Prognostic value of DCE-MRI in breast cancer patients undergoing neoadjuvant chemotherapy: a comparison with traditional survival indicators

Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods

PURPOSE

To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods.

METHODS

A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE-MRI data of 53 women with malignant (n = 25) or benign (n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE-MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k-space data and assessing the impact of theB 1 + $$ {\mathrm{B}}_1^{+} $$ field inhomogeneity on estimated parameters.

RESULTS

The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy ofv p $$ {v}_p $$ andPS $$ \mathrm{PS} $$ increase to about 33% and 51% without correction forB 1 + $$ {\mathrm{B}}_1^{+} $$ field.

CONCLUSION

We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.

Early Identification of Pathologic Complete Response to Neoadjuvant Chemotherapy Using Multiphase DCE-MRI by Siamese Network in Breast Cancer: A Longitudinal Multicenter Study

BACKGROUND

Siamese network (SN) using longitudinal DCE-MRI for pathologic complete response (pCR) identification lack a unified approach to phases selection.

PURPOSE

To identify pCR in early-stage NAC, using SN with longitudinal DCE-MRI and introducing IPS for phases selection.

STUDY TYPE

Multicenter, longitudinal.

POPULATION

Center A: 162 female patients (50.63 ± 8.41 years) divided 7:3 into training and internal validation cohorts. Center B: 61 female patients (50.08 ± 7.82 years) were used as an external validation cohort.

FIELD STRENGTH/SEQUENCE

Center A: single vendor 3.0 T with a compressed-sensing volume interpolated breath-hold examination sequence. Center B: single vendor 1.5 T with volume interpolated breath-hold examination sequence.

ASSESSMENT

Patients underwent DCE-MRI before and after two NAC cycles, with tumor regions of interest (ROI) manually delineated. Histopathology was the reference for pCR identification. Models developed included a clinical one, four SN models based on IPS-selected phases, and integrated models combining clinical and SN features.

STATISTICAL TESTS

Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The DeLong test was used to compare AUCs. Net reclassification improvement and integrated discrimination improvement (IDI) tests were employed for performance comparison. P < 0.05 was considered significant.

RESULTS

In internal and external validation cohorts, the clinical model showed AUCs of 0.760 and 0.718. SN and integrated models, with increasing phases via IPS, achieved AUCs ranging from 0.813 to 0.951 and 0.818 to 0.922. Notably, SN-3 and integrated-3 and integrated-4 outperformed the clinical model. However, input phases beyond 20% did not significantly enhance performance (IDI test: SN-4 vs. SN-3, P = 0.314 and 0.630; integrated-4 vs. integrated-3, P = 0.785 and 0.709).

DATA CONCLUSION

The longitudinal multiphase DCE-MRI based on the SN demonstrates promise for identifying pCR in breast cancer.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 4.

Early prediction of pathological complete response to neoadjuvant chemotherapy combining DCE-MRI and apparent diffusion coefficient values in breast Cancer

INTRODUCTION

Improving the early prediction of neoadjuvant chemotherapy (NAC) efficacy in breast cancer can lead to an improved prediction of the final prognosis of patients, which would be useful for promoting individualized treatment. This study aimed to explore the value of the combination of dynamic contrast-enhanced (DCE)-MRI parameters and apparent diffusion coefficient (ADC) values in the early prediction of pathological complete response (pCR) to NAC for breast cancer.

METHODS

A total of 119 (range, 28-69 years) patients with biopsy-proven breast cancer who received two cycles of NAC before breast surgery were retrospectively enrolled from our hospital database. Patients were divided into pCR and non pCR groups according to their pathological responses; a total of 24 patients achieved pCR, while 95 did not. The quantitative (K^trans; K_ep; V_e; IAUC) and semiquantitative parameters (W-in; W-out; TTP) of DCE-MRI that were significantly different between groups were combined with ADC values to explore their value in the early prediction of pCR to NAC for breast cancer. The independent T test was performed to compare the differences in DCE-MRI parameters and ADC values between the two groups. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC), sensitivity and specificity were calculated to evaluate the performance of the prediction.

RESULTS

The K^trans, K_ep, IAUC, ADC, W-in and TTP values were significantly different between the pCR and non pCR groups after NAC. The AUC (0.845) and specificity (95.79%) of the combined K^trans, K_ep, IAUC and ADC values were both higher than those of the individual parameters. The combination of W-in, TTP and ADC values had the highest AUC value (0.886) in predicting pCR, with a sensitivity and specificity of 87.5% and 82.11%, respectively.

CONCLUSIONS

The results suggested that the combination of ADC values and quantitative and semiquantitative DCE-MRI parameters, especially the combination of W-in, TTP, and ADC values, may improve the early prediction of pCR in breast cancer.

Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer

Neoadjuvant chemotherapy (NAC) is increasingly widely used in breast cancer treatment, and accurate evaluation of its response provides essential information for treatment and prognosis. Thus, the imaging tools used to quantify the disease response are critical in evaluating and managing patients treated with NAC. We discussed the recent progress, advantages, and disadvantages of common imaging methods in assessing the efficacy of NAC for breast cancer.

Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Dynamic Contrast-enhanced and Diffusion-weighted MRI

RATIONALE AND OBJECTIVES

The study investigated the potential of the combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging in predicting the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) after two cycles of NAC.

MATERIALS AND METHODS

Eighty-seven patients with breast cancer who underwent MR examination before and after two cycles of NAC were enrolled. The patients were randomly assigned to a training cohort and a validation cohort (3:1 ratio). MRI parameters including tumor longest diameter, time-signal intensity curve, early enhanced ratio (E₉₀), maximal enhanced ratio and ADC value were measured, and percentage change in MRI parameters were calculated. Univariate analysis and multivariate logistic regression analysis were used to evaluate independent predictors of pCR in the training cohort. The validation cohort was used to test the prediction model, and the nomogram was created based on the prediction model.

RESULTS

This study demonstrated that the ADC value after two cycles of NAC (OR = 1.041, 95% CI (1.002, 1.081); p = 0.037), percentage decrease in E₉₀ (OR = 0.927, 95% CI (0.881, 0.977); p =0.004) and percentage decrease in tumor size (OR = 0.948, 95% CI (0.909, 0.988); p = 0.011) were significantly important for independently predicting pCR. The prediction model yielded AUC of 0.939 and 0.944 in the training cohort and the validation cohort, respectively.

CONCLUSION

The combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging could accurately predict pCR after two cycles of NAC. The prediction model and the nomogram had strong predictive value to NAC.

Improved Classification of Benign and Malignant Breast Lesions Using Deep Feature Maximum Intensity Projection MRI in Breast Cancer Diagnosis Using Dynamic Contrast-enhanced MRI

PURPOSE

To develop a deep transfer learning method that incorporates four-dimensional (4D) information in dynamic contrast-enhanced (DCE) MRI to classify benign and malignant breast lesions.

MATERIALS AND METHODS

The retrospective dataset is composed of 1990 distinct lesions (1494 malignant and 496 benign) from 1979 women (mean age, 47 years ± 10). Lesions were split into a training and validation set of 1455 lesions (acquired in 2015-2016) and an independent test set of 535 lesions (acquired in 2017). Features were extracted from a convolutional neural network (CNN), and lesions were classified as benign or malignant using support vector machines. Volumetric information was collapsed into two dimensions by taking the maximum intensity projection (MIP) at the image level or feature level within the CNN architecture. Performances were evaluated using the area under the receiver operating characteristic curve (AUC) as the figure of merit and were compared using the DeLong test.

RESULTS

The image MIP and feature MIP methods yielded AUCs of 0.91 (95% CI: 0.87, 0.94) and 0.93 (95% CI: 0.91, 0.96), respectively, for the independent test set. The feature MIP method achieved higher performance than the image MIP method (∆AUC 95% CI: 0.003, 0.051; P = .03).

CONCLUSION

Incorporating 4D information in DCE MRI by MIP of features in deep transfer learning demonstrated superior classification performance compared with using MIP images as input in the task of distinguishing between benign and malignant breast lesions.Keywords: Breast, Computer Aided Diagnosis (CAD), Convolutional Neural Network (CNN), MR-Dynamic Contrast Enhanced, Supervised learning, Support vector machines (SVM), Transfer learning, Volume Analysis © RSNA, 2021.

Neoadjuvant breast cancer treatment response; tumor size evaluation through different conventional imaging modalities in the NeoDense study

BACKGROUND

Neoadjuvant chemotherapy (NACT) is offered to an increasing number of breast cancer (BC) patients, and comprehensive monitoring of treatment response is of utmost importance. Several imaging modalities are available to follow tumor response, although likely to provide different clinical information. We aimed to examine the association between early radiological response by three conventional imaging modalities and pathological complete response (pCR). Further, we investigated the agreement between these modalities pre-, during, and post-NACT, and the accuracy of predicting pathological residual tumor burden by these imaging modalities post-NACT.

MATERIAL AND METHODS

This prospective Swedish cohort study included 202 BC patients assigned to NACT (2014-2019). Breast imaging with clinically used modalities: mammography, ultrasound, and tomosynthesis was performed pre-, during, and post-NACT. We investigated the agreement of tumor size by the different imaging modalities, and their accuracy of tumor size estimation. Patients with a radiological complete response or radiological partial response (≥30% decrease in tumor diameter) during NACT were classified as radiological early responders.

RESULTS

Patients with an early radiological response by ultrasound had 2.9 times higher chance of pCR than early radiological non-responders; the corresponding relative chance for mammography and tomosynthesis tumor size measures was 1.8 and 2.8, respectively. Post-NACT, each modality, separately, could accurately estimate tumor size (within 5 mm margin compared to pathological evaluation) in 43-46% of all tumors. The diagnostic precision in predicting pCR post-NACT was similar between the three imaging modalities; however, tomosynthesis had slightly higher specificity and positive predictive values.

CONCLUSION

Breast imaging modalities correctly estimated pathological tumor size in less than half of the tumors. Based on this finding, predicting residual tumor size post-NACT is challenging using conventional imaging. Patients with early radiological non-response might need improved monitoring during NACT and be considered for changed treatment plans.

A Novel Marker, Based on Ultrasound Tomography, for Monitoring Early Response to Neoadjuvant Chemotherapy

OBJECTIVE

To evaluate the combination of tumor volume and sound speed as a potential imaging marker for assessing neoadjuvant chemotherapy (NAC) response.

METHODS

This study was carried out under an IRB-approved protocol (written consent required). Fourteen patients undergoing NAC for invasive breast cancer were examined with ultrasound tomography (UST) throughout their treatment. The volume (V) and the volume-averaged sound speed (VASS) of the tumors and their changes were measured for each patient. Time-dependent response curves of V and VASS were constructed individually for each patient and then as averages for the complete versus partial response groups in order to characterize differences between the two groups. Differences in group means were assessed for statistical significance using t-tests. Differences in shapes of group curves were evaluated with Kolmogorov-Smirnoff tests.

RESULTS

On average, tumor volume and sound speed in the partial response group showed a gradual decline in the first 60 days of treatment, while the complete response group showed a much steeper decline (P < 0.05). The shapes of the response curves of the two groups, corresponding to the entire treatment period, were also found to be significantly different (P < 0.05). Furthermore, large simultaneous drops in volume and sound speed in the first 3 weeks of treatment were characteristic only of the complete responders (P < 0.05).

CONCLUSION

This study demonstrates the feasibility of using UST to monitor NAC response, warranting future studies to better define the potential of UST for noninvasive, rapid identification of partial versus complete responders in women undergoing NAC.

3.0 T Magnetic Resonance Functional Imaging Quantitative Parameters for Differential Diagnosis of Benign and Malignant Lesions of the Breast

Objective: To investigate value of quantitative dynamic contrast-enhanced magnetic resonance imaging (MRI) parameters and apparent diffusion coefficient (ADC) value in differential diagnosis of breast benign and malignant lesions, and their correlation with prognostic factors of breast cancer. Methods: The study collected MRI images and clinical data from 232 female patients suspected of breast cancer. Philips INGENIA 3.0T superconducting magnetic resonance scanner was used for imaging examination. Complete pathological data of patients were collected, and the expression of ER, PR, HER-2, and Ki-67 were further investigated. Results: K_ep was higher in malignant breast lesion group than that in benign breast lesion group, and ADC value was lower in the former group than that in the latter group (both p < 0.05). The areas under the receiver operating characteristic curves for K_ep, ADC, and extravascular volume fraction (V_e) were 0.904 (95% confidence interval [CI]: 0.863-0.945), 0.813 (95% CI: 0.752-0.875), and 0.774 (95% CI: 0.707-0.841), respectively. Furthermore, according to the maximum Youden index, the specificity of K_ep and the sensitivity of ADC were high, which were 97.20% and 96.00%, respectively, with a cutoff value of 0.314 and 0.151, respectively. K_ep value in ER-positive expression group was significantly higher than that in ER-negative expression group (p < 0.05). K_ep value in PR-positive expression group was significantly higher than that in PR-negative expression group (p < 0.05). There was positive correlation between K_ep and expression of Ki-67 (p < 0.05). ADC value was negatively correlated with Ki-67 expression (p < 0.05). Conclusion: Quantitative parameters K_ep and ADC of 3.0 T MR functional imaging can be used as reference indexes for differential diagnosis of benign and malignant breast lesions and for biological behavior evaluation, indicating potential clinical value for noninvasive preoperative evaluation of breast cancer.

Textural radiomic features and time-intensity curve data analysis by dynamic contrast-enhanced MRI for early prediction of breast cancer therapy response: preliminary data

BACKGROUND

To investigate the potential of semiquantitative time-intensity curve parameters compared to textural radiomic features on arterial phase images by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for early prediction of breast cancer neoadjuvant therapy response.

METHODS

A retrospective study of 45 patients subjected to DCE-MRI by public datasets containing examination performed prior to the start of treatment and after the treatment first cycle ('QIN Breast DCE-MRI' and 'QIN-Breast') was performed. In total, 11 semiquantitative parameters and 50 texture features were extracted. Non-parametric test, receiver operating characteristic analysis with area under the curve (ROC-AUC), Spearman correlation coefficient, and Kruskal-Wallis test with Bonferroni correction were applied.

RESULTS

Fifteen patients with pathological complete response (pCR) and 30 patients with non-pCR were analysed. Significant differences in median values between pCR patients and non-pCR patients were found for entropy, long-run emphasis, and busyness among the textural features, for maximum signal difference, washout slope, washin slope, and standardised index of shape among the dynamic semiquantitative parameters. The standardised index of shape had the best results with a ROC-AUC of 0.93 to differentiate pCR versus non-pCR patients.

CONCLUSIONS

The standardised index of shape could become a clinical tool to differentiate, in the early stages of treatment, responding to non-responding patients.

Kinetic volume analysis on dynamic contrast-enhanced MRI of triple-negative breast cancer: associations with survival outcomes

OBJECTIVE

To evaluate the associations between computer-aided diagnosis (CAD)-generated kinetic volume parameters and survival in triple-negative breast cancer (TNBC) patients.

METHODS

40 patients with TNBC who underwent pre-operative MRI between March 2008 and March 2014 were included. We analyzed CAD-generated parameters on dynamic contrast-enhanced MRI, visual MRI assessment, and histopathological data. Cox proportional hazards models were used to determine associations with survival outcomes.

RESULTS

12 of the 40 (30.0%) patients experienced recurrence and 7 died of breast cancer after a median follow-up of 73.6 months. In multivariate analysis, higher percentage volume (%V) with more than 200% initial enhancement rate correlated with worse disease-specific survival (hazard ratio, 1.12; 95% confidence interval, 1.02-1.22; p-value, 0.014) and higher %V with more than 100% initial enhancement rate followed by persistent curve type at 30% threshold correlated with worse disease-specific survival (hazard ratio, 1.33; 95% confidence interval, 1.10-1.61; p-value, 0.004) and disease-free survival (hazard ratio, 1.27; 95% confidence interval, 1.12-1.43; p-value, 0.000).

CONCLUSION

CAD-generated kinetic volume parameters may correlate with survival in TNBC patients. Further study would be necessary to validate our results on larger cohorts.

ADVANCES IN KNOWLEDGE

CAD generated kinetic volume parameters on breast MRI can predict recurrence and survival outcome of patients in TNBC. Varying the enhancement threshold improved the predictive performance of CAD generated kinetic volume parameter.

Quantitative mathematical objective evaluation of contrast-enhanced spectral mammogram in the assessment of response to neoadjuvant chemotherapy and prediction of residual disease in breast cancer

Background

The aim of the study is to initiate a new quantitative mathematical objective tool for evaluation of response to neoadjuvant chemotherapy (NAC) and prediction of residual disease in breast cancer using contrast-enhanced spectral mammography (CESM). Forty-two breast cancer patients scheduled for receiving NAC were included. All patients underwent two CESM examinations: pre and post NAC. To assess the response to neoadjuvant chemotherapy, we used a mathematical image analysis software that can calculate the difference in the intensity of enhancement between the pre and post neoadjuvant contrast images (MATLAB and Simulink) (Release 2013b). The proposed technique used the pre and post neoadjuvant contrast images as inputs. The technique consists of three main steps: (1) preprocessing, (2) extracting the region of interest (ROI), and (3) assessment of the response to chemotherapy by measuring the percentage of change in the intensity of enhancement of malignant lesions in the pre and post neoadjuvant CESM studies using a quantitative mathematical technique. This technique depends on the analysis of number of pixels included within the ROI. We compared this technique with the currently used method of evaluation: RECIST 1.1 (response evaluation criteria in solid tumors 1.1) and using another combined response evaluation approach using both RECIST 1.1 in addition to a subjective visual evaluation. Results were then correlated with the postoperative pathology evaluation using Miller–Payne grades. For statistical evaluation, patients were classified into responders and non-responders in all evaluation methods.

Results

According to the Miller–Payne criteria, 39/42 (92.9%) of the participants were responders (Miller–Payne grades III, IV, and IV) and 3/42 (7.1%) were non-responders (Miller–Payne grades I and II). Using the proposed technique, 39/39 (100%) were responders in comparison to 38/39 patients (97.4%) using the combined criteria and 34/39 (87.2%) using the RECIST 1.1 evaluation. The calculated correlation coefficient of the proposed quantitative objective mathematical technique, RECIST 1.1 criteria, and the combined method was 0.89, 0.59, and 0.69 respectively. With classification of patients into responder and non-responders, the objective mathematical evaluation showed higher sensitivity, positive and negative predictive values, and overall accuracy (100%, 97.5%, 100%, and 85.7% respectively) compared to RECIST 1.1 evaluation (87.2%, 97.1%, 28.6%, and 54.8% respectively) and the combined response method (97.4%, 97.4%, 66.7%, and 85.7% respectively).

Conclusion

Quantitative mathematical objective evaluation using CESM images allows objective quantitative and accurate evaluation of the response of breast cancer to chemotherapy and is recommended as an alternative to the subjective techniques as a part of the pre-operative workup.

Whole-lesion histogram and texture analyses of breast lesions on inline quantitative DCE mapping with CAIPIRINHA-Dixon-TWIST-VIBE

PURPOSE

To investigate the diagnostic capability of whole-lesion (WL) histogram and texture analysis of dynamic contrast-enhanced (DCE) MRI inline-generated quantitative parametric maps using CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) to differentiate malignant from benign breast lesions and breast cancer subtypes.

MATERIALS AND METHODS

From February 2018 to November 2018, DCE MRI using CDTV was performed on 211 patients. The inline-generated parametric maps included K^trans, k_ep, V_e, and IAUGC₆₀. Histogram and texture features were extracted from the above parametric maps respectively based on a WL analysis. Student's t tests, one-way ANOVAs, Mann-Whitney U tests, Jonckheere-Terpstra tests, and ROC curves were used for statistical analysis.

RESULTS

Compared with benign breast lesions, malignant breast lesions showed significantly higher K^trans_{_median, 5th percentile, entropy, and diff-entropy}, IAUGC_{60_median, 5th percentile, entropy, and diff-entropy}, k_{ep_mean, median, 5th percentile, entropy, and diff-entropy}, and V_{e_95th percentile, diff-variance, and contrast}, and significantly lower k_{ep_skewness} and V_{e_SD, entropy, diff-entropy, and skewness} (all p ≤ 0.011). The combination of all the extracted parameters yielded an AUC of 0.85 (sensitivity 76%, specificity 86%). k_{ep_contrast} showed a significant difference among different subtypes of breast cancer (p = 0.006). k_{ep_skewness} showed a significant difference between lymph node-positive and lymph node-negative breast cancer (p = 0.007). The IAGC_{60_5th percentile} had an AUC of 0.71 (sensitivity 50%, specificity 91%) for differentiating between high- and low-proliferation groups of breast cancer.

CONCLUSIONS

The WL histogram and texture analyses of CDTV-DCE-derived parameters may give additional information for further evaluation of breast cancer.

KEY POINTS

• Inline DCE mapping with CDTV is effective and time-saving. • WL histogram and texture-extracted features could distinguish breast cancer from benign lesions accurately. • k_{ep_contrast}, k_{ep_skewness}, and IAUGC_{60_5th percentile} could predict breast cancer subtypes, lymph node metastasis, and proliferation abilities, respectively.

Dynamic contrast-enhanced MR imaging of the prostate: intraindividual comparison of gadoterate meglumine and gadobutrol

OBJECTIVES

To intraindividually compare the signal-enhancing effect of 0.5 M gadoterate meglumine and 1.0 M gadobutrol in dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging of the prostate.

METHODS

Fifty patients who underwent two 3-T MR examinations of the prostate were included in this IRB-approved retrospective uncontrolled, unrandomized study. All received two scans (mean time interval, 20.5 months) including T1-weighted DCE-MR imaging, one with 0.5 M gadoterate meglumine and one with 1.0 M gadobutrol. Equimolar doses of gadolinium (0.1 mmol/kg body weight) were administered with identical injection speed (2 mL/s), resulting in differing gadolinium delivery rate. An identical region of interest (ROI_tz) within a BPH-node was identified on both scans. The area under the time-enhancement curve of each ROI_tz from 0 to 180 s post contrast arrival and pharmacokinetic parameters were calculated. Relative enhancement and signal-to-noise (SNR) and contrast-to-noise (CNR) ratios in the delayed phase at about 180 s were compared between both agents.

RESULTS

There was a significantly larger area under the time-enhancement curve (5.53 vs 4.97 p = 0.0007) and higher relative enhancement of BPH nodules (2.23 vs 1.96 p < 0.0001) with gadobutrol compared with gadoterate meglumine. There were no significant differences in SNR (44.55 vs 37.63 p = 0.12), CNR (31.22 vs 26.39 p = 0.18), and pharmacokinetic parameters Ktrans (0.31 vs 0.32 p = 0.86), Ve (1.36 vs 0.98 p = 0.13), and Kep (0.34 vs 0.36 p = 0.12).

CONCLUSIONS

At equimolar doses, increased gadolinium delivery over time using gadobutrol provides higher relative enhancement parameters in BPH nodules compared with gadoterate meglumine, but does not translate into improved SNR or CNR.

KEY POINTS

• At equal injection rate and equimolar total dose, gadobutrol compared with gadoterate meglumine provides a significantly greater relative enhancement in DCE-MR imaging of BPH over the first 180 s. • There are no significant differences in SNRs, CNRs, and pharmacokinetic parameters between the two GBCAs.

Differentiating the lymph node metastasis of breast cancer through dynamic contrast-enhanced magnetic resonance imaging

Objective:

Lymph node metastasis is an important trait of breast cancer, and tumors with different lymph node statuses require various clinical treatments. This study was designed to evaluate the lymph node metastasis of breast cancer through pharmacokinetic and histogram analysis via dynamic contrast-enhanced (DCE) MRI.

Methods and materials:

A retrospective analysis was conducted to quantitatively evaluate the lymph node statuses of patients with breast cancer. A total of 75 patients, i.e. 34 patients with lymph node metastasis and 41 patients without lymph node metastasis, were involved in this research. Of the patients with lymph node metastases, 19 had sentinel lymph node metastasis, and 15 had axillary lymph node metastasis. MRI was conducted using a 3.0 T imaging device. Segmentation was carried out on the regions of interest (ROIs) in breast tumors under DCE-MRI, and pharmacokinetic and histogram parameters were calculated from the same ROIs. Mann–Whitney U test was performed, and receiver operating characteristic curves for the parameters of the two groups were constructed to determine their diagnostic values.

Results:

Pharmacokinetic parameters, including Ktrans, Kep, area under the curve of time–concentration, and time to peak, which were derived from the extended Tofts linear model for DCE-MRI, could highlight the tumor areas in the breast and reveal the increased perfusion. Conversely, the pharmacokinetic parameters showed no significant difference between the patients with and without lymph node metastases. By contrast, the parameters from the histogram analysis yielded promising results. The entropy of the ROIs exhibited the best diagnostic ability between patients with and without lymph node metastases (p < 0.01, area under the curve of receiver operating characteristic = 0.765, specificity = 0.706, sensitivity = 0.780).

Conclusion:

In comparison with the pharmacokinetic parameters, the histogram analysis of the MR images could reveal the differences between patients with and without lymph node metastases. The entropy from the histogram indicated that the diagnostic ability was highly sensitive and specific.

Advances in knowledge:

This research gave out a promising result on the differentiating lymph node metastases through histogram analysis on tumors in DCE-MR images. Histogram could reveal the tumors heterogenicity between patients with different lymph node status.

Morphological and Semiquantitative Kinetic Analysis on Dynamic Contrast Enhanced MRI in Triple Negative Breast Cancer Patients

RATIONALE AND OBJECTIVES

The aim of this study was to retrospectly investigate the association between different breast cancer (BC) immunohistochemical subtypes and morphological and semiquantitative kinetic analysis on breast magnetic resonance imaging (MRI) performed before surgery treatment. Specifically we aimed to assess MRI features of triple-negative breast cancer (TNBC) compared to the other BC subtypes (nTNBC).

MATERIALS AND METHODS

Patients undergone to breast MRI and then diagnosed with BC by core-needle biopsy were included. The MRI morphological and kinetic features were studied. Parametric and non-parametric tests were used, as appropriate.

RESULTS

Seventy-five BC patients were considered, 30 patients included in TNBC Group and 45 patients included in nTNBC Group. We found in TNBC Group a greater mean lesion size (P <0.001), a rim enhancement imaging (P=0.003), and a higher intratumoral signal intensity on T2-weighted images (P=0.03) with respect to nTNBC Group. We noticed that TNBC patients presented a lower grade of BPE when compared to the nTBC Group (P< 0.02). TNBC Group showed lower EPeak values (P=0.003) and higher SER values (P=0.02) with respect to the nTNBC Group. In addition, stratifying kinetics parameters according to the tumor grade, the TNBC Group presented higher tumor grade (G3) (P< 0.005) and this subgroup had higher SER values when compared to TNBCs showing a lower tumor grade (G1 and G2) (P=0.03).

CONCLUSION

After validation by large-scale studies, the morphological and semiquantitative kinetic analysis on dynamic contrast enhanced MRI may help in the pretreatment risk stratification of patients with TNBC and in evidence-based clinical decision support.

Diffuse optical spectroscopy for monitoring the responses of patients with breast cancer to neoadjuvant chemotherapy: A meta-analysis

BACKGROUND

This study aimed to investigate the potential of diffuse optical spectroscopy (DOT) for monitoring the responses of patients with breast cancer to neoadjuvant chemotherapy (NAC).

METHODS

We searched PubMed, EMBASE, Cochrane Database of Systematic Reviews, and Web of Science for relevant studies. Data were extracted for pooled analysis, heterogeneity testing, threshold effect testing, sensitivity analysis, publication bias analysis, and subgroup analysis.

RESULTS

The pooled meta-analysis of the 10 eligible studies that included 422 patients indicated the high performance of DOT for monitoring total patient responses to NAC (OR = 14.78, 95% CI: 8.23-26.54, P < .001), with low significant heterogeneity (I = 7.2%, P = .375). DOT possessed an area under the curve of 0.84 (95% CI: 0.81-0.87) to distinguish total patient responses to NAC. Subgroup analysis showed that the pooled sensitivity of DOT for monitoring pathologic complete response to NAC was 87%, and the pooled specificity was 70%. Meanwhile, the pooled sensitivity of DOT for monitoring pathologic complete and partial responses to NAC was 82%, and the pooled specificity was 82%. Although Begg's funnel plot (P = .049) indicated the presence of publication bias among the included studies, trim-and-fill method verified the stability of the pooled outcomes.

CONCLUSION

Our meta-analysis of available published data indicated that DOT can be potentially used to predict and monitor patient responses to NAC. A larger study population is needed to fully assess the use of DOT for guiding therapies and predicting responses of individual subjects to NAC.

Contrast agents in dynamic contrast-enhanced magnetic resonance imaging

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive method to assess angiogenesis, which is widely used in clinical applications including diagnosis, monitoring therapy response and prognosis estimation in cancer patients. Contrast agents play a crucial role in DCE-MRI and should be carefully selected in order to improve accuracy in DCE-MRI examination. Over the past decades, there was much progress in the development of optimal contrast agents in DCE-MRI. In this review, we describe the recent research advances in this field and discuss properties of contrast agents, as well as their advantages and disadvantages. Finally, we discuss the research perspectives for improving this promising imaging method.

Spatiotemporal features of DCE-MRI for breast cancer diagnosis

BACKGROUND AND OBJECTIVE

Breast cancer is a major cause of mortality among women if not treated in early stages. Previous works developed non-invasive diagnosis methods using imaging data, focusing on specific sets of features that can be called spatial features or temporal features. However, limited set of features carry limited information, requiring complex classification methods to diagnose the disease. For non-invasive diagnosis, different imaging modalities can be used. DCE-MRI is one of the best imaging techniques that provides temporal information about the kinetics of the contrast agent in suspicious lesions along with acceptable spatial resolution.

METHODS

We have extracted and studied a comprehensive set of features from spatiotemporal space to obtain maximum available information from the DCE-MRI data. Then, we have applied a feature fusion technique to remove common information and extract a feature set with maximum information to be used by a simple classification method. We have also implemented conventional feature selection and classification methods and compared them with our proposed approach.

RESULTS

Experimental results obtained from DCE-MRI data of 26 biopsy or short-term follow-up proven patients illustrate that the proposed method outperforms alternative methods. The proposed method achieves a classification accuracy of 99% without missing any of the malignant cases.

CONCLUSIONS

The proposed method may help physicians determine the likelihood of malignancy in breast cancer using DCE-MRI without biopsy.

Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks

Deep learning methods have been shown to improve breast cancer diagnostic and prognostic decisions based on selected slices of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). However, incorporation of volumetric and temporal components into DCE-MRIs has not been well studied. We propose maximum intensity projection (MIP) images of subtraction MRI as a way to simultaneously include four-dimensional (4-D) images into lesion classification using convolutional neural networks (CNN). The study was performed on a dataset of 690 cases. Regions of interest were selected around each lesion on three MRI presentations: (i) the MIP image generated on the second postcontrast subtraction MRI, (ii) the central slice of the second postcontrast MRI, and (iii) the central slice of the second postcontrast subtraction MRI. CNN features were extracted from the ROIs using pretrained VGGNet. The features were utilized in the training of three support vector machine classifiers to characterize lesions as malignant or benign. Classifier performances were evaluated with fivefold cross-validation and compared based on area under the ROC curve (AUC). The approach using MIPs [Formula: see text] outperformed that using central-slices of either second postcontrast MRIs [Formula: see text] or second postcontrast subtraction MRIs [Formula: see text], at statistically significant levels.

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis

This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI (p<0.001). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.

Interim heterogeneity changes measured using entropy texture features on T2-weighted MRI at 3.0 T are associated with pathological response to neoadjuvant chemotherapy in primary breast cancer

OBJECTIVES

To investigate whether interim changes in hetereogeneity (measured using entropy features) on MRI were associated with pathological residual cancer burden (RCB) at final surgery in patients receiving neoadjuvant chemotherapy (NAC) for primary breast cancer.

METHODS

This was a retrospective study of 88 consenting women (age: 30-79 years). Scanning was performed on a 3.0 T MRI scanner prior to NAC (baseline) and after 2-3 cycles of treatment (interim). Entropy was derived from the grey-level co-occurrence matrix, on slice-matched baseline/interim T2-weighted images. Response, assessed using RCB score on surgically resected specimens, was compared statistically with entropy/heterogeneity changes and ROC analysis performed. Association of pCR within each tumour immunophenotype was evaluated.

RESULTS

Mean entropy percent differences between examinations, by response category, were: pCR: 32.8%, RCB-I: 10.5%, RCB-II: 9.7% and RCB-III: 3.0%. Association of ultimate pCR with coarse entropy changes between baseline/interim MRI across all lesions yielded 85.2% accuracy (area under ROC curve: 0.845). Excellent sensitivity/specificity was obtained for pCR prediction within each immunophenotype: ER+: 100%/100%; HER2+: 83.3%/95.7%, TNBC: 87.5%/80.0%.

CONCLUSIONS

Lesion T2 heterogeneity changes are associated with response to NAC using RCB scores, particularly for pCR, and can be useful across all immunophenotypes with good diagnostic accuracy.

KEY POINTS

• Texture analysis provides a means of measuring lesion heterogeneity on MRI images. • Heterogeneity changes between baseline/interim MRI can be linked with ultimate pathological response. • Heterogeneity changes give good diagnostic accuracy of pCR response across all immunophenotypes. • Percentage reduction in heterogeneity is associated with pCR with good accuracy and NPV.

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.

Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer

Purpose To identify the molecular basis of quantitative imaging characteristics of tumor-adjacent parenchyma at dynamic contrast material-enhanced magnetic resonance (MR) imaging and to evaluate their prognostic value in breast cancer. Materials and Methods In this institutional review board-approved, HIPAA-compliant study, 10 quantitative imaging features depicting tumor-adjacent parenchymal enhancement patterns were extracted and screened for prognostic features in a discovery cohort of 60 patients. By using data from The Cancer Genome Atlas (TCGA), a radiogenomic map for the tumor-adjacent parenchymal tissue was created and molecular pathways associated with prognostic parenchymal imaging features were identified. Furthermore, a multigene signature of the parenchymal imaging feature was built in a training cohort (n = 126), and its prognostic relevance was evaluated in two independent cohorts (n = 879 and 159). Results One image feature measuring heterogeneity (ie, information measure of correlation) was significantly associated with prognosis (false-discovery rate < 0.1), and at a cutoff of 0.57 stratified patients into two groups with different recurrence-free survival rates (log-rank P = .024). The tumor necrosis factor signaling pathway was identified as the top enriched pathway (hypergeometric P < .0001) among genes associated with the image feature. A 73-gene signature based on the tumor profiles in TCGA achieved good association with the tumor-adjacent parenchymal image feature (R² = 0.873), which stratified patients into groups regarding recurrence-free survival (log-rank P = .029) and overall survival (log-rank P = .042) in an independent TCGA cohort. The prognostic value was confirmed in another independent cohort (Gene Expression Omnibus GSE 1456), with log-rank P = .00058 for recurrence-free survival and log-rank P = .0026 for overall survival. Conclusion Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with the tumor necrosis signaling pathway and poor survival in breast cancer. ^© RSNA, 2017 Online supplemental material is available for this article.

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.

Assessing tumor vascularization as a potential biomarker of imatinib resistance in gastrointestinal stromal tumors by dynamic contrast-enhanced magnetic resonance imaging

BACKGROUND

Most metastatic gastrointestinal stromal tumors (GISTs) develop resistance to the first-line imatinib treatment. Recently, increased vessel density and angiogenic markers were reported in GISTs with a poor prognosis, suggesting that angiogenesis is implicated in GIST tumor progression and resistance. The purpose of this study was to investigate the relationship between tumor vasculature and imatinib resistance in different GIST mouse models using a noninvasive magnetic resonance imaging (MRI) functional approach.

METHODS

Immunodeficient mice (n = 8 for each cell line) were grafted with imatinib-sensitive (GIST882 and GIST-T1) and imatinib-resistant (GIST430) human cell lines. Dynamic contrast-enhanced MRI (DCE-MRI) was performed on GIST xenografts to quantify tumor vessel permeability (K ^trans) and vascular volume fraction (v _p). Microvessel density (MVD), permeability (mean dextran density, MDD), and angiogenic markers were evaluated by immunofluorescence and western blot assays.

RESULTS

Dynamic contrast-enhanced magnetic resonance imaging showed significantly increased vessel density (P < 0.0001) and permeability (P = 0.0002) in imatinib-resistant tumors compared to imatinib-sensitive ones. Strong positive correlations were observed between MRI estimates, K ^trans and v _p, and their related ex vivo values, MVD (r = 0.78 for K ^trans and r = 0.82 for v _p) and MDD (r = 0.77 for K ^trans and r = 0.94 for v _p). In addition, higher expression of vascular endothelial growth factor receptors (VEGFR2 and VEFGR3) was seen in GIST430.

CONCLUSIONS

Dynamic contrast-enhanced magnetic resonance imaging highlighted marked differences in tumor vasculature and microenvironment properties between imatinib-resistant and imatinib-sensitive GISTs, as also confirmed by ex vivo assays. These results provide new insights into the role that DCE-MRI could play in GIST characterization and response to GIST treatment. Validation studies are needed to confirm these findings.

Early perfusion changes within 1 week of systemic treatment measured by dynamic contrast-enhanced MRI may predict survival in patients with advanced hepatocellular carcinoma

OBJECTIVES

To correlate early changes in the parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) within 1 week of systemic therapy with overall survival (OS) in patients with advanced hepatocellular carcinoma (HCC).

METHODS

Eighty-nine patients with advanced HCC underwent DCE-MRI before and within 1 week following systemic therapy. The relative changes of six DCE-MRI parameters (Peak, Slope, AUC, Ktrans, Kep and Ve) of the tumours were correlated with OS using the Kaplan-Meier model and the double-sided log-rank test.

RESULTS

All patients died and the median survival was 174 days. Among the six DCE-MRI parameters, reductions in Peak, AUC, and Ktrans, were significantly correlated with one another. In addition, patients with a high Peak reduction following treatment had longer OS (P = 0.023) compared with those with a low Peak reduction. In multivariate analysis, a high Peak reduction was an independent favourable prognostic factor in all patients [hazard ratio (HR), 0.622; P = 0.038] after controlling for age, sex, treatment methods, tumour size and stage, and Eastern Cooperative Oncology Group performance status.

CONCLUSIONS

Early perfusion changes within 1 week following systemic therapy measured by DCE-MRI may aid in the prediction of the clinical outcome in patients with advanced HCC.

KEY POINTS

• DCE-MRI is helpful to evaluate perfusion changes of HCC after systemic treatment. • Early perfusion changes within 1 week after treatment may predict overall survival. • High Peak reduction was an independent favourable prognostic factor after systemic treatment.

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.

Can Dynamic Contrast-Enhanced MRI (DCE-MRI) and Diffusion-Weighted MRI (DW-MRI) Evaluate Inflammation Disease: A Preliminary Study of Crohn's Disease

The aim of the study was to investigate diagnosis efficacy of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) in Crohn's disease (CD). To find out the correlations between functional MRI parameters including K, Kep, Ve, Vp, and apparent diffusion coefficient (ADC) with a serologic biomarker. The relationships between pharmacokinetic parameters and ADC were also studied.Thirty-two patients with CD (22 men, 10 women; mean age: 30.5 years) and 18 healthy volunteers without any inflammatory disease (10 men, 8 women; mean age, 34.11 years) were enrolled into this approved prospective study. Pearson analysis was used to evaluate the correlation between K, Kep, Ve, Vp, and C-reactive protein (CRP), ADC, and CRP respectively. The diagnostic efficacy of the functional MRI parameters in terms of sensitivity and specificity were analyzed by receiver operating characteristic (ROC) curve analyses. Optimal cut-off values of each functional MRI parameters for differentiation of inflammatory from normal bowel were determined according to the Youden criterion.Mean value of K in the CD group was significantly higher than that of normal control group. Similar results were observed for Kep and Ve. On the contrary, the ADC value was lower in the CD group than that in the control group. K and Ve were shown to be correlated with CRP (r = 0.725, P < 0.001; r = 0.533, P = 0.002), meanwhile ADC showed negative correlation with CRP (r = -0.630, P < 0.001). There were negative correlations between the pharmacokinetic parameters and ADC, such as K to ADC (r = -0.856, P < 0.001), and Ve to ADC (r = -0.451, P = 0.01). The area under the curve (AUC) was 0.994 for K (P < 0.001), 0.905 for ADC (P < 0.001), 0.806 for Ve (P < 0.001), and 0.764 for Kep (P = 0.002). The cut-off point of the K was found to be 0.931 min. This value provided the best trade-off between sensitivity (93.8%) and specificity (100%). The best cut-off point of ADC was 1.11 × 10 mm/s. At this level, sensitivity was 100% and specificity was 68.8%.DCE-MRI and DW-MRI were helpful in the diagnosis of CD. Quantitative MRI parameters could be used to assess the severity of inflammation. The relationships between pharmacokinetic parameters (K and Ve) and ADC reflected microstructure and microcirculation of CD to some extent.

Relating Doses of Contrast Agent Administered to TIC and Semi-Quantitative Parameters on DCE-MRI: Based on a Murine Breast Tumor Model

Objective

To explore the changes in the time-signal intensity curve(TIC) type and semi-quantitative parameters of dynamic contrast-enhanced(DCE)imaging in relation to variations in the contrast agent(CA) dosage in the Walker 256 murine breast tumor model, and to determine the appropriate parameters for the evaluation ofneoadjuvantchemotherapy(NAC)response.

Materials and Methods

Walker 256 breast tumor models were established in 21 rats, which were randomly divided into three groups of7rats each. Routine scanning and DCE-magnetic resonance imaging (MRI) of the rats were performed using a 7T MR scanner. The three groups of rats were administered different dosages of the CA0.2mmol/kg, 0.3mmol/kg, and 0.5mmol/kg, respectively; and the corresponding TICs the semi-quantitative parameters were calculated and compared among the three groups.

Results

The TICs were not influenced by the CA dosage and presented a washout pattern in all of the tumors evaluated and weren’t influenced by the CA dose. The values of the initial enhancement percentage(E_first), initial enhancement velocity(V_first), maximum signal(S_max), maximum enhancement percentage(E_max), washout percentage(E_wash), and signal enhancement ratio(SER) showed statistically significant differences among the three groups (F = 16.952, p = 0.001; F = 69.483, p<0.001; F = 54.838, p<0.001; F = 12.510, p = 0.003; F = 5.248, p = 0.031; F = 9.733, p = 0.006, respectively). However, the values of the time to peak(T_peak), maximum enhancement velocity(V_max), and washout velocity(V_wash)did not differ significantly among the three dosage groups (F = 0.065, p = 0.937; F = 1.505, p = 0.273; χ2 = 1.423, p = 0.319, respectively); the washout slope(Slope_wash), too, was uninfluenced by the dosage(F = 1.654, p = 0.244).

Conclusion

The CA dosage didn’t affect the TIC type, T_peak, V_max, V_wash or Slope_wash. These dose-independent parameters as well as the TIC type might be more useful for monitoring the NAC response because they allow the comparisons of the DCE data obtained using different CA dosages.

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.

Incorporating Imaging Into the Locoregional Management of Breast Cancer

Although some breast cancers present as palpable masses or with other clinical findings, many are detected at screening. Most screening is currently done with digital mammography, but high-risk patients or those with dense breast tissue may undergo additional screening examinations with magnetic resonance imaging or ultrasound. Additionally, digital breast tomosynthesis, contrast-enhanced mammography, and molecular breast imaging are newer technologies available at some sites. Optimal usage of breast imaging technologies remains controversial, both in screening and diagnostic settings following a new diagnosis of breast cancer. This article will review well established and newer, alternative breast imaging technologies as well as recent data regarding their role in optimizing patient care.