A simplified scoring protocol to improve diagnostic accuracy with the breast imaging reporting and data system in breast magnetic resonance imaging

External validation of multiparametric magnetic resonance imaging-based decision rules for characterizing breast lesions and comparison to Kaiser score and breast imaging reporting and data system (BI-RADS) category

Background

Breast imaging reporting and data system (BI-RADS) provides standard descriptors but not detailed decision rules for characterizing breast lesions. Diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) are also not incorporated in the BI-RADS. Several multiparametric magnetic resonance imaging (mpMRI)-based decision rules have been developed to differentiate breast lesions, but lack external validation. This study aims to externally validate several mpMRI-based decision rules for characterizing breast lesions and compare them with Kaiser score and BI-RADS category.

Methods

There were 206 patients with 218 pathology-proven breast lesions (99 malignancies) included in this retrospective study from January 2018 to May 2018. Two radiologists blinded to pathology evaluated breast lesions according to the three mpMRI-based decision rules (Kim, Istomin, Zhong) and Kaiser score. BI-RADS category was extracted from radiology reports and also analysed. The diagnostic performances of the four decision rules and BI-RADS category were calculated and compared for different lesion types [mass and non-mass enhancement (NME)] and size (≤10 and >10 mm). The unnecessary biopsy rates for BI-RADS 4 lesions were calculated by the four decision rules.

Results

The three mpMRI-based decision rules showed area under the curve (AUC) of 0.81-0.87 for all lesions, 0.86-0.92 for mass lesions, 0.68-0.82 for NME, and 0.68-0.87 for lesion size ≤10 mm, 0.82-0.87 for lesion size >10 mm. Kaiser score showed the highest diagnostic performance for all subgroups except for lesion size ≤10 mm. No significant differences were found in AUC between Kaiser score and BI-RADS category. The mpMRI-based decision rules showed high sensitivity of 100% in all subgroups at the expense of low specificity (range, 2.9-41.2%). In contrast, Kaiser score demonstrated a significantly higher specificity of 73.5-92.9% than the three mpMRI-based decision rules at the cost of a decreased sensitivity (range, 60.0-93.6%) in different subgroups. The unnecessary biopsy rates for BI-RADS 4 lesions were 9.8% (Istomin), 12.2% (Zhong), 14.6% (Kim) and 70.7% (Kaiser score), respectively.

Conclusions

The mpMRI-based decision rules showed high sensitivity but low specificity for characterizing breast lesions, and their diagnostic efficiencies were inferior to Kaiser score and BI-RADS category.

A background parenchymal enhancement quantification framework of breast magnetic resonance imaging

Background

Background parenchymal enhancement (BPE) is defined as the enhanced proportion of normal fibroglandular tissue on enhanced magnetic resonance imaging. BPE shows promise as a quantitative imaging biomarker (QIB). However, the lack of consensus among radiologists in their semi-quantitative grading of BPE limits its clinical utility.

Methods

The main objective of this study was to develop a BPE quantification model according to clinical expertise, with the BPE integral being used as a QIB to incorporate both the volume and intensity of the enhancement metrics. The model was applied to 2,786 cases to compare our quantitative results with radiologists' semi-quantitative BPE grading to evaluate the effectiveness of using the BPE integral as a QIB for analyzing BPE. Comparisons between multiple groups of nonnormally distributed BPE integrals were performed using the Kruskal-Wallis test.

Results

Our study found a considerable degree of concordance between our BPE quantitative integral and radiologists' semi-quantitative assessments. Specifically, our research results revealed significant variability in BPE integral attained through the BPE quantification framework among all semi-quantitative BPE grading groups labeled by experienced radiologists, including mild-moderate (P<0.001), mild-marked (P<0.001), and moderate-marked (P<0.001). Furthermore, there was an apparent correlation between BPE integral and BPE grades, with marked BPE displaying the highest BPE integral, followed by moderate BPE, with mild BPE exhibiting the lowest BPE integral value.

Conclusions

The study developed and implemented a BPE quantification framework, which incorporated both the volume and intensity of enhancement and which could serve as a QIB for BPE.

Quantitative evaluation of Kaiser score in diagnosing breast dynamic contrast-enhanced magnetic resonance imaging for patients with high-grade background parenchymal enhancement

Background

High-grade background parenchymal enhancement (BPE), including moderate and marked, poses a considerable challenge for the diagnosis of breast disease due to its tendency to increase the rate of false positives and false negatives. The purpose of our study was to explore whether the Kaiser score can be used for more accurate assessment of benign and malignant lesions in high-grade BPE compared with the Breast Imaging Reporting and Data System (BI-RADS).

Methods

A retrospective review was conducted on consecutive breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans from 2 medical centers. Included were patients who underwent DCE-MRI demonstrating high-grade BPE and who had a pathology-confirmed diagnosis. Excluded were patients who had received neoadjuvant chemotherapy or who had undergone biopsy prior to MRI examination. Two physicians with more than 7 years of experience specializing in breast imaging diagnosis jointly reviewed breast magnetic resonance (MR) images. The Kaiser score was used to determine the sensitivity, specificity, and positive predictive value (PPV), and negative predictive value (NPV) of the BI-RADS from different BPE groups and different enhancement types. The performance of the Kaiser score and BI-RADS were compared according to diagnostic accuracy.

Results

A total of 126 cases of high-grade BPE from 2 medical centers were included in this study. The Kaiser score had a higher specificity and PPV than did the BI-RADS (87.5% vs. 46.3%) as well as a higher PPV (94.3% vs. 79.8%). The value of diagnostic accuracy and 95% confidence interval (CI) for the Kaiser score (accuracy 0.928; 95% CI: 0.883-0.973) was larger than that for BI-RADS (accuracy 0.810; 95% CI: 0.741-0.879). Moreover, the Kaiser score had a significantly higher value of diagnostic accuracy for both mass and non-mass enhancement, especially mass lesions (Kaiser score: accuracy 0.947, 95% CI: 0.902-0.992; BI-RADS: accuracy 0.821, 95% CI: 0.782-0.860), with a P value of 0.006.

Conclusions

The Kaiser score is a useful diagnostic tool for the evaluation of high-grade BPE lesions, with a higher specificity, PPV, and diagnostic accuracy as compared to the BI-RADS.

A new scoring system for differentiating malignant from benign "second-look" breast lesions detected by MRI in patients with known breast cancer

AIM

To propose a scoring system made of reproducible and objective criteria to aid in differentiating malignant from benign "second-look" breast lesions detected at magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Data were collected retrospectively for "second-look" lesions identified on breast MRI studies performed at the University Hospitals of Leicester NHS Trust breast unit over a 2-year period (from January 2020 to January 2022). Ninety-five "second look" MRI-detected lesions were included in this retrospective study. Lesions were assessed according to margins, T2 signal, internal enhancement patterns, contrast kinetics, and diffusion-weighted imaging (DWI) patterns.

RESULTS

Fifty-two per cent of the included lesions were confirmed at histopathology to be malignant. The most common contrast kinetics identified in malignant lesions was the plateau pattern followed by the washout pattern while the most common pattern in benign lesions was the progressive pattern. The apparent diffusion coefficient (ADC) cut-off value for separating benign and malignant lesions at the unit was found to be 1.1 × 10-3 mm2/s. Based on the MRI features described above, a scoring system is suggested to help differentiate benign from malignant "second-look" lesions. According to the present results, setting a score of 2 or more points as an indication for biopsy was 100% reliable in identifying malignant lesions and avoiding biopsies in >30% of lesions.

CONCLUSION

The suggested scoring system could avoid biopsy of >30% of the "second-look" lesions detected by MRI without missing any malignant lesions.

Histogram analysis of breast diffusion kurtosis imaging: a comparison between readout-segmented and single-shot echo-planar imaging sequence

Background

Histogram analysis of the diffusion-weighted imaging (DWI) parameters is widely used to differentiate the breast lesions. However, histogram analysis of the diffusion-kurtosis imaging (DKI) parameters for the single-shot echo-planar imaging (ss-EPI) and readout-segmented echo planar imaging (rs-EPI) sequences has not been compared in breast cancer. Thus, this study is to investigate the diagnostic accuracy and reliability of the histogram parameters derived from the rs-EPI and ss-EPI sequences of DKI parameters in distinguishing between the benign and malignant breast lesions.

Methods

This single-center, retrospective cohort study enrolled 205 consecutive patients with breast lesions (65 benign and 140 malignant). The patients underwent breast magnetic resonance imaging (MRI) with a 3T scanner using the rs-EPI and ss-EPI sequences with 4 b values (0, 50, 1,000, and 2,000 s/mm²). The regions of interest (ROIs) were manually delineated for all the lesion images from both the sequences, and the histogram parameters were extracted from the apparent diffusion coefficient (ADC) and apparent diffusional kurtosis (K_app) maps. Statistical analysis was performed using the Kolmogorov-Smirnov test, the student's t-test, and the receiver operating characteristic (ROC) curves.

Results

The mean, 25th, 50th, 75th, and 100th percentiles, skewness, and kurtosis values derived from apparent diffusion for non-Gaussian distribution (D_app) and K_app maps showed good or excellent intra-observer agreement (ICC: 0.695 to 0.863).The mean and the 25th, 50th, 75th, and 100th percentile values for D_app were significantly lower and the mean and the 25th, 50th, 75th, and 100th percentile values for K_app were significantly higher in the malignant breast lesions compared with those in the benign breast lesions for both the rs-EPI and ss-EPI sequences (all P<0.05). The majority of the histogram K_app and D_app parameters (except skewness and kurtosis) for the benign and malignant lesions showed significant differences between the ss-EPI and the rs-EPI sequences (P<0.05). ROC curve analysis showed that the AUC values for the 75th percentile of K_app (0.854 for rs-EPI, 0.844 for ss-EPI) and the 25th percentile of D_app (0.866 for rs-EPI, 0.858 for ss-EPI) were highest for both DKI sequences. The diagnostic performance of the rs-EPI sequence was better than the ss-EPI sequence for all the histogram parameters except the skewness value of D_app.

Conclusions

Histogram parameters from the rs-EPI sequence were more reliable and accurate in differentiating malignant and benign breast lesions than those from the ss-EPI sequence.