The effectiveness of an ultrafast breast MRI protocol in the differentiation of benign and malignant breast lesions

The diagnostic performance of ultrafast MRI to differentiate benign from malignant breast lesions: a systematic review and meta-analysis

OBJECTIVES

To assess the diagnostic performance of ultrafast magnetic resonance imaging (UF-DCE MRI) in differentiating benign from malignant breast lesions.

MATERIALS AND METHODS

A comprehensive search was conducted until September 1, 2023, in Medline, Embase, and Cochrane databases. Clinical studies evaluating the diagnostic performance of UF-DCE MRI in breast lesion stratification were screened and included in the meta-analysis. Pooled summary estimates for sensitivity, specificity, diagnostic odds ratio (DOR), and hierarchic summary operating characteristics (SROC) curves were pooled under the random-effects model. Publication bias and heterogeneity between studies were calculated.

RESULTS

A final set of 16 studies analyzing 2090 lesions met the inclusion criteria and were incorporated into the meta-analysis. Using UF-DCE MRI kinetic parameters, the pooled sensitivity, specificity, DOR, and area under the curve (AUC) for differentiating benign from malignant breast lesions were 83% (95% CI 79-88%), 77% (95% CI 72-83%), 18.9 (95% CI 13.7-26.2), and 0.876 (95% CI 0.83-0.887), respectively. We found no significant difference in diagnostic accuracy between the two main UF-DCE MRI kinetic parameters, maximum slope (MS) and time to enhancement (TTE). DOR and SROC exhibited low heterogeneity across the included studies. No evidence of publication bias was identified (p = 0.585).

CONCLUSIONS

UF-DCE MRI as a stand-alone technique has high accuracy in discriminating benign from malignant breast lesions.

CLINICAL RELEVANCE STATEMENT

UF-DCE MRI has the potential to obtain kinetic information and stratify breast lesions accurately while decreasing scan times, which may offer significant benefit to patients.

KEY POINTS

• Ultrafast breast MRI is a novel technique which captures kinetic information with very high temporal resolution. • The kinetic parameters of ultrafast breast MRI demonstrate a high level of accuracy in distinguishing between benign and malignant breast lesions. • There is no significant difference in accuracy between maximum slope and time to enhancement kinetic parameters.

Ultrafast Dynamic Contrast-Enhanced MRI of the Breast: From Theory to Practice

The development of ultrafast dynamic contrast-enhanced (UF-DCE) MRI has occurred in tandem with fast MRI scan techniques, particularly view-sharing and compressed sensing. Understanding the strengths of each technique and optimizing the relevant parameters are essential to their implementation. UF-DCE MRI has now shifted from research protocols to becoming a part of clinical scan protocols for breast cancer. UF-DCE MRI is expected to compensate for the low specificity of abbreviated MRI by adding kinetic information from the upslope of the time-intensity curve. Because kinetic information from UF-DCE MRI is obtained from the shape and timing of the initial upslope, various new kinetic parameters have been proposed. These parameters may be associated with receptor status or prognostic markers for breast cancer. In addition to the diagnosis of malignant lesions, more emphasis has been placed on predicting and evaluating treatment response because hyper-vascularity is linked to the aggressiveness of breast cancers. In clinical practice, it is important to note that breast lesion images obtained from UF-DCE MRI are slightly different from those obtained by conventional DCE MRI in terms of morphology. A major benefit of using UF-DCE MRI is avoidance of the marked or moderate background parenchymal enhancement (BPE) that can obscure the target enhancing lesions. BPE is less prominent in the earlier phases of UF-DCE MRI, which offers better lesion-to-noise contrast. The excellent contrast of early-enhancing vessels provides a key to understanding the detailed pathological structure of tumor-associated vessels. UF-DCE MRI is normally accompanied by a large volume of image data for which automated/artificial intelligence-based processing is expected to be useful. In this review, both the theoretical and practical aspects of UF-DCE MRI are summarized. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Thyroid imaging reporting and data system with MRI morphological features for thyroid nodules: diagnostic performance and unnecessary biopsy rate

BACKGROUND

To assess MRI-based morphological features in improving the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) for categorizing thyroid nodules.

METHODS

A retrospective analysis was performed on 728 thyroid nodules (453 benign and 275 malignant) that postoperative pathology confirmed. Univariate and multivariate logistic regression analyses were used to find independent predictors of MRI morphological features in benign and malignant thyroid nodules. The improved method involved increasing the ACR-TIRADS level by one when there are independent predictors of MRI-based morphological features, whether individually or in combination, and conversely decreasing it by one. The study compared the performance of conventional ACR-TIRADS and different improved versions.

RESULTS

Among the various MRI morphological features analyzed, restricted diffusion and reversed halo sign were determined to be significant independent risk factors for malignant thyroid nodules (OR = 45.1, 95% CI = 23.2-87.5, P < 0.001; OR = 38.0, 95% CI = 20.4-70.7, P < 0.001) and were subsequently included in the final assessment of performance. The areas under the receiver operating characteristic curves (AUCs) for both the conventional and four improved ACR-TIRADSs were 0.887 (95% CI: 0.861-0.909), 0.945 (95% CI: 0.926-0.961), 0.947 (95% CI: 0.928-0.962), 0.945 (95% CI: 0.926-0.961) and 0.951 (95% CI: 0.932-0.965), respectively. The unnecessary biopsy rates for the conventional and four improved ACR-TIRADSs were 62.8%, 30.0%, 27.1%, 26.8% and 29.1%, respectively, while the malignant missed diagnosis rates were 1.1%, 2.8%, 3.7%, 5.4% and 1.2%.

CONCLUSIONS

MRI morphological features with ACR-TIRADS has improved diagnostic performance and reduce unnecessary biopsy rate while maintaining a low malignant missed diagnosis rate.

Optimizing ultrafast dynamic contrast-enhanced MRI scan duration in the differentiation of benign and malignant breast lesions

OBJECTIVE

To determine the optimal scan duration for ultrafast DCE-MRI in effectively differentiating benign from malignant breast lesions.

METHODS

The study prospectively recruited participants who underwent breast ultrafast DCE-MRI from September 2021 to March 2023. A 30-phase breast ultrafast DCE-MRI on a 3.0-T MRI system was conducted with a 4.5-s temporal resolution. Scan durations ranged from 40.5 s to 135.0 s, during which the analysis is performed at three-phase intervals, forming eight dynamic sets (scan duration [SD]40.5s: 40.5 s, SD54s: 54.0 s, SD67.5s: 67.5 s, SD81s: 81.0 s, SD94.5s: 94.5 s, SD108s: 108.0 s, SD121.5s: 121.5 s, and SD135s: 135.0 s). Two ultrafast DCE-MRI parameters, maximum slope (MS) and initial area under the curve in 60 s (iAUC), were calculated for each dynamic set and compared between benign and malignant lesions. Areas under the receiver operating characteristic curve (AUCs) were used to assess their diagnostic performance.

RESULTS

A total of 140 women (mean age, 47 ± 11 years) with 151 lesions were included. MS and iAUC from eight dynamic sets exhibited significant differences between benign and malignant lesions (all p < 0.05), except iAUC at SD40.5s. The AUC of MS (AUC = 0.804) and iAUC (AUC = 0.659) at SD67.5s were significantly higher than their values at SD40.5s (AUC = 0.606 and 0.516; corrected p < 0.05). No significant differences in AUCs for MS and iAUC were observed from SD67.5s to SD135s (all corrected p > 0.05).

CONCLUSIONS

Ultrafast DCE-MRI with a 67.5-s scan duration appears optimal for effectively differentiating malignant from benign breast lesions.

CRITICAL RELEVANCE STATEMENT

By evaluating scan durations (40.5-135 s) and analyzing two ultrafast DCE-MRI parameters, we found a scan duration of 67.5 s optimal for discriminating between these lesions and offering a balance between acquisition time and diagnostic efficacy.

KEY POINTS

Ultrafast DCE-MRI can effectively differentiate malignant from benign breast lesions. A minimum of 67.5-sec ultrafast DCE-MRI scan duration is required to differentiate benign and malignant lesions. Extending the scan duration beyond 67.5 s did not significantly improve diagnostic accuracy.

An innovative breast cancer detection framework using multiscale dilated densenet with attention mechanism

Cancer-related deadly diseases affect both developed and underdeveloped nations worldwide. Effective network learning is crucial to more reliably identify and categorize breast carcinoma in vast and unbalanced image datasets. The absence of early cancer symptoms makes the early identification process challenging. Therefore, from the perspectives of diagnosis, prevention, and therapy, cancer continues to be among the healthcare concerns that numerous researchers work to advance. It is highly essential to design an innovative breast cancer detection model by considering the complications presented in the classical techniques. Initially, breast cancer images are gathered from online sources and it is further subjected to the segmentation region. Here, it is segmented using Adaptive Trans-Dense-Unet (A-TDUNet), and their parameters are tuned using the developed Modified Sheep Flock Optimization Algorithm (MSFOA). The segmented images are further subjected to the breast cancer detection stage and effective breast cancer detection is performed by Multiscale Dilated Densenet with Attention Mechanism (MDD-AM). Throughout the result validation, the Net Present Value (NPV) and accuracy rate of the designed approach are 96.719% and 93.494%. Hence, the implemented breast cancer detection model secured a better efficacy rate than the baseline detection methods in diverse experimental conditions.

Abbreviated breast magnetic resonance imaging (MRI) or digital breast tomosynthesis for breast cancer detection in dense breasts? A retrospective preliminary study with comparable results

AIM

To compare the diagnostic performance of abbreviated breast magnetic resonance (AB-MR) imaging (MRI) and digital breast tomosynthesis (DBT) for breast cancer detection in Malaysian women with dense breasts, using histopathology as the reference standard.

MATERIALS AND METHODS

This was a single-centre cross-sectional study of 115 women with American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BIRADS) breast density C and D on DBT with breast lesions who underwent AB-MR from June 2018 to December 2021. AB-MR was performed on a 3 T MRI system with an imaging protocol consisting of three sequences: axial T1 fat-saturated unenhanced; axial first contrast-enhanced; and subtracted first contrast-enhanced with maximum intensity projection (MIP). DBT and AB-MR images were evaluated by two radiologists blinded to the histopathology and patient outcomes. Diagnostic accuracy (sensitivity, specificity, positive predictive value [PPV] and negative predictive value [NPV]) was assessed.

RESULT

Of the 115 women, the mean age was 50.6 years. There were 48 (41.7%) Malay, 54 (47%) Chinese, and 12 (10.4%) Indian women. The majority (n=87, 75.7%) were from the diagnostic population. Sixty-one (53.1%) were premenopausal and 54 (46.9%) postmenopausal. Seventy-eight (72.4%) had an increased risk of developing breast cancer. Ninety-one (79.1%) women had density C and 24 (20.9%) had density D. There were 164 histopathology-proven lesions; 69 (42.1%) were malignant and 95 (57.9%) were benign. There were 62.8% (n=103/164) lesions detected at DBT. All the malignant lesions 100% (n=69) and 35.7% (n=34) of benign lesions were detected. Of the 61 lesions that were not detected, 46 (75.4%) were in density C, and 15 (24.6%) were in density D. The sensitivity, specificity, PPV, and NPV for DBT were 98.5%, 34.6%, 66.3%, and 94.7%, respectively. There were 65.2% (n=107/164) lesions detected on AB-MR, with 98.6% (n=68) malignant and 41.1% (39) benign lesions detected. The sensitivity, specificity, PPV, and NPV for AB-MR were 98.5%, 43.9%, 67.2%, and 96.2%, respectively. One malignant lesion (0.6%), which was a low-grade ductal carcinoma in-situ (DCIS), was missed on AB-MR.

CONCLUSION

The present findings suggest that both DBT and AB-MR have comparable effectiveness as an imaging method for detecting breast cancer and have high NPV for low-risk lesions in women with dense breasts.