Role of quantitative pharmacokinetic parameter (transfer constant: K(trans)) in the characterization of breast lesions on MRI

High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4

BACKGROUND

BI-RADS 4 breast lesions are suspicious for malignancy with a range from 2 to 95%, indicating that numerous benign lesions are unnecessarily biopsied. Thus, we aimed to investigate whether high-temporal-resolution dynamic contrast-enhanced MRI (H_DCE-MRI) would be superior to conventional low-temporal-resolution DCE-MRI (L_DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions.

METHODS

This single-center study was approved by the IRB. From April 2015 to June 2017, patients with breast lesions were prospectively included and randomly assigned to undergo either H_DCE-MRI, including 27 phases, or L_DCE-MRI, including 7 phases. Patients with BI-RADS 4 lesions were diagnosed by the senior radiologist in this study. Using a two-compartment extended Tofts model and a three-dimensional volume of interest, several pharmacokinetic parameters reflecting hemodynamics, including K^trans, K_ep, V_e, and V_p, were obtained from the intralesional, perilesional and background parenchymal enhancement areas, which were labeled the Lesion, Peri and BPE areas, respectively. Models were developed based on hemodynamic parameters, and the performance of these models in discriminating between benign and malignant lesions was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 140 patients were included in the study and underwent H_DCE-MRI (n = 62) or L_DCE-MRI (n = 78) scans; 56 of these 140 patients had BI-RADS 4 lesions. Some pharmacokinetic parameters from H_DCE-MRI (Lesion_K^trans, K_ep, and V_p; Peri_K^trans, K_ep, and V_p) and from L_DCE-MRI (Lesion_K_ep, Peri_V_p, BPE_K^trans and BPE_V_p) were significantly different between benign and malignant breast lesions (P < 0.01). ROC analysis showed that Lesion_K^trans (AUC = 0.866), Lesion_K_ep (AUC = 0.929), Lesion_V_p (AUC = 0.872), Peri_K^trans (AUC = 0.733), Peri_K_ep (AUC = 0.810), and Peri_V_p (AUC = 0.857) in the H_DCE-MRI group had good discrimination performance. Parameters from the BPE area showed no differentiating ability in the H_DCE-MRI group. Lesion_K_ep (AUC = 0.767), Peri_V_p (AUC = 0.726), and BPE_K^trans and BPE_V_p (AUC = 0.687 and 0.707) could differentiate between benign and malignant breast lesions in the L_DCE-MRI group. The models were compared with the senior radiologist's assessment for the identification of BI-RADS 4 breast lesions. The AUC, sensitivity and specificity of Lesion_K_ep (0.963, 100.0%, and 88.9%, respectively) in the H_DCE-MRI group were significantly higher than those of the same parameter in the L_DCE-MRI group (0.663, 69.6% and 75.0%, respectively) for the assessment of BI-RADS 4 breast lesions. The DeLong test was conducted, and there was a significant difference only between Lesion_K_ep in the H_DCE-MRI group and the senior radiologist (P = 0.04).

CONCLUSIONS

Pharmacokinetic parameters (K^trans, K_ep and V_p) from the intralesional and perilesional regions on high-temporal-resolution DCE-MRI, especially the intralesional K_ep parameter, can improve the assessment of benign and malignant BI-RADS 4 breast lesions to avoid unnecessary biopsy.

Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast

PURPOSE

A method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition.

THEORY

Simplicial complexes are generated from the Kety-Tofts model pharmacokinetic parameters Ktrans and ve . A geometric search selects optimal time points for accurate estimation of perfusion parameters.

METHODS

The DCE-MRI data acquired in women with invasive breast cancer (N = 27) were used to retrospectively compare parameter maps fit to full and subsampled time courses. Simplicial complexes were generated for a fixed range of Kety-Tofts model parameters and for the parameter ranges weighted by estimates from the fully sampled data. The largest-area manifolds determined the optimal three time points for each case. Simulations were performed along with retrospectively subsampled data fits. The agreement was computed between the model parameters fit to three points and those fit to all points.

RESULTS

The optimal three-point sample times were from the data-informed simplicial complex analysis and determined to be 65, 204, and 393 s after arrival of the contrast agent to breast tissue. In the patient data, tumor-median parameter values fit using all points and the three selected time points agreed with concordance correlation coefficients of 0.97 for Ktrans and 0.67 for ve .

CONCLUSION

It is possible to accurately estimate pharmacokinetic parameters from three properly selected time points inserted into a clinical DCE-MRI breast exam. This technique can provide guidance on when to capture images for quantitative data between high-spatial-resolution DCE-MRI images.

Quantitative transport mapping (QTM) for differentiating benign and malignant breast lesion: Comparison with traditional kinetics modeling and semi-quantitative enhancement curve characteristics

PURPOSE

To test the feasibility of using quantitative transport mapping (QTM) method, which is based on the inversion of transport equation using spatial deconvolution without any arterial input function, for automatically postprocessing dynamic contrast enhanced MRI (DCE-MRI) to differentiate malignant and benign breast tumors.

MATERIALS AND METHODS

Breast DCE-MRI data with biopsy confirmed malignant (n = 13) and benign tumors (n = 13) was used to assess QTM velocity (|u|) and diffusion coefficient (D), volume transfer constant (K^trans), volume fraction of extravascular extracellular space (V_e) from kinetics method, and traditional enhancement curve characteristics (ECC: amplitude A, wash-in rate α, wash-out rate β). A Mann-Whitney U test and receiver operating characteristic curve (ROC) analysis were performed to assess the diagnostic performance of these parameters for distinguishing between benign and malignant tumors.

RESULTS

Between malignant and benign tumors, there was a significant difference in |u| and K^trans, (p = 0.0066, 0.0274, respectively), but not in D, V_e, A, α and β (p = 0.1119, 0.2382, 0.4418,0.2592 and 0.9591, respectively). ROC area-under-the-curve was 0.82, 0.75 (95% confidence level 0.60-0.95, 0.51-0.90) for |u| and K^trans, respectively.

CONCLUSION

QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with traditional kinetics method and ECC, QTM method showed better diagnostic accuracy in differentiating benign from malignant breast tumors in this study.

Use of Multiple-Tube Phantom: A Method to Globally Correct Native T1 Relaxation Time Inhomogeneity in Dedicated Molecular Magnetic Resonance Breast Coil

Background:

Native T1 relaxation time (T1₀) presents an important prerequisite to reliably quantify pharmacokinetic parameter like K^trans (volume transfer constant). Native T1 value can be varied because of the inhomogeneity in the breast coil, thus influencing the K^trans measurement.

Purpose:

The current study aims to design and use a phantom with multiple tubes for both breast cuffs to assess native T1 inhomogeneity across the dedicated molecular magnetic resonance (mMR) breast coil and adopt corrective method to spatially normalize T1 values to improve homogeneity.

Materials and Methods:

Two phantoms with multiple tubes (19 tubes) specially designed and filled with contrast medium with known T1 value were placed in each mMR breast coil cuff. Native T1 at various spatial locations was calculated applying dual flip angle sequence. Correction factors were derived at various spatial locations as a function of deviation of the native T1 value from phantom and applied to correct the native T1 relaxation time.

Results:

A statistically significant difference between native T1 values of the right and left anterior (P = 0.0095), middle (P = 0.0081), and posterior (P = 0.0004) parts of the breast coil. No significant difference was seen in the corrected T1 values between anterior (P = 0.402), middle (P = 0.305), and posterior (P = 0.349) aspects of both sides of the breast coil.

Conclusion:

Inhomogeneity in the native T1 value exists in dedicated mMR breast coil, and significant improvement can be achieved using specially designed external phantom with multiple tubes.

The clinical value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) semi-quantitative parameters in monitoring neoadjuvant chemotherapy response of osteosarcoma

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a non-invasive technique which could monitor tumor morphology, blood vessel dynamics, and micro-environmental changes.

PURPOSE

To evaluate the value of DCE-MRI semi-quantitative parameters in monitoring the neoadjuvant chemotherapy (NAC) response of osteosarcoma.

MATERIAL AND METHODS

Twenty-five patients pathologically confirmed as osteosarcoma received four cycles of NAC followed by surgery. All patients underwent conventional and dynamic MRI twice, before starting chemotherapy and before surgical treatment. With a reference standard of histological response (tumor necrosis rate), semi-quantitative parameters were compared between good response group (TNR ≥ 90%) and non-response group (TNR < 90%). The differences between intra- and inter-group parameters before and after NAC were analyzed by Mann-Whitney U test. Receiver operating characteristic (ROC) analysis was generated to assess the parameters' efficacy in predicting the outcome of NAC.

RESULTS

The changes were statistically significant on slope, maximum signal intensity (SI_max), time to peak (TTP), signal enhanced extent (SEE), peak percent enhancement (PPE), washout rate (WOR), and enhancement rate (ER) in the good response group (P < 0.05), while only SI_max and SEE were different in the non-response group after NAC. The changes in Slope, SI_max, TTP, SEE, WOR, and ER were markedly different (P < 0.05) between the two groups after NAC. Also, at the threshold values of 3.2%/s, 175 s, and 5.4% (slope, TTP, and ER), the sensitivity and specificity for predicting good response to chemotherapy were 83.3% and 92.3%, 91.7% and 69.2%, 84.6% and 75.0%, respectively.

CONCLUSION

Slope, TTP, and ER values could be used to evaluate and predict the response to NAC in osteosarcoma.

Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer

This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety-Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviated time course data could reproduce the pharmacokinetic parameters, K^trans (volume transfer constant) and v_e (extravascular/extracellular volume fraction), when compared to the full time course data. SKT analysis of simulated abbreviated time courses (ATCs) based on the imaging parameters from two available datasets (collected with a 3T MRI scanner) at a temporal resolution of 15 s (N = 15) and 7.23 s (N = 15) found a concordance correlation coefficient (CCC) greater than 0.80 for ATCs of length 3.0 and 2.5 min, respectively, for the K^trans parameter. Analysis of the experimental data found that at least 90% of patients met this CCC cut-off of 0.80 for the ATCs of the aforementioned lengths. Patlak analysis of experimental data found that 80% of patients from the 15 s resolution dataset and 90% of patients from the 7.27 s resolution dataset met the 0.80 CCC cut-off for ATC lengths of 1.25 and 1.09 min, respectively. This study provides evidence for both the feasibility and potential utility of performing a quantitative analysis of abbreviated breast DCE-MRI in conjunction with acquisition of current standard-of-care high resolution scans without significant loss of information in the community setting.

Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization

Simple Summary

Confirming whether a breast lesion is benign or malignant usually involves an invasive tissue sample with an image-guided breast biopsy, which may cause substantial inconvenience to the patient. The purpose of this study was to investigate whether imaging biomarkers obtained from noninvasive dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast can help differentiate benign from malignant lesions and characterize breast cancers to the same extent as a biopsy. In a sample of 37 patients with suspicious findings on mammography or ultrasound, we found that the radiologists’ diagnostic accuracy was improved when subjective Breast Imaging-Reporting and Data System (BI-RADS) evaluation was augmented with the use of pharmacokinetic markers. This study serves as a starting point for future collaborative research with the potential of providing valuable noninvasive tools for improved breast cancer diagnosis.

Abstract

The purpose of this study was to investigate whether ultra-high-field dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast at 7T using quantitative pharmacokinetic (PK) analysis can differentiate between benign and malignant breast tumors for improved breast cancer diagnosis and to predict molecular subtypes, histologic grade, and proliferation rate in breast cancer. In this prospective study, 37 patients with 43 lesions suspicious on mammography or ultrasound underwent bilateral DCE-MRI of the breast at 7T. PK parameters (K^Trans, k_ep, V_e) were evaluated with two region of interest (ROI) approaches (2D whole-tumor ROI or 2D 10 mm standardized ROI) manually drawn by two readers (senior reader, R1, and R2) independently. Histopathology served as the reference standard. PK parameters differentiated benign and malignant lesions (n = 16, 27, respectively) with good accuracy (AUCs = 0.655–0.762). The addition of quantitative PK analysis to subjective BI-RADS classification improved breast cancer detection from 88.4% to 97.7% for R1 and 86.04% to 97.67% for R2. Different ROI approaches did not influence diagnostic accuracy for both readers. Except for K^Trans for whole-tumor ROI for R2, none of the PK parameters were valuable to predict molecular subtypes, histologic grade, or proliferation rate in breast cancer. In conclusion, PK-enhanced BI-RADS is promising for the noninvasive differentiation of benign and malignant breast tumors.

Breast density, MR imaging biomarkers, and breast cancer risk

Mammographic breast density and various breast MRI features are imaging biomarkers that can predict a woman's future risk of breast cancer. While mammographic density (MD) has been established as an independent risk factor for the development of breast cancer, MD assessment methods need to be accurate and reproducible for widespread clinical use in stratifying patients based on their risk. In addition, a number of breast MRI biomarkers using contrast-enhanced and noncontrast-enhanced techniques are also being investigated as risk predictors. The validation and standardization of these breast MRI biomarkers will be necessary for population-based clinical implementation of patient risk stratification, as well. This review provides an update on MD assessment methods, breast MRI biomarkers, and their ability to predict breast cancer risk.

Role of Various DW MRI and DCE MRI Parameters as Predictors of Malignancy in Solid Pulmonary Lesions

PURPOSE

We aimed to evaluate various diffusion and dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) parameters in differentiating malignant from benign pulmonary lesions.

METHODS

We enrolled 31 (22 males) patients who had solid pulmonary lesion(s) >2 cm in our cross sectional study. Of these, 23 (74.2%) were found to be malignant on histopathology. Dynamic contrast-enhanced MRI was performed using 36 dynamic measurements (volumetric interpolated breath-hold examination). Diffusion-weighted MRI (DW MRI) performed at b value of 800 s/mm². We measured different diffusion and perfusion parameters, for example, diffusion-weighted imaging (DWI) SI, mean apparent diffusion coefficient (ADC), minimum ADC, lesion-to-spinal cord ratio, DWI score, T2 score, K^trans, K_ep, and V_e. We stratified values of each parameter as high if it was >median of values observed in our data set and low if it was ≤median. Normally distributed data were compared by unpaired t test, whereas non-normal continuous data were compared by Kruskal Wallis-H test. We applied Wilson score method to calculate sensitivity, specificity, and predictive values of parameters that were statistically significant by type of lesion with reference to histopathological examination as gold standard.

RESULTS

Diffusion-weighted imaging SI, mean ADC, minimum ADC, DWI score and K^trans values were found to be significantly different (P value < .05) by type of lesion. K^trans was found to have the highest diagnostic accuracy (74.2%) among these parameters.

CONCLUSION

K^trans and mean ADC had similar sensitivity of 65.2%. However, K^trans had highest diagnostic accuracy among various DWI and DCE MRI parameters in predicting malignancy in solid pulmonary lesions. In our study, we found a cutoff value 0.251 min^-1 for K^trans as 100% specific.

Comparison of Qualitative (Time Intensity Curve Analysis), Semi-Quantitative, and Quantitative Multi-Phase 3T DCEMRI Parameters as Predictors of Malignancy in Adnexal

Objective:

The present study aimed to compare the qualitative (time intensity curve analysis), the semi-quantitative and the quantitative multiphase 3T dynamic contrast-enhanced (DCE) MRI parameters as predictors of malignancy in adnexal masses. Materials and

Methods:

In this prospective study, women with an adnexal mass who were scheduled for surgical resection or were followed for more than one year period to confirm the benignity of their lesions, underwent multiphase 3T DCE-MRI. The qualitative (time intensity curve), semi-quantitative (SImax, SIrel, WIR) and quantitative (Ktrans, Kep, Vb) analyses were performed on DCE-MRI sequences and their predictive values were compared.

Results:

A total of 17 benign and 14 malignant lesions were included. According to the qualitative analysis, none of the lesions with Type I time intensity curves (TIC) were malignant and none of the masses with Type III TICs were benign. The accuracy of the quantitative parameters in detection of malignancy was found to be higher than that of semi-quantitative variables, particularly when calculated for a small ROI within the high signal area of the mass (sROI) rather than the largest ROI including the whole mass (lROI), and when inter-MRI variations were omitted using ratios. The Kep(tumor)/Kep(myometrium) ratio measured from sROI was the best parameter for differentiating a malignant lesion with a sensitivity of 100% and a specificity of 92.3%.

Conclusion:

We concluded that a Type I TIC confirms a benign lesion, and a type III TIC confirms the malignancy and further evaluation is not recommended for these lesions. So complementary quantitative analysis is only recommended for adnexal masses with type II TICs.

Feasibility study of dual parametric 2D histogram analysis of breast lesions with dynamic contrast-enhanced and diffusion-weighted MRI

Background

This study aimed to investigate the diagnostic value of a dual-parametric 2D histogram classification method for breast lesions.

Methods

This study included 116 patients with 72 malignant and 44 benign breast lesions who underwent CAIPIRINHA-Dixon-TWIST-VIBE dynamic contrast-enhanced (CDT-VIBE DCE) and readout-segmented diffusion-weighted magnetic resonance examination. The volume of interest (VOI), which encompassed the entire lesion, was segmented from the last phase of DCE images. For each VOI, a 1D histogram analysis (mean, median, 10th percentile, 90th percentile, kurtosis and skewness) was performed on apparent diffusion coefficient (ADC) and volume transfer constant (Ktrans) maps; a 2D histogram image (Ktrans-ADC) was generated from the pixelwise aligned maps, and its kurtosis and skewness were calculated. Each parameter was correlated with pathological results using the Mann–Whitney test and receiver operating characteristic curve analysis.

Results

For the Ktrans histogram, the area under the curve (AUC) of the mean, median, 90th percentile and kurtosis had statistically diagnostic values (mean: 0.760; median: 0.661; 90th percentile: 0.781; and kurtosis: 0.620). For the ADC histogram, the AUC of the mean, median, 10th percentile, skewness and kurtosis had statistically diagnostic values (mean: 0.661; median: 0.677; 10th percentile: 0.656; skewness: 0.664; and kurtosis: 0.620). For the 2D Ktrans-ADC histogram, the skewness and kurtosis had statistically higher diagnostic values (skewness: 0.831, kurtosis: 0.828) than those of the 1D histogram (all P < 0.05).

Conclusions

The dual-parametric 2D histogram analysis revealed better diagnostic accuracy for breast lesions than single parametric histogram analysis of either Ktrans or ADC maps.

A radiomic nomogram based on an apparent diffusion coefficient map for differential diagnosis of suspicious breast findings

Objective

To develop and validate a radiomic nomogram based on an apparent diffusion coefficient (ADC) map for differentiating benign and malignant lesions in suspicious breast findings classified as Breast Imaging Reporting and Data System (BI-RADS) category 4 on breast magnetic resonance imaging (MRI).

Methods

Eighty-eight patients diagnosed with BI-RADS 4 findings on breast MRI in the First Affiliated Hospital of China Medical University from December 2014 to December 2015 were retrospectively analyzed in this study. Sixty-three were randomized electronically to establish forecasting models, and the other 25 were used for validation. Radiomic features based on the ADC map were generated automatically by Artificial Intelligence Kit software (A.K. software; GE Healthcare, China). Feature reduction was conducted using the Mann-Whitney test and Spearman correlation after pre-treatment. A prediction model of ADC radiomics was established by logistic linear regression and cross-validation. A nomogram was established based on ADC radiomic features, pharmacokinetics and clinical features, including the morphology and ADC value for breast BI-RADS 4 lesions on MRI.

Results

A total of 396 radiomic features were extracted automatically by the A.K. software. Five features were selected after pre-processing, Mann-Whitney tests and Spearman correlation analysis. The area under the ROC curve of the prediction model comprising ADC radiomic features was 0.79 when the cutoff value was 0.45, and the accuracy, sensitivity and specificity were 80.0%, 0.813 and 0.778, respectively. A visualized differential nomogram based on the radiomic score, pharmacokinetics and clinical features was established. The decision curve showed good consistency.

Conclusions

ADC radiomic features could provide an important reference for differential diagnosis between benign and malignant lesions in suspicious BI-RADS 4 lesions. The visualized nomogram based on ADC radiomic features, pharmacokinetics and clinical features may have good prospects for clinical application.

Role of pharmacokinetic parameters derived with high temporal resolution DCE MRI using simultaneous PET/MRI system in breast cancer: A feasibility study

PURPOSE

To evaluate the reliability of pharmacokinetic parameters like K^trans, Kep and v_e derived through DCE MRI breast protocol using 3T Simultaneous PET/MRI (3Tesla Positron Emission Tomography/Magnetic Resonance Imaging) system in distinguishing benign and malignant lesions.

MATERIALS AND METHODS

High temporal resolution DCE (Dynamic Contrast Enhancement) MRI performed as routine breast MRI for diagnosis or as a part of PET/MRI for cancer staging using a 3T simultaneous PET/MRI system in 98 women having 109 breast lesions were analyzed for calculation of pharmacokinetic parameters (K^trans, v_e, and Kep) at 60s time point using an in-house developed computation scheme.

RESULTS

Receiver operating characteristic (ROC) curve analysis revealed a cut off value for K^trans, Kep, v_e as 0.50, 2.59, 0.15 respectively which reliably distinguished benign and malignant breast lesions. Data analysis revealed an overall accuracy of 94.50%, 79.82% and 87.16% for K^trans, Kep, v_e respectively. Introduction of native T1 normalization with an externally placed phantom showed a higher accuracy (94.50%) than without native T1 normalization (93.50%) with an increase in specificity of 87% vs 84%.

CONCLUSION

Overall the results indicate that reliable measurement of pharmacokinetic parameters with reduced acquisition time is feasible in a 3TMRI embedded PET/MRI system with reasonable accuracy and application may be extended to exploit the potential of simultaneous PET/MRI in further work on breast cancer.

Discrimination of metastatic from non-metastatic mesorectal lymph nodes in rectal cancer using quantitative dynamic contrast-enhanced magnetic resonance imaging

Preoperative detection of lymph nodes (LNs) metastasis is always highly challenging for radiologists nowadays. The utility of quantitative dynamic contrast-enhanced magnetic resonance imaging (QDCE-MRI) in identifying LNs metastasis is not well understood. In the present study, 59 patients with histologically proven rectal carcinoma underwent preoperative QDCE-MRI. The short axis diameter ratio, long axis diameter ratio, short-to-long axis diameter ratio and QDEC-MRI parameters (K(trans), Kep, fPV and Ve) values were compared between the non-metastatic (n=44) and metastatic (n=35) LNs groups based on pathological examination. Compared with the non-metastatic group, the metastatic group exhibited significantly higher short axis diameter (7.558±0.668 mm vs. 5.427±0.285 mm), K(trans) (0.483±0.198 min(-1) vs. 0.218±0.116 min(-1)) and Ve (0.399±0.118 vs. 0.203±0.096) values (all P<0.05). The short-to-long axis diameter ratio, long axis diameter ratio, Kep and fPV values did not show significant differences between the two groups. In conclusion, our results showed that for LNs larger than 5 mm in rectal cancer, there are distinctive differences in the K(trans) and Ve values between the metastatic and non-metastatic LNs, suggesting that QDCE-MRI may be potentially helpful in identifying LNs status.