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

Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis

PURPOSE

We sought to evaluate the diagnostic performance of diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant breast tumors by performing a meta-analysis.

METHODS

We comprehensively searched the electronic databases PubMed and Embase from January 2000 to April 2020 for studies in English. Studies were included if they reported the sensitivity and specificity for identifying benign and malignant breast lesions using DWI or IVIM. Studies were reviewed according to QUADAS-2. The data inhomogeneity and publication bias were also assessed. In order to explore the influence of different field strengths and different b values on diagnostic efficiency, we conducted subgroup analysis.

RESULTS

We analyzed 79 studies, which included a total of 6294 patients with 4091 malignant lesions and 2793 benign lesions. Overall, the pooled sensitivity and specificity of ADC for detecting malignant breast tumors were 0.87 (0.86-0.88) and 0.80 (0.78-0.81), respectively. The PLR was 5.09 (4.16-6.24); the NLR was 0.15 (0.13-0.18); and the DOR was 38.95 (28.87-52.54). The AUC value was 0.9297. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity and specificity was 0.85 (0.82-0.88) and 0.87(0.83-0.90), respectively; the PLR was 5.65 (3.91-8.18); the NLR was 0.17 (0.12-0.26); and the DOR was 38.44 (23.57-62.69). The AUC value was 0.9265. Most of parameters demonstrated considerable statistically significant heterogeneity (P < 0.05, I²>50 %) except the pooled DOR, PLR of D and the pooled DOR and NLR of D*.

CONCLUSIONS

Our meta-analysis indicated that DWI and IVIM had high sensitivity and specificity in the differential diagnosis of breast lesions; and compared with DWI, IVIM could not further increase the diagnostic performance. There was no significant difference in diagnostic accuracy.

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

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 B_HER2-, luminal B_HER2+, 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.

Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis

BACKGROUND

Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC).

METHODS AND FINDINGS

In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and ≥ 800 s/mm2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I2) and considering covariables: field strength, lowest b-value, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84 (0.81-0.87), 0.86 (0.82-0.88) for BTTS4, respectively. Significant heterogeneity was found between studies (I2 = 95).

CONCLUSIONS

None of the breast tissue selection (BTTS) methodologies outperformed in differentiating benign from malignant breast lesions. The high heterogeneity of ADC data acquisition demands further standardization, such as DWI acquisition parameters and tumor tissue selection to substantially increase the reliability of DWI of the breast.

The Role of Combined Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MRI for Differentiating Malignant From Benign Breast Lesions Presenting Washout Curve

PURPOSE

The aim of this study is to evaluate the diagnostic performance of combined breast magnetic resonance imaging (MRI) protocol including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in patients with enhancing lesions that demonstrated washout curve and to determine whether applying apparent diffusion coefficient (ADC) cutoff value could improve the diagnostic value of breast MRI.

METHODS

The retrospective study included 116 patients with 116 suspicious breast lesions, which showed washout curve on DCE-MRI, who underwent subsequent biopsy. Morphologic characteristics on DCE-MRI and ADC values on DWI were evaluated. Apparent diffusion coefficient values and morphologic features of benign and malignant lesions were compared. Diagnostic values of DCE-MRI and combined MRI, including DCE-MRI and DWI (applying an ADC cutoff value) for distinguishing malignancy from benign lesions, were calculated.

RESULTS

Of the 116 breast lesions, 79 were malignant and 37 were benign. The ADC value of malignant tumors (median ADC, 0.72 × 10^-3 mm²/s) was significantly lower than that of benign lesions (median ADC, 1.03 × 10^-3 mm²/s; P < .000). The sensitivity and specificity of an ADC cutoff value of 0.89 × 10^-3 mm²/s were 92% and 95%, respectively. Dynamic contrast-enhanced MRI alone presented 100% sensitivity and 59.4% specificity. Adding an ADC cutoff value of 0.89 × 10^-3 mm²/s provided 100% sensitivity and 81% specificity, which would have prevented biopsy for 21.6% of benign lesions without missing any malignancies.

CONCLUSION

Applying an ADC cutoff value to DCE-MRI provides an improvement in the diagnostic value of breast MRI for differentiating among lesions presenting washout curve.

Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions

BACKGROUND

The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions.

METHODS

MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions.

RESULTS

The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10^- 3 mm²/s and the mean value of the benign lesions was 1.5 × 10^- 3 mm²/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10^- 3 mm²/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area).

CONCLUSION

An ADC threshold of 1.00 × 10^- 3 mm²/s can be recommended for distinguishing breast cancers from benign lesions.

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization

Background Various techniques are available to assess diffusion properties of breast lesions as a marker of malignancy at MRI. The diagnostic performance of these diffusion markers has not been comprehensively assessed. Purpose To compare by meta-analysis the diagnostic performance of parameters from diffusion-weighted imaging (DWI), diffusion-tensor imaging (DTI), and intravoxel incoherent motion (IVIM) in the differential diagnosis of malignant and benign breast lesions. Materials and Methods PubMed and Embase databases were searched from January to March 2018 for studies in English that assessed the diagnostic performance of DWI, DTI, and IVIM in the breast. Studies were reviewed according to eligibility and exclusion criteria. Publication bias and heterogeneity between studies were assessed. Pooled summary estimates for sensitivity, specificity, and area under the curve were obtained for each parameter by using a bivariate model. A subanalysis investigated the effect of MRI parameters on diagnostic performance by using a Student t test or a one-way analysis of variance. Results From 73 eligible studies, 6791 lesions (3930 malignant and 2861 benign) were included. Publication bias was evident for studies that evaluated apparent diffusion coefficient (ADC). Significant heterogeneity (P < .05) was present for all parameters except the perfusion fraction (f). The pooled sensitivity, specificity, and area under the curve for ADC was 89%, 82%, and 0.92, respectively. The highest performing parameter for DTI was the prime diffusion coefficient (λ1), and pooled sensitivity, specificity, and area under the curve was 93%, 90%, and 0.94, respectively. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity, specificity, and area under the curve was 88%, 79%, and 0.90. Choice of MRI parameters had no significant effect on diagnostic performance. Conclusion Diffusion-weighted imaging, diffusion-tensor imaging, and intravoxel incoherent motion have comparable diagnostic accuracy with high sensitivity and specificity. Intravoxel incoherent motion is comparable to apparent diffusion coefficient. Diffusion-tensor imaging is potentially promising but to date the number of studies is limited. © RSNA, 2019 Online supplemental material is available for this article.

Correlation between minimum apparent diffusion coefficient values and the histological grade of breast invasive ductal carcinoma

The present study aimed to investigate the correlation between the minimum apparent diffusion coefficient (ADC_min) value and the histological grade of breast invasive ductal carcinoma (IDC). In total, 129 pathologically verified lesions that were subjected to dynamic breast magnetic resonance imaging and diffusion weighted imaging prior to biopsy were included. The ADC_min value was calculated and its correlation with the tumor histological grade was investigated. Tumors of lower grades demonstrated significantly higher ADC_min values as compared with tumors of higher grades (F=33.49; P<0.01). The mean ADC_min values for IDC of grades I, II and III were (1.14±0.11)×10^-3, (0.99±0.12)×10^-3 and (0.86±0.13)×10^-3 mm²/sec, respectively. Statistically significant differences were detected in the mean ADC_min value between tumors of grades II and III (P<0.01), as well as between tumors of grades I and II (P<0.01). In addition, the mean ADC_min values for the less aggressive (grades I and II) and more aggressive (grade III) groups were (1.01±0.13)×10^-3 and (0.86±0.13)×10^-3 mm²/sec, respectively (t=5.76, P<0.01). In conclusion, these data indicated that the ADC_min value was correlated with the IDC histological grade, and lower ADC_min values were associated with a higher histological grade and more aggressiveness. Thus, the ADC_min value may be considered as a promising prognostic parameter in identifying tumor aggressiveness.

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

BACKGROUND This 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 AND METHODS This 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. RESULTS ADC 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. CONCLUSIONS Fischer's scoring system combined with ADC could reasonably subdivide Category 4 breast lesions with high specificity and sensitivity.

Is the necrosis/wall ADC ratio useful for the differentiation of benign and malignant breast lesions?

OBJECTIVE

To determine whether the necrosis/wall apparent diffusion coefficient (ADC) ratio is useful for the malignant-benign differentiation of necrotic breast lesions.

METHODS

Breast MRI was performed using a 3-T system. In this retrospective study, calculation of the necrosis/wall ADC ratio was based on ADC values measured from the necrosis and from the wall of malignant and benign breast lesions by diffusion-weighted imaging (DWI). By synchronizing post-contrast T₁ weighted images, the separate parts of wall and necrosis were maintained. All the diagnoses were pathologically confirmed. Statistical analyses were conducted using an independent sample t-test and receiver operating characteristic analysis. The intraclass and interclass correlations were evaluated.

RESULTS

A total of 66 female patients were enrolled, 38 of whom had necrotic breast carcinomas and 28 of whom had breast abscesses. The ADC values were obtained from both the wall and necrosis. The mean necrosis/wall ADC ratio (± standard deviation) was 1.61 ± 0.51 in carcinomas, and it was 0.65 ± 0.33 in abscesses. The area under the curve values for necrosis ADC, wall ADC and the necrosis/wall ADC ratio were 0.680, 0.068 and 0.942, respectively. A wall/necrosis ADC ratio cut-off value of 1.18 demonstrated a sensitivity of 97%, specificity of 93%, a positive-predictive value of 95%, a negative-predictive value of 96% and an accuracy of 95% in determining the malignant nature of necrotic breast lesions. There was a good intra- and interclass reliability for the ADC values of both necrosis and wall.

CONCLUSION

The necrosis/wall ADC ratio appears to be a reliable and promising tool for discriminating breast carcinomas from abscesses using DWI. Advances in knowledge: ADC values of the necrosis obtained by DWI are valuable for malignant-benign differentiation in necrotic breast lesions. The necrosis/wall ADC ratio appears to be a reliable and promising tool in the breast imaging field.