Differentiation between malignant and benign breast masses: combination of semi-quantitative analysis on DCE-MRI and histogram analysis of ADC maps

Liu, H.-L.; Zong, Min‐Hua; Han, Wei; Lou, Jianjuan; Wang, S.-Q.; Zou, Qigui; Shi, Haibin; Jiang, Yanni

doi:10.1016/j.crad.2017.11.026

Cited by 18 publications

(23 citation statements)

References 30 publications

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“…Four studies used histopathology as the reference standard (30–33), whereas only one used histopathology and clinical follow-up (34). MRI reviewers blinded to other test results and clinical data were reported in three studies (30,31,34) and two studies were unclear (32,33). Five studies provided diagnostic findings for 10th percentile ADC value (30–34), three studies provided diagnostic findings for 90th percentile ADC value (30,33,34) and mean (30,33,34), two studies provided diagnostic findings for 50th percentile ADC value (30,34), skewness (33,34), and mode (30,34), and one study provided diagnostic findings for 5th percentile (30), 25th percentile (30), 75th percentile (30), 95th percentile (30), maximum (30), median (33), entropy (33), and kurtosis (34).…”

Section: Resultsmentioning

confidence: 99%

Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis

Liang

Guo

et al. 2020

Acta Radiol

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Background Although whole-lesion apparent diffusion coefficient (ADC) histogram has been increasingly used for breast lesions, it has not been routinely used in clinical practice as an emergent promising imaging tool. Purpose To evaluate the performance of whole-lesion ADC histogram analysis metrics for differentiating benign and malignant breast lesions. Material and Methods A systematic PubMed/EMBASE/Cochrane electronic database search was performed for original diagnostic studies from 1 January 1970 to 2 January 2019. Summary estimates of diagnostic accuracy were generated and meta-regression was performed to explore sources of heterogeneity according to study and magnetic resonance imaging characteristics. Results Five original articles involving 493 patients were included in the meta-analysis. The pooled sensitivity and specificity of whole-lesion ADC histogram analysis were 0.85 (95% confidence interval [CI] = 0.81–0.89) and 0.79 (95% CI = 0.72–0.84) for distinguishing benign and malignant breast lesions, respectively. The area under the curve (AUC) was 0.9178. No publication bias was detected ( P = 0.51). In subgroup analysis, the summary sensitivity and specificity of 50th percentile ADC value were 0.81 (95% CI = 0.71–0.88) and 0.86 (95% CI = 0.74–0.94), respectively. Meta-regression analysis indicated no covariates were sources of heterogeneity ( P > 0.05). Conclusion Whole-lesion ADC histogram analysis demonstrated good diagnostic performance for differentiating between benign and malignant breast lesions, with 50th percentile ADC value showing higher diagnostic accuracy than other histogram parameters. Given the limited number of studies included in the analysis, the findings from our meta-analysis will need further confirmation in future research.

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Section: Resultsmentioning

confidence: 99%

Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis

Liang

Guo

et al. 2020

Acta Radiol

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“…The use of ADC as a tool for the differentiation between benign and malignant lesions has been widely explored in several studies. Malignant tumors usually have lower ADC values than benign lesions due to high cellularity [20, 21]. Likewise, invasive ductal carcinomas have lower ADC values than ductal carcinomas in situ [22].…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, invasive ductal carcinomas have lower ADC values than ductal carcinomas in situ [22]. ADC has been also investigated for the prediction of prognostic factors, such as positive axilla and lymphovascular invasion, with promising results [20, 21, 23–25].…”

Section: Discussionmentioning

confidence: 99%

Histogram Analysis and Visual Heterogeneity of Diffusion-Weighted Imaging with Apparent Diffusion Coefficient Mapping in the Prediction of Molecular Subtypes of Invasive Breast Cancers

Horvat

Iyer

Morris

et al. 2019

Contrast Media & Molecular Imaging

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Objective To investigate if histogram analysis and visually assessed heterogeneity of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping can predict molecular subtypes of invasive breast cancers. Materials and Methods In this retrospective study, 91 patients with invasive breast carcinoma who underwent preoperative magnetic resonance imaging (MRI) with DWI at our institution were included. Two radiologists delineated a 2-D region of interest (ROI) on ADC maps in consensus. Tumors were also independently classified into low and high heterogeneity based on visual assessment of DWI. First-order statistics extracted through histogram analysis within the ROI of the ADC maps (mean, 10th percentile, 50th percentile, 90th percentile, standard deviation, kurtosis, and skewness) and visually assessed heterogeneity were evaluated for associations with tumor receptor status (ER, PR, and HER2 status) as well as molecular subtype. Results HER2-positive lesions demonstrated significantly higher mean (p=0.034), Perc50 (p=0.046), and Perc90 (p=0.040), with AUCs of 0.605, 0.592, and 0.652, respectively, than HER2-negative lesions. No significant differences were found in the histogram values for ER and PR statuses. Neither quantitative histogram analysis based on ADC maps nor qualitative visual heterogeneity assessment of DWI images was able to significantly differentiate between molecular subtypes, i.e., luminal A versus all other subtypes (luminal B, HER2-enriched, and triple negative) combined, luminal A and B combined versus HER2-enriched and triple negative combined, and triple negative versus all other types combined. Conclusion Histogram analysis and visual heterogeneity assessment cannot be used to differentiate molecular subtypes of invasive breast cancer.

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“…Wang et al 10 showed features, such as shapes, edges, enhancement characteristics, and involvement that were significantly different between benign and malignant breast lesions. Liu et al 11 investigated the performance of semiquantitative parameters and proved that the signal intensity (SI) slope exhibited the best diagnostic performance in predicting malignancy. Honda et al 12 Among these diverse features, TIC features computed from the TIC are known as some of the strongest breast cancer risk indicators because TIC has the unique property of reflecting the dynamic characteristics of lesions.…”

mentioning

confidence: 99%

“…Wang et al showed features, such as shapes, edges, enhancement characteristics, and involvement that were significantly different between benign and malignant breast lesions. Liu et al investigated the performance of semiquantitative parameters and proved that the signal intensity (SI) slope exhibited the best diagnostic performance in predicting malignancy. Honda et al developed a computer‐aided diagnosis (CAD) scheme with 11 objective features that exhibited high classification accuracy.…”

mentioning

confidence: 99%

Assessing the performance of benign and malignant breast lesion classification with bilateral TIC differentiation and other effective features in DCE‐MRI

Sun

Liu

et al. 2019

Magnetic Resonance Imaging

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Background Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is increasingly being used to detect and diagnose breast cancer. However, the effective methods based on quantitative feature analysis need to be explored. Purpose To investigate a new approach for improving the performance of benign and malignant breast lesion classification by combining bilateral time–intensity curve (TIC) differentiation features with other effective features. Study Type Retrospective. Population In all, 112 DCE‐MRI biopsy‐proven breast examinations (45 malignant tumors and 67 benign tumors). Field Strength/Sequence 3 T MR with a breast coil. Precontrast images were acquired before contrast administration. Subsequently, postcontrast images were acquired approximately every minute directly following administration of the contrast agent (gadopentetate dimeglumine). The postcontrast images contain eight sequences in total. Each sequence contains 78 slices. Assessment: Accuracy, sensitivity, specificity, and the area under the curve (AUC) of the classification was calculated and compared with the published result. Statistical Tests Five‐fold crossvalidation was used. Results Areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.8461, 0.7914, 0.8514, and 0.9058 were achieved using conventional features, unilateral TIC features, bilateral TIC differentiation features, and all the selected features, respectively. In terms of accuracy, the use of only unilateral TIC features or conventional features achieved an accuracy of 0.7321 or 0.8482 (sensitivity of 0.7556 or 0.7111 and specificity of 0.7164 or 0.9403). However, the accuracy increased to 0.9196 (sensitivity of 0.8889 and specificity of 0.9403) when bilateral TIC differentiation features were included. Data Conclusion Bilateral differentiation TIC features can serve as a stronger indicator in differentiating benign and malignant breast lesions than the unilateral TIC features computed from one side of the breast only and some conventional features that are commonly used in MR image analysis. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:465–473.

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Differentiation between malignant and benign breast masses: combination of semi-quantitative analysis on DCE-MRI and histogram analysis of ADC maps

Cited by 18 publications

References 30 publications

Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis

Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis

Histogram Analysis and Visual Heterogeneity of Diffusion-Weighted Imaging with Apparent Diffusion Coefficient Mapping in the Prediction of Molecular Subtypes of Invasive Breast Cancers

Assessing the performance of benign and malignant breast lesion classification with bilateral TIC differentiation and other effective features in DCE‐MRI

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