Evaluation of Kinetic Entropy of Breast Masses Initially Found on MRI using Whole-lesion Curve Distribution Data: Comparison with the Standard Kinetic Analysis

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis

PURPOSE

To evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer.

METHODS

This prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses.

RESULTS

kep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944).

CONCLUSION

The inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.

Assessment of Enhancement Kinetics Improves the Specificity of Abbreviated Breast MRI: Performance in an Enriched Cohort

Objective: To investigate the added value of kinetic information for breast lesion evaluation on abbreviated breast MRI (AB-MRI). Methods: This retrospective study analyzed 207 breast lesions with Breast Imaging Reporting and Data System categories 3, 4, or 5 on AB-MRI in 198 consecutive patients who had breast MRI for screening after breast cancer surgery between January 2017 and December 2019. All lesions were pathologically confirmed or stable on follow-up images for 2 years or more. Kinetic information of the lesions regarding the degree and rate of enhancement on the first post-contrast-enhanced image and the enhancement curve type from two post-contrast-enhanced images were analyzed on a commercially available computer-assisted diagnosis system. The diagnostic performances of AB-MRI with morphological analysis alone and with the addition of kinetic information were compared using the McNemar test. Results: Of 207 lesions, 59 (28.5%) were malignant and 148 (71.5%) were benign. The addition of an enhancement degree of ≥90% to the morphological analysis significantly increased the specificity of AB-MRI (29.7% vs. 52.7%, p < 0.001) without significantly reducing the sensitivity (94.9% vs. 89.8%, p = 0.083) compared to morphological analysis alone. Unnecessary biopsy could have been avoided in 34 benign lesions, although three malignant lesions could have been missed. For detecting invasive cancer, adding an enhancement degree ≥107% to the morphological analysis significantly increased the specificity (26.5% vs. 57.6%, p < 0.001) without significantly decreasing the sensitivity (94.6% vs. 86.5%, p = 0.083). Conclusion: Adding the degree of enhancement on the first post-contrast-enhanced image to the morphological analysis resulted in higher AB-MRI specificity without compromising its sensitivity.

Can the delayed phase of quantitative contrast-enhanced mammography improve the diagnostic performance on breast masses?

Background

Contrast-enhanced mammography (CEM) is an imaging tool for breast cancer detection. Most quantitative analyses of CEM involve two phases, and it is unknown whether an added delayed phase can improve its diagnostic performance compared to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This study aimed to evaluate whether the delayed phase improves the diagnostic performance of CEM in distinguishing malignant and benign masses.

Methods

This prospective study enrolled 111 women with 111 pathologically confirmed breast masses. CEM was performed after the injection of contrast agent between 2-3 minutes (T1, early phase), 4-5 minutes (T2, second phase), and 7-9 minutes (T3, delayed phase). The quantitative enhanced gray value of lesions (LGV) and the lesion to background grey value ratio (LBR) were measured within each phase's corresponding region of interest (ROI). Based on their changes, the kinetic enhancement pattern was assessed among the three phases, and the diagnostic performance was subsequently measured.

Results

The LGV and LBR of malignant masses were significantly greater than those of benign lesions. The diagnostic performance of LGV and LBR at the delayed phase was consistent with that of the second phase but poorer than that of the early phase. The sensitivity of LGV_T1 + LGV_T2 + LGV_T3 was less than that of LGV_T1 + LGV_T2 (86.5% vs. 95.1%) with a similar area under the curve (AUC), specificity, positive-predictive value (PPV), negative-predictive value (NPV), and accuracy. The sensitivity of LBR_T1 + LBR_T2 + LBR_T3 increased by 19.6%, and specificity decreased by 20.7% compared with LBR_T1 + LBR_T2. The LGV_T1 + LGV_T2 + LGV_T3 + kinetic enhancement (T1-T3) had the lowest sensitivity (67.0%), but the highest specificity (75.8%), and the sensitivity of LBR_T1 + LBR_T2 + LBR_T3 + kinetic enhancement (T1-T3) was higher than that of LBR_T1 + LBR_T2 + kinetic enhancement (T1-T2) (90.2% vs. 63.4%, respectively).

Conclusions

The addition of a delayed CEM phase for breast cancer diagnosis yielded limited performance improvement. The quantitative analysis combined with enhancement patterns between the two consecutive phases has great potential to distinguish between malignant and benign lesions.

A Heterogeneity Radiomic Nomogram for Preoperative Differentiation of Primary Gastric Lymphoma From Borrmann Type IV Gastric Cancer

OBJECTIVE

This study aimed to preoperatively differentiate primary gastric lymphoma from Borrmann type IV gastric cancer by heterogeneity nomogram based on routine contrast-enhanced computed tomographic images.

METHODS

We enrolled 189 patients from 2 hospitals (90 in the training cohort and 99 in the validation cohort). Subjective findings, including high-enhanced mucosal sign, high-enhanced serosa sign, nodular or an irregular outer layer of the gastric wall, and perigastric fat infiltration, were assessed to construct a subjective finding model. A deep learning model was developed to segment tumor areas, from which 1680 three-dimensional heterogeneity radiomic parameters, including first-order entropy, second-order entropy, and texture complexity, were extracted to build a heterogeneity signature by least absolute shrinkage and selection operator logistic regression. A nomogram that integrates heterogeneity signature and subjective findings was developed by multivariate logistic regression. The diagnostic performance of the nomogram was assessed by discrimination and clinical usefulness.

RESULTS

High-enhanced serosa sign and nodular or an irregular outer layer of the gastric wall were identified as independent predictors for building the subjective finding model. High-enhanced serosa sign and heterogeneity signature were significant predictors for differentiating the 2 groups (all, P < 0.05). The area under the curve with heterogeneity nomogram was 0.932 (95% confidence interval, 0.863-0.973) in the validation cohort. Decision curve analysis and stratified analysis confirmed the clinical utility of the heterogeneity nomogram.

CONCLUSIONS

The proposed heterogeneity radiomic nomogram on contrast-enhanced computed tomographic images may help differentiate primary gastric lymphoma from Borrmann type IV gastric cancer preoperatively.

Percentage volume of delayed kinetics in computer-aided diagnosis of MRI of the breast to reduce false-positive results and unnecessary biopsies

AIM

To investigate the best cut-off percentage volume of delayed kinetics using magnetic resonance imaging (MRI) with computer-aided diagnosis (CAD) to reduce unnecessary biopsies in patients with newly diagnosed breast cancer.

MATERIALS AND METHODS

Between January 2017 and December 2018, 94 malignant and 56 benign masses were analysed using MRI CAD. All malignant and benign masses measured <2 cm and were confirmed histopathologically. The optimal cut-off values for washout, plateau, and persistent components were determined using the maximum Youden Index. The positive predictive value (PPV) was analysed using morphological descriptors and combining the percentage volume of delayed kinetics.

RESULTS

The area under the curve (AUC) was highest at ≤73% persistent component (AUC=0.759). In the subgroup analyses of masses <1 cm, the AUC was highest a plateau of >26% (AUC=0.697). When the persistent ≤73% criterion was applied to the lesions of C4a, the positive predictive value (PPV) increased from 61.9% to 72.44% with reduced false-negative cases and when applied to the lesions of C4a and C4b, the PPV increased from 61.9% to 78.1% with slightly increased false-negative cases. For subcentimetre lesions, the PPV increased from 46.77% to 54.72% with the same number of false-negative cases, when a plateau of >26% was applied to C4a, and the PPV increased from 46.77% to 61.36% with five false-negative cases when applied to C4a and C4b.

CONCLUSION

The percentage volume of delayed kinetics has the potential to improve the PPV of breast MRI. When suspicious masses <2 cm do not show ≤73% persistence, follow-up rather than biopsy could be considered; however, to avoid increasing false-negative cases, delayed kinetic information should be used with caution and accurate margin assessment is essential.

Nonmass Enhancement Breast Lesions: Diagnostic Performance of Kinetic Assessment on Ultrafast and Standard Dynamic Contrast-Enhanced MRI in Comparison With Morphologic Evaluation

OBJECTIVE. The purpose of this article was to evaluate the diagnostic performance of the kinetic parameters of ultrafast and standard dynamic contrast-enhanced MRI (DCE-MRI) compared with morphologic evaluation in differentiating benign from malignant nonmass enhancement (NME) breast lesions. MATERIALS AND METHODS. A total of 77 consecutive patients with 77 NMEs (23 benign and 54 malignant) underwent 3-T MRI, including one unenhanced and eight contrast-enhanced ultrafast DCE-MRI scans (7-second scans) and standard DCE-MRI scans. The two readers evaluated the lesions' likelihood of malignancy on a continuous scale from 0 to 100% as the morphologic score using standard DCE-MRI. The kinetic curves of ultrafast DCE-MRI were fitted using an empirical mathematical model, ΔS(t) = A × (1 - e^-αt), where A is the upper limit of signal intensity, e is the Euler number, and alpha (s^-1) is the rate of signal increase. The initial slope of the kinetic curve (A × α) and the initial AUC (AUC₃₀, which is the integration of the kinetic curve from 0 to 30 seconds) were calculated. From standard DCE-MRI, initial enhancement rate and signal enhancement ratio (SER) were calculated. These parameters were compared between benign and malignant NMEs. RESULTS. The morphologic score of malignant NME was statistically significantly higher than that of benign NME (p < 0.0001). The upper limit of signal intensity, rate of signal increase, initial slope of the kinetic curve, and AUC₃₀ of ultrafast DCE-MRI, initial enhancement rate, SER of standard DCE-MRI of malignant NMEs were statistically significantly higher than those of benign NMEs (p = 0.0011, 0.0045, < 0.0001, < 0.0001, 0.0017, and < 0.0001, respectively). AUC ROC analysis found no statistically significant difference between morphologic score, AUC₃₀ of ultrafast DCE-MRI, or SER of standard DCE-MRI. CONCLUSION. The kinetic parameters of ultrafast and standard DCE-MRI were as effective as morphologic evaluation for differentiation between benign and malignant NMEs.

Correlation Between Enhancement Intensity in Contrast Enhancement Spectral Mammography and Types of Kinetic Curves in Magnetic Resonance Imaging

Background

Due to the decreased sensitivity of mammography in glandular breasts, new diagnostic modalities, like contrast-enhanced spectral mammography (CESM) and digital breast tomosynthesis (DBT) have been developed. The aim of this study was to compare qualitative enhancement levels on CESM with type of kinetic enhancement curves on MRI examination.

Material/Methods

Patients qualified for the CESM examination presented some diagnostic doubts – suspected multifocality, multicentricity, or having dense glandular breast tissue. The enhancement level on CESM was described as weak, medium, or strong. Enhancement on achieved MR images was assessed on the basis of enhancement kinetic curves. The level of enhancement on CESM was associated with enhancement curves type on MRI. All lesions detected on CESM and MRI were histopathologically verified.

Results

The study involved 107 lesions diagnosed in 94 patients: 71 lesions (66%) appeared to be infiltrating on histopathological examination, 9 lesions (8%) were non-infiltrating cancers, and 27 lesions (25%) were benign. Data analysis revealed that lesions with wash-out curve on MRI most often presented strong enhancement on CESM, while in lesions with progressive enhancement curve, strong enhancement on CESM was the rarest. The relationship between enhancement level on CESM and curve type on contrast-enhanced MRI depends on the nature of the lesion. The type of MRI curve was found to be associated with enhancement level on CESM.

Conclusions

We compared subjective assessments of contrast enhancement on CESM with enhancement kinetic curves on MRI. The results showed that the level of enhancement on CESM and type of kinetic curves on MRI depends on the lesion type.

Differentiation between subcentimeter carcinomas and benign lesions using kinetic parameters derived from ultrafast dynamic contrast-enhanced breast MRI

OBJECTIVES

This study aims to evaluate ultrafast DCE-MRI-derived kinetic parameters that reflect contrast agent inflow effects in differentiating between subcentimeter BI-RADS 4-5 breast carcinomas and benign lesions.

METHODS

We retrospectively reviewed consecutive 3-T MRI performed from February to October 2017, during which ultrafast DCE-MRI was performed as part of a hybrid clinical protocol with conventional DCE-MRI. In total, 301 female patients with 369 biopsy-proven breast lesions were included. Ultrafast DCE-MRI was acquired continuously over approximately 60 s (temporal resolution, 2.7-7.1 s/phase) starting simultaneously with the start of contrast injection. Four ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS], contrast enhancement ratio [CER], bolus arrival time [BAT], and initial area under gadolinium contrast agent concentration [IAUGC]) and one conventional DCE-MRI-derived kinetic parameter (signal enhancement ratio [SER]) were calculated for each lesion. Wilcoxon rank sum test or Fisher's exact test was performed to compare kinetic parameters, volume, diameter, age, and BI-RADS morphological descriptors between subcentimeter carcinomas and benign lesions. Univariate/multivariate logistic regression analyses were performed to determine predictive parameters for subcentimeter carcinomas.

RESULTS

In total, 125 lesions (26 carcinomas and 99 benign lesions) were identified as BI-RADS 4-5 subcentimeter lesions. Subcentimeter carcinomas demonstrated significantly larger MS and SER and shorter BAT than benign lesions (p = 0.0117, 0.0046, and 0.0102, respectively). MS, BAT, and age were determined as significantly predictive for subcentimeter carcinoma (p = 0.0208, 0.0023, and < 0.0001, respectively).

CONCLUSIONS

Ultrafast DCE-MRI-derived kinetic parameters may be useful in differentiating subcentimeter BI-RADS 4 and 5 carcinomas from benign lesions.

KEY POINTS

• Ultrafast DCE-MRI can generate kinetic parameters, effectively differentiating breast carcinomas from benign lesions. • Subcentimeter carcinomas demonstrated significantly larger maximum slope and shorter bolus arrival time than benign lesions. • Maximum slope and bolus arrival time contribute to better management of suspicious subcentimeter breast lesions.

Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial

Comparative preliminary analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data collected in the International Breast MR Consortium 6883 multicenter trial was performed to distinguish benign and malignant breast tumors. Prebiopsy DCE-MRI data from 45 patients with suspicious breast lesions were obtained. Semiquantitative mean signal-enhancement ratio ([Formula: see text]) was calculated for all lesions, and quantitative pharmacokinetic, parameters [Formula: see text], [Formula: see text], and [Formula: see text], were calculated for the subset with available [Formula: see text] maps ([Formula: see text]). Diagnostic performance was estimated for DCE-MRI parameters and compared to standard clinical MRI assessment. Quantitative and semiquantitative metrics discriminated benign and malignant lesions, with receiver operating characteristic area under the curve (AUC) values of 0.71, 0.70, and 0.82 for [Formula: see text], [Formula: see text], and [Formula: see text], respectively ([Formula: see text]). At equal 94% sensitivity, the specificity and positive predictive value of [Formula: see text] (53% and 63%, respectively) and K^trans (42% and 58%) were higher than clinical MRI assessment (32% and 54%). A multivariable model combining [Formula: see text] and clinical MRI assessment had an AUC value of 0.87. Quantitative pharmacokinetic and semiquantitative analyses of DCE-MRI improves discrimination of benign and malignant breast tumors, with our findings suggesting higher diagnostic accuracy using [Formula: see text]. [Formula: see text] has potential to help reduce unnecessary biopsies resulting from routine breast imaging.

Kinetic Analysis of Benign and Malignant Breast Lesions With Ultrafast Dynamic Contrast-Enhanced MRI: Comparison With Standard Kinetic Assessment

OBJECTIVE

The purposes of this study were to evaluate diagnostic parameters measured with ultrafast MRI acquisition and with standard acquisition and to compare diagnostic utility for differentiating benign from malignant lesions.

MATERIALS AND METHODS

Ultrafast acquisition is a high-temporal-resolution (7 seconds) imaging technique for obtaining 3D whole-breast images. The dynamic contrast-enhanced 3-T MRI protocol consists of an unenhanced standard and an ultrafast acquisition that includes eight contrast-enhanced ultrafast images and four standard images. Retrospective assessment was performed for 60 patients with 33 malignant and 29 benign lesions. A computer-aided detection system was used to obtain initial enhancement rate and signal enhancement ratio (SER) by means of identification of a voxel showing the highest signal intensity in the first phase of standard imaging. From the same voxel, the enhancement rate at each time point of the ultrafast acquisition and the AUC of the kinetic curve from zero to each time point of ultrafast imaging were obtained.

RESULTS

There was a statistically significant difference between benign and malignant lesions in enhancement rate and kinetic AUC for ultrafast imaging and also in initial enhancement rate and SER for standard imaging. ROC analysis showed no significant differences between enhancement rate in ultrafast imaging and SER or initial enhancement rate in standard imaging.

CONCLUSION

Ultrafast imaging is useful for discriminating benign from malignant lesions. The differential utility of ultrafast imaging is comparable to that of standard kinetic assessment in a shorter study time.

Kinetic Curve Type Assessment for Classification of Breast Lesions Using Dynamic Contrast-Enhanced MR Imaging

OBJECTIVE

The aim of this study was to employ a kinetic model with dynamic contrast enhancement-magnetic resonance imaging to develop an approach that can efficiently distinguish malignant from benign lesions.

MATERIALS AND METHODS

A total of 43 patients with 46 lesions who underwent breast dynamic contrast enhancement-magnetic resonance imaging were included in this retrospective study. The distribution of malignant to benign lesions was 31/15 based on histological results. This study integrated a single-compartment kinetic model and dynamic contrast enhancement-magnetic resonance imaging to generate a kinetic modeling curve for improving the accuracy of diagnosis of breast lesions. Kinetic modeling curves of all different lesions were analyzed by three experienced radiologists and classified into one of three given types. Receiver operating characteristic and Kappa statistics were used for the qualitative method. The findings of the three radiologists based on the time-signal intensity curve and the kinetic curve were compared.

RESULTS

An average sensitivity of 82%, a specificity of 65%, an area under the receiver operating characteristic curve of 0.76, and a positive predictive value of 82% and negative predictive value of 63% was shown with the kinetic model (p = 0.017, 0.052, 0.068), as compared to an average sensitivity of 80%, a specificity of 55%, an area under the receiver operating characteristic of 0.69, and a positive predictive value of 79% and negative predictive value of 57% with the time-signal intensity curve method (p = 0.003, 0.004, 0.008). The diagnostic consistency of the three radiologists was shown by the κ-value, 0.857 (p<0.001) with the method based on the time-signal intensity curve and 0.826 (p<0.001) with the method of the kinetic model.

CONCLUSIONS

According to the statistic results based on the 46 lesions, the kinetic modeling curve method showed higher sensitivity, specificity, positive and negative predictive values as compared with the time-signal intensity curve method in lesion classification.