Diffusion-weighted MRI for Unenhanced Breast Cancer Screening

Amornsiripanitch, Nita; Bickelhaupt, Sebastian; Shin, Hee Jung; Dang, Madeline; Rahbar, Habib; Pinker, Katja; Partridge, Savannah C.

doi:10.1148/radiol.2019182789

Cited by 100 publications

(118 citation statements)

References 95 publications

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“…With restricted water diffusion, breast cancer usually shows hyperintensity on DW images [ 6 ]. DWI contributes to the differential diagnosis of breast lesions and may be a promising tool in breast cancer detection [ 7 ]. In differentiating malignant and benign breast lesions, the diagnostic performance of contrast-enhanced magnetic resonance imaging (MRI) with DWI is higher than that of contrast-enhanced MRI with time-intensity curves [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

Zhou

Liu

et al. 2020

BMC Cancer

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Background The clinicopathological classification of breast cancer is proposed according to therapeutic purposes. It is simplified and can be conducted easily in clinical practice, and this subtyping undoubtedly contributes to the treatment selection of breast cancer. This study aims to investigate the feasibility of using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI for predicting the clinicopathological subtypes of breast cancer. Methods Patients who underwent breast magnetic resonance imaging were confirmed by retrieving data from our institutional picture archiving and communication system (PACS) between March 2013 and September 2017. Five clinicopathological subtypes were determined based on the status of ER, PR, HER2 and Ki-67 from the immunohistochemical test. The radiomic features of diffusion-weighted imaging were derived from the volume of interest (VOI) of each tumour. Fisher discriminant analysis was performed for clinicopathological subtyping by using a backward selection method. To evaluate the diagnostic performance of the radiomic features, ROC analyses were performed to differentiate between immunohistochemical biomarker-positive and -negative groups. Results A total of 84 radiomic features of four statistical methods were included after preprocessing. The overall accuracy for predicting the clinicopathological subtypes was 96.4% by Fisher discriminant analysis, and the weighted accuracy was 96.6%. For predicting diverse clinicopathological subtypes, the prediction accuracies ranged from 92 to 100%. According to the cross-validation, the overall accuracy of the model was 82.1%, and the accuracies of the model for predicting the luminal A, luminal BHER2-, luminal BHER2+, HER2 positive and triple negative subtypes were 79, 77, 88, 92 and 73%, respectively. According to the ROC analysis, the radiomic features had excellent performance in differentiating between different statuses of ER, PR, HER2 and Ki-67. Conclusions The Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI is a reliable method for the prediction of clinicopathological breast cancer subtypes.

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Section: Introductionmentioning

confidence: 99%

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

Zhou

Liu

et al. 2020

BMC Cancer

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“…Diffusion-weighted (DW) MRI (where water molecule mobility is decreased in the cancer microenvironment and contrast is not required) may be an adjunct to existing MRI techniques [ 19 ]. DW-MRI may be useful in the future in increasing detection rates, however it still requires further standardisation to ascertain its true sensitivity and specificity [ 20 ].…”

Section: Diagnostic Techniques and Their Effect On The Incidence Andmentioning

confidence: 99%

Occult breast cancer: Where are we at?

Ofri¹,

Moore

2020

The Breast

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Occult breast cancer (OBC) is described as an axillary metastatic carcinoma without detection of a primary breast lesion and is uncommon. Significant advances in breast imaging have occurred since its description, decreasing its incidence. However current management is based upon old studies, with variable clinical, radiological and pathological definitions of OBC. We suggest standardised definitions of OBC to facilitate more homogenous data representation in the literature. This review also discusses the conflicting heterogeneous data and its influence in determining the current management guidelines. We discuss whether the current significant surgical recommendations are necessary and postulate whether they could be safely substituted with less invasive management.

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“…Diffusion-weighted imaging (DWI) is increasingly incorporated into breast MRI protocols worldwide [1][2][3]. DWI using apparent diffusion coefficient (ADC) mapping has reported sensitivities of up to 96% and specificities of up to 100% for breast cancer detection [4,5]. Currently, the prime focus of DWI is to differentiate between benign and malignant lesions to prevent unnecessary breast biopsies.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have demonstrated that the sensitivity of unenhanced MRI with DWI was equal to or superior to mammography [4,15]; however, there is still room for improvement [16]. Diffusion sensitivity, better known as "b-value," has important implications for tumor conspicuity and can be controlled by modifying the magnitude and duration of the diffusion gradients.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic value of diffusion-weighted imaging with synthetic b-values in breast tumors: comparison with dynamic contrast-enhanced and multiparametric MRI

et al. 2020

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Objectives To assess DWI for tumor visibility and breast cancer detection by the addition of different synthetic b-values. Methods Eighty-four consecutive women who underwent a breast-multiparametric-MRI (mpMRI) with enhancing lesions on DCE-MRI (BI-RADS 2-5) were included in this IRB-approved retrospective study from September 2018 to March 2019. Three readers evaluated DW acquired b-800 and synthetic b-1000, b-1200, b-1500, and b-1800 s/mm 2 images for lesion visibility and preferred b-value based on lesion conspicuity. Image quality (1-3 scores) and breast composition (BI-RADS) were also recorded. Diagnostic parameters for DWI were determined using a 1-5 malignancy score based on qualitative imaging parameters (acquired + preferred synthetic b-values) and ADC values. BI-RADS classification was used for DCE-MRI and quantitative ADC values + BI-RADS were used for mpMRI. Results Sixty-four malignant (average = 23 mm) and 39 benign (average = 8 mm) lesions were found in 80 women. Although b-800 achieved the best image quality score, synthetic b-values 1200-1500 s/mm 2 were preferred for lesion conspicuity, especially in dense breast. b-800 and synthetic b-1000/b-1200 s/mm 2 values allowed the visualization of 84-90% of cancers visible with DCE-MRI performing better than b-1500/b-1800 s/mm 2. DWI was more specific (86.3% vs 65.7%, p < 0.001) but less sensitive (62.8% vs 90%, p < 0.001) and accurate (71% vs 80.7%, p = 0.003) than DCE-MRI for breast cancer detection, where mpMRI was the most accurate modality accounting for less false positive cases. Conclusion The addition of synthetic b-values enhances tumor conspicuity and could potentially improve tumor visualization particularly in dense breast. However, its supportive role for DWI breast cancer detection is still not definite. Key Points • The addition of synthetic b-values (1200-1500 s/mm 2) to acquired DWI afforded a better lesion conspicuity without increasing acquisition time and was particularly useful in dense breasts. • Despite the use of synthetic b-values, DWI was less sensitive and accurate than DCE-MRI for breast cancer detection. • A multiparametric MRI modality still remains the best approach having the highest accuracy for breast cancer detection and thus reducing the number of unnecessary biopsies.

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Diffusion-weighted MRI for Unenhanced Breast Cancer Screening

Cited by 100 publications

References 95 publications

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI

Occult breast cancer: Where are we at?

Diagnostic value of diffusion-weighted imaging with synthetic b-values in breast tumors: comparison with dynamic contrast-enhanced and multiparametric MRI

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