The diagnostic value of diffusion‐weighted imaging and the apparent diffusion coefficient values in the differentiation of benign and malignant breast lesions

Çabuk, Gonca; Duce, Meltem Nass; Özgür, Anıl; Apaydın, Feramuz Demir; Polat, Ayşe; Örekici, Gülhan

doi:10.1111/1754-9485.12273

Cited by 16 publications

(10 citation statements)

References 28 publications

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“…The motion of water molecules in tissue can be affected by tissue cellularity and membrane integrity [6]. DWI has been applied in the detection of breast cancer [34], differentiation of benign and malignant lesions [35], and monitoring the response to neoadjuvant chemotherapy [36,37]. However, there is no consensus that DWI is a reliable stand-alone method.…”

Section: Discussionmentioning

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

Preprint

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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: Discussionmentioning

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

Preprint

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“…However, high sensitivity leads to the problem of over-treatment, resulting in a high rate of puncture biopsies [ 17 – 19 ]. Many studies have tested the effectiveness of DWI in benign-malignant differentiation of breast lesions and find that ADC values can increase the specificity of breast MRI [ 20 , 21 ]. For example, Li et al [ 22 ] found that ADC values were significantly lower in malignant than in benign lesions.…”

Section: Discussionmentioning

confidence: 99%

Apparent Diffusion Coefficient to Subdivide Breast Imaging Reporting and Data System Magnetic Resonance Imaging (BI-RADS-MRI) Category 4 Lesions

Liu

et al. 2018

Med Sci Monit

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BackgroundThis study aims to subdivide BI-RADS-MRI (Breast Imaging Reporting and Data System Magnetic Resonance Imaging) Category 4 lesions and to evaluate the role of Fischer’s scoring system, apparent diffusion coefficient (ADC), and Fischer’s + ADC in differential diagnosis of breast lesions.Material/MethodsThis study retrospectively analyzed the data of 143 patients (150 breast lesions), who were diagnosed by biopsy, and received dynamic contrast enhancement and diffusion-weighted imaging. The diagnostic efficacies of ADC, Fischer’s scoring system, and the Fischer’s + ADC were analyzed by the receiver operating characteristics curve. The area under the curve (AUC) was calculated. Fischer’s scoring system and the Fischer’s + ADC were used to subdivide BI-RADS Category 4 breast lesions.ResultsADC value was negatively correlated with the tumor grade. The AUC of Fischer’s + ADC (0.949) was significantly higher than that of ADC (0.855) and Fischer’s (0.912) (P=0.0008 and 0.001, respectively). Scored by Fischer’s scoring system, Category 4 and 5 indicated a likely malignant threshold with sensitivity and specificity of 98.70% and 65.75%, respectively. Scored by the Fischer’s + ADC method, Category 4B and 4C indicated a likely malignant threshold with sensitivity of 97.40% and specificity of 82.19%. Kappa values were 0.63 (ADC), 0.65 (Fischer’s), and 0.80 (Fischer’s + ADC), respectively. The positive predictive value of BI-RADS 4A, 4B, and 4C were 7.69%, 52.38% and 89.29%, respectively.ConclusionsFischer’s scoring system combined with ADC could reasonably subdivide Category 4 breast lesions with high specificity and sensitivity.

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“…Often, but not always, cancerous breast tissue is associated with increased cellularity and increased intracellular density, such that the motion of water is diminished, and the apparent diffusion coefficient (ADC) is lowered. On the other hand, the ADC is often increased with non-cancerous breast lesions [50][51][52]. There are, however, exceptions: fibrotic or benign proliferative processes are also associated with diminished ADC, whereas necrotic tissue within a cancer is hypocellular, and, therefore, associated with increased ADC.…”

Section: Diffusion-weighted Imaging For Identifying Breast Cancer Andmentioning

confidence: 99%

Mathematically-optimized magnetic resonance spectroscopy in breast cancer diagnostics: implications for personalized cancer medicine

Belkić

2015

J Math Chem

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We comprehensively examine the performance of the fast Padé transform (FPT) for handling noise-corrupted magnetic resonance spectroscopic (MRS) data from normal breast, fibroadenoma and breast cancer in the controlled setting compared to conventional processing through the fast Fourier transform (FFT) for a wide chemical shift region. Included is an assessment of the performance of the FPT (+) for very heavily noise-corrupted MRS time signals from the breast. For all three categories of breast tissue, the FFT produced only rudimentary total shape spectra with coarse, broad, shortened peaks at full signal length, whereas the FPT (−) fully resolved all the tightly-packed, and some completely overlapping resonances, accurately reconstructing the spectral parameters from which all the metabolite concentrations were exactly computed. With added noise of standard deviation σ ∼ 3 root-mean-square, the FPT (+) clearly identified and exactly reconstructed all the genuine resonances and distinguished these from the much more abundant spurious content. The powerful capability of the FPT (+) to induce convergence into divergent series via analytical continuation appears to result in more noise cancellation, with pole-zero coincidence remaining complete even at very high noise levels. These results represent a critical step towards efficient in vivo implementations of MRS, where there is no input data with which to check. Detailed consideration is made of how these results could improve the diagnostic yield of MRS for breast cancer, and how this could potentially impact upon a more personalized approach to care of patients afflicted with or at risk for this malignancy.

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The diagnostic value of diffusion‐weighted imaging and the apparent diffusion coefficient values in the differentiation of benign and malignant breast lesions

Abstract: DWI can be an effective radiological method in the differentiation of benign and malignant breast lesions.

Cited by 16 publications

References 28 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

Apparent Diffusion Coefficient to Subdivide Breast Imaging Reporting and Data System Magnetic Resonance Imaging (BI-RADS-MRI) Category 4 Lesions

Mathematically-optimized magnetic resonance spectroscopy in breast cancer diagnostics: implications for personalized cancer medicine

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