Role of diffusion weighted images combined with breast MRI in improving the detection and differentiation of breast lesions

Accuracy of high b-value diffusion-weighted imaging in identifying benign and malignant breast lesions: a systematic review and meta-analysis

OBJECTIVE

To evaluate the diagnostic accuracy of high b-value diffusion-weighted imaging (DWI) in differentiating malignant from benign breast lesions.

METHODS

A comprehensive literature search was conducted across PubMed, Embase, Cochrane Library, Scopus, and Web of Science for English-language studies on high b-value DWI in breast lesions, from inception to June 2024. Study quality was assessed, and data were extracted. Heterogeneity analysis, forest plots, Deek's funnel plots, and summary receiver operating characteristic (SROC) curves were generated using Stata 16.0 and Meta-Disc 1.4 software. Meta-regression identified sources of heterogeneity.

RESULTS

The meta-analysis included 12 studies with 1,747 patients and 1,861 breast lesions (1,000 malignant, 861 benign). Pooled diagnostic metrics were: sensitivity, 0.91 (95% CI: 0.87-0.94); specificity, 0.93 (95% CI: 0.87-0.96); positive likelihood ratio, 12.21 (95% CI: 6.91-21.57); negative likelihood ratio, 0.09 (95% CI: 0.06-0.14); diagnostic odds ratio, 130.75 (95% CI: 56.95-300.21); and AUC, 0.97 (95% CI: 0.95-0.98).

CONCLUSION

High b-value DWI has high diagnostic accuracy in differentiating between benign and malignant breast lesions, demonstrating potential as a reliable imaging marker.

REGISTRATION

PROSPERO (CRD42024568777).

Multimodality imaging review of metastatic melanoma involving the breast

Melanoma is among the most commonly reported non-mammary primary tumors to metastasize to the breast. Unfortunately, evidence of melanoma metastasis to any site portends a poor prognosis. Imaging studies can be useful in the early detection of metastatic melanoma which is essential for appropriate management of this disease. There have been very few previous studies on the imaging findings of metastatic melanoma especially across multiple imaging modalities. This review aims to describe these imaging features seen on mammography, ultrasound, magnetic resonance imaging (MRI) and fluorodeoxyglucose-positron emission tomography computed tomography (FDG PET/CT) using three case examples. Our findings, consistent with previous studies, describe melanoma metastases to the breast as largely non-specific, round or oval masses with circumscribed margins and homogeneous internal enhancement.

Diagnostic efficiency of multi-modal MRI based deep learning with Sobel operator in differentiating benign and malignant breast mass lesions-a retrospective study

Purpose

To compare the diagnostic efficiencies of deep learning single-modal and multi-modal for the classification of benign and malignant breast mass lesions.

Methods

We retrospectively collected data from 203 patients (207 lesions, 101 benign and 106 malignant) with breast tumors who underwent breast magnetic resonance imaging (MRI) before surgery or biopsy between January 2014 and October 2020. Mass segmentation was performed based on the three dimensions-region of interest (3D-ROI) minimum bounding cube at the edge of the lesion. We established single-modal models based on a convolutional neural network (CNN) including T2WI and non-fs T1WI, the dynamic contrast-enhanced (DCE-MRI) first phase was pre-contrast T1WI (d1), and Phases 2, 4, and 6 were post-contrast T1WI (d2, d4, d6); and Multi-modal fusion models with a Sobel operator (four_mods:T2WI, non-fs-T1WI, d1, d2). Training set (n = 145), validation set (n = 22), and test set (n = 40). Five-fold cross validation was performed. Accuracy, sensitivity, specificity, negative predictive value, positive predictive value, and area under the ROC curve (AUC) were used as evaluation indicators. Delong's test compared the diagnostic performance of the multi-modal and single-modal models.

Results

All models showed good performance, and the AUC values were all greater than 0.750. Among the single-modal models, T2WI, non-fs-T1WI, d1, and d2 had specificities of 77.1%, 77.2%, 80.2%, and 78.2%, respectively. d2 had the highest accuracy of 78.5% and showed the best diagnostic performance with an AUC of 0.827. The multi-modal model with the Sobel operator performed better than single-modal models, with an AUC of 0.887, sensitivity of 79.8%, specificity of 86.1%, and positive prediction value of 85.6%. Delong's test showed that the diagnostic performance of the multi-modal fusion models was higher than that of the six single-modal models (T2WI, non-fs-T1WI, d1, d2, d4, d6); the difference was statistically significant (p = 0.043, 0.017, 0.006, 0.017, 0.020, 0.004, all were greater than 0.05).

Conclusions

Multi-modal fusion deep learning models with a Sobel operator had excellent diagnostic value in the classification of breast masses, and further increase the efficiency of diagnosis.

Diagnostic role of dynamic contrast-enhanced magnetic resonance imaging in differentiating breast lesions

OBJECTIVE

This study aimed to assess the diagnostic role of perfusion weighted image (DCE-PWI) to differentiate benign from malignant breast lesions.

PATIENTS AND METHODS

The study comprised 32 women who had mammography and/or breast ultrasonography findings that were clinically questionable. All patients were fasting during the magnetic resonance imaging (MRI) test to avoid nausea or dynamic contrast-enhanced vomiting from the contrast medium.

RESULT

In this study, we observed the form of the dynamic curve (time and signal intensity curve) type I (persistent curve) was noted in 12 lesions (37.5%): 10 lesions were benign and two lesions were malignant; type II (plateau curve) was noted in eight lesions (25%): three lesions were benign and five lesions were malignant, and type III (washout curve) noted in 12 lesions (37.5%): one lesion was benign and 11 lesions were malignant.

CONCLUSIONS

The dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) perfusion technique plays an important role in differentiating benign and malignant tumors in breast cancer.

Quantitative Evaluation for Differential Diagnosis of Breast Lesions in Diffusion-Weighted MR Imaging

Diffusion-weighted MR Imaging is a rapidly emerging technique, that allows in-vivo mapping processes of the water diffusion in tissues. It has the potential capabilities for clinical application in breast imaging. The aim of this study was to find out the optimal b-value for calculation of ADC value for differential diagnosis of breast lesions. A total of 124 subjects (mean age 46 years) with 141 lesions were included. The protocol consists of axial T2 sequence for lesion localization and measurement and DW sequence with three sets of b-values of 0, 300, 600, and 1000 s/mm2. The mean ADC values of the breast lesions for b-values (0, 300, 600, and 1000) were 1.75 ± 0.18 × 10−3mm2/sec, 1.66 ± 0.12 × 10−3mm2/sec and 1.57 ± 0.15 × 10−3mm2/sec for the benign lesions and 1.26 ± 0.048 × 10−3mm2/sec, 1.14 ± 0.11 × 10−3mm2/sec and 0.93 ± 0.14 × 10−3mm2/sec for malignant lesions respectively. Statistical significant differences were noted on the ADC value of benign and malignant lesions among the three sets of b values (p = 0.001). ADC values of malignant lesion was significantly lower compared to benign lesions. The AUC (0.998) was substantially large for b-value of 0,600 s/mm2 with a threshold ADC cut off value of 1.28 × 10−3mm2/sec with 98.4% sensitivity, 93.2% specificity and 98.5% positive predictive value(PPV). In conclusion, diffusion weighted imaging has the ability for differential diagnosis of breast lesions with the optimal b value of 0,600 s/ mm2. DWI is a reliable tool for characterising breast lesions and may increase the overall specificity of breast MRI.

Accuracy of multiparametric MRI in distinguishing the breast malignant lesions from benign lesions: a meta-analysis

BACKGROUND

The sensitivity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for detecting breast cancer was high and the specificity was relatively low. However, diffusion-weighted imaging (DWI) has a high specificity in the diagnosis of malignant lesions.

PURPOSE

To evaluate the accuracy of the multiparametric MRI (mp-MRI) in distinguishing the breast malignant lesions from the benign lesions.

MATERIAL AND METHODS

A comprehensive search of the PubMed, Embase, and Cochrane Library electronic databases was conducted up to March 2020. Data were analyzed for the following indexes: pooled sensitivity and specificity; positive likelihood ratio; negative likelihood ratio; diagnostic odds ratio; and the area under the curve.

RESULTS

A total of 2356 patients with 1604 malignant and 967 benign breast lesions were included from 22 studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve for mp-MRI were 0.93, 0.85, 6.3, 0.08, 81, and 0.96, respectively. The pooled sensitivity, specificity, and area under the curve for DCE-MRI alone were 0.95, 0.71, and 0.92, respectively. The pooled sensitivity, specificity, and area under the curve for DWI alone were 0.88, 0.84, and 0.93, respectively.

CONCLUSION

The mp-MRI did not improve the sensitivity but increased the specificity for the diagnosis of breast malignant lesions.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Sinomenine restrains breast cancer cells proliferation, migration and invasion via modulation of miR-29/PDCD-4 axis

Sinomenine (Sino) is diffusely applied in heal rheumatoid arthritis and neuralgia. Howbeit, the activities of Sino in breast cancer cells remain confused. The research attempted to probe the anti-tumor function of Sino in breast cancer cells and divulge the feasible molecular mechanism. Sion at the 1-16 μM concentrations was exploited for the exposure of MDA-MB-231 or MCF7 cells, and cell growth, migration, invasion, cell cycle-relevant and apoptosis-correlative factors were estimated. Micro RNA (miR)-29 expression was evaluated via enforcing qRT-PCR, and the actions of miR-29 in MDA-MB-231 cells growth, migration and invasion were appraised after the overexpressed or suppressed vectors transfection. The functions of PDCD-4 in JNK and MEK/ERK pathways were estimated by employing western blot. We found that, Sino exposure impeded cell proliferation, provoked cell apoptosis and barricaded cell migration and invasion in MDA-MB-231 and MCF7 cells. Enhancement of miR-29 was observed in Sino-managed cells, and miR-29 overexpression further potentiated the activities of Sino in MDA-MB-231 cells. Additionally, Sino remarkably enhanced PCDC-4 expression via adjusting miR-29 in MDA-MB-231 cells. Beyond that, overexpressed PCDC-4 obstructed JNK and MEK/ERK pathways in MDA-MB-231 cells. Taken together, the explorations unveiled that Sino restrained MDA-MB-231 cells proliferation, migration, invasion, and provoked apoptosis through modulation of miR-29/PDCD-4 axis. Highlight Sino inhibits MDA-MB-231 and MCF7 cells proliferation and provokes apoptosis; Sino restrains MDA-MB-231 and MCF7 cells migration and invasion; Sino ascends miR-29 expression in MDA-MB-231 and MCF7 cells; Sino adjusts cell growth, migration and invasion via modulating miR-29; Sino up-regulates PDCD-4 expression through mediating miR-29; PDCD-4 obstructs JNK and MEK/ERK pathways in MDA-MB-231 cells.

Fat Necrosis of the Breast: Magnetic Resonance Imaging Characteristics and Pathologic Correlation

RATIONALE AND OBJECTIVES

This study aims to describe the magnetic resonance imaging (MRI) features of fat necrosis on magnetic resonance mammography, which may downstage a suspicious lesion to a merely benign finding.

MATERIALS AND METHODS

This prospective study included 82 female patients (mean age 50 years) who were diagnosed to have suspicious lesions by mammography, ultrasonography or both. All patients underwent MRI including diffusion-weighted imaging and spectroscopy. Image postprocessing and analysis included signal intensity, enhancement characteristics, diffusion restriction, and spectroscopic analysis. All patients underwent histopathological analysis for confirmation. Sensitivity, specificity, positive predictive value (PPV), and negative (NPV) predictive value were calculated.

RESULTS

To label a lesion as fat necrosis on MRI analysis, presence of fat signal in a lesion revealed sensitivity of 98.04%, specificity of 100%, PPV of 100%, and NPP of 96.88%, whereas nonenhancement of the lesion itself revealed sensitivity of 96.08%, specificity of 100%, PPV of 100%, and NPP of 93.94%. However, adding both the nonrestriction on diffusion analysis and the lack of tCholine at 3.22 ppm increased the sensitivity and specificity to 100%, as well as PPV of 100% for fat necrosis and hence a NPV for malignancy of 100%.

CONCLUSIONS

MRI proved to be of value in differentiating fat necrosis from malignancy based on the molecular composition of fat necrosis, clearly depicted by MRI without the need for invasive confirmation by biopsy.