Estimation of T2* Relaxation Time of Breast Cancer: Correlation with Clinical, Imaging and Pathological Features

Noninvasive identification of HER2-zero, -low, or -overexpressing breast cancers: Multiparametric MRI-based quantitative characterization in predicting HER2-low status of breast cancer

PURPOSE

To evaluate the effectiveness of both synthetic magnetic resonance imaging (SyMRI) and conventional diffusion-weighted imaging (DWI) for identifying the human epidermal growth factor receptor 2 (HER2) status in breast cancer (BC) patients.

METHOD

In this retrospective study, 114 women with DWI and SyMRI were pathologically classified into three groups: HER2-overexpressing (n = 40), HER2-low-expressing (n = 53), and HER2-zero-expressing (n = 21). T1 and T2 relaxation times and proton density (PD) were assessed before and after enhancement, and the resulting quantitative parameters produced by SyMRI were recorded as T1, T2, and PD and T1e, T2e, and PDe. Logistic regression was used to identify the best indicators for classifying patients based on HER2 expression. The discriminative performance of the models was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

Our preliminary study revealed significant differences in progesterone receptor (PR) status, Ki-67 index, and axillary lymph node (ALN) count among the HER2-zero, -low, and -overexpressing groups (p < 0.001 to p = 0.03). SyMRI quantitative indices showed significant differences among BCs in the three HER2 subgroups, except for ΔT2 (p < 0.05). our results indicate that PDe achieved an area under the curve（AUC）of 0.849 (95 % CI: 0.760-0.915) for distinguishing HER2-low and -overexpressing BCs. Further investigation revealed that both the PDe and ADC were indicators for predicting differences among patients with HER2-zero and HER2-low-expressing BC, with AUCs of 0.765(95 % CI: 0.652-0.855) and 0.684(95 % CI: 0.565-0.787), respectively. The addition of the PDe to the ADC improved the AUC to 0.825(95 % CI: 0.719-0.903).

CONCLUSIONS

SyMRI could noninvasively and robustly predict the HER2 expression status of patients with BC.

Multiparametric MRI model with synthetic MRI, DWI multi-quantitative parameters, and differential sub-sampling with cartesian ordering enables BI-RADS 4 lesions diagnosis with high accuracy

Objective

To assess the feasibility and diagnostic performances of synthetic magnetic resonance imaging (SyMRI) combined with diffusion-weighted imaging (DWI) and differential subsampling with cartesian ordering (DISCO) in breast imaging reporting and data system (BI-RADS) 4 lesions.

Methods

A total of 98 BI-RADS 4 patients, including 68 cases assigned to a malignant group and 33 cases assigned to a benign group, were prospectively enrolled, and their MRI and clinical information were collected. Two physicians jointly analyzed the characteristics of conventional MRI. T1, T2, proton density (PD), and ADC values were obtained from three different regions of interest (ROIs). Logistic regression analyses were used to select features and build models, and a nomogram was constructed with the best model.

Results

Using the ROI delineation method at the most obvious enhancement to measure the ADC value revealed the best diagnostic performance in diagnosing BI-RADS type 4 mass lesions. The diagnostic efficiency of the maximum level drawing method of the quantitative relaxation model was better than that of the whole drawing method and the most obvious enhancement method. The best relaxation model (model A) was composed of two parameters: T2stand and ΔT1%stand (AUC=0.887), and the BI-RADS model (model B) was constructed by two MRI features of edge and TIC curve (AUC=0.793). Using the quantitative parameters of SyMRI and DWI of the best ROC method combined with DISCO enhanced MRI features to establish a joint diagnostic model (model C: edge, TIC curve type, ADClocal, T2stand, ΔT1%stand) showed the best diagnostic efficiency (AUC=0.953). The nomogram also had calibration curves with good overlap.

Conclusions

The combined diagnosis model of SyMRI and DWI quantitative parameters combined with DISCO can improve the diagnostic efficiency of BI-RADS 4 types of mass lesions. Also, the line diagram based on this model can be used as an auxiliary diagnostic tool.

Synthetic MRI in breast cancer: differentiating benign from malignant lesions and predicting immunohistochemical expression status

To evaluate and compare the performance of synthetic magnetic resonance imaging (SyMRI) in classifying benign and malignant breast lesions and predicting the expression status of immunohistochemistry (IHC) markers. We retrospectively analysed 121 patients with breast lesions who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and SyMRI before surgery in our hospital. DCE-MRI was used to assess the lesions, and then regions of interest (ROIs) were outlined on SyMRI (before and after enhancement), and apparent diffusion coefficient (ADC) maps to obtain quantitative values. After being grouped according to benign and malignant status, the malignant lesions were divided into high and low expression groups according to the expression status of IHC markers. Logistic regression was used to analyse the differences in independent variables between groups. The performance of the modalities in classification and prediction was evaluated by receiver operating characteristic (ROC) curves. In total, 57 of 121 lesions were benign, the other 64 were malignant, and 56 malignant lesions performed immunohistochemical staining. Quantitative values from proton density-weighted imaging prior to an injection of the contrast agent (PD-Pre) and T2-weighted imaging (T2WI) after the injection (T2-Gd), as well as its standard deviation (SD of T2-Gd), were valuable SyMRI parameters for the classification of benign and malignant breast lesions, but the performance of SyMRI (area under the curve, AUC = 0.716) was not as good as that of ADC values (AUC = 0.853). However, ADC values could not predict the expression status of breast cancer markers, for which SyMRI had excellent performance. The AUCs of androgen receptor (AR), estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), p53 and Ki-67 were 0.687, 0.890, 0.852, 0.746, 0.813 and 0.774, respectively. SyMRI had certain value in distinguishing between benign and malignant breast lesions, and ADC values were still the ideal method. However, to predict the expression status of IHC markers, SyMRI had an incomparable value compared with ADC values.

Can the apparent transverse relaxation rate (R2*) evaluate the efficacy of concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma? a preliminary experience

BACKGROUND

The use of the apparent transverse relaxation rate (R2^*) in nasopharyngeal carcinoma (NPC) has not been previously reported in the literature. The aim of this study was to investigate the role of the R2^* value in evaluating response to concurrent chemoradiotherapy (CCRT) in patients with NPC.

METHODS

Forty-one patients with locoregionally advanced NPC confirmed by pathology were examined by blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) before and after CCRT, and conventional MRI was performed 3 months after the completion of CCRT. All patients were divided into a responding group (RG) and a nonresponding group (NRG), according to MRI findings 3 months after the end of treatment. The R2^* values before (R2*_preT) and after (R2^*_postT) CCRT and the ΔR2^* (ΔR2^*=R2^*_postT - R2^*_preT) were calculated in the tumor.

RESULTS

Among the 41 patients, 26 were in the RG and 15 were in the NRG. There was no statistical difference in the R2^*_preT between RG and NRG (P = 0.307); however, there were significant differences in R2^*_postT and ΔR2^* (P < 0.001). The area under the curve of R2^*_postT and ΔR2^* for predicting the therapeutic response of NPC was 0.897 and 0.954, respectively, with cutoff values of 40.95 and 5.50 Hz, respectively.

CONCLUSION

The R2^* value can be used as a potential imaging indicator to evaluate the therapeutic response of locoregionally advanced NPC.

Measurement of Tumor T2* Relaxation Times after Iron Oxide Nanoparticle Administration

T2* relaxometry is one of the established methods to measure the effect of superparamagnetic iron oxide nanoparticles on tumor tissues with magnetic resonance imaging (MRI). Iron oxide nanoparticles shorten the T1, T2, and T2* relaxation times of tumors. While the T1 effect is variable based on the size and composition of the nanoparticles, the T2 and T2* effects are usually predominant, and T2* measurements are the most time-efficient in a clinical context. Here, we present our approach to measuring tumor T2* relaxation times, using multi-echo gradient echo sequences, external software, and a standardized protocol for creating a T2* map with scanner-independent software. This facilitates the comparison of imaging data from different clinical scanners, different vendors, and co-clinical research work (i.e., tumor T2* data obtained in mouse models and patients). Once the software is installed, the T2 Fit Map plugin needs to be installed from the plugin manager. This protocol provides step-by-step procedural details, from importing the multi-echo gradient echo sequences into the software, to creating color-coded T2* maps and measuring tumor T2* relaxation times. The protocol can be applied to solid tumors in any body part and has been validated based on preclinical imaging data and clinical data in patients. This could facilitate tumor T2* measurements for multi-center clinical trials and improve the standardization and reproducibility of tumor T2* measurements in co-clinical and multi-center data analyses.

Cellular 1H MR Relaxation Times in Healthy and Cancer Three-Dimensional (3D) Breast Cell Culture

Noninvasive measurements of ¹H Magnetic Resonance Imaging (MR) relaxation times in a three-dimensional (3D) cell culture construct are presented. Trastuzumab was used as a pharmacological component delivered to the cells in vitro. The purpose of this study was to evaluate the Trastuzumab delivery by relaxation times in 3D cell cultures. The bioreactor has been designed and used for 3D cell cultures. Four bioreactors were prepared, two with normal cells and two with breast cancer cells. The relaxation times of HTB-125 and CRL 2314 cell cultures were determined. An immunohistochemistry (IHC) test was performed before MRI measurements to confirm the amount of HER2 protein in the CRL-2314 cancer cells. The results showed that the relaxation time of CRL2314 cells is lower than normal HTB-125 cells in both cases, before and after treatment. An analysis of the results showed that 3D culture studies have potential in evaluating treatment efficacy using relaxation times measurements with a field of 1.5 Tesla. The use ¹H MRI relaxation times allows for the visualization of cell viability in response to treatment.

Quantitative Values from Synthetic MRI Correlate with Breast Cancer Subtypes

The purpose of this study is to correlate quantitative T1, T2, and proton density (PD) values with breast cancer subtypes. Twenty-eight breast cancer patients underwent MRI of the breast including synthetic MRI. T1, T2, and PD values were correlated with Ki-67 and were compared between ER-positive and ER-negative cancers, and between Luminal A and Luminal B cancers. The effectiveness of T1, T2, and PD in differentiating the ER-negative from the ER-positive group and Luminal A from Luminal B cancers was evaluated using receiver operating characteristic analysis. Mean T2 relaxation of ER-negative cancers was significantly higher than that of ER-positive cancers (p < 0.05). The T1, T2, and PD values exhibited a strong positive correlation with Ki-67 (Pearson’s r = 0.75, 0.69, and 0.60 respectively; p < 0.001). Among ER-positive cancers, T1, T2, and PD values of Luminal A cancers were significantly lower than those of Luminal B cancers (p < 0.05). The area under the curve (AUC) of T2 for discriminating ER-negative from ER-positive cancers was 0.87 (95% CI: 0.69−0.97). The AUC of T1 for discriminating Luminal A from Luminal B cancers was 0.83 (95% CI: 0.61−0.95). In conclusion, quantitative values derived from synthetic MRI show potential for subtyping of invasive breast cancers.

Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review

Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.

Contrast-free MRI quantitative parameters for early prediction of pathological response to neoadjuvant chemotherapy in breast cancer

OBJECTIVES

To assess early changes in synthetic relaxometry after neoadjuvant chemotherapy (NAC) for breast cancer and establish a model with contrast-free quantitative parameters for early prediction of pathological response.

METHODS

From March 2019 to January 2021, breast MRI were performed for a primary cohort of women with breast cancer before (n = 102) and after the first (n = 93) and second (n = 90) cycle of NAC. Tumor size, synthetic relaxometry (T1/T2 relaxation time [T1/T2], proton density), and ADC were obtained, and the changes after treatment were calculated. Prediction models were established by multivariate logistic regression; evaluated with discrimination, calibration, and clinical application; and compared with Delong tests, net reclassification (NRI), and integrated discrimination index (IDI). External validation was performed from February to June 2021 with an independent cohort of 35 patients.

RESULTS

In the primary cohort, all parameters changed after early treatment. Synthetic relaxometry decreased to a greater degree in major histologic responders (MHR, Miller-Payne G4-5) compared with non-MHR (Miller-Payne G1-3). A model combining ADC after treatment, changes in T1 and tumor size, and cancer subtype achieved the highest AUC after the first (primary/validation cohort, 0.83/0.82) and second cycles (primary/validation cohort, 0.85/0.84). No difference of AUC (p ≥ 0.27), NRI (p ≥ 0.31), and IDI (p ≥ 0.32) was found between models with different cycles and size-measured sequences. Model calibration and decision curves demonstrated a good fitness and clinical benefit, respectively.

CONCLUSIONS

Early reduction in synthetic relaxometry indicated pathological response to NAC. Contrast-free T1 and ADC combined with size and cancer subtype predicted effectively pathological response after one NAC cycle.

KEY POINTS

• Synthetic MRI relaxometry changed after early neoadjuvant chemotherapy, which demonstrated pathological response for mass-like breast cancers. • Contrast-free quantitative parameters including T1 relaxation time and apparent diffusion coefficient, combined with tumor size and cancer subtype, stratified major histologic responders. • A contrast-free model predicted an early pathological response after the first treatment cycle of neoadjuvant chemotherapy.

Multiparameter MRI Model With DCE-MRI, DWI, and Synthetic MRI Improves the Diagnostic Performance of BI-RADS 4 Lesions

Objectives

To evaluate the value of synthetic magnetic resonance imaging (syMRI), diffusion-weighted imaging (DWI), DCE-MRI, and clinical features in breast imaging–reporting and data system (BI-RADS) 4 lesions, and develop an efficient method to help patients avoid unnecessary biopsy.

Methods

A total of 75 patients with breast diseases classified as BI-RADS 4 (45 with malignant lesions and 30 with benign lesions) were prospectively enrolled in this study. T1-weighted imaging (T1WI), T2WI, DWI, and syMRI were performed at 3.0 T. Relaxation time (T1 and T2), apparent diffusion coefficient (ADC), conventional MRI features, and clinical features were assessed. “T” represents the relaxation time value of the region of interest pre-contrast scanning, and “T+” represents the value post-contrast scanning. The rate of change in the T value between pre- and post-contrast scanning was represented by ΔT%.

Results

ΔT1%, T2, ADC, age, body mass index (BMI), menopause, irregular margins, and heterogeneous internal enhancement pattern were significantly associated with a breast cancer diagnosis in the multivariable logistic regression analysis. Based on the above parameters, four models were established: model 1 (BI-RADS model, including all conventional MRI features recommended by BI-RADS lexicon), model 2 (relaxation time model, including ΔT1% and T2), model 3 [multi-parameter (mp)MRI model, including ΔT1%, T2, ADC, margin, and internal enhancement pattern], and model 4 (combined image and clinical model, including ΔT1%, T2, ADC, margin, internal enhancement pattern, age, BMI, and menopausal state). Among these, model 4 has the best diagnostic performance, followed by models 3, 2, and 1.

Conclusions

The mpMRI model with DCE-MRI, DWI, and syMRI is a robust tool for evaluating the malignancies in BI-RADS 4 lesions. The clinical features could further improve the diagnostic performance of the model.

Triple-negative breast cancer on contrast-enhanced MRI and synthetic MRI: A comparison with non-triple-negative breast carcinoma

PURPOSE

This study aimed to compare the characteristics of triple-negative breast cancer (TNBC) with non-TNBC on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and synthetic MRI.

METHOD

This retrospective study included 79 patients with histopathologically proven breast cancer (TNBC: 16, non-TNBC: 63) who underwent synthetic MRI. Using synthetic MR images, we obtained T1 and T2 relaxation times in breast lesions before (Pre-T1, Pre-T2, Pre-PD) and after (Gd-T1, Gd-T2, Gd-PD) contrast agent injection. Subsequently, we calculated the ΔT1 (Pre-T1 - Gd-T1), ΔT2 (Pre-T2 - Gd-T2), Pre-T1/T2, and Gd-T1/T2. We compared the aforementioned quantitative values, as well as several morphologic features between TNBCs and non-TNBCs that were identified on DCE-MRI.

RESULTS

The multivariate analyses revealed that the Pre-T2 (P = 0.037) and the presence of rim enhancement (P-RIM) (P = 0.034) were significant and independent predictors of TNBC. The area under the receiver operating characteristics curve for all breast cancers was greater when a combination of Pre-T2 and P-RIM (Pre-T2＋P-RIM; Method 3, AUC (area under the curve) = 0.858) was used to distinguish between TNBCs and non-TNBCs versus the use of either Pre-T2 alone (Method 1, AUC = 0.786) or P-RIM alone (Method 2, AUC = 0.747).

CONCLUSIONS

Pre-T2 obtained using synthetic MRI and P-RIM identified on DCE-MRI allowed the differentiation between TNBCs and non-TNBCs. However, these results are preliminary and need to be verified by further studies.

Intratumoral Intensity in T2-weighted MRI and the Association With Histological and Molecular Prognostic Factors in Women With Invasive Breast Cancer

OBJECTIVE

To compare the intratumoral T2 signal intensity on MRI and histopathological and molecular expression of biomarkers of aggressiveness (histological grade, hormonal status, HER2, and Ki-67).

METHODS

This retrospective study included all women with invasive breast cancer undergoing MRI from January 2014 to October 2016. The intratumoral T2 signal as interpreted at consensus by two radiologists was compared to histopathological and molecular prognostic factors from the surgical specimen. Statistical analyses used Pearson χ 2 test with a confidence level of 95% (P ≤ 0.05).

RESULTS

Fifty patients with 50 lesions met study criteria (mean age 65.8 ± 13.5 years). Mean lesion size was 28 mm ± 15.7 mm (range, 15 to 76 mm). Cancer types were invasive ductal (35/50, 70%), invasive lobular (10/50, 20%), and mixed (5/50, 10%). Most lesions were histological grade 1 or 2 (41/50, 82%) and luminal type (45/50, 90%). On T2 images, lesions were hypointense in 62% (31/50), isointense in 20% (10/50), and hyperintense in 18% (9/50) of cases. Among hypointense lesions, 94% (29/31) were low or intermediate grade tumors (P = 0.02), low HER2 overexpression (30/31, 97%) (P = 0.005), and high ER status (30/31, 97%) (P = 0.006), high PR (26/31, 84%) (P = 0.02), and low incidence of necrosis (2/31, 6%). The difference in Ki-67 tumoral expression between groups was not significant.

CONCLUSION

Intratumoral T2 hypointensity in invasive breast cancer is associated with better prognostic tumors, such as histological low-grade high hormone receptor status.

Histogram analysis of quantitative parameters from synthetic MRI: Correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer

PURPOSE

To evaluate intra-tumoral heterogeneity through a histogram analysis of quantitative parameters obtained from synthetic MRI (magnetic resonance imaging), and determine correlations of these histogram characteristics with prognostic factors and molecular subtypes of invasive ductal carcinoma (IDC).

METHODS

A total of 122 IDC from 122 women who underwent preoperative synthetic MRI and DCE (dynamic contrast enhancement)-MRI were investigated. The synthetic MRI parameters (T1, T2, and PD (proton density)) were obtained. For each parameter, the minimum, 10th percentile, mean, median, 90th percentile, maximum, skewness, and kurtosis values of tumor were calculated, and correlations with prognostic factors and subtypes were assessed. The Mann-Whitney U test or the Student^'s t test were utilized to analyze the association between the histogram features of synthetic MRI parameters and prognostic factors. The Kruskal-Wallis test followed by the post-hoc test was used to analyze differences of synthetic MRI parameters among molecular subtypes.

RESULTS

IDC with high histopathologic grade showed statistically higher PD_maxium, T1_mean and T1_median values than those with low grade (p = 0.003, p = 0.007, p = 0.003). The T1_10th were significantly higher in cancers with PR (progesterone receptor) negativity than those with PR positivity (p = 0.005). ER-negative cancers had significant higher values of T2_10th, T2_mean, and T2_median than ER-positive cancers (p = 0.006, 0.002, and 0.006, respectively). The values of PD_median were significantly higher in IDC with HER2 (human epidermal growth factor receptor 2) positivity than those with HER2 negativity (p = 0.001). When discriminating molecular subtypes of IDC, the T2_mean achieved the highest performance. The T2_mean values of TN (triple-negative), luminal B and luminal A types are arranged in descending order (p <  0.0001).

CONCLUSIONS

Histogram features derived from synthetic MRI quantifies the distributions of tissue relaxation time and proton density, and may serve as a potential biomarker for discriminating histopathological grade, hormone receptor status, HER2 expression status and breast cancer subtypes.

The diagnostic performance of quantitative mapping in breast cancer patients: a preliminary study using synthetic MRI

Background

Previous studies have indicated that quantitative MRI (qMR) is beneficial for diagnosis of breast cancer. As a novel qMR technology, synthetic MRI (syMRI) may be advantageous by offering simultaneous generation of T1 and T2 mapping in one scan within a few minutes and without concern to the deposition of the gadolinium contrast agent in cell nucleus. In this study, the potential of quantitative mapping derived from Synthetic MRI (SyMRI) to diagnose breast cancer was investigated.

Methods

From April 2018 to May 2019, a total of 87 patients with suspicious breast lesions underwent both conventional and SyMRI before treatment. The quantitative metrics derived from SyMRI, including T1 and T2 values, were measured in breast lesions. The diagnostic performance of SyMRI was evaluated with unpaired Student’s t-tests, receiver operating characteristic curve analysis and multivariate logistic regression analysis. The AUCs of quantitative values were compared using Delong test.

Results

Among 77 patients who met the inclusion criteria, 48 were diagnosed with histopathological confirmed breast cancers, and the rest had benign lesions. The breast cancers showed significantly higher T1 (1611.61 ± 215.88 ms) values and lower T2 (80.93 ± 7.51 ms) values than benign lesions. The area under the ROC curve (AUC) values were 0.931 (95% CI: 0.874–0.989) and 0.883 (95% CI: 0.810–0.956) for T1 and T2 maps, respectively, in diagnostic discrimination between breast cancers and benign lesions. A slightly increased AUC of 0.978 (95% CI: 0.915–0.993) was achieved by combining those two relaxation-based quantitative metrics.

Conclusion

In conclusion, our preliminary study showed that the quantitative T1 and T2 values obtained by SyMRI could distinguish effectively between benign and malignant breast lesions, and T1 relaxation time showed the highest diagnostic efficiency. Furthermore, combining the two quantitative relaxation metrics further improved their diagnostic performance.

Improved discrimination of molecular subtypes in invasive breast cancer: Comparison of multiple quantitative parameters from breast MRI

PURPOSE

To compare multiple quantitative parameters from breast magnetic resonance imaging (MRI) with the synthetic MRI sequence included for discrimination of molecular subtypes of invasive breast cancer.

MATERIALS AND METHODS

Between March 2019 and September 2020, two hundred breast cancer patients underwent preoperative breast multiparametric MRI examinations including synthetic MRI, diffusion weighted imaging (DWI) and dynamic contrast enhancement (DCE)-MRI sequences. MRI morphological features, T1 and T2 relaxation times (T1, T2) and proton density (PD) values from synthetic MRI, K_trans, K_ep, and V_e from DCE-MRI, mean apparent diffusion coefficient (ADC) from DWI and tumor volume were measured. Quantitative parameters were compared according to molecular markers and subtypes. Logistic regression were performed to find the related MRI parameters and establish combined parameters. The comparison between single and combined quantitative parameters by using DeLong tests.

RESULTS

T1, T2 values were significantly higher in hormone receptor (HR)- negative and Ki67 > 14% tumors (p < 0.05). Human epidermal growth factor receptor 2 (HER2)-positive tumors demonstrated significantly higher K_trans and K_ep (p < 0.01). Mean ADC values were significantly decreased in HR-positive and Ki67 > 14% tumors (p < 0.01). Tumor volumes were significantly higher in HER2-positive and Ki67 > 14% tumors (p < 0.05). Independent influencing factors were lower T2 values (p < 0.001), smaller tumor volume (p = 0.031) and higher mean ADC (p = 0.002) associated with luminal A subtype, while T1 values (p = 0.007) was the only quantitative parameter associated with triple-negative subtype. The diagnostic efficiency of combined parameters (T2 + mean ADC + volume) (AUC = 0.765) was significantly higher than that of mean ADC (AUC = 0.666, p = 0.031 by DeLong test) and volume (AUC = 0.650, p = 0.008 by DeLong test) for separating luminal A subtype.

CONCLUSIONS

MRI quantitative parameters could help distinguish molecular markers and subtypes. The emerging synthetic MRI parameters - T1 values were associated with the TN subtype, and combined parameters with added T2 values might improve the discrimination of the luminal A subtype. Application of synthetic MRI can enrich quantitative descriptors from breast MRI.

Confounding factors in breast magnetic resonance fingerprinting: B 1 + , slice profile, and diffusion effects

PURPOSE

Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane B 1 + , slice profile and diffusion effects on T₁ and T₂ estimation in the female breast at 1.5T.

METHODS

Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE.

RESULTS

Theoretic analysis predicts T₁ under-/T₂ overestimation if the nominal flip angles are underestimated and inversely, T₁ under-/T₂ overestimation if omitting slice profile correction, and T₁ under-/T₂ underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane B 1 + effects occurred in vivo, causing T₂ left-right inhomogeneity between both breasts. Their correction decreased the T₂ difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T₂ most strongly, resulting in -22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions.

CONCLUSION

Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.

Utility of synthetic MRI in predicting the Ki-67 status of oestrogen receptor-positive breast cancer: a feasibility study

AIM

To evaluate the utility of synthetic magnetic resonance imaging (MRI) of the breast in predicting the Ki-67 status in patients with oestrogen receptor (ER)-positive breast cancer.

MATERIALS AND METHODS

Forty-nine patients with 50 histopathologically proven breast cancers who underwent additional synthetic MRI were enrolled in the present study. Using synthetic MRI images, T1 and T2 relaxation times and their standard deviations (SD) in the breast lesions before (T1-Pre, T2-Pre, PD-Pre, SD of T1-Pre, SD of T2-Pre, SD of PD-Pre) and after (T1-Gd, T2-Gd, PD-Gd, SD of T1-Gd, SD of T2-Gd, SD of PD-Gd) contrast agent injection were obtained. These quantitative values were compared between the low Ki-67 expression (<14%) lesions (low-proliferation group: n=23) and high Ki-67 expression (≥14%) lesions (high-proliferation group: n=27).

RESULTS

The univariate analysis showed that the SD of T1-Gd (p<0.001) and T2-Gd (p=0.042) were significantly higher in the high-proliferation group than in the low-proliferation group. Multivariate analysis further showed that the SD of T1-Gd was a significant and independent predictor of Ki-67 expression, with an area under the receiver operating characteristic (AUROC) curve of 0.885. The sensitivity, specificity, and accuracy of the SD of T1-Gd with an optimal cut-off value of 98.5 were 77.8%, 87%, and 82%, respectively.

CONCLUSION

The SD of T1-Gd obtained from synthetic MRI was useful to predict Ki-67 status.

Kinetic Features of Invasive Breast Cancers on Computer-Aided Diagnosis Using 3T MRI Data: Correlation with Clinical and Pathologic Prognostic Factors

Objective

To investigate the correlation of kinetic features of breast cancers on computer-aided diagnosis (CAD) of preoperative 3T magnetic resonance imaging (MRI) data and clinical-pathologic factors in breast cancer patients.

Materials and Methods

Between July 2016 and March 2017, 85 patients (mean age, 54 years; age range, 35–81 years) with invasive breast cancers (mean, 1.8 cm; range, 0.8–4.8 cm) who had undergone MRI and surgery were retrospectively enrolled. All magnetic resonance images were processed using CAD, and kinetic features of tumors were acquired. The relationships between kinetic features and clinical-pathologic factors were assessed using Spearman correlation test and binary logistic regression analysis.

Results

Peak enhancement and angio-volume were significantly correlated with histologic grade, Ki-67 index, and tumor size: r = 0.355 (p = 0.001), r = 0.330 (p = 0.002), and r = 0.231 (p = 0.033) for peak enhancement, r = 0.410 (p = 0.005), r = 0.341 (p < 0.001), and r = 0.505 (p < 0.001) for angio-volume. Delayed-plateau component was correlated with Ki-67 (r = 0.255 [p = 0.019]). In regression analysis, higher peak enhancement was associated with higher histologic grade (odds ratio [OR] = 1.004; 95% confidence interval [CI]: 1.001–1.008; p = 0.024), and higher delayed-plateau component and angio-volume were associated with higher Ki-67 (OR = 1.051; 95% CI: 1.011–1.094; p = 0.013 for delayed-plateau component, OR = 1.178; 95% CI: 1.023–1.356; p = 0.023 for angio-volume).

Conclusion

Of the CAD-assessed kinetic features, higher peak enhancement may correlate with higher histologic grade, and higher delayed-plateau component and angio-volume correlate with higher Ki-67 index. These results support the clinical application of kinetic features in prognosis assessment.

How clinical imaging can assess cancer biology

Human cancers represent complex structures, which display substantial inter- and intratumor heterogeneity in their genetic expression and phenotypic features. However, cancers usually exhibit characteristic structural, physiologic, and molecular features and display specific biological capabilities named hallmarks. Many of these tumor traits are imageable through different imaging techniques. Imaging is able to spatially map key cancer features and tumor heterogeneity improving tumor diagnosis, characterization, and management. This paper aims to summarize the current and emerging applications of imaging in tumor biology assessment.

The feasibility of synthetic MRI in breast cancer patients: comparison of T2 relaxation time with multiecho spin echo T2 mapping method

OBJECTIVE

To compare the T2 relaxation times acquired with synthetic MRI to those of multi-echo spin-echo sequences and to evaluate the usefulness of synthetic MRI in the clinical setting.

METHODS

From January 2017 to May 2017, we included 51 patients with newly diagnosed breast cancer, who underwent additional synthetic MRI and multiecho spin echo (MESE) T2 mapping sequences. Synthetic MRI technique uses a multiecho and multidelay acquisition method for the simultaneous quantification of physical properties such as T1 and T2 relaxation times and proton density image map. A radiologist with 9 years of experience in breast imaging drew region of interests manually along the tumor margins on two consecutive axial sections including the center of tumor mass and in the fat tissue of contralateral breast on both synthetic T2 map and MESE T2 map images.

RESULTS

The mean T2 relaxation time of the cancer was 84.75 ms (± 15.54) by synthetic MRI and 90.35 ms (± 19.22) by MESE T2 mapping. The mean T2 relaxation time of the fat was 129.22 ms (± 9.53) and 102.11 ms (± 13.9), respectively. Bland-Altman analysis showed mean difference of 8.4 ms for the breast cancer and a larger mean difference of 27.8 ms for the fat tissue. Spearman's correlation test showed that there was significant positive correlation between synthetic MRI and MESE sequences for the cancer (r = 0.713, p < 0.001) and for the fat (r = 0.551, p < 0.001). The positive estrogen receptor and low histologic grade were associated with little differences between two methods (p = 0.02  and = 0.043, respectively).

CONCLUSION

T2 relaxation times of breast cancer acquired with synthetic MRI showed positive correlation with those of MESE T2 mapping. Synthetic MRI could be useful for the evaluation of tissue characteristics by simultaneous acquisition of several quantitative physical properties.

ADVANCES IN KNOWLEDGE

Synthetic MRI is useful for the evaluation of T2 relaxation times of the breast cancers.

Age of Data in Contemporary Research Articles Published in Representative General Radiology Journals

Objective

To analyze and compare the age of data in contemporary research articles published in representative general radiology journals.

Materials and Methods

We searched for articles reporting original research studies analyzing patient data that were published in the print issues of the Korean Journal of Radiology (KJR), European Radiology (ER), and Radiology in 2017. Eligible articles were reviewed to extract data collection period (time from first patient recruitment to last patient follow-up) and age of data (time between data collection end and publication). The journals were compared in terms of the proportion of articles reporting the data collection period to the level of calendar month and regarding the age of data.

Results

There were 50, 492, and 254 eligible articles in KJR, ER, and Radiology, respectively. Of these, 44 (88%; 95% confidence interval [CI]: 75.8-94.8%), 359 (73%; 95% CI: 68.9-76.7%), and 211 (83.1%; 95% CI: 78-87.2%) articles, respectively, provided enough details of data collection period, revealing a significant difference between ER and Radiology (p = 0.002). The age of data was significantly greater in KJR (median age: 826 days; range: 299-2843 days) than in ER (median age: 570 days; range: 56-4742 days; p < 0.001) and Radiology (median age: 618; range: 75-4271 days; p < 0.001).

Conclusion

Korean Journal of Radiology did not fall behind ER or Radiology in reporting of data collection period, but showed a significantly greater age of data than ER and Radiology, suggesting that KJR should take measures to improve the timeliness of its data.

Identification of Preoperative Magnetic Resonance Imaging Features Associated with Positive Resection Margins in Breast Cancer: A Retrospective Study

Objective

To determine which preoperative breast magnetic resonance imaging (MRI) findings and clinicopathologic features are associated with positive resection margins at the time of breast-conserving surgery (BCS) in patients with breast cancer.

Materials and Methods

We reviewed preoperative breast MRI and clinicopathologic features of 120 patients (mean age, 53.3 years; age range, 27–79 years) with breast cancer who had undergone BCS in 2015. Tumor size on MRI, multifocality, patterns of enhancing lesions (mass without non-mass enhancement [NME] vs. NME with or without mass), mass characteristics (shape, margin, internal enhancement characteristics), NME (distribution, internal enhancement patterns), and breast parenchymal enhancement (BPE; weak, strong) were analyzed. We also evaluated age, tumor size, histology, lymphovascular invasion, T stage, N stage, and hormonal receptors. Univariate and multivariate logistic regression analyses were used to determine the correlation between clinicopathological features, MRI findings, and positive resection margins.

Results

In univariate analysis, tumor size on MRI, multifocality, NME with or without mass, and segmental distribution of NME were correlated with positive resection margins. Among the clinicopathological factors, tumor size of the invasive breast cancer and in situ components were significantly correlated with a positive resection margin. Multivariate analysis revealed that NME with or without mass was an independent predictor of positive resection margins (odds ratio [OR] = 7.00; p < 0.001). Strong BPE was a weak predictor of positive resection margins (OR = 2.59; p = 0.076).

Conclusion

Non-mass enhancement with or without mass is significantly associated with a positive resection margin in patients with breast cancer. In patients with NME, segmental distribution was significantly correlated with positive resection margins.