Use of dynamic contrast-enhanced MR imaging to predict survival in patients with primary breast cancer undergoing neoadjuvant chemotherapy

Quantitative DCE-MRI prediction of breast cancer recurrence following neoadjuvant chemotherapy: a preliminary study

INTRODUCTION

Breast cancer patients treated with neoadjuvant chemotherapy (NACT) are at risk of recurrence depending on clinicopathological characteristics. This preliminary study aimed to investigate the predictive performances of quantitative dynamic contrast-enhanced (DCE) MRI parameters, alone and in combination with clinicopathological variables, for prediction of recurrence in patients treated with NACT.

METHODS

Forty-seven patients underwent pre- and post-NACT MRI exams including high spatiotemporal resolution DCE-MRI. The Shutter-Speed model was employed to perform pharmacokinetic analysis of the DCE-MRI data and estimate the Ktrans, ve, kep, and τi parameters. Univariable logistic regression was used to assess predictive accuracy for recurrence for each MRI metric, while Firth logistic regression was used to evaluate predictive performances for models with multi-clinicopathological variables and in combination with a single MRI metric or the first principal components of all MRI metrics.

RESULTS

Pre- and post-NACT DCE-MRI parameters performed better than tumor size measurement in prediction of recurrence, whether alone or in combination with clinicopathological variables. Combining post-NACT Ktrans with residual cancer burden and age showed the best improvement in predictive performance with ROC AUC = 0.965.

CONCLUSION

Accurate prediction of recurrence pre- and/or post-NACT through integration of imaging markers and clinicopathological variables may help improve clinical decision making in adjusting NACT and/or adjuvant treatment regimens to reduce the risk of recurrence and improve survival outcome.

Prospective Evaluation of Ultrafast Breast MRI for Predicting Pathologic Response after Neoadjuvant Therapies

Background Ultrafast dynamic contrast-enhanced (DCE) MRI parameters are associated with breast cancer aggressiveness. However, the role of these parameters as predictive biomarkers for pathologic response after neoadjuvant chemotherapy (NAC) has been poorly investigated. Purpose To assess whether semiquantitative perfusion parameters calculated at initial ultrafast DCE MRI are associated with early prediction for pathologic response after NAC in participants with breast cancer. Materials and Methods This prospective single-center study included consecutive women with nonmetastatic invasive breast cancer treated with NAC followed by surgery who underwent initial ultrafast DCE MRI between December 2020 and August 2021. Six semiquantitative ultrafast DCE MRI parameters were calculated for each participant from the fitted time-signal intensity curve. Multivariable logistic regression was used to identify independent predictors of pathologic complete response (pCR) and residual cancer burden (RCB). Results Fifty women (mean age, 49 years ± 12 [SD]) were included in the study; 20 achieved pCR and 25 achieved low RCB (RCB-0 and I). A wash-in slope (WIS) cutoff value of 1.6% per second had a sensitivity of 94% (17 of 18 participants) and a specificity of 59% (19 of 32 participants) for pCR. A WIS of more than 1.6% per second (odds ratio [OR], 8.4 [95% CI: 1.5, 48.2]; P = .02), human epidermal growth factor receptor 2 (HER2) positivity (OR, 6.3 [95% CI: 1.5, 27.4]; P = .01), and tumor-infiltrating lymphocytes of more than 10% (OR, 6.9 [95% CI: 1.3, 37.7]; P = .03) were independent predictive factors of pCR. The area under the receiver operating characteristic curve of the three-component model, which included WIS, tumor-infiltrating lymphocytes, and HER2 positivity, was 0.92 (95% CI: 0.84, 0.99). A WIS of more than 1.6% per second was associated with higher pCR rates in the HER2-positive (OR, 21.7 [95% CI: 1.8, 260.6]; P = .02) breast cancer subgroup. For luminal HER2-negative and triple-negative breast cancers, a WIS of more than 1.6% per second was associated with low RCB (OR, 11.0 [95% CI: 1.1, 106.4]; P = .04). Conclusion The wash-in slope (WIS) assessment at initial ultrafast dynamic contrast-enhanced MRI may be used to predict pathologic complete response (pCR) in participants with breast cancer. The WIS value was used to identify two subsets of human epidermal growth factor receptor 2-positive cancers with distinct pCR rates. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.

Quantitative Predictors of Response to Neoadjuvant Chemotherapy on Dynamic Contrast-enhanced 3T Breast MRI

OBJECTIVE

To assess whether changes in quantitative parameters on breast MRI better predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer than change in volume.

METHODS

This IRB-approved retrospective study included women with newly diagnosed breast cancer who underwent 3T MRI before and during NAC from January 2013 to December 2019 and underwent surgery at our institution. Clinical data such as age, histologic diagnosis and grade, biomarker status, clinical stage, maximum index cancer dimension and volume, and surgical pathology (presence or absence of in-breast pCR) were collected. Quantitative parameters were calculated using software. Correlations between clinical features and MRI quantitative measures in pCR and non-pCR groups were assessed using univariate and multivariate logistic regression.

RESULTS

A total of 182 women with a mean age of 52 years (range, 26-79 years) and 187 cancers were included. Approximately 45% (85/182) of women had pCR at surgery. Stepwise multivariate regression analysis showed statistical significance for changes in quantitative parameters (increase in time to peak and decreases in peak enhancement, wash out, and Kep [efflux rate constant]) for predicting pCR. These variables in combination predicted pCR with 81.2% accuracy and an area under the curve (AUC) of 0.878. The AUCs of change in index cancer volume and maximum dimension were 0.767 and 0.613, respectively.

CONCLUSION

Absolute changes in quantitative MRI parameters between pre-NAC MRI and intra-NAC MRI could help predict pCR with excellent accuracy, which was greater than changes in index cancer volume and maximum dimension.

Dynamic contrast-enhanced magnetic resonance imaging for evaluating early response to radiosurgery in patients with vestibular schwannoma

PURPOSE

This study aimed to use dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate early treatment response in vestibular schwannoma (VS) patients after radiosurgery.

METHODS

Twenty-four VS patients who underwent gamma knife radiosurgery were prospectively followed up for at least four years. DCE-MRI sequences, in addition to standard MRI protocol, were obtained prior to radiosurgery, at 3 and 6 months. Conventionally, treatment responses based on tumor volume changes were classified as regression or stable (RS), transient tumor enlargement (TTE), and continuous tumor enlargement (CTE). DCE-MRI parameters, such as K^trans, K_ep and V_e, were compared according to follow-up periods and between groups. The diagnostic performance was tested using receiver operating characteristic (ROC) curves.

RESULTS

Changes in tumor volume were as follows at the last 48 months of follow-up: RS in 11 patients (45.8%), TTE in 10 patients (41.7%), and CTE in three patients (12.5%). The median time required to distinguish TTE from CTE using conventional MRI was 12 months (range 9-18). The K^trans and V_e were significantly decreased in patients with RS and TTE at 3 and 6 months, but did not differ significantly in patients with CTE. There were no significant differences in K^trans and V_e between patients with RS and TTE at 3 and 6 months. Both K^trans and V_e demonstrated high diagnostic performance in evaluating early treatment response to radiosurgery in patients with VS.

CONCLUSION

DCE-MRI may aid in the monitoring and early prediction of treatment response in patients with VS following radiosurgery.

The diagnostic performance of dynamic contrast-enhanced MRI and its correlation with subtypes of breast cancer

To evaluate diagnostic performance of perfusion-weighted imaging in differentiating benign from malignant breast lesions, and the correlation between the prognostic factors/subtypes of breast cancers and the perfusion parameters.A total of 76 patients (59 cases with breast cancer) were included in our study. The Wilcoxon rank-sum test or the Kruskal-Wallis test were adopted for comparisons according to the dichotomous histopathologic prognostic factors or immunohistochemical subtypes. Receiver operating characteristic curves were used to determine the area under the curve (AUC) values for perfusion parameters to assess discrimination ability.Confirming by pathology after operation, the percentage of benign lesions is 22.37% (17/76), malignant lesions (breast cancer) is 77.63% (59/76). According to puncture and pathological findings after operation, the standard of the molecular subtypes of breast cancer, triple negative account for 13.6% (8/59), non-triple negative account for 86.4% (51/59). The value of mean Ktrans and Kep were lower in benign than malignant lesions (P ≤ .001). The AUC of the 3 indicators are significantly improved after adjusting for age (AUC = 0.858 for Ktrans, AUC = 0.926 for Kep, and AUC = 0.827 for Ve). Moreover, the Ve index showed better discrimination performance than other indicators in identifying patients with triple-negative subtypes. Similarly, the identification ability came to the highest when combing Kep and Ve.Perfusion parameters on dynamic enhanced magnetic resonance imaging are statistically significant in distinguishing benign from malignant breast lesion, and may potentially be used as biomarkers in discriminating patients with triple-negative molecular subtypes of breast cancer.

Diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging after radiation therapy for bone metastases in patients with hepatocellular carcinoma

The objectives of this study were to assess changes in apparent diffusion coefficient (ADC) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters after radiation therapy (RT) for bone metastases from hepatocellular carcinoma (HCC) and to evaluate their prognostic value. This prospective study was approved by the Institutional Review Board. Fourteen patients with HCC underwent RT (30 Gy in 10 fractions once daily) for bone metastases. The ADC and DCE-MRI parameters and the volume of the target lesions were measured before (baseline) and one month after RT (post-RT). The Wilcoxon signed-rank test was used to compare the parameters between the baseline and post-RT MRI. The parameters were compared between patients with or without disease progression in RT fields using the Mann–Whitney test. Intraclass correlation coefficients were used to evaluate the interobserver agreement. The medians of the ADC, rate constant [k_ep], and volume fraction of the extravascular extracellular matrix [v_e] in the baseline and post-RT MRI were 0.67 (range 0.61–0.72) and 0.75 (range 0.63–1.43) (× 10^–3 mm²/s) (P = 0.027), 836.33 (range 301.41–1082.32) and 335.80 (range 21.86–741.87) (× 10^–3/min) (P = 0.002), and 161.54 (range 128.38–410.13) and 273.99 (range 181.39–1216.95) (× 10^–3) (P = 0.027), respectively. The medians of the percent change in the ADC of post-RT MRI in patients with progressive disease and patients without progressive disease were − 1.35 (range − 6.16 to 6.79) and + 46.71 (range 7.71–112.81) (%) (P = 0.011), respectively. The interobserver agreements for all MRI parameters were excellent (intraclass correlation coefficients > 0.8). In conclusion, the ADC, k_ep, and v_e of bone metastases changed significantly after RT. The percentage change in the ADC was closely related to local tumor progression.

Value of quantitative dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging in predicting extramural venous invasion in locally advanced gastric cancer and prognostic significance

Background

Extramural venous invasion (EMVI) has been found to be related to poor prognosis in gastric cancer. Preoperative diagnosis of EMVI is challenging, as it can only be detected by surgical pathology. The present study aimed to investigate the value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in predicting EMVI preoperatively, and to determine the relationship between prediction results and prognosis in patients with locally advanced gastric cancer (LAGC).

Methods

Between January, 2015, and June, 2017, 79 LAGC patients underwent MRI preoperatively were enrolled in this study. Pathological EMVI (pEMVI) was used as the gold standard for diagnosis. The differences in quantitative DCE-MRI and DWI parameters between groups with different pEMVI status were analyzed. Multivariate logistic regression was used to build the combined prediction model for pEMVI with statistically significant quantitative parameters. The performance of the model for predicting pEMVI was evaluated using receiver operating characteristic (ROC) analysis. Patients were grouped based on MRI-predicted EMVI (mrEMVI). Kaplan-Meier analysis was used to investigate the relationship between mrEMVI and 2-year recurrence-free survival (RFS).

Results

Of the 79 LAGC patients who underwent MRI, 29 were pEMVI positive and 50 were pEMVI negative. Among the patients' clinical and pathological characteristics, only postoperative staging showed a significant difference between the 2 groups (P=0.015). The pEMVI-positive group had higher volume transfer constant (K^trans) and rate constant (k_ep), and lower apparent diffusion coefficient (ADC) values than the negative group (0.189 vs. 0.082 min^-1, 0.687 vs. 0.475 min^-1, and 1.230×10^-3 vs. 1.463×10^-3 mm²/s, respectively; P<0.05). Quantitative parameters, K^trans and k_ep, and ADC values, were independently associated with pEMVI which odds ratio values were 3.66, 2.65, and 0.30 (P<0.05), respectively, using multivariate logistic regression. ROC analysis showed that the area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value in predicting pEMVI using combined K^trans, k_ep, and ADC values were 0.879, 72.4%, 96%, 91.3%, and 85.7%, respectively. A total of 23 cases were considered to be mrEMVI positive, and 56 cases were considered to be mrEMVI negative, according to the predictive results. The median RFS of the mrEMVI-positive group was significant lower than the negative group (21.7 vs. 31.2 months), and the 2-year RFS rate in the mrEMVI-positive group was significantly lower than that of the negative group (43.6% vs. 72.5%, P=0.010).

Conclusions

The quantitative DCE-MRI parameters, K^trans and k_ep, and DWI parameter, ADC, are independent predictors of pEMVI in LAGC; mrEMVI was confirmed to be a poor prognostic predictor for RFS.

Drug concentration estimation using contrast-enhanced MRI in intra-arterial chemotherapy for head and neck cancers

OBJECTIVE

In cases of head and neck cancer treated with intra-arterial chemotherapy, no objective indices are available for determining the distribution of anticancer drugs administered to multiple arteries. To establish such indices, noninvasive measurements of drug concentrations are required in the arterial perfusion area of each artery. In MRI, changes in 1/T₁ (Δ1/T₁) are correlated with the contrast agent concentration. We focused on these properties and investigated whether it is possible to estimate anticancer drug concentrations within tissue based on Δ1/T₁.

METHODS

We employed the fast spin echo (FSE) sequence to determine optimum imaging parameters using a phantom. Subsequently, contrast agent was administered via the lingual and external carotid arteries for seven cases of tongue cancer. Δ1/T₁ were then measured in tumor and nontumor tissues. The results of this study were compared with those of a previous study in which intratumor concentrations of anticancer agent were measured in excised specimens.

RESULTS

The optimum imaging parameters for the FSE was two repetition times (TR, 500 and 1000 ms). When compared with the external carotid artery administration, the lingual artery administration of contrast agent resulted in significantly higher Δ1/T1 in both tumor and nontumor tissues (2.13 and 2.62 times, respectively). The multiplying factor for the nontumor tissue and high homogeneity of the contrast agent concentration were reasonably consistent with the results of the previous study.

CONCLUSION

This method can be applied to estimating intratissue concentrations of intra-arterially administered anticancer drugs, thus possibly providing useful information in determining the distribution of anticancer drugs.

Image quality of the CAIPIRINHA-Dixon-TWIST-VIBE technique for ultra-fast breast DCE-MRI: Comparison with the conventional GRE technique

PURPOSE

The aim of this study was to evaluate image quality of the CAIPIRINHA-Dixon-TWIST-Volume-Interpolated Breath-hold Examination (CDT-VIBE) technique for ultra-fast breast dynamic contrast enhanced (DCE) MRI with respect to conventional Gradient-Recalled Echo (GRE) technique.

METHODS

A total of 58 patients underwent a DCE-MRI based on CDT-VIBE sequence (temporal resolution: 11.9 s), immediately followed by 1 phase of a conventional T1 weighted GRE sequence (acquisition time: 68 s). The Signal-to-Noise Ratio (SNR) on phantom images, lesion/parenchyma signal ratio (LPSR), image quality, and morphological characterization were compared between the last phase of CDT-VIBE and conventional GRE images. The image quality was assessed by visual grading analysis (VGA). Reader agreement was assessed using Kappa analysis.

RESULTS

There was no significant difference in SNR (phantom) or LPSR (patient) between CDT-VIBE and conventional GRE images (P > 0.05). Significant parallel acquisition technique (PAT) noise and mild blurriness was observed on CDT-VIBE images. Visual grading analysis (VGA) confirmed significantly worse ratings for CDT-VIBE compared to the conventional GRE sequence in terms of PAT noise, lesion's internal feature clarity, and therefore overall image quality (area under contrast curve [AUC] values: 0.578 ‒ 0.764, P < 0.05), but edge sharpness and lesion conspicuity were equivalent (P > 0.05). Kappa analysis revealed good agreement on image quality scores (к = 0.725 ‒ 0.908) and on morphologic terms (к = 0.745-1.000).

CONCLUSION

The CDT-VIBE sequence provides excellent spatial resolution and adequate image quality in ultra-fast breast DCE-MRI. Further improvement in PAT noise and internal structure blurriness may be necessary.

Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy

In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.

Kinetic Heterogeneity of Breast Cancer Determined Using Computer-aided Diagnosis of Preoperative MRI Scans: Relationship to Distant Metastasis-Free Survival

Background Higher peak enhancement and washout component values measured on preoperative breast MRI scans with computer-aided diagnosis (CAD) are presumed to be associated with worse recurrence-free survival. Purpose To investigate whether CAD-extracted kinetic features of breast cancer and the heterogeneity of these features at preoperative MRI are associated with distant metastasis-free survival in women with invasive breast cancer. Materials and Methods Consecutive women with newly diagnosed invasive breast cancer who underwent preoperative MRI were retrospectively evaluated between 2011 and 2012. A commercially available CAD system was used to extract the peak enhancement and delayed enhancement profiles of each breast cancer case from preoperative MRI data. The kinetic heterogeneity of these features (a measure of heterogeneity in the proportions of tumor pixels with delayed washout, plateau, and persistent components within a tumor) was calculated to evaluate intratumoral heterogeneity. Cox proportional hazards models were used to investigate the associations between CAD-extracted kinetic features and distant metastasis-free survival after adjusting for clinical-pathologic factors. Results A total of 276 consecutive women (mean age, 53 years) were evaluated. In 28 of 276 (10.1%) women, distant metastasis developed at a median follow-up of 79 months. A higher degree of kinetic heterogeneity was observed in women with distant metastases than in those without distant metastases (mean, 0.70 ± 0.2 vs 0.43 ± 0.3; P < .001). Multivariable Cox proportional hazards analysis revealed that a higher degree of kinetic heterogeneity (hazard ratio [HR], 19.2; 95% confidence interval [CI]: 4.2, 87.1; P < .001), higher peak enhancement (HR, 1.001; 95% CI: 1.000, 1.002; P = .045), the presence of lymphovascular invasion (HR, 3.3; 95% CI: 1.5, 7.5; P = .004), and a higher histologic grade (ie, grade 3) (HR, 2.2; 95% CI: 1.0, 4.9; P = .044) were associated with worse distant metastasis-free survival. Conclusion Higher values of kinetic heterogeneity and peak enhancement as determined with computer-aided diagnosis of preoperative MRI were associated with worse distant metastasis-free survival in women with invasive breast cancer. © RSNA, 2020 See also the editorial by El Khouli and Jacobs in this issue.

Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer

To investigate whether automated volumetric radiomic analysis of breast cancer vascularization (VAV) can improve survival prediction in primary breast cancer. 314 consecutive patients with primary invasive breast cancer received standard clinical MRI before the initiation of treatment according to international recommendations. Diagnostic work-up, treatment, and follow-up was done at one tertiary care, academic breast-center (outcome: disease specific survival/DSS vs. disease specific death/DSD). The Nottingham Prognostic Index (NPI) was used as the reference method with which to predict survival of breast cancer. Based on the MRI scans, VAV was accomplished by commercially available, FDA-cleared software. DSD served as endpoint. Integration of VAV into the NPI gave NPI_VAV. Prediction of DSD by NPI_VAV compared to standard NPI alone was investigated (Cox regression, likelihood-test, predictive accuracy: Harrell's C, Kaplan Meier statistics and corresponding hazard ratios/HR, confidence intervals/CI). DSD occurred in 35 and DSS in 279 patients. Prognostication of the survival outcome by NPI (Harrell's C = 75.3%) was enhanced by VAV (NPI_VAV: Harrell's C = 81.0%). Most of all, the NPI_VAV identified patients with unfavourable outcome more reliably than NPI alone (hazard ratio/HR = 4.5; confidence interval/CI = 2.14-9.58; P = 0.0001). Automated volumetric radiomic analysis of breast cancer vascularization improved survival prediction in primary breast cancer. Most of all, it optimized the identification of patients at higher risk of an unfavorable outcome. Future studies should integrate MRI as a "gate keeper" in the management of breast cancer patients. Such a "gate keeper" could assist in selecting patients benefitting from more advanced diagnostic procedures (genetic profiling etc.) in order to decide whether are a more aggressive therapy (chemotherapy) is warranted.

Tumor segmentation analysis at different post-contrast time points: A possible source of variability of quantitative DCE-MRI parameters in locally advanced breast cancer

PURPOSE

to assess if tumor segmentation analysis performed at different post-contrast time points (TPs) on dynamic images could influence the extraction of dynamic contrast enhanced (DCE)-MRI parameters in locally advanced breast cancer (LABC), and potentially represent a source of variability.

METHOD

forty patients with forty-two LABC lesions were prospectively enrolled and underwent breast DCE-MRI examination at 3 T. On post-processed dynamic images, enhancing tumor lesions were manually segmented at four different TPs: at the first post-contrast dynamic image in which the lesion was appreciable (TP 1) and at 1, 5 and 10 min after contrast-agent administration (TPs 2, 3 and 4, respectively) and corresponding DCE-MRI parameters were extracted. Friedman's test followed by Bonferroni-adjusted Wilcoxon signed rank test for post-hoc analysis was used to compare DCE-MRI parameters. Intra- and inter-observer reliability of DCE-MRI parameters measurements was assessed using the Intraclass Correlation Coefficient (ICC) analysis.

RESULTS

Ktrans, Kep and iAUC were significantly higher when extracted from ROIs placed at TP1 and progressively decreased from TP 2-4. The intra-observer reliability ranged from good to excellent (ICC's: 0.894 to 0.990). The inter-observer reliability varied from moderate to excellent (0.770 to 0.942). The inter-observer reliability was significantly higher for Ktrans and Kep extracted at TPs1 and 2 as compared to TPs 3 and 4.

CONCLUSIONS

A significant variability of DCE-MRI quantitative parameters occurs when tumor segmentation is performed at different TPs. We suggest to performing tumor delineation at an established TP, preferably the earliest, in order to extract reliable and comparable DCE-MRI data.

Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps

We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (K^trans), in the training and testing sets, respectively. The differences in AUC were statistically significant (P < .05) for several parametric maps. Thus, multiresolution analysis that decomposes the texture at various spatial-frequency scales may more accurately capture changes in tumor vascular heterogeneity as measured by DCE-MRI, and therefore provide better early prediction of NACT response.

The kinetic analysis of breast cancer: An investigation of the optimal temporal resolution for dynamic contrast-enhanced MR imaging

INTRODUCTION

There is wide agreement that morphologic features and enhancement kinetics should be evaluated for MRI of the breast, although there has been no clear consensus concerning optimal temporal resolutions. The objective of this study was to investigate the optimal temporal resolution for the kinetic analysis of breast cancers.

METHODS

Thirty-four patients with 34 enhancing lesions of breast cancer who underwent dynamic contrast-enhanced MRI (DCE-MRI) on a 3.0-T scanner were included in this retrospective study. DCE-MRI was performed with an original temporal resolution of 10-s, and the values of pharmacokinetic parameters (Ktrans, Ve, Kep, and area under the curve (AUC)) were compared with selected data of 30-s and 60-s time intervals.

RESULTS

Among the 34 lesions, 10 showed a wash out pattern, 16 showed a plateau pattern, and 8 showed a persistent enhancement pattern. The Ktrans value in the wash-out pattern was significantly higher than that of other time-intensity curve patterns (p < 0.01). The Kep and AUC also showed significant differences between the wash-out pattern and other types (p < 0.01). On comparing the perfusion parameters among different temporal resolutions, simulations showed that only the AUC differed significantly between the data acquired at a 10-s temporal resolution and that acquired at a 60-s time interval (p < 0.01). Although the comparison of the AUC between the 30-s and 60-s data also showed significant differences (p = 0.01), there was no significant difference between the 10-s and 30-s data (p = 0.17).

CONCLUSIONS

DCE-MRI with a temporal resolution of 30-s preserves the kinetic information. Further prospective studies will be needed to investigate the trade-off between temporal and spatial resolution in DCE-MRI.

Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI

BACKGROUND

Early prediction of nonresponse is essential in order to avoid inefficient treatments.

PURPOSE

To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24-72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response.

STUDY TYPE

This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study.

POPULATION

Sixty patients were initially recruited, with 39 women participating in the final cohort.

FIELD STRENGTH/SEQUENCE

A 1.5T scanner was used for MRI examinations.

ASSESSMENT

Dynamic contrast-enhanced (DCE)-MR images were acquired at baseline (timepoint 1, TP1), 24-72 hours after the first chemotherapy (TP2), and after the end of anthracycline treatment (TP3). PRM was performed after fusion of T₁ subtraction images from TP1 and TP2 using an affine registration algorithm. Pixels with an increase of more than 10% of their value (PRMdce+) were corresponding nonresponding regions of the tumor. Patients with a decrease of maximum diameter (%dDmax) between TP1 and TP3 of more than 30% were defined as EMR responders. pCR patients achieved a residual cancer burden score of 0.

STATISTICAL TESTS

T-test, receiver operating characteristic (ROC) curves, and logistic regression were used for the analysis.

RESULTS

PRM showed a statistical difference between pCR response groups (P < 0.01) and AUC of 0.88 for the prediction of non-pCR. Logistic regression analysis demonstrated that PRMdce+ and Grade II were significant (P < 0.01) for non-pCR prediction (AUC = 0.94). Peripheral tumor region demonstrated higher performance for the prediction of non-pCR (AUC = 0.85) than intermediate and central zones; however, statistical comparison showed no significant difference.

DATA CONCLUSION

PRM could be predictive of non-pCR 24-72 hours after initiation of chemotherapy treatment. Moreover, the peripheral region showed increased AUC for non-pCR prediction and increased signal intensity during treatment for non-pCR tumors, information that could be used for optimal tissue sampling.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;51:1403-1411.

Comparison of Pathologic Response Evaluation Systems After Neoadjuvant Chemotherapy in Breast Cancers: Correlation With Computer-Aided Diagnosis of MRI Features

OBJECTIVE. The objective of our study was to compare pathologic response assessment systems with dynamic contrast-enhanced MRI (DCE-MRI) features and evaluate the predictive performance of DCE-MRI features relative to different pathologic response assessment systems after neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS. This retrospective study included 223 women (mean age, 47.2 years; age range, 31-71 years) who underwent DCE-MRI before and after completing NAC before definitive surgery between January and December 2015. Six MRI features (i.e., tumor size; angio volume; peak enhancement; and persistent, plateau, and washout enhancing components) were measured and correlated with the Miller-Payne grading system, residual cancer burden (RCB) class, and modified in-breast RCB index. Percentage changes in MRI features were correlated with pathologic assessment systems using the Spearman rank correlation test; ROC curves were analyzed to predict pathologic outcomes. RESULTS. All six MRI features correlated most strongly with the in-breast RCB index (r = -0.75 to -0.51, p < 0.001) followed by the Miller-Payne system (r = 0.47-0.72, p < 0.001) and RCB class (r = -0.58 to -0.41, p < 0.001). The in-breast RCB index correlated most strongly with the angio volume reduction rate (r = -0.75, p < 0.001) followed by maximum diameter (r = -0.69, p < 0.001), peak enhancement (r = -0.67, p < 0.001), washout component (r = -0.60, p < 0.001), plateau component (r = -0.59, p < 0.001), and persistent component (r = -0.51, p < 0.001). CONCLUSION. The in-breast RCB index correlated best with changes in DCE-MRI features, and the MRI-measured angio volume reduction rate correlated best with pathologic tumor responses.

Diffusion-weighted Imaging of Invasive Breast Cancer: Relationship to Distant Metastasis-free Survival

Background Combined minimum apparent diffusion coefficient (ADC) and ADC difference value may improve the diagnostic performance of breast diffusion-weighted imaging (DWI) compared with mean ADC. Purpose To investigate whether ADC parameters at DWI are associated with distant metastasis-free survival in women with invasive breast cancer. Materials and Methods Between 2013 and 2014, 258 consecutive women (mean age ± standard deviation, 50.9 years ± 10.5; age range, 23-85 years) with newly diagnosed invasive breast cancer who underwent preoperative MRI with DWI were evaluated. All DWI examinations were retrospectively reviewed by two radiologists blinded to clinical information. The mean, minimum, and maximum ADCs were measured by manually placing regions of interest within the lesions, and the ADC difference value (the difference between minimum and maximum ADCs) was calculated to evaluate intratumoral heterogeneity. Cox proportional hazards models were used to determine the associations between ADC parameters and distant metastasis-free survival after adjustment for clinical-pathologic factors. Results In 25 of the 258 women (9.7%), distant metastasis developed at a median follow-up of 51 months. The ADC difference value was higher in women with distant metastasis than in those without distant metastasis (mean, 0.743 × 10^-3 mm²/sec vs 0.566 × 10^-3 mm²/sec, respectively; P < .001). Multivariable Cox proportional hazards analysis showed that a higher ADC difference value (>0.698 × 10^-3 mm²/sec) (hazard ratio [HR], 4.5; 95% confidence interval [CI]: 2.0, 10.0; P < .001), presence of axillary node metastasis (HR, 3.3; 95% CI: 1.2, 9.3; P = .02), and estrogen receptor negativity (HR, 2.6; 95% CI: 1.2, 5.7; P = .02) were associated with poorer distant metastasis-free survival. Conclusion A higher apparent diffusion coefficient difference value at diffusion-weighted imaging is associated with poorer distant metastasis-free survival of women with invasive breast cancer. © RSNA, 2019 See also the editorial by Taourel in this issue.

Predicting the response to neoadjuvant therapy for early-stage breast cancer: tumor-, blood-, and imaging-related biomarkers

Neoadjuvant therapy (NAT) has been used increasingly in patients with locally advanced or early-stage breast cancer. However, the accurate evaluation and prediction of response to NAT remain the great challenge. Biomarkers could prove useful to identify responders or nonresponders, or even to distinguish between early and delayed responses. These biomarkers could include markers from the tumor itself, such as versatile proteins, genes, and ribonucleic acids, various biological factors or peripheral blood cells, and clinical and pathological features. Possible predictive markers could also include multiple features from functional imaging, such as standard uptake values in positron emission tomography, apparent diffusion coefficient in magnetic resonance, or radiomics imaging biomarkers. In addition, cells that indirectly present the immune status of tumor cells and/or their host could also potentially be used as biomarkers, eg, tumor-infiltrating lymphocytes, tumor-associated macrophages, and myeloid-derived suppressor cells. Though numerous biomarkers have been widely investigated, only estrogen and/or progesterone receptors and human epidermal growth factor receptor have been proven to be reliable biomarkers to predict the response to NAT. They are the only biomarkers recommended in several international guidelines. The other aforementioned biomarkers warrant further validation studies. Some multigene profiling assays that are commercially available, eg, Oncotype DX and MammaPrint, should be used with caution when extrapolated to NAT settings. A panel of combined multilevel biomarkers might be able to predict the response to NAT more robustly than individual biomarkers. To establish such a panel and its prediction model, reliable methods and extensive clinical validation are warranted.

Shear Wave Speed of the Lesion in Preoperative Breast Ultrasonography: Association with Disease-free Survival of Patients with Primary Operable Invasive Breast Cancer

RATIONALE AND OBJECTIVES

We aimed to investigate the relationship between shear wave speed (SWS) of the lesion on preoperative breast ultrasonography (US) and disease-free survival of patients with primary operable invasive breast cancer.

MATERIALS AND METHODS

This retrospective study was approved by our Institutional Review Board. The requirement for informed consent was waived. A total of 195 consecutive newly diagnosed invasive breast cancer patients (age 33-83 years; mean 54.0 years) with preoperative breast US with SWS measurement of the lesion were identified. They underwent surgery between May 2012 and May 2013. SWS was measured at the center and three marginal zones in the main tumors, and the maximum value was used for analyses. For 35 patients who underwent primary systemic therapy (PST), the maximum SWS before PST was used. Cox proportional hazards modeling was used to identify the relationship between clinical-pathologic factors and disease-free survival.

RESULTS

Fourteen recurrences occurred at 6-47 months (mean 22.3 months) after surgery. On multivariate analysis, a positive history of PST (hazard ratio [HR] = 4.93; 95% confidence interval [CI]: 1.66, 14.70; P = .004), adjuvant chemotherapy (HR = 3.67; 95% CI: 1.11, 12.1; P = .033), and higher maximum SWS (HR = 1.55; 95% CI: 1.07, 2.23; P = .020) were associated with poorer disease-free survival.

CONCLUSION

Higher maximum SWS on preoperative US, in addition to a positive history of PST and adjuvant chemotherapy, was significantly associated with poorer disease-free survival of patients with invasive breast cancer.

Total choline quantification measured by 1H MR spectroscopy as early predictor of response after neoadjuvant treatment for locally advanced breast cancer: The impact of immunohistochemical status

BACKGROUND

Validation of new biomarkers is essential for the early evaluation of neoadjuvant treatments.

PURPOSE

To determine whether measurements of total choline (tCho) by 1H spectroscopy could predict morphological or pathological complete response (pCR) of neoadjuvant treatment and whether breast cancer subgroups are related to prediction accuracy.

STUDY TYPE

Prospective, nonrandomized, monocentric, diagnostic study.

POPULATION

Sixty patients were initially included with 39 women participating in the final cohort.

FIELD STRENGTH/SEQUENCE

A 1.5T scanner was used for acquisition and MRS was performed using the syngo GRACE sequence.

ASSESSMENT

MRS and MRI examinations were performed at baseline (TP1), 24-72 hours after first chemotherapy (TP2), after the end of anthracycline treatment (TP3), and MRI only after the end of taxane treatment (TP4). Early (EMR) and late (LMR) morphological response were defined as %ΔDmax13 or %ΔDmax14, respectively. Responders were patients with %ΔDmax >30. Pathological complete response (pCR) patients achieved a residual cancer burden score of 0.

STATISTICAL TESTS

T-test, receiver operating characteristic (ROC) curves, multiple regression, logistic regression, one-way analysis of variance (ANOVA) analysis were used for the analysis.

RESULTS

At TP1 there was a significant difference between response groups for tCho1 concerning EMR prediction (P = 0.05) and pCR (P < 0.05) and for K_ep 1 (P = 0.03) concerning LMR prediction. At TP2, no modification of tCho and other parameters could predict response. At TP3, ΔtCho, ΔDmax, and ΔVol could predict LMR (P < 0.05 for all parameters), pCR (P < 0.05 for all parameters), and ΔK^trans could predict only pCR (P = 0.04). Logistic regression at baseline showed the highest area under the curve (AUC) of 0.9 for prediction of pCR. The triple negative (TN) subgroup showed significantly higher tCho at baseline (P = 0.02) and higher ΔtCho levels at TP3 (P < 0.05).

DATA CONCLUSION

Baseline measurements of tCho in combination with clinicopathological criteria could predict non-pCR with a high AUC. Furthermore, tCho quantification for prediction of pCR was more sensitive for TN tumors.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2018;48:982-993.

Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results

Pathologic complete response following neoadjuvant therapy (NAT) is used as a short-term surrogate marker of eventual outcome in patients with breast cancer. Analyzing voxel-level heterogeneity in MRI-derived parametric maps, obtained before and after the first cycle of NAT ([Formula: see text]), in conjunction with receptor status, may improve the predictive accuracy of tumor response to NAT. Toward that end, we incorporated two MRI-derived parameters, the apparent diffusion coefficient and efflux rate constant, with receptor status in a logistic ridge-regression model. The area under the curve (AUC) and Brier score of the model computed via 10-fold cross validation were 0.94 (95% CI: 0.85, 0.99) and 0.11 (95% CI: 0.06, 0.16), respectively. These two statistics strongly support the hypothesis that our proposed model outperforms the other models that we investigated (namely, models without either receptor information or voxel-level information). The contribution of the receptor information was manifested by an 8% to 15% increase in AUC and a 14% to 21% decrease in Brier score. These data indicate that combining multiparametric MRI with hormone receptor status has a high likelihood of improved prediction of pathologic response to NAT in breast cancer.

DCE- and DW-MRI as early imaging biomarkers of treatment response in a preclinical model of triple negative breast cancer

This work evaluates quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parameters as early biomarkers of response in a preclinical model of triple negative breast cancer (TNBC). The standard Tofts' model of DCE-MRI returns estimates of the volume transfer constant (K^trans ) and the extravascular extracellular volume fraction (v_e ). DW-MRI returns estimates of the apparent diffusion coefficient (ADC). Mice (n = 38) were injected subcutaneously with MDA-MB-231. Tumors were grown to approximately 275 mm³ and sorted into the following groups: saline controls, low-dose Abraxane (15 mg/kg) and high-dose Abraxane (25 mg/kg). Animals were imaged at days zero, one and three. On day three, tumors were extracted for immunohistochemistry. The positive percentage change in ADC on day one was significantly higher in both treatment groups relative to the control group (p < 0.05). In addition, the positive percentage change in K^trans was significantly higher than controls (p < 0.05) on day one for the high-dose group and on days one and three for the low-dose group. The percentage change in tumor volume was significantly different between the high-dose and control groups on day three (p = 0.006). Histology confirmed differences at day three through reduced numbers of proliferating cells (Ki67 staining) in the high-dose group (p = 0.03) and low-dose group (p = 0.052) compared with the control group. Co-immunofluorescent staining of vascular maturity [using von Willebrand Factor (vWF) and α-smooth muscle actin (α-SMA)] indicated significantly higher vascular maturation in the low-dose group compared with the controls on day three (p = 0.03), and trending towards significance in the high-dose group compared with controls on day three (p = 0.052). These results from quantitative imaging with histological validation indicate that ADC and K^trans have the potential to serve as early biomarkers of treatment response in murine studies of TNBC.

Ductal Carcinoma in Situ: Quantitative Preoperative Breast MR Imaging Features Associated with Recurrence after Treatment

Purpose To investigate whether specific imaging features on breast magnetic resonance (MR) images are associated with ductal carcinoma in situ (DCIS) recurrence risk after definitive treatment. Materials and Methods Patients with DCIS who underwent preoperative dynamic contrast material-enhanced (DCE) MR imaging between 2004 and 2014 with ipsilateral recurrence more than 6 months after definitive surgical treatment were retrospectively identified. For each patient, a control subject with DCIS that did not recur was identified and matched on the basis of clinical, histopathologic, and treatment features known to affect recurrence risk. On DCE MR images, lesion characteristics (longest diameter, functional tumor volume [FTV], peak percentage enhancement [PE], peak signal enhancement ratio [SER], and washout fraction) and normal tissue features (background parenchymal enhancement [BPE] volume, mean BPE) were quantitatively measured. MR imaging features were compared between patients and control subjects by using the Wilcoxon signed-rank test, with adjustment for multiple comparisons. Results Of 415 subjects with DCIS who underwent preoperative MR imaging, 14 experienced recurrence and 11 had an identifiable matching control subject (final cohort, 11 patients and 11 control subjects). Median time to recurrence was 14 months, and median follow-up for control subjects was 102 months. When compared with matched control subjects, patients with DCIS recurrence exhibited significantly greater FTV (median, 9.3 cm³ vs 1.3 cm³, P = .01), lesion peak SER (median, 1.7 vs 1.2; P = .03), and mean BPE (median, 58.3% vs 41.1%; P = .02). Conclusion Quantitative lesion and normal breast tissue characteristics at preoperative MR imaging in women with newly diagnosed DCIS show promise for association with breast cancer recurrence after treatment. ^© RSNA, 2017.

DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

This study investigates the effectiveness of hundreds of texture features extracted from voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps for early prediction of breast cancer response to neoadjuvant chemotherapy (NAC). In total, 38 patients with breast cancer underwent DCE-MRI before (baseline) and after the first of the 6-8 NAC cycles. Quantitative pharmacokinetic (PK) parameters and semiquantitative metrics were estimated from DCE-MRI time-course data. The residual cancer burden (RCB) index value was computed based on pathological analysis of surgical specimens after NAC completion. In total, 1043 texture features were extracted from each of the 13 parametric maps of quantitative PK or semiquantitative metric, and their capabilities for early prediction of RCB were examined by correlating feature changes between the 2 MRI studies with RCB. There were 1069 pairs of feature-map combinations that showed effectiveness for response prediction with 4 correlation coefficients >0.7. The 3-dimensional gray-level cooccurrence matrix was the most effective feature extraction method for therapy response prediction, and, in general, the statistical features describing texture heterogeneity were the most effective features. Quantitative PK parameters, particularly those estimated with the shutter-speed model, were more likely to generate effective features for prediction response compared with the semiquantitative metrics. The best feature-map pair could predict pathologic complete response with 100% sensitivity and 100% specificity using our cohort. In conclusion, breast tumor heterogeneity in microvasculature as measured by texture features of voxel-based DCE-MRI parametric maps could be a useful biomarker for early prediction of NAC response.

Estimating breast tumor blood flow during neoadjuvant chemotherapy using interleaved high temporal and high spatial resolution MRI

PURPOSE

To evaluate an interleaved MRI sampling strategy that acquires both high temporal resolution (HTR) dynamic contrast-enhanced (DCE) data for quantifying breast tumor blood flow (TBF) and high spatial resolution (HSR) DCE data for clinical reporting, following a single standard injection of contrast agent.

METHODS

A simulation study was used to evaluate the performance of the interleaved technique under different conditions. In a prospective clinical study, 18 patients with primary breast cancer, who were due to undergo neoadjuvant chemotherapy (NACT), were examined using interleaved HTR and HSR DCE-MRI at 1.5 Tesla. Tumor regions of interest were analyzed with a two-compartment tracer kinetic model. Paired parameters (n = 10) from the data acquired before and post-cycle 2 of NACT were compared using the nonparametric Wilcoxon signed-rank test.

RESULTS

Simulations demonstrated that TBF was reliably estimated using the proposed strategy. The region of interest analysis revealed significant changes in TBF (0.81-0.43 mL/min/mL; P = 0.002) following two cycles of NACT. The HSR data were reported in the normal way and enabled the assessment of tumor volume, which decreased by 53% following NACT (P = 0.065).

CONCLUSIONS

TBF can be measured reliably using the proposed strategy without compromising a standard clinical protocol. Furthermore, in our feasibility study, TBF decreased significantly following NACT, whereas capillary permeability surface-area product did not. Magn Reson Med 79:317-326, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Computer-aided Diagnosis-generated Kinetic Features of Breast Cancer at Preoperative MR Imaging: Association with Disease-free Survival of Patients with Primary Operable Invasive Breast Cancer

Purpose To retrospectively investigate the relationship between the kinetic features of breast cancer assessed with computer-aided diagnosis (CAD) at preoperative magnetic resonance (MR) imaging and disease-free survival in patients with primary operable invasive breast cancer. Materials and Methods This retrospective study was approved by the institutional review board. The requirement to obtain informed consent was waived. The authors identified 329 consecutive women (mean age, 52.9 years; age range, 32-88 years) with newly diagnosed invasive breast cancer who had undergone preoperative MR imaging and surgery between January 2012 and February 2013. All MR images were retrospectively reviewed by using a commercially available CAD system, and the following kinetic parameters were noted for each lesion: peak enhancement (highest pixel signal intensity in the first series obtained after administration of contrast material), angio-volume (total volume of the enhancing lesion), and delayed enhancement profiles (the proportions of washout, plateau, and persistently enhancing component within a tumor). Cox proportional hazards modeling was used to identify the relationship between CAD-generated kinetics and disease-free survival after adjusting for clinical-pathologic variables. Results A total of 36 recurrences developed at a median follow-up of 50 months (range, 15-55 months). CAD-measured peak enhancement at preoperative MR imaging enabled differentiation between patients with and patients without recurrence (area under the receiver operating characteristic curve = 0.728; 95% confidence interval [CI]: 0.676, 0.775; P < .001). Multivariate Cox analysis showed that a higher peak enhancement (hazard ratio [HR] = 1.001; 95% CI: 1.000, 1.002; P = .004), a higher washout component (HR = 1.029; 95% CI: 1.005, 1.054; P = .017), and lymphovascular invasion at histopathologic examination (HR = 3.011; 95% CI: 1.302, 6.962; P = .010) were associated with poorer disease-free survival. Conclusion Higher values of CAD-measured peak enhancement and washout component at preoperative MR imaging were significantly associated with poorer disease-free survival of patients with primary operable breast cancer. ^© RSNA, 2017.

Dynamic Contrast-Enhanced MRI Perfusion Parameters as Imaging Biomarkers of Angiogenesis

Hypoxia in the tumor microenvironment is the leading factor in angiogenesis. Angiogenesis can be identified by dynamic contrast-enhanced breast MRI (DCE MRI). Here we investigate the relationship between perfusion parameters on DCE MRI and angiogenic and prognostic factors in patients with invasive ductal carcinoma (IDC). Perfusion parameters (K^trans, k_ep and v_e) of 81 IDC were obtained using histogram analysis. Twenty-fifth, 50th and 75th percentile values were calculated and were analyzed for association with microvessel density (MVD), vascular endothelial growth factor (VEGF) and conventional prognostic factors. Correlation between MVD and v_e50 was positive (r = 0.33). K^trans₅₀ was higher in tumors larger than 2 cm than in tumors smaller than 2 cm. In multivariate analysis, K^trans₅₀ was affected by tumor size and MVD with 12.8% explanation. There was significant association between K^trans₅₀ and tumor size and MVD. Therefore we conclude that DCE MRI perfusion parameters are potential imaging biomarkers for prediction of tumor angiogenesis and aggressiveness.

Arterial input functions in dynamic contrast-enhanced magnetic resonance imaging: which model performs best when assessing breast cancer response?

OBJECTIVE

To evaluate the performance of six models of population arterial input function (AIF) in the setting of primary breast cancer and neoadjuvant chemotherapy (NAC). The ability to fit patient dynamic contrast-enhanced MRI (DCE-MRI) data, provide physiological plausible data and detect pathological response was assessed.

METHODS

Quantitative DCE-MRI parameters were calculated for 27 patients at baseline and after 2 cycles of NAC for 6 AIFs. Pathological complete response detection was compared with change in these parameters from a reproduction cohort of 12 patients using the Bland-Altman approach and receiver-operating characteristic analysis.

RESULTS

There were fewer fit failures pre-NAC for all models, with the modified Fritz-Hansen having the fewest pre-NAC (3.6%) and post-NAC (18.8%), contrasting with the femoral artery AIF (19.4% and 43.3%, respectively). Median transfer constant values were greatest for the Weinmann function and also showed greatest reductions with treatment (-68%). Reproducibility (r) was the lowest for the Weinmann function (r = -49.7%), with other AIFs ranging from r = -27.8 to -39.2%.

CONCLUSION

Using the best performing AIF is essential to maximize the utility of quantitative DCE-MRI parameters in predicting response to NAC treatment. Applying our criteria, the modified Fritz-Hansen and cosine bolus approximated Parker AIF models performed best. The Fritz-Hansen and biexponential approximated Parker AIFs performed less well, and the Weinmann and femoral artery AIFs are not recommended.

ADVANCES IN KNOWLEDGE

We demonstrate that using the most appropriate AIF can aid successful prediction of response to NAC in breast cancer.

Effect of Radiofrequency Transmit Field Correction on Quantitative Dynamic Contrast-enhanced MR Imaging of the Breast at 3.0 T

PURPOSE

To investigate the effects of radiofrequency transmit field (B1(+)) correction on (a) the measured T1 relaxation times of normal breast tissue and malignant lesions and (b) the pharmacokinetically derived parameters of malignant breast lesions at 3 T.

MATERIALS AND METHODS

Ethics approval and informed consent were obtained. Between May 2013 and January 2014, 30 women (median age, 58 years; range, 32-83 years) with invasive ductal carcinoma of at least 10 mm were recruited to undergo dynamic contrast material-enhanced magnetic resonance (MR) imaging before surgery. B1(+) and T1 mapping sequences were performed to determine the effect of B1(+) correction on the native tissue relaxation time (T10) of fat, parenchyma, and malignant lesions in both breasts. Pharmacokinetic parameters were calculated before and after correction for B1(+) variations. Results were correlated with histologic grade by using the Kruskal-Wallis test.

RESULTS

Measurements showed a mean 37% flip angle difference between the right and left breast, which resulted in a 61% T10 difference in fat and a 41.5% difference in parenchyma between the two breasts. The T1 of lesions in the right breast increased by 58%, whereas that of lesions in the left breast decreased by 30% after B1(+) correction. The whole-tumor transendothelial permeability across the vascular compartment(K(trans)) of lesions in the right breast decreased by 41%, and that of lesions in the left breast increased by 46% after correction. A systematic increase in K(trans) was observed, with significant differences found across the histologic grades (P < .001). The effect size of B1(+) correction on K(trans) calculation was large for lesions in the right breast and moderate for lesions in the left breast (Cohen effect size, d = 0.86 and d = 0.59, respectively).

CONCLUSION

B1(+) correction demonstrates a substantial effect on the results of quantitative dynamic contrast-enhanced analysis of breast tissue at 3 T, which propagates into the pharmacokinetic analysis of tumors that is dependent on whether the tumor is located in the right or left breast.

18F-FDG PET-Derived Tumor Blood Flow Changes After 1 Cycle of Neoadjuvant Chemotherapy Predicts Outcome in Triple-Negative Breast Cancer

Previous studies have suggested that early changes in blood flow (BF) in response to neoadjuvant chemotherapy and evaluated with ¹⁵O-water are a surrogate biomarker of outcome in women with breast cancer. This study investigates, in the triple-negative breast cancer subtype, the prognostic relevance of tumor BF changes (ΔBF) in response to chemotherapy, assessed using a short dynamic ¹⁸F-FDG PET acquisition.

METHODS

Forty-six consecutive women with triple-negative breast cancer and an indication for neoadjuvant chemotherapy were prospectively included. Women benefited from a baseline ¹⁸F-FDG PET examination with a 2-min chest-centered dynamic acquisition, started at the time of ¹⁸F-FDG injection. Breast tumor perfusion was calculated from this short dynamic image using a first-pass model. This dynamic PET acquisition was repeated after the first cycle of chemotherapy to measure early ΔBF. Delayed static PET acquisitions were also performed (90 min after ¹⁸F-FDG injection) to measure changes in tumor glucose metabolism (ΔSUV_max). The association between tumor BF, clinicopathologic characteristics, and patients' overall survival (OS) was evaluated.

RESULTS

Median baseline tumor BF was 21 mL/min/100 g (range, 6-46 mL/min/100 g) and did not significantly differ according to tumor size, Scarf-Bloom-Richardson grade, or Ki-67 expression. Median tumor ∆BF was -30%, with highly scattered values (range, -93% to +118%). A weak correlation was observed between ΔBF and ∆SUV_max (r = +0.40, P = 0.01). The median follow-up was 30 mo (range, 6-73 mo). Eight women developed recurrent disease, 7 of whom died. Low OS was associated with menopausal history (P = 0.03), persistent or increased tumor vascularization on the interim PET (ΔBF cutoff = -30%; P = 0.03), non-breast-conserving surgery (P = 0.04), and the absence of a pathologic complete response (pCR) (P = 0.01). ΔBF and pCR provided incremental prognostic stratification: 3-y OS was 100% in pCR women, 87% in no-pCR women but achieving an early tumor BF response, and only 48% in no-pCR/no-BF-response women (ΔBF cutoff = -30%, P < 0.001).

CONCLUSION

This study suggests the clinical usefulness of an early user- and patient-friendly 2-min dynamic acquisition to monitor breast tumor ΔBF to neoadjuvant chemotherapy using ¹⁸F-FDG PET/CT. Monitoring tumor perfusion and angiogenesis response to treatment seems to be a promising target for PET tracers.

Multi-parametric MRI in the early prediction of response to neo-adjuvant chemotherapy in breast cancer: Value of non-modelled parameters

OBJECTIVE

To prospectively evaluate individual functional MRI metrics for the early prediction of pathological complete response (pCR) to neo-adjuvant chemotherapy (NAC) in breast cancer.

MATERIALS AND METHODS

Thirty-two women (median age 52 years; range 32-71 years) with biopsy proven breast cancer due to receive neo-adjuvant anthracycline and/or taxane-based chemotherapy were prospectively recruited following local research ethics committee approval and written informed consent. Breast MRI was performed prior to and after two cycles of NAC and pCR was assessed after surgery. The enhancement fraction (EF), tumour volume, initial area under the gadolinium curve (IAUGC), pharmacokinetic parameters (K(trans), kep and ve), the apparent diffusion coefficient (ADC) and R2* values, along with the percentage change in these parameters after two cycles were evaluated according to pCR status using an independent samples t-test. The area under the receiver operating characteristics curve (AUC) was calculated for each parameter. Linear discriminant analysis (LDA) determined the most important parameter in predicting pCR.

RESULTS

A reduction in the EF (-41% ± 38%) and tumour volume (-80% ± 25%) after 2 cycles of NAC were significantly greater in those achieving pCR (p=0.025, p=0.011 respectively). A reduction in the EF of 7% after 2 cycles of NAC identified those more likely to achieve pCR (AUC 0.76). AUC changes in other parameters were tumour volume (0.77), IAUGC (0.64), K(trans) (0.60), kep (0.68), ve (0.58), ADC (0.69) and R2* (0.41).

CONCLUSION

In a multi-parametric MRI model, the decrease in a non-model based vascular parameter the enhancement fraction as well as the tumour volume are the most important early predictors of pCR in breast cancer.

DCE-MRI and IVIM-MRI of rabbit Vx2 tumors treated with MR-HIFU-induced mild hyperthermia

Background

The purpose of this study is to investigate whether changes could be detected in dynamic contrast-enhanced (DCE) and intra-voxel incoherent motion (IVIM) MR parameters upon MR-guided high-intensity focused ultrasound (MR-HIFU)-induced hyperthermia in a rabbit Vx2 tumor model.

Methods

Five Vx2 tumor-bearing New Zealand white rabbits were treated with hyperthermia using a clinical MR-HIFU system. Data were acquired before and after hyperthermia. For the DCE analysis, the extended Tofts model was used. For the IVIM analysis, a Bayesian approach was used. Maps were reconstructed of the DCE parameters (K^trans, k_ep, and v_p) and IVIM parameters (D_t, f_p, and D_p). Individual parameter histograms and two-dimensional cross-correlation histograms were constructed to analyze changes in the parameters after hyperthermia. Changes in median values were tested for statistical significance with the Mann-Whitney U test.

Results

The MR temperature measurements confirmed that mild hyperthermia (40 to 42 °C) was successfully achieved in all rabbits. One rabbit died during treatment and was excluded from the analysis. In the remaining four rabbits, an increase in D_t was observed. In three rabbits, an increase in K^trans was observed, while in the other rabbits, all three DCE parameter values decreased. Mixed changes were seen for v_p and f_p.

Conclusions

Changes in DCE and IVIM parameters were detected after hyperthermia and were variable between the rabbits. DCE- and IVIM-MRI may be promising tools to assess tumor responses to hyperthermia. Further research in a larger number of subjects is necessary in order to assess their value for treatment response monitoring.

Background Parenchymal Enhancement on Preoperative Magnetic Resonance Imaging: Association With Recurrence-Free Survival in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy

To retrospectively investigate whether background parenchymal enhancement (BPE) of the contralateral breast on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is associated with therapeutic outcomes following neoadjuvant chemotherapy (NAC) in unilateral invasive breast cancer. The institutional review board approved this study, and informed consent was waived. Between 2009 and 2011, 93 women with unilateral invasive breast cancer (43 premenopausal women who performed pre-NAC MRI between days 7 and 20 of the menstrual cycle and 50 postmenopausal women) underwent NAC with pre- and post-NAC DCE-MRI before surgery. MRI features (BPE [minimal, mild, moderate, marked] of the contralateral breast, lesion size and number, lesion kinetics, and changes in lesion size) and clinicopathologic features were analyzed. Patients were grouped according to BPE category (high [moderate or marked] or low [minimal or mild]). Cox regression modeling was used to determine associations between MRI features and recurrence-free survival (RFS) after controlling for clinicopathologic variables. The mean follow-up period was 48.2 months. Twenty-three recurrences occurred (2 ipsilateral breasts, 6 regional, and 15 distant). On multivariate analysis, high BPE on pre-NAC MRI (hazard ratio [HR] = 3.851, P = 0.006) and triple-negative cancer (HR = 3.192, P = 0.002) were independent factors associated with worse RFS. A greater reduction of lesion size on post-NAC MRI (HR = 0.984, P = 0.021) was associated with better RFS. High BPE on pre-NAC MRI is significantly associated with worse RFS in an NAC setting. This study suggests that BPE on pre-NAC DCE-MRI may have potential as a predictor of long-term outcomes in breast cancer patients who undergo NAC.

Assessment of Tumor Angiogenesis: Dynamic Contrast-enhanced MR Imaging and Beyond

Dynamic contrast-enhanced (DCE) MR imaging is used increasingly often to evaluate tumor angiogenesis and the efficacy of antiangiogenic drugs. In clinical practice DCE-MR imaging applications are largely centered on lesion detection, characterization, and localization. In research, DCE-MR imaging helps inform decision making in early-phase clinical trials by showing efficacy and by selecting dose and schedule. However, the role of these techniques in patient selection is uncertain. Future research is required to optimize existing DCE-MR imaging methods and to fully validate these biomarkers for wider use in patient care and in drug development.

Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI

The purpose is to compare quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) metrics with imaging tumor size for early prediction of breast cancer response to neoadjuvant chemotherapy (NACT) and evaluation of residual cancer burden (RCB). Twenty-eight patients with 29 primary breast tumors underwent DCE-MRI exams before, after one cycle of, at midpoint of, and after NACT. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed with the standard Tofts and Shutter-Speed models (TM and SSM). After one NACT cycle the percent changes of DCE-MRI parameters K^trans (contrast agent plasma/interstitium transfer rate constant), v_e (extravascular and extracellular volume fraction), k_ep (intravasation rate constant)_, and SSM-unique τ_i (mean intracellular water lifetime) are good to excellent early predictors of pathologic complete response (pCR) vs. non-pCR, with univariate logistic regression C statistics value in the range of 0.804 to 0.967. v_e values after one cycle and at NACT midpoint are also good predictors of response, with C ranging 0.845 to 0.897. However, RECIST LD changes are poor predictors with C = 0.609 and 0.673, respectively. Post-NACT K^trans, τ_i, and RECIST LD show statistically significant (P < .05) correlations with RCB. The performances of TM and SSM analyses for early prediction of response and RCB evaluation are comparable. In conclusion, quantitative DCE-MRI parameters are superior to imaging tumor size for early prediction of therapy response. Both TM and SSM analyses are effective for therapy response evaluation. However, the τ_i parameter derived only with SSM analysis allows the unique opportunity to potentially quantify therapy-induced changes in tumor energetic metabolism.

Molecular classification of infiltrating breast cancer: toward personalized therapy

Breast cancer is a heterogeneous disease, which comprises several molecular and genetic subtypes, each with characteristic clinicobiologic behavior and imaging patterns. Traditional classification of breast cancer is based on the histopathologic features but offers limited prognostic value. Novel molecular characterization of breast cancer with cellular markers has allowed a new classification that offers prognostic value, with predictive categories of disease aggressiveness. These molecular signatures also open the door to personalized therapeutic options, with new receptor-targeted therapies. For example, invasive cancer subtypes such as the luminal A and B subtypes show better prognosis and response to hormone receptor-targeted therapies compared with the triple-negative subtypes; on the other hand, triple-negative tumors respond better than luminal tumors to chemotherapy. Tumors that display amplification of the oncogene ERBB2 (also known as the HER2/neu oncogene) respond to drugs directed against this oncogene, such as trastuzumab. The imaging aspects of tumors correlate with molecular subgroups, as well as other pathologic features such as nuclear grade. Smooth tumor margins at mammography may be suggestive of a triple-negative breast cancer, and a human epidermal growth factor receptor 2 (HER2)-positive tumor is characteristically a spiculated mass with calcifications. Low-grade ductal carcinoma in situ (DCIS) is better detected with mammography, although magnetic resonance (MR) imaging may allow better characterization of high-grade DCIS. MR imaging diffusion sequences show higher values for the apparent diffusion coefficient for triple-negative and HER2-positive subtypes, compared with luminal A and B tumors. MR imaging is also a useful tool in the prediction of tumor response after chemotherapy, especially for triple-negative and HER2-positive subtypes.

Texture analysis on MR images helps predicting non-response to NAC in breast cancer

BACKGROUND

To assess the performance of a predictive model of non-response to neoadjuvant chemotherapy (NAC) in patients with breast cancer based on texture, kinetic, and BI-RADS parameters measured from dynamic MRI.

METHODS

Sixty-nine patients with invasive ductal carcinoma of the breast who underwent pre-treatment MRI were studied. Morphological parameters and biological markers were measured. Pathological complete response was defined as the absence of invasive and in situ cancer in breast and nodes. Pathological non-responders, partial and complete responders were identified. Dynamic imaging was performed at 1.5 T with a 3D axial T1W GRE fat-suppressed sequence. Visual texture, kinetic and BI-RADS parameters were measured in each lesion. ROC analysis and leave-one-out cross-validation were used to assess the performance of individual parameters, then the performance of multi-parametric models in predicting non-response to NAC.

RESULTS

A model based on four pre-NAC parameters (inverse difference moment, GLN, LRHGE, wash-in) and k-means clustering as statistical classifier identified non-responders with 84 % sensitivity. BI-RADS mass/non-mass enhancement, biological markers and histological grade did not contribute significantly to the prediction.

CONCLUSION

Pre-NAC texture and kinetic parameters help predicting non-benefit to NAC. Further testing including larger groups of patients with different tumor subtypes is needed to improve the generalization properties and validate the performance of the predictive model.

Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms

PURPOSE

The purpose of this study is to investigate the association between breast cancer recurrence-free survival and breast magnetic resonance imaging (MRI) tumor enhancement dynamics which are quantified semi-automatically using computer algorithms.

METHODS

In this retrospective IRB-approved study, we analyzed data from 275 breast cancer patients at a single institution. Recurrence-free survival data were obtained from the medical record. Routine clinical pre-operative breast MRIs were performed in all patients. The tumors were marked on the MRIs by fellowship-trained breast radiologists. A previously developed computer algorithm was applied to the marked tumors to quantify the enhancement dynamics relative to the automatically assessed background parenchymal enhancement. To establish whether the contrast enhancement feature quantified by the algorithm was associated with recurrence-free survival, we constructed a Cox proportional hazards regression model with the computer-extracted feature as a covariate. We controlled for tumor grade and size (major axis length), patient age, patient race/ethnicity, and menopausal status.

RESULTS

The analysis showed that the semi-automatically obtained feature quantifying MRI tumor enhancement dynamics was independently predictive of recurrence-free survival (p=0.024).

CONCLUSION

Semi-automatically quantified tumor enhancement dynamics on MRI are predictive of recurrence-free survival in breast cancer patients.

Automatic ROI construction for analyzing time-signal intensity curve in dynamic contrast-enhanced MR imaging of the breast

Our purpose in this study was to construct a 3-dimensional (3D) region of interest (ROI) for analyzing the time-signal intensity curve (TIC) semi-automatically in dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging of the breast. DCE-MR breast imaging datasets were acquired by a 3.0-Tesla MR system with the use of a 3D fast gradient echo sequence. The essential idea in the new method was to analyze each pixel and to construct an ROI made up of pixels with similar TICs. First, an analyst selected a starting point in the contrast media-enhanced tumor. Second, we calculated Pearson's correlation coefficients (CCs) between the TIC in the starting coordinate selected by the analyst and the TIC in the other coordinates. Third, ROI pixels were selected if their CC threshold satisfied a level of coefficient variation of the ROI determined by prior research performed in our institution. We made a retrospective review of patients who underwent breast DCE-MR examination for pre-operative diagnosis. To confirm the feasibility of the resulting 3D-ROI from TIC analysis, we compared Fischer's score obtained from 3D-ROI by applying a new method to a score obtained from a manually selected 2-dimensional (2D) ROI which was used during routine clinical examination. The Fischer's scores obtained from both the automatically selected 3D-ROI and the manually selected 2D-ROI showed almost equivalent results. Thus, we considered that the new method was comparable to the conventional method. Furthermore, the new method has the potential to be used for evaluation of the extent of tumors.