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

Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study

Background

Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC).

Methods

This is a retrospective study. The IDC of 30 and ILC of 28 patients from Dukes breast cancer MRI data set of The Cancer Imaging Archive (TCIA), were included. The RF categories such as shape based, Gray level dependence matrix (GLDM), Gray level co-occurrence matrix (GLCM), First order, Gray level run length matrix (GLRLM), Gray level size zone matrix (GLSZM), NGTDM (Neighbouring gray tone difference matrix) were extracted from the DCE-MRI sequence using a 3D slicer. The maximum relevance and minimum redundancy (mRMR) was applied using Google Colab for identifying the top fifteen relevant radiomic features. The Mann-Whitney U test was performed to identify significant RF for differentiating IDC and ILC. Receiver Operating Characteristic (ROC) curve analysis was performed to ascertain the accuracy of RF in distinguishing between IDC and ILC.

Results

Ten DCE MRI-based RFs used in our study showed a significant difference (p <0.001) between IDC and ILC. We noticed that DCE RF, such as Gray level run length matrix (GLRLM) gray level variance (sensitivity (SN) 97.21%, specificity (SP) 96.2%, area under curve (AUC) 0.998), Gray level co-occurrence matrix (GLCM) difference average (SN 95.72%, SP 96.34%, AUC 0.983), GLCM interquartile range (SN 95.24%, SP 97.31%, AUC 0.968), had the strongest ability to differentiate IDC and ILC.

Conclusions

MRI-based RF derived from DCE sequences can be used in clinical settings to differentiate malignant lesions of the breast, such as IDC and ILC, without requiring intrusive procedures.

Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom

Purpose

Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Approach

A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to 300    μ m / 60    s ) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to 300    μ m / 1 to 10 s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity.

Results

The observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization.

Conclusion

An in silico validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.

An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data

PURPOSE

To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data.

METHODS

The ConvDecoder (CD) neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle data. Fully-sampled variable-flip angle k-space data were retrospectively accelerated by factors of R = {8, 12, 18, 36} and reconstructed with CD, CD with the proposed regularization (CD + r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD + r training were evaluated at the "argmin" of the regularization loss; whereas the CD, LR, and L1 reconstructions were chosen optimally based on ground truth data. The performance measures used were the normalized RMS error, the concordance correlation coefficient, and the structural similarity index.

RESULTS

The CD + r reconstructions, chosen using the stopping condition, yielded structural similarity indexs that were similar to the CD (p = 0.47) and LR structural similarity indexs (p = 0.95) across R and that were significantly higher than the L1 structural similarity indexs (p = 0.04). The concordance correlation coefficient values for the CD + r T1 maps across all R and subjects were greater than those corresponding to the L1 (p = 0.15) and LR (p = 0.13) T1 maps, respectively. For R ≥ 12 (≤4.2 min scan time), L1 and LR T1 maps exhibit a loss of spatially refined details compared to CD + r.

CONCLUSION

The use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.

Fluid Mechanics Approach to Perfusion Quantification: Vasculature Computational Fluid Dynamics Simulation, Quantitative Transport Mapping (QTM) Analysis of Dynamics Contrast Enhanced MRI, and Application in Nonalcoholic Fatty Liver Disease Classification

OBJECTIVE

We quantify liver perfusion using quantitative transport mapping (QTM) method that is free of arterial input function (AIF). QTM method is validated in a vasculature computational fluid dynamics (CFD) simulation and is applied for processing dynamic contrast enhanced (DCE) MRI images in differentiating liver with nonalcoholic fatty liver disease (NAFLD) from healthy controls using pathology reference in a preclinical rabbit model.

METHODS

QTM method was validated on a liver perfusion simulation based on fluid dynamics using a rat liver vasculature model and the mass transport equation. In the NAFLD grading task, DCE MRI images of 7 adult rabbits with methionine choline-deficient diet-induced nonalcoholic steatohepatitis (NASH), 8 adult rabbits with simple steatosis (SS) were acquired and processed using QTM method and dual-input two compartment Kety's method respectively. Statistical analysis was performed on six perfusion parameters: velocity magnitude | u | derived from QTM, liver arterial blood flow LBFa, liver venous blood flow LBFv, permeability Ktrans, blood volume Vp and extravascular space volume Ve averaged in liver ROI.

RESULTS

In the simulation, QTM method successfully reconstructed blood flow, reduced error by 48% compared to Kety's method. In the preclinical study, only QTM |u| showed significant difference between high grade NAFLD group and low grade NAFLD group.

CONCLUSION

QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with Kety's method, QTM method showed higher accuracy and better differentiation in NAFLD classification task.

SIGNIFICANCE

We propose to apply QTM method in liver DCE MRI perfusion quantification.

A simplified scoring protocol to improve diagnostic accuracy with the breast imaging reporting and data system in breast magnetic resonance imaging

BACKGROUND

The breast imaging reporting and data system (BI-RADS) lexicon provides a standardized terminology for describing leision characteristics but does not provide defined rules for converting specific imaging features into diagnostic categories. The inter-reader agreement of the BI-RADS is moderate. In this study, we explored the use of a simplified protocol and scoring system for BI-RADS categorization which integrates the morphologic features (MF), kinetic time-intensity curve (TIC), and apparent diffusion coefficient (ADC) values with equal weights, with a view to providing a convenient and practical method for breast magnetic resonance imaging (MRI) and improving the inter-reader agreement and diagnostic performance of BI-RADS.

METHODS

This cross-sectional, retrospective, single-center study included 879 patients with 898 histopathologically verified lesions who underwent an MRI scan on a 3.0 Tesla GE Discovery 750 MRI scanner between January 1, 2017, and June 30, 2020. The BI-RADS categorization of the studied lesions was assessed according to the sum of the assigned scores (the presence of malignant MF, lower ADC, and suspicious TIC each warranted a score of +1). Total scores of +2 and +3 were classified as category 5, scores of +1 were classified as category 4, and scores of +0 but with other lesions of interest were classified as category 3. The receiver operating characteristic (ROC) curves were plotted, and the sensitivity, specificity, and accuracy of this categorization were investigated to assess its efficacy and its consistency with pathology.

RESULTS

There were 472 malignant, 104 risk, and 322 benign lesions. Our simplified scoring protocol had high diagnostic accuracy, with an area under curve (AUC) value of 0.896. In terms of the borderline effect of pathological risk and category 4 lesions, our results showed that when risk lesions were classified together with malignant ones, the AUC value improved (0.876 vs. 0.844 and 0.909 vs. 0.900). When category 4 and 5 lesions were classified as malignant, the specificity, accuracy, and AUC value decreased (82.3% vs. 93.2%, 89.3% vs. 90.2%, and 0.876 vs. 0.909, respectively). Therefore, to improve the diagnostic accuracy of the protocol for BI-RADS categorization, only category 5 lesions should be considered to be malignant.

CONCLUSIONS

Our simplified scoring protocol that integrates MF, TIC, and ADC values with equal weights for BI-RADS categorization could improve both the diagnostic performance of the protocol for BI-RADS categorization in clinical practice and the understanding of the benign-risk-malignant breast diseases.

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.

Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.

Discrimination of Malignant and Benign Breast Lesions Using Quantitative Multiparametric MRI: A Preliminary Study

We aimed to compare diagnostic performance in discriminating malignant and benign breast lesions between two intravoxel incoherent motion (IVIM) analysis methods for diffusion-weighted magnetic resonance imaging (DW-MRI) data and between DW- and dynamic contrast-enhanced (DCE)-MRI, and to determine if combining DW- and DCE-MRI further improves diagnostic accuracy. DW-MRI with 12 b-values and DCE-MRI were performed on 26 patients with 28 suspicious breast lesions before biopsies. The traditional biexponential fitting and a 3-b-value method were used for independent IVIM analysis of the DW-MRI data. Simulations were performed to evaluate errors in IVIM parameter estimations by the two methods across a range of signal-to-noise ratio (SNR). Pharmacokinetic modeling of DCE-MRI data was performed. Conventional radiological MRI reading yielded 86% sensitivity and 21% specificity in breast cancer diagnosis. At the same sensitivity, specificity of individual DCE- and DW-MRI markers improved to 36%–57% and that of combined DCE- or combined DW-MRI markers to 57%–71%, with DCE-MRI markers showing better diagnostic performance. The combination of DCE- and DW-MRI markers further improved specificity to 86%–93% and the improvements in diagnostic accuracy were statistically significant (P < .05) when compared with standard clinical MRI reading and most individual markers. At low breast DW-MRI SNR values (<50), like those typically seen in clinical studies, the 3-b-value approach for IVIM analysis generates markers with smaller errors and with comparable or better diagnostic performances compared with biexponential fitting. This suggests that the 3-b-value method could be an optimal IVIM-MRI method to be combined with DCE-MRI for improved diagnostic accuracy.

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.

Multinuclear MRI to disentangle intracellular sodium concentration and extracellular volume fraction in breast cancer

The purpose of this work was to develop a novel method to disentangle the intra- and extracellular components of the total sodium concentration (TSC) in breast cancer from a combination of proton ([Formula: see text]H) and sodium ([Formula: see text]) magnetic resonance imaging (MRI) measurements. To do so, TSC is expressed as function of the intracellular sodium concentration ([Formula: see text]), extracellular volume fraction (ECV) and the water fraction (WF) based on a three-compartment model of the tissue. TSC is measured from [Formula: see text] MRI, ECV is calculated from baseline and post-contrast [Formula: see text]H [Formula: see text] maps, while WF is measured with a [Formula: see text]H chemical shift technique. [Formula: see text] is then extrapolated from the model. Proof-of-concept was demonstrated in three healthy subjects and two patients with triple negative breast cancer. In both patients, TSC was two to threefold higher in the tumor than in normal tissue. This alteration mainly resulted from increased [Formula: see text] ([Formula: see text] 30 mM), which was [Formula: see text] 130% greater than in healthy conditions (10-15 mM) while the ECV was within the expected range of physiological values (0.2-0.25). Multinuclear MRI shows promise for disentangling [Formula: see text] and ECV by taking advantage of complementary [Formula: see text]H and [Formula: see text] measurements.

Dynamic Contrast-enhanced MRI Detects Responses to Stroma-directed Therapy in Mouse Models of Pancreatic Ductal Adenocarcinoma

PURPOSE

The dense stroma underlies the drug resistance of pancreatic ductal adenocarcinoma (PDA) and has motivated the development of stroma-directed drugs. Our objective is to test the concept that dynamic contrast-enhanced (DCE) MRI using FDA-approved contrast media, an imaging method sensitive to the tumor microenvironment, can detect early responses to stroma-directed drug.

EXPERIMENTAL DESIGN

Imaging studies were performed in three mouse models exhibiting high desmoplastic reactions: the autochthonous PDA in genetically engineered mice (KPC), an orthotopic model in syngeneic mice, and a xenograft model of human PDA in athymic mice. An investigational drug, PEGPH20 (pegvorhyaluronidase alfa), which degrades hyaluronan (HA) in the stroma of PDA, was injected alone or in combination with gemcitabine.

RESULTS

At 24 hours after a single injection of PEGPH20, Ktrans , a DCE-MRI-derived marker that measures how fast a unit volume of contrast media is transferred from capillaries to interstitial space, increased 56% and 50% from baseline in the orthotopic and xenograft tumors, respectively, compared with a 4% and 6% decrease in vehicle groups (both P < 0.05). Similarly, after three combined treatments, Ktrans in KPC mice increased 54%, whereas it decreased 4% in controls treated with gemcitabine alone (P < 0.05). Consistently, after a single injection of PEGPH20, tumor HA content assessed by IHC was reduced substantially in all three models while drug delivery (measured by paclitaxel accumulation in tumor) was increased by 2.6-fold.

CONCLUSIONS

These data demonstrated a DCE-MRI marker, Ktrans , can detect early responses to stroma-directed drug and reveal the sustained effect of combination treatment (PEGPH20+ gemcitabine).

Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis

Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. Furthermore, the combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics that increases diagnostic accuracy towards individualized therapy planning. More importantly, radiomics can now be complemented by the emerging deep learning techniques for further process automation and correlation with other clinical data which facilitate the monitoring of treatment response, as well as the prediction of patient's outcome, by means of unravelling of the complex underlying pathophysiological mechanisms which are reflected in tissue phenotype. The scope of this review is to provide applications and limitations of radiomics towards the development of clinical decision support systems for breast cancer diagnosis and prognosis.