3D T2-weighted spin echo imaging in the breast

Quantitative measurement of mammographic density in breast-tissue explants using portable NMR: Precision and accuracy

PURPOSE

Single-sided portable NMR (pNMR) has previously been demonstrated to be suitable for quantification of mammographic density (MD) in excised breast tissue samples. Here we investigate the precision and accuracy of pNMR measurements of MD ex vivo as compared with the gold standards.

METHODS

Forty-five breast-tissue explants from 9 prophylactic mastectomy patients were measured. The relative tissue water content was taken as the MD-equivalent quantity. In each sample, the water content was measured using some combination of three pNMR techniques (apparent T2, diffusion, and T1 measurements) and two gold-standard techniques (computed microtomography [μCT] and hematoxylin and eosin [H&E] histology). Pairwise correlation plots and Bland-Altman analysis were used to quantify the degree of agreement between pNMR techniques and the gold standards.

RESULTS

Relative water content measured from both apparent T2 relaxation spectra, and diffusion decays exhibited strong correlation with the H&E and μCT results. Bland-Altman analysis yielded average bias values of -0.4, -2.6, 2.6, and 2.8 water percentage points (pp) and 95% confidence intervals of 13.1, 7.5, 11.2, and 11.8 pp for the H&E - T2, μCT - T2, H&E - diffusion, and μCT - diffusion comparison pairs, respectively. T1-based measurements were found to be less reliable, with the Bland-Altman confidence intervals of 27.7 and 33.0 pp when compared with H&E and μCT, respectively.

CONCLUSION

Apparent T2-based and diffusion-based pNMR measurements enable quantification of MD in breast-tissue explants with the precision of approximately 10 pp and accuracy of approximately 3 pp or better, making pNMR a promising measurement modality for radiation-free quantification of MD.

Application of Texture and Volume Model Analysis to Dedicated Axillary High-resolution 3D T2-weighted MR Imaging: A Novel Method for Diagnosing Lymph Node Metastasis in Patients with Clinically Node-negative Breast Cancer

PURPOSE

To evaluate the effectiveness of the texture analysis of axillary high-resolution 3D T2-weighted imaging (T2WI) in distinguishing positive and negative lymph node (LN) metastasis in patients with clinically node-negative breast cancer.

METHODS

Between December 2017 and May 2021, 242 consecutive patients underwent high-resolution 3D T2WI and were classified into the training (n = 160) and validation cohorts (n = 82). We performed manual 3D segmentation of all visible LNs in axillary level I to extract the texture features. As the additional parameters, the number of the LNs and the total volume of all LNs for each case were calculated. The least absolute shrinkage and selection operator algorithm and Random Forest were used to construct the models. We constructed the texture model using the features from the LN with the largest least axis length in the training cohort. Furthermore, we constructed the 3 models combining the selected texture features of the LN with the largest least axis length, the number of LNs, and the total volume of all LNs: texture-number model, texture-volume model, and texture-number-volume model. As a conventional method, we manually measured the largest cortical diameter. Moreover, we performed the receiver operating curve analysis in the validation cohort and compared area under the curves (AUCs) of the models.

RESULTS

The AUCs of the texture model, texture-number model, texture-volume model, texture-number-volume model, and conventional method in the validation cohort were 0.7677, 0.7403, 0.8129, 0.7448, and 0.6851, respectively. The AUC of the texture-volume model was higher than those of other models and conventional method. The sensitivity, specificity, positive predictive value, and negative predictive value of the texture-volume model were 90%, 69%, 49%, and 96%, respectively.

CONCLUSION

The texture-volume model of high-resolution 3D T2WI effectively distinguished positive and negative LN metastasis for patients with clinically node-negative breast cancer.

Accelerated 3D high-resolution T2-weighted breast MRI with deep learning constrained compressed sensing, comparison with conventional T2-weighted sequence on 3.0 T

PURPOSE

To evaluate the feasibility of isotropic 3D high-resolution T2-weighted imaging (T2WI) MRI sequences and compare the images reconstructed by integrating artificial intelligence-compressed sensing (AI-CS), compressed sensing (CS), and conventional 2D T2WI sequences for quality.

MATERIALS AND METHODS

Fifty-two female patients (ages: 26-80 years) with suspected breast cancer were enrolled. They underwent breast MRI examinations using three sequences: conventional T2WI, CS 3D T2WI, and AI-CS 3D T2WI. Image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio, tumor volume, and maximal tumor diameter were compared using the Friedman test. Image quality was scored on a 5-point scale, with 1 indicating nonassessable quality and 5 indicating excellent quality. Tumor volume and maximal tumor diameter were compared based on AI-CS 3D T2WI (slightly high signal), conventional T2WI, and dynamic contrast-enhanced (DCE) sequences.

RESULTS

All three T2WI were successfully performed in all patients. 3D CS and AI-CS were significantly better than conventional T2WI in terms of lesion conspicuity and morphology, structural details, overall image quality, diagnostic information for breast lesions, and breast tissue delineation (P < 0.001). The SNR of conventional T2WI was significantly higher for 3D T2WI sequences. The contrast-to-noise ratio was significantly higher for AI-CS 3D T2WI than for conventional T2WI sequence. There was no significant difference in tumor volume between DCE (8.08 ± 16.51) and AI-CS 3D T2WI (8.25 ± 16.29) sequences and no significant differences in tumor diameter among DCE, AI-CS 3D T2WI, and conventional T2WI sequences.

CONCLUSION

Isotropic-resolution 3D T2WI sequences can be acquired using AI-CS while maintaining image quality and diagnostic value, which may pave the way for isotropic 3D high-resolution T2WI for clinical application.

Solitary, Well-Circumscribed, T2 Hyperintense Masses on MRI Have Very Low Malignancy Rates

OBJECTIVE

The purpose of this study was to determine the malignancy rate of solitary MRI masses with benign BI-RADS descriptors.

METHODS

A retrospective review was conducted of all breast MRI reports that described a mass with a final BI-RADS assessment of 3, 4, or 5, from February 1, 2005, through February 28, 2014 (n = 1510). Studies were excluded if the mass was not solitary, did not meet formal criteria for a mass, or had classically suspicious BI-RADS features (e.g., washout kinetics, and spiculated margin). The masses were reviewed by 2 fellowship-trained breast radiologists who reported consensus BI-RADS mass margin, shape, internal-enhancement, and kinetics descriptors. The T2 signal was reported as hyperintense if equal to or greater than the signal intensity of the axillary lymph nodes. Pathology results or 2 years of imaging follow-up were recorded. Comparisons were made between mass descriptors and clinical outcomes.

RESULTS

There were 127 women with 127 masses available for analysis. There were 76 (60%) masses that underwent biopsy for an overall malignancy rate of 4% (5/127): 2 ductal carcinoma in situ (DCIS) and 3 invasive ductal carcinoma. The malignancy rate was 2% (1/59) for T2 hyperintense solitary masses. The malignancy rate was greater than 2% for all of the following BI-RADS descriptors: oval (3%, 3/88), round (5%, 2/39), circumscribed (4%, 5/127), homogeneous (4%, 3/74), and dark internal septations (4%, 2/44).

CONCLUSION

T2 hyperintense solitary masses without associated suspicious features have a low malignancy rate, and they could be considered for a BI-RADS 3 final assessment.

Hypoenhancing prostate cancers on dynamic contrast-enhanced MRI are associated with poor outcomes in high-risk patients: results of a hypothesis generating study

PURPOSE

To investigate the association of hypoenhancement on dynamic Contrast enhanced (DCE) with prostate cancer patient outcomes.

MATERIAL AND METHODS

This was a single-institution retrospective Institutional Review Board (IRB)-approved cohort study of 54 men who had prostate Magnetic Resonance Imaging (MRI) within 6 months of cancer diagnosis between 01/2012 to 03/2014. Two readers independently identified the dominant MRI-lesions utilizing Prostate Imaging-Reporting and Data System-version2- guidelines. These lesions were classified as hypoenhancing or hyperenhancing, compared to normal peripheral zone using quantitative DCE analysis. The t test for unequal sample sizes and the two-sample Wilcoxon rank-sum tests were used to compare groups. Logistic regression determined if DCE characteristics predict the development of metastases or prostate cancer death.

RESULTS

Time-to-progression was significantly shorter for hypoenhancing tumors (6.2 vs. 24.8 months, p = 0.05). Men with these lesions had a higher odds of having poor outcome (univariate logistic regression, odds ratio (OR) 6.79, 95% confidence interval (CI) 1.45-31.72, p = 0.02; multivariate analysis, OR 2.05, 95% CI 0.30-13.72, p = 0.47). Hypoenhancing tumors were larger (33.1 vs. 19.1 mm, p < 0.001) and more likely to be intermediate (Gleason scores 3 + 4 and 4 + 3) and high-grade (Gleason scores ≥ 4 + 4) prostate cancers (p = 0.05). Men in the hypoenhancing group had a higher mean prostate-specific antigen (PSA) value (87.6 vs. 24.8 ng/dL, p = 0.01) and PSA density (1.54 vs. 0.72, p = 0.03). The mean K_trans and k_ep of hypoenhancing lesion were lower when compared to hyperenhancing lesions (p = 0.03 and p = 0.04). V_e values did not differ (p = 0.25).

CONCLUSION

Men with hypoenhancing prostate cancers may have a worse prognosis than men with hyperenhancing tumors.

Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy

Background

The MRI Breast Imaging‐Reporting and Data System (BI‐RADS) lexicon recommends that a breast MRI protocol contain T₂‐weighted and dynamic contrast‐enhanced (DCE) MRI sequences. The addition of diffusion‐weighted imaging (DWI) significantly improves diagnostic accuracy. This study aims to clarify which descriptors from DCE‐MRI, DWI, and T₂‐weighted imaging are most strongly associated with a breast cancer diagnosis.

Purpose/Hypothesis

To develop a multiparametric MRI (mpMRI) model for breast cancer diagnosis incorporating American College of Radiology (ACR) BI‐RADS recommended descriptors for breast MRI with DCE, T₂‐weighted imaging, and DWI with apparent diffusion coefficient (ADC) mapping.

Study Type

Retrospective.

Subjects

In all, 188 patients (mean 51.6 years) with 210 breast tumors (136 malignant and 74 benign) who underwent mpMRI from December 2010 to September 2014.

Field Strength/Sequence

IR inversion recovert DCE‐MRI dynamic contrast‐enhanced magnetic resonance imaging VIBE Volume‐Interpolated‐Breathhold‐Examination FLASH turbo fast‐low‐angle‐shot TWIST Time‐resolved angiography with stochastic Trajectories.

Assessment

Two radiologists in consensus and another radiologist independently evaluated the mpMRI data. Characteristics for mass (n = 182) and nonmass (n = 28) lesions were recorded on DCE and T₂‐weighted imaging according to BI‐RADS, as well as DWI descriptors. Two separate models were analyzed, using DCE‐MRI BI‐RADS descriptors, T₂‐weighted imagines, and ADCmean as either a continuous or binary form using a previously published ADC cutoff value of ≤1.25 × 10⁻³ mm²/sec for differentiation between benign and malignant lesions. Histopathology was the standard of reference.

Statistical Tests

χ² test, Fisher's exact test, Kruskal–Wallis test, Pearson correlation coefficient, multivariate logistic regression analysis, Hosmer–Lemeshow test of goodness‐of‐fit, receiver operating characteristics analysis.

Results

In Model 1, ADCmean (P = 0.0031), mass margins with DCE (P = 0.0016), and delayed enhancement with DCE (P = 0.0016) were significantly and independently associated with breast cancer diagnosis; Model 2 identified ADCmean (P = 0.0031), mass margins with DCE (P = 0.0012), initial enhancement (P = 0.0422), and delayed enhancement with DCE (P = 0.0065) to be significantly independently associated with breast cancer diagnosis. T₂‐weighted imaging variables were not included in the final models.

Data Conclusion

mpMRI with DCE‐MRI and DWI with ADC mapping enables accurate breast cancer diagnosis. A model using quantitative and qualitative descriptors from DCE‐MRI and DWI identifies breast cancer with a high diagnostic accuracy. T₂‐weighted imaging does not significantly contribute to breast cancer diagnosis.

Level of Evidence: 3

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2019;49:864–874.

Using quantitative features extracted from T2-weighted MRI to improve breast MRI computer-aided diagnosis (CAD)

Computer-aided diagnosis (CAD) has been proposed for breast MRI as a tool to standardize evaluation, to automate time-consuming analysis, and to aid the diagnostic decision process by radiologists. T2w MRI findings are diagnostically complementary to T1w DCE-MRI findings in the breast and prior research showed that measuring the T2w intensity of a lesion relative to a tissue of reference improves diagnostic accuracy. The diagnostic value of this information in CAD has not been yet quantified. This study proposes an automatic method of assessing relative T2w lesion intensity without the need to select a reference region. We also evaluate the effect of adding this feature to other T2w and T1w image features in the predictive performance of a breast lesion classifier for differential diagnosis of benign and malignant lesions. An automated feature of relative T2w lesion intensity was developed using a quantitative regression model. The diagnostic performance of the proposed feature in addition to T2w texture was compared to the performance of a conventional breast MRI CAD system based on T1w DCE-MRI features alone. The added contribution of T2w features to more conventional T1w-based features was investigated using classification rules extracted from the lesion classifier. After institutional review board approval that waived informed consent, we identified 627 breast lesions (245 malignant, 382 benign) diagnosed after undergoing breast MRI at our institution between 2007 and 2014. An increase in diagnostic performance in terms of area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was observed with the additional T2w features and the proposed quantitative feature of relative T2w lesion intensity. AUC increased from 0.80 to 0.83 and this difference was statistically significant (adjusted p-value = 0.020).

Takeaways from Pre-Contrast T1 and T2 Breast Magnetic Resonance Imaging in Women with Recently Diagnosed Breast Cancer

Background

Dynamic contrast-enhanced magnetic resonance imaging (DCE - MRI) has been widely used in the management of breast cancer, and its diagnostic value in breast imaging has been demonstrated. There have only been a few reports regarding the usefulness of pre-contrast imaging. Knowledge about clinically significant findings of preoperative, pre-contrast T1 and T2 MR images will allow more accurate decisions regarding patient treatment and management.

Objectives

The aim of this study was to evaluate the clinically significant findings of preoperative, pre-contrast T1 and T2 MR images in recently diagnosed breast cancer patients.

Patients and Methods

We analyzed 390 preoperative 3-T MRIs of recently diagnosed breast cancer patients in whom the diagnosis was confirmed by a core needle biopsy.

Results

MRI findings that were correlated with post-core needle-biopsy changes were observed in 27.9% of the pre-contrast T1 and T2 MRIs (n = 109/390). Two of 35 cases that had a subareolar ductal high signal area on the pre-contrast T1 were confirmed by surgery as having nipple-areolar complex involvement.

Conclusion

A subareolar ductal high signal area on a pre-contrast T1 MRI must be carefully assessed in combination with dynamic, contrast-enhanced images for proper surgical management.

Rim sign in breast lesions on diffusion-weighted magnetic resonance imaging: diagnostic accuracy and clinical usefulness

PURPOSE

To investigate the diagnostic accuracy and clinical usefulness of the rim sign in breast lesions observed in diffusion-weighted magnetic resonance imaging (DWI).

MATERIALS AND METHODS

The magnetic resonance imaging (MRI) findings of 98 pathologically confirmed lesions (62 malignant and 36 benign) in 84 patients were included. Five breast radiologists were asked to independently review the breast MRI results, to grade the degree of high peripheral signal, the "rim sign," in the DWI, and to confirm the mean apparent diffusion coefficient (ADCmean ) values. We analyzed the diagnostic accuracy and compared the consensus (when ≥ 4 of 5 independent reviewers agreed) results of the rim sign with the ADCmean values. Additionally, we evaluated the correlation between the dynamic contrast-enhanced (DCE)-MRI morphologic appearance and DWI rim sign.

RESULTS

According to the consensus results, the rim sign in DWI was observed on 59.7% of malignant lesions and 19.4% of benign lesions. The sensitivity, specificity, and area under the curve (AUC) value for the rim sign in DWI were 59.7%, 80.6%, and 0.701, respectively. The sensitivity, specificity, and AUC value for the ADCmean value (criteria ≤ 1.46 × 10(-3) mm(2) /sec) were 82.3%, 63.9%, and 0.731, respectively. Based on consensus, no correlation was observed between the DCE-MRI and DWI rim signs.

CONCLUSION

In DWI, a high-signal rim is a valuable morphological feature for improving specificity in DWI.