The significant difference in values of sADC in progesterone receptor status (P = .002) was noted. sADC was significantly different between histologic subtypes (P = .006). Conclusion Approaches that combined various IVIM and non-Gaussian diffusion MR imaging parameters may provide BI-RADS-equivalent scores almost comparable to BI-RADS categories without the use of contrast agents. Non-Gaussian diffusion parameters also differed by biologic prognostic factors. RSNA, 2017 Online supplemental material is available for this article.

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Section: Relationship Of Non-gaussian Dw Imaging and Ivim Parameters supporting

confidence: 91%

Intravoxel Incoherent Motion and Quantitative Non-Gaussian Diffusion MR Imaging: Evaluation of the Diagnostic and Prognostic Value of Several Markers of Malignant and Benign Breast Lesions

et al. 2018

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“…The association of spiculated border characteristics with low grade, low Ki-67 index (<20%), and association of annular contrast enhancement with high histologic grade and ER/PR negative subtype were reported in the literature (10,11). Similarly, we demonstrated that spiculated border characteristics were associated with ER/PR positive cancers, particularly with luminal A subtype.…”

Section: Resultssupporting

confidence: 79%

Can Breast MRI Findings Predict Molecular Subtypes of Breast Cancer?

Polat¹,

Öztürk²,

Özgür³

et al. 2019

Akd Med J

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Objective:To investigate the association between the morphologic and kinetic results obtained with the dynamic contrast-enhanced magnetic resonance imaging (MRI), and the apparent diffusion coefficient (ADC) values obtained using diffusion-weighted imaging (DWI) in breast cancer with the histopathologic subtypes of tumors. Material and Methods: The MRI results of 271 breast lesions of 258 patients were retrospectively evaluated. Lesion morphology and contrast-enhancement characteristics were evaluated using conventional MRI, and ADC measurements were performed with DWI. Results: An association was detected between regular margins in the masses, the presence of intratumoral necrosis, and annular contrast enhancement with triple-negative type (TN), spiculated margin luminal A type. Higher histological grade was mostly detected in TN (45.7%), and human epidermal growth factor receptor 2 positive (HER2+) tumors (47.1%) (p<0.001). The mean ADC value was measured as 1001x10 -6 mm 2 /second. No significant difference was detected in molecular subtypes considering the ADC values (p=0.396). No correlation was detected between the Ki-67 proliferation index and the mean ADC values (p=0.207). Conclusion: Although the morphological results of dynamic contrast-enhanced breast MRI indicated particular molecular subtypes, it may be suggested that the ADC values obtained using DWI were not decisive in identifying molecular subtypes.

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“…The tumour ADC has been evaluated as a candidate biomarker for the prediction of responses to neoadjuvant chemotherapy, and has been used to make differential diagnoses between benign and malignant breast masses . Moreover, ADC has been utilized as an indicator of tumour aggressiveness, Ki‐67 expression, tumour cellularity and the expression of prognosis biomarkers in breast cancer . Recent studies have reported a correlation between ADC and the molecular markers of breast cancer .…”

Section: Introductionmentioning

confidence: 99%

“…[16][17][18] Moreover, ADC has been utilized as an indicator of tumour aggressiveness, Ki-67 expression, tumour cellularity and the expression of prognosis biomarkers in breast cancer. [19][20][21][22][23] Recent studies have reported a correlation between ADC and the molecular markers of breast cancer. [24][25][26][27] In addition to direct analysis of the entire tumour, DWI information on the breast tumour-stromal boundary and adjacent tissue surrounding the tumour has also been combined to associate DWI parameters with pathological factors or to predict responses to treatment.…”

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confidence: 99%

Diffusion‐weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer

Fan

Zhang

et al. 2017

NMR in Biomedicine

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Breast cancer heterogeneity is the main obstacle preventing the identification of patients with breast cancer with poor prognoses and treatment responses; however, such heterogeneity has not been well characterized. The purpose of this retrospective study was to reveal heterogeneous patterns in the apparent diffusion coefficient (ADC) signals in tumours and the surrounding stroma to predict molecular subtypes of breast cancer. A dataset of 126 patients with breast cancer, who underwent preoperative diffusion-weighted imaging (DWI) on a 3.0-T image system, was collected. Breast images were segmented into regions comprising the tumour and surrounding stromal shells in which features that reflect heterogeneous ADC signal distribution were extracted. For each region, imaging features were computed, including the mean, minimum, variance, interquartile range (IQR), range, skewness, kurtosis and entropy of ADC values. Univariate and stepwise multivariate logistic regression modelling was performed to identify the magnetic resonance imaging features that optimally discriminate luminal A, luminal B, human epidermal growth factor 2 (HER2)-enriched and basal-like molecular subtypes. The performance of the predictive models was evaluated using the area under the receiver operating characteristic curve (AUC). Univariate logistic regression analysis showed that the skewness in the tumour boundary achieved an AUC of 0.718 for discrimination between luminal A and non-luminal A tumours, whereas the IQR of the ADC value in the tumour boundary had an AUC of 0.703 for classification of the HER2-enriched subtype. Imaging features in the tumour boundary and the proximal peritumoral stroma corresponded to a higher overall prediction performance than those in other regions. A multivariate logistic regression model combining features in all the regions achieved an overall AUC of 0.800 for the classification of the four tumour subtypes. These findings suggest that features in the tumour boundary and stroma around the tumour may be further assessed as potential predictors of molecular subtypes of breast cancer.

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Differences in morphological features and minimum apparent diffusion coefficient values among breast cancer subtypes using 3-tesla MRI

Abstract: Conclusion:Morphology and minimum ADC would be useful in distinguishing breast cancer subtypes. pg. 2 KeywordBreast cancer subtypes, Magnetic resonance imaging, Diffusion weighed imaging, pg. 3

Cited by 33 publications

References 19 publications

Intravoxel Incoherent Motion and Quantitative Non-Gaussian Diffusion MR Imaging: Evaluation of the Diagnostic and Prognostic Value of Several Markers of Malignant and Benign Breast Lesions

Intravoxel Incoherent Motion and Quantitative Non-Gaussian Diffusion MR Imaging: Evaluation of the Diagnostic and Prognostic Value of Several Markers of Malignant and Benign Breast Lesions

Can Breast MRI Findings Predict Molecular Subtypes of Breast Cancer?

Diffusion‐weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer

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