Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors

Ultrafast Breast MRI: A Narrative Review

Breast magnetic resonance imaging (MRI) is considered the most effective method for detecting breast cancer due to its high sensitivity. Yet multiple factors limit its widespread use, including high direct and indirect costs, a prolonged acquisition time with consequent patient discomfort, and a lack of trained radiologists. During the last decade, new strategies have been followed to increase the availability of breast MRI, including the omission of non-essential sequences to generate abbreviated MRI protocols (AB-MRIs) aimed at reducing the acquisition time with the potential of improving the patient's experience and accommodating a higher number of MRI examinations per day. An alternative method is ultrafast MRI (UF-MRI), a novel technique that gathers kinetic data within the first minute after contrast injection, offering high temporal resolution. This enables the analysis of early contrast wash-in curves, showing promising outcomes. In this study, we reviewed the role of UF-MRI in breast imaging and detailed how the integration of this new approach with radiomics and mathematical models might further improve diagnostic accuracy and even have a prognostic role, a fundamental characteristic in the modern scenarios of personalized medicine. In addition, possible clinical applications and advantages of UF-MRI will be discussed.

Institutional Variability in Ultrafast Breast MR Imaging: Comparing Compressed Sensing and View Sharing Techniques with Different Patient Populations and Contrast Injection Protocols

PURPOSE

To assess the institutional variability in ultrafast dynamic contrast-enhanced (UF-DCE) breast MRI using time-resolved angiography with stochastic trajectories (TWIST)-volumetric interpolated breath-hold examination (VIBE) and compressed sensing (CS)-VIBE sequences acquired at 2 different institutions with different patient populations and contrast injection protocols.

METHODS

UF-DCE MR images of 18 patients from site A acquired using a TWIST-VIBE sequence, and UF-DCE MR images of 18 patients from site B acquired with a CS-VIBE sequence, were retrospectively evaluated and compared. The 2-site patient cohort was matched for patient age, background parenchymal enhancement, malignancy or benignity, and lesion size. Qualitative assessments included noise, blurring, poor fat suppression, aliasing artifact, motion artifact, lesion conspicuity, lesion morphology, time-intensity-curve smoothness, and vessel delineation. For quantitative assessment, the bolus arrival time was evaluated for each lesion, and its diagnostic performance in discriminating between benign and malignant lesions was examined using receiver operating characteristics analysis.

RESULTS

Thirteen malignant and five benign lesions were included from each site. Qualitative evaluation revealed that poor fat suppression and aliasing artifacts were visible in images from site A with TWIST-VIBE (P = 0.004 and P < 0.001), whereas motion artifacts were present in images from site B with CS-VIBE (P = 0.04). Lesion morphology assessments (P < 0.001) and vessel delineation (P < 0.001) were superior for images from site B with CS-VIBE. Bolus arrival time was significantly longer with TWIST-VIBE than with CS-VIBE, for both benign and malignant lesions (P < 0.001). The area under the receiver operating characteristics curve was 0.55 for site A and 0.69 for site B (P = 0.39).

CONCLUSION

Both acquisitions allowed evaluation of breast lesions with good lesion conspicuity and time-intensity-curve smoothness, whereas CS-VIBE was superior to TWIST-VIBE for morphological evaluation of breast lesions and depiction of blood vessels in the breast. Injection rate appears to have a significant impact on semi-quantitative parameters derived from UF-DCE MRI.

Current trends in the characterization and monitoring of vascular response to cancer therapy

Tumor vascular physiology is an important determinant of disease progression as well as the therapeutic outcome of cancer treatment. Angiogenesis or the lack of it provides crucial information about the tumor's blood supply and therefore can be used as an index for cancer growth and progression. While standalone anti-angiogenic therapy demonstrated limited therapeutic benefits, its combination with chemotherapeutic agents improved the overall survival of cancer patients. This could be attributed to the effect of vascular normalization, a dynamic process that temporarily reverts abnormal vasculature to the normal phenotype maximizing the delivery and intratumor distribution of chemotherapeutic agents. Longitudinal monitoring of vascular changes following antiangiogenic therapy can indicate an optimal window for drug administration and estimate the potential outcome of treatment. This review primarily focuses on the status of various imaging modalities used for the longitudinal characterization of vascular changes before and after anti-angiogenic therapies and their clinical prospects.

Ultrafast Dynamic Contrast-Enhanced MRI of the Breast: From Theory to Practice

The development of ultrafast dynamic contrast-enhanced (UF-DCE) MRI has occurred in tandem with fast MRI scan techniques, particularly view-sharing and compressed sensing. Understanding the strengths of each technique and optimizing the relevant parameters are essential to their implementation. UF-DCE MRI has now shifted from research protocols to becoming a part of clinical scan protocols for breast cancer. UF-DCE MRI is expected to compensate for the low specificity of abbreviated MRI by adding kinetic information from the upslope of the time-intensity curve. Because kinetic information from UF-DCE MRI is obtained from the shape and timing of the initial upslope, various new kinetic parameters have been proposed. These parameters may be associated with receptor status or prognostic markers for breast cancer. In addition to the diagnosis of malignant lesions, more emphasis has been placed on predicting and evaluating treatment response because hyper-vascularity is linked to the aggressiveness of breast cancers. In clinical practice, it is important to note that breast lesion images obtained from UF-DCE MRI are slightly different from those obtained by conventional DCE MRI in terms of morphology. A major benefit of using UF-DCE MRI is avoidance of the marked or moderate background parenchymal enhancement (BPE) that can obscure the target enhancing lesions. BPE is less prominent in the earlier phases of UF-DCE MRI, which offers better lesion-to-noise contrast. The excellent contrast of early-enhancing vessels provides a key to understanding the detailed pathological structure of tumor-associated vessels. UF-DCE MRI is normally accompanied by a large volume of image data for which automated/artificial intelligence-based processing is expected to be useful. In this review, both the theoretical and practical aspects of UF-DCE MRI are summarized. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer

BACKGROUND

To develop and validate a peritumoral vascular and intratumoral radiomics model to improve pretreatment predictions for pathologic complete responses (pCRs) to neoadjuvant chemoradiotherapy (NAC) in patients with triple-negative breast cancer (TNBC).

METHODS

A total of 282 TNBC patients (93 in the primary cohort, 113 in the validation cohort, and 76 in The Cancer Imaging Archive [TCIA] cohort) were retrospectively included. The peritumoral vasculature on the maximum intensity projection (MIP) from pretreatment DCE-MRI was segmented by a Hessian matrix-based filter and then edited by a radiologist. Radiomics features were extracted from the tumor and peritumoral vasculature of the MIP images. The LASSO method was used for feature selection, and the k-nearest neighbor (k-NN) classifier was trained and validated to build a predictive model. The diagnostic performance was assessed using the ROC analysis.

RESULTS

One hundred of the 282 patient (35.5%) with TNBC achieved pCRs after NAC. In predicting pCRs, the combined peritumoral vascular and intratumoral model (fusion model) yields a maximum AUC of 0.82 (95% confidence interval [CI]: 0.75, 0.88) in the primary cohort, a maximum AUC of 0.67 (95% CI: 0.57, 0.76) in the internal validation cohort, and a maximum AUC of 0.65 (95% CI: 0.52, 0.78) in TCIA cohort. The fusion model showed improved performance over the intratumoral model and the peritumoral vascular model, but not significantly (p > 0.05).

CONCLUSION

This study suggested that combined peritumoral vascular and intratumoral radiomics model could provide a non-invasive tool to enable prediction of pCR in TNBC patients treated with NAC.

Optimizing ultrafast dynamic contrast-enhanced MRI scan duration in the differentiation of benign and malignant breast lesions

OBJECTIVE

To determine the optimal scan duration for ultrafast DCE-MRI in effectively differentiating benign from malignant breast lesions.

METHODS

The study prospectively recruited participants who underwent breast ultrafast DCE-MRI from September 2021 to March 2023. A 30-phase breast ultrafast DCE-MRI on a 3.0-T MRI system was conducted with a 4.5-s temporal resolution. Scan durations ranged from 40.5 s to 135.0 s, during which the analysis is performed at three-phase intervals, forming eight dynamic sets (scan duration [SD]40.5s: 40.5 s, SD54s: 54.0 s, SD67.5s: 67.5 s, SD81s: 81.0 s, SD94.5s: 94.5 s, SD108s: 108.0 s, SD121.5s: 121.5 s, and SD135s: 135.0 s). Two ultrafast DCE-MRI parameters, maximum slope (MS) and initial area under the curve in 60 s (iAUC), were calculated for each dynamic set and compared between benign and malignant lesions. Areas under the receiver operating characteristic curve (AUCs) were used to assess their diagnostic performance.

RESULTS

A total of 140 women (mean age, 47 ± 11 years) with 151 lesions were included. MS and iAUC from eight dynamic sets exhibited significant differences between benign and malignant lesions (all p < 0.05), except iAUC at SD40.5s. The AUC of MS (AUC = 0.804) and iAUC (AUC = 0.659) at SD67.5s were significantly higher than their values at SD40.5s (AUC = 0.606 and 0.516; corrected p < 0.05). No significant differences in AUCs for MS and iAUC were observed from SD67.5s to SD135s (all corrected p > 0.05).

CONCLUSIONS

Ultrafast DCE-MRI with a 67.5-s scan duration appears optimal for effectively differentiating malignant from benign breast lesions.

CRITICAL RELEVANCE STATEMENT

By evaluating scan durations (40.5-135 s) and analyzing two ultrafast DCE-MRI parameters, we found a scan duration of 67.5 s optimal for discriminating between these lesions and offering a balance between acquisition time and diagnostic efficacy.

KEY POINTS

Ultrafast DCE-MRI can effectively differentiate malignant from benign breast lesions. A minimum of 67.5-sec ultrafast DCE-MRI scan duration is required to differentiate benign and malignant lesions. Extending the scan duration beyond 67.5 s did not significantly improve diagnostic accuracy.

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.

Dynamic Contrast-Enhanced (DCE) MRI

The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment

ABSTRACT

A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

Pharmacokinetic Analysis of Enhancement-Constrained Acceleration (ECA) reconstruction-based high temporal resolution breast DCE-MRI

The high spatial and temporal resolution of dynamic contrast-enhanced MRI (DCE-MRI) can improve the diagnostic accuracy of breast cancer screening in patients who have dense breasts or are at high risk of breast cancer. However, the spatiotemporal resolution of DCE-MRI is limited by technical issues in clinical practice. Our earlier work demonstrated the use of image reconstruction with enhancement-constrained acceleration (ECA) to increase temporal resolution. ECA exploits the correlation in k-space between successive image acquisitions. Because of this correlation, and due to the very sparse enhancement at early times after contrast media injection, we can reconstruct images from highly under-sampled k-space data. Our previous results showed that ECA reconstruction at 0.25 seconds per image (4 Hz) can estimate bolus arrival time (BAT) and initial enhancement slope (iSlope) more accurately than a standard inverse fast Fourier transform (IFFT) when k-space data is sampled following a Cartesian based sampling trajectory with adequate signal-to-noise ratio (SNR). In this follow-up study, we investigated the effect of different Cartesian based sampling trajectories, SNRs and acceleration rates on the performance of ECA reconstruction in estimating contrast media kinetics in lesions (BAT, iSlope and Ktrans) and in arteries (Peak signal intensity of first pass, time to peak, and BAT). We further validated ECA reconstruction with a flow phantom experiment. Our results show that ECA reconstruction of k-space data acquired with 'Under-sampling with Repeated Advancing Phase' (UnWRAP) trajectories with an acceleration factor of 14, and temporal resolution of 0.5 s/image and high SNR (SNR ≥ 30 dB, noise standard deviation (std) < 3%) ensures minor errors (5% or 1 s error) in lesion kinetics. Medium SNR (SNR ≥ 20 dB, noise std ≤ 10%) was needed to accurately measure arterial enhancement kinetics. Our results also suggest that accelerated temporal resolution with ECA with 0.5 s/image is practical.

Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer

BACKGROUND

Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient's response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient's response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform.

METHODS

A single-blinded study was performed using a retrospective database from a single institution (9/2014-12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm³ (≥ 99.9% reduction).

RESULTS

The cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2- (35%), HR-/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10^-7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% - 96.4%; Clopper-Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): - 1.99; 95% CI [- 3.96, - 0.02]; p = 0.043) and clinically assessed pCR (HR: - 1.76; 95% CI [- 3.75, 0.23]; p = 0.054).

CONCLUSION

We demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis

The peritumoral vasogenic edema (PVE) in brain tumors exhibits varied characteristics. Brain metastasis (BM) and meningioma barely have tumor cells in PVE, while glioblastoma (GB) show tumor cell infiltration in most subjects. The purpose of this study was to investigate the PVE of these three pathologies using radiomics features in FLAIR images, with the hypothesis that the tumor cells might influence textural variation. Ex vivo experimentation of radiomics analysis of T1-weighted images of the culture medium with and without suspended tumor cells was also attempted to infer the possible influence of increasing tumor cells on radiomics features. This retrospective study involved magnetic resonance (MR) images acquired using a 3.0-T MR machine from 83 patients with 48 GB, 21 BM, and 14 meningioma. The 93 radiomics features were extracted from each subject's PVE mask from three pathologies using T1-dynamic contrast-enhanced MR imaging. Statistically significant (< 0.05, independent samples T-test) features were considered. Features maps were also computed for qualitative investigation. The same was carried out for T1-weighted cell line images but group comparison was carried out using one-way analysis of variance. Further, a random forest (RF)-based machine learning model was designed to classify the PVE of GB and BM. Texture-based variations, especially higher nonuniformity values, were observed in the PVE of GB. No significance was observed between BM and meningioma PVE. In cell line images, the culture medium had higher nonuniformity and was considerably reduced with increasing cell densities in four features. The RF model implemented with highly significant features provided improved area under the curve results. The possible infiltrative tumor cells in the PVE of the GB are likely influencing the texture values and are higher in comparison with BM PVE and may be of value in the differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with a larger cohort and across different scanners in the future.

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.

Development and validation of a four-dimensional registration technique for DCE breast MRI

BACKGROUND

Patient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI.

RESULTS

After applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman's rho: 0.83, p < 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient K^trans as surrogate parameter for motion artifacts. Mean K^trans decreased from 0.103 (± 0.077) before registration to 0.097 (± 0.070) after registration (p < 0.001). Uncertainty in perfusion quantification was reduced by 7.4% after registration (± 15.5, p < 0.001).

CONCLUSIONS

Four-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.

Evaluation of the Effects of Anti-PD-1 Therapy on Triple-Negative Breast Cancer in Mice by Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced Imaging

BACKGROUND

The monitoring of immunotherapies is still based on changes in the tumor size in imaging, with a long evaluation period and low sensitivity.

PURPOSE

To investigate the effectiveness of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the therapeutic efficacy of anti-programmed death-1 (PD-1) therapy in a mouse triple negative breast cancer (TNBC) model.

STUDY TYPE

Prospective.

ANIMAL MODEL

A total of 54 BALB/c mouse subcutaneous 4 T1 transplantation models of TNBC.

FIELD STRENGTH/SEQUENCE

A 3.0-T; turbo spin echo (TSE) T2-weighted imaging, DKI with seven b values (0, 500, 1000, 1500, 2000, 2500, and 3000 sec/mm² ) and T1-twist DCE acquisition series.

ASSESSMENT

DKI and DCE-MRI parameters were evaluated by two radiologists independently. Regions of interest (ROIs) were drawn manually on the maximum cross-sectional area of the lesion; care was taken to avoid necrotic areas. The tumor cell density, the CD45 and CD31 levels were analyzed by two pathologists.

STATISTICAL TESTS

The two-tailed unpaired t-test, Mann-Whitney U test, Fisher's exact test and Pearson correlation coefficient were performed. A P < 0.05 was considered statistically significant.

RESULTS

The apparent diffusion coefficient (ADC), mean diffusivity (MD), K_trans and K_ep values were significantly different between the two groups at each time point after treatment. There were significant differences in the mean kurtosis (MK) and V_e values between the two groups at 5 and 10 days after treatment but no significant differences at 15 days (P = 0.317 and 0.183, respectively). The ADC and MD values were significantly correlated with tumor cell density (ADC, r = -0.833; MD, r = 0.890) and the CD45 level (ADC, r = 0.720; MD, r = 0.718). The K_trans and K_ep values were significantly correlated with the CD31 level (K_trans , r = 0.820; K_ep , r = 0.683).

DATA CONCLUSION

DKI and DCE-MRI could reflect the changes in tumor microstructure and tumor tissue vasculature after anti-PD-1 therapy, respectively.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 4.

Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics

OBJECTIVE

This study establishes a fluid dynamics model personalized with patient-specific imaging data to optimize neoadjuvant therapy (i.e., doxorubicin) protocols for breast cancers.

METHODS

Ten patients recruited at the University of Chicago were included in this study. Quantitative dynamic contrast-enhanced and diffusion weighted magnetic resonance imaging data are leveraged to estimate patient-specific hemodynamic properties, which are then used to constrain the mechanism-based drug delivery model. Then, computer simulations of this model yield the subsequent drug distribution throughout the breast. By systematically varying the dosing schedule, we identify an optimized regimen for each patient using the maximum safe therapeutic duration (MSTD), which is a metric balancing treatment efficacy and toxicity.

RESULTS

With an individually optimized dose (range = 12.11-15.11 mg/m2 per injection), a 3-week regimen consisting of a uniform daily injection significantly outperforms all other scheduling strategies (P < 0.001). In particular, the optimal protocol is predicted to significantly outperform the standard protocol (P < 0.001), improving the MSTD by an average factor of 9.93 (range = 6.63 to 14.17).

CONCLUSION

A clinical-mathematical framework was developed by integrating quantitative MRI data, advanced image processing, and computational fluid dynamics to predict the efficacy and toxicity of neoadjuvant therapy protocols, thus enabling the rational identification of an optimal therapeutic regimen on a patient-specific basis.

SIGNIFICANCE

Our clinical-computational approach has the potential to enable optimization of therapeutic regimens on a patient-specific basis and provide guidance for prospective clinical trials aimed at refining neoadjuvant therapy protocols for breast cancers.

Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

PURPOSE

The tumor-associated vasculature (TAV) differs from healthy blood vessels by its convolutedness, leakiness, and chaotic architecture, and these attributes facilitate the creation of a treatment-resistant tumor microenvironment. Measurable differences in these attributes might also help stratify patients by likely benefit of systemic therapy (e.g., chemotherapy). In this work, we present a new category of computational image-based biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancer types, imaging modalities, and treatment regimens involving chemotherapy.

EXPERIMENTAL DESIGN

We isolated tumor vasculature and extracted mathematical measurements of twistedness and organization from routine pretreatment radiology (CT or contrast-enhanced MRI) of a total of 558 patients, who received one of four first-line chemotherapy-based therapeutic intervention strategies for breast (n = 371) or non-small cell lung cancer (NSCLC, n = 187).

RESULTS

Across four chemotherapy-based treatment strategies, classifiers of QuanTAV measurements significantly (P < 0.05) predicted response in held out testing cohorts alone (AUC = 0.63-0.71) and increased AUC by 0.06-0.12 when added to models of significant clinical variables alone. Similarly, we derived QuanTAV risk scores that were prognostic of recurrence-free survival in treatment cohorts who received surgery following chemotherapy for breast cancer [P = 0.0022; HR = 1.25; 95% confidence interval (CI), 1.08-1.44; concordance index (C-index) = 0.66] and chemoradiation for NSCLC (P = 0.039; HR = 1.28; 95% CI, 1.01-1.62; C-index = 0.66). From vessel-based risk scores, we further derived categorical QuanTAV high/low risk groups that were independently prognostic among all treatment groups, including patients with NSCLC who received chemotherapy only (P = 0.034; HR = 2.29; 95% CI, 1.07-4.94; C-index = 0.62). QuanTAV response and risk scores were independent of clinicopathologic risk factors and matched or exceeded models of clinical variables including posttreatment response.

CONCLUSIONS

Across these domains, we observed an association of vascular morphology on CT and MRI-as captured by metrics of vessel curvature, torsion, and organizational heterogeneity-and treatment outcome. Our findings suggest the potential of shape and structure of the TAV in developing prognostic and predictive biomarkers for multiple cancers and different treatment strategies.

Differences Between Ipsilateral and Contralateral Early Parenchymal Enhancement Kinetics Predict Response of Breast Cancer to Neoadjuvant Therapy

RATIONALE AND OBJECTIVES

To determine whether kinetics measured with ultrafast dynamic contrast-enhanced magnetic resonance imaging in tumor and normal parenchyma pre- and post-neoadjuvant therapy (NAT) can predict the response of breast cancer to NAT.

MATERIALS AND METHODS

Twenty-four patients with histologically confirmed invasive breast cancer were enrolled. They were scanned with ultrafast dynamic contrast-enhanced magnetic resonance imaging (3-7 seconds/frame) pre- and post-NAT. Four kinetic parameters were calculated in the segmented tumors, and ipsi- and contra-lateral normal parenchyma: (1) tumor (tSE30) or background parenchymal relative enhancement at 30 seconds (BPE30), (2) maximum relative enhancement slope (MaxSlope), (3) bolus arrival time (BAT), and (4) area under relative signal enhancement curve for the initial 30 seconds (AUC30). The tumor kinetics and the differences between ipsi- and contra-lateral parenchymal kinetics were compared for patients achieving pathologic complete response (pCR) vs those who had residual disease after NAT. The chi-squared test and two-sided t-test were used for baseline demographics. The Wilcoxon rank sum test and one-way analysis of variance were used for differential responses to therapy.

RESULTS

Patients with similar pre-NAT mean BPE30, median BAT and mean AUC30 in the ipsi- and contralateral normal parenchyma were more likely to achieve pCR following NAT (p < 0.02). Patients classified as having residual cancer burden (RCB) II after NAT showed higher post-NAT tSE30 and tumor AUC30 and higher post-NAT MaxSlope in ipsilateral normal parenchyma compared to those classified as RCB I or pCR (p < 0.05).

CONCLUSION

Bilateral asymmetry in normal parenchyma could predict treatment outcome prior to NAT. Post-NAT tumor kinetics could evaluate the aggressiveness of residual tumor.

A 1D-0D-3D coupled model for simulating blood flow and transport processes in breast tissue

In this work, we present mixed dimensional models for simulating blood flow and transport processes in breast tissue and the vascular tree supplying it. These processes are considered, to start from the aortic inlet to the capillaries and tissue of the breast. Large variations in biophysical properties and flow conditions exist in this system necessitating the use of different flow models for different geometries and flow regimes. In total, we consider four different model types. First, a system of 1D nonlinear hyperbolic partial differential equations (PDEs) is considered to simulate blood flow in larger arteries with highly elastic vessel walls. Second, we assign 1D linearized hyperbolic PDEs to model the smaller arteries with stiffer vessel walls. The third model type consists of ODE systems (0D models). It is used to model the arterioles and peripheral circulation. Finally, homogenized 3D porous media models are considered to simulate flow and transport in capillaries and tissue within the breast volume. Sink terms are used to account for the influence of the venous and lymphatic systems. Combining the four model types, we obtain two different 1D-0D-3D coupled models for simulating blood flow and transport processes: The first model results in a fully coupled 1D-0D-3D model covering the complete path from the aorta to the breast combining a generic arterial network with a patient specific breast network and geometry. The second model is a reduced one based on the separation of the generic and patient specific parts. The information from a calibrated fully coupled model is used as inflow condition for the patient specific sub-model allowing a significant computational cost reduction. Several numerical experiments are conducted to calibrate the generic model parameters and to demonstrate realistic flow simulations compared to existing data on blood flow in the human breast and vascular system. Moreover, we use two different breast vasculature and tissue data sets to illustrate the robustness of our reduced sub-model approach.

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer

This study identifies physiological habitats using quantitative magnetic resonance imaging (MRI) to elucidate intertumoral differences and characterize microenvironmental response to targeted and cytotoxic therapy. BT-474 human epidermal growth factor receptor 2 (HER2+) breast tumors were imaged before and during treatment (trastuzumab, paclitaxel) with diffusion-weighted MRI and dynamic contrast-enhanced MRI to measure tumor cellularity and vascularity, respectively. Tumors were stained for anti-CD31, anti-ɑSMA, anti-CD45, anti-F4/80, anti-pimonidazole, and H&E. MRI data was clustered to identify and label each habitat in terms of vascularity and cellularity. Pre-treatment habitat composition was used stratify tumors into two "tumor imaging phenotypes" (Type 1, Type 2). Type 1 tumors showed significantly higher percent tumor volume of the high-vascularity high-cellularity (HV-HC) habitat compared to Type 2 tumors, and significantly lower volume of low-vascularity high-cellularity (LV-HC) and low-vascularity low-cellularity (LV-LC) habitats. Tumor phenotypes showed significant differences in treatment response, in both changes in tumor volume and physiological composition. Significant positive correlations were found between histological stains and tumor habitats. These findings suggest that the differential baseline imaging phenotypes can predict response to therapy. Specifically, the Type 1 phenotype indicates increased sensitivity to targeted or cytotoxic therapy compared to Type 2 tumors.

Predicting cancer outcomes with radiomics and artificial intelligence in radiology

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.

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 (Ktrans), volume fraction of extravascular extracellular space (Ve) 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 Ktrans, (p = 0.0066, 0.0274, respectively), but not in D, Ve, 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 Ktrans, 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.

Difference of DCE-MRI Parameters at Different Time Points and Their Predictive Value for Axillary Lymph Node Metastasis of Breast Cancer

RATIONALE AND OBJECTIVES

To assess differences of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) parameters at different postcontrast time points (TPs), and to explore the predictive value of DCE-MRI parameters for axillary lymph node (ALN) metastasis of breast cancer.

MATERIALS AND METHODS

A total of 107 breast cancer patients were included retrospectively, and 50 phases were collected on DCE-MRI for each patient. DCE-MRI parameters Ktrans, Kep, Ve, TTP, Peak, Washin, Washout, and AUC were extracted from the images at 67.8 seconds, 128.5 seconds, 189.2 seconds, 249.9 seconds, and 310.5 seconds (regard as TP1, 2, 3, 4, and 5). Wilcoxon signed rank test was used to compare DCE-MRI parameters at different postcontrast TPs. Logistic regression was performed to analyze the predictive value of DCE-MRI parameters for ALN metastasis of breast cancer, and receiver operating characteristic (ROC) curve was constructed to evaluate the predictive performance.

RESULTS

The difference of DCE-MRI parameters between TP1, 2, 3, 4, and 5 was statistically significant (p < 0.01) in breast cancer. The TPs are considered as the optimal TPs when DCE-MRI parameters values reach the maximum. The optimal TPs of Ktrans, Kep, and Ve were respectively at TP2, TP2, and TP4 (Ktrans₂, Kep₂, and Ve₄). The optimal TPs of TTP, Peak, and AUC were at TP5 (TTP₅, Peak₅, and AUC₅). AUC₅ showed the ability to predict ALN metastasis of breast cancer (area under ROC curve = 0.656, p < 0.05).

CONCLUSIONS

DCE-MRI parameters values were different at different postcontrast TPs. AUC₅ may be an independent predictor of ALN metastasis in breast cancer.

Ultrafast Dynamic Contrast-enhanced MRI of the Breast: How Is It Used?

Ultrafast dynamic contrast-enhanced (UF-DCE) MRI is a new approach to capture kinetic information in the very early post-contrast period with high temporal resolution while keeping reasonable spatial resolution. The detailed timing and shape of the upslope in the time–intensity curve are analyzed. New kinetic parameters obtained from UF-DCE MRI are useful in differentiating malignant from benign lesions and in evaluating prognostic markers of the breast cancers. Clinically, UF-DCE MRI contributes in identifying hypervascular lesions when the background parenchymal enhancement (BPE) is marked on conventional dynamic MRI. This review starts with the technical aspect of accelerated acquisition. Practical aspects of UF-DCE MRI include identification of target hypervascular lesions from marked BPE and diagnosis of malignant and benign lesions based on new kinetic parameters derived from UF-DCE MRI: maximum slope (MS), time to enhance (TTE), bolus arrival time (BAT), time interval between arterial and venous visualization (AVI), and empirical mathematical model (EMM). The parameters derived from UF-DCE MRI are compared in terms of their diagnostic performance and association with prognostic markers. Pitfalls of UF-DCE MRI in the clinical situation are also covered. Since UF-DCE MRI is an evolving technique, future prospects of UF-DCE MRI are discussed in detail by citing recent evidence. The topic covers prediction of treatment response, multiparametric approach using DWI-derived parameters, evaluation of tumor-related vessels, and application of artificial intelligence for UF-DCE MRI. Along with comprehensive literature review, illustrative clinical cases are used to understand the value of UF-DCE MRI.

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.

Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting

This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2-3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction-diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.

Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI

In patients with dense breasts or at high risk of breast cancer, dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive diagnostic tool. However, its specificity is highly variable and sometimes low; quantitative measurements of contrast uptake parameters may improve specificity and mitigate this issue. To improve diagnostic accuracy, data need to be captured at high spatial and temporal resolution. While many methods exist to accelerate MRI temporal resolution, not all are optimized to capture breast DCE-MRI dynamics. We propose a novel, flexible, and powerful framework for the reconstruction of highly-undersampled DCE-MRI data: enhancement-constrained acceleration (ECA). Enhancement-constrained acceleration uses an assumption of smooth enhancement at small time-scale to estimate points of smooth enhancement curves in small time intervals at each voxel. This method is tested in silico with physiologically realistic virtual phantoms, simulating state-of-the-art ultrafast acquisitions at 3.5s temporal resolution reconstructed at 0.25s temporal resolution (demo code available here). Virtual phantoms were developed from real patient data and parametrized in continuous time with arterial input function (AIF) models and lesion enhancement functions. Enhancement-constrained acceleration was compared to standard ultrafast reconstruction in estimating the bolus arrival time and initial slope of enhancement from reconstructed images. We found that the ECA method reconstructed images at 0.25s temporal resolution with no significant loss in image fidelity, a 4x reduction in the error of bolus arrival time estimation in lesions (p < 0.01) and 11x error reduction in blood vessels (p < 0.01). Our results suggest that ECA is a powerful and versatile tool for breast DCE-MRI.

An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom

Quantitative evaluation of an image processing method to perform as designed is central to both its utility and its ability to guide the data acquisition process. Unfortunately, these tasks can be quite challenging due to the difficulty of experimentally obtaining the "ground truth" data to which the output of a given processing method must be compared. One way to address this issue is via "digital phantoms", which are numerical models that provide known biophysical properties of a particular object of interest.  In this contribution, we propose an in silico validation framework for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition and analysis methods that employs a novel dynamic digital phantom. The phantom provides a spatiotemporally-resolved representation of blood-interstitial flow and contrast agent delivery, where the former is solved by a 1D-3D coupled computational fluid dynamic system, and the latter described by an advection-diffusion equation. Furthermore, we establish a virtual simulator which takes as input the digital phantom, and produces realistic DCE-MRI data with controllable acquisition parameters. We assess the performance of a simulated standard-of-care acquisition (Protocol A) by its ability to generate contrast-enhanced MR images that separate vasculature from surrounding tissue, as measured by the contrast-to-noise ratio (CNR). We find that the CNR significantly decreases as the spatial resolution (SRA, where the subscript indicates Protocol A) or signal-to-noise ratio (SNRA) decreases. Specifically, with an SNRA / SRA = 75 dB / 30 μm, the median CNR is 77.30, whereas an SNRA / SRA = 5 dB / 300 μm reduces the CNR to 6.40. Additionally, we assess the performance of simulated ultra-fast acquisition (Protocol B) by its ability to generate DCE-MR images that capture contrast agent pharmacokinetics, as measured by error in the signal-enhancement ratio (SER) compared to ground truth (PESER). We find that PESER significantly decreases the as temporal resolution (TRB) increases. Similar results are reported for the effects of spatial resolution and signal-to-noise ratio on PESER. For example, with an SNRB / SRB / TRB = 5 dB / 300 μm / 10 s, the median PESER is 21.00%, whereas an SNRB / SRB / TRB = 75 dB / 60 μm / 1 s, yields a median PESER of 0.90%. These results indicate that our in silico framework can generate virtual MR images that capture effects of acquisition parameters on the ability of generated images to capture morphological or pharmacokinetic features. This validation framework is not only useful for investigations of perfusion-based MRI techniques, but also for the systematic evaluation and optimization new MRI acquisition, reconstruction, and image processing techniques.

Functional Tumor Volume by Fast Dynamic Contrast-Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple-Negative Breast Cancer

BACKGROUND

Dynamic contrast-enhanced (DCE) MRI is useful for diagnosis and assessment of treatment response in breast cancer. Fast DCE MRI offers a higher sampling rate of contrast enhancement curves in comparison to conventional DCE MRI, potentially characterizing tumor perfusion kinetics more accurately for measurement of functional tumor volume (FTV) as a predictor of treatment response.

PURPOSE

To investigate FTV by fast DCE MRI as a predictor of neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC).

STUDY TYPE

Prospective.

POPULATION/SUBJECTS

Sixty patients with biopsy-confirmed TNBC between December 2016 and September 2020.

FIELD STRENGTH/SEQUENCE

A 3.0 T/3D fast spoiled gradient echo-based DCE MRI ASSESSMENT: Patients underwent MRI at baseline and after four cycles (C4) of NAST, followed by definitive surgery. DCE subtraction images were analyzed in consensus by two breast radiologists with 5 (A.H.A.) and 2 (H.S.M.) years of experience. Tumor volumes (TV) were measured on early and late subtractions. Tumors were segmented on 1 and 2.5-minute early phases subtractions and FTV was determined using optimized signal enhancement thresholds. Interpolated enhancement curves from segmented voxels were used to determine optimal early phase timing.

STATISTICAL TESTS

Tumor volumes were compared between patients who had a pathologic complete response (pCR) and those who did not using the area under the receiver operating curve (AUC) and Mann-Whitney U test.

RESULTS

About 26 of 60 patients (43%) had pCR. FTV at 1 minute after injection at C4 provided the best discrimination between pCR and non-pCR, with AUC (95% confidence interval [CI]) = 0.85 (0.74,0.95) (P < 0.05). The 1-minute timing was optimal for FTV measurements at C4 and for the change between C4 and baseline. TV from the early phase at C4 also yielded a good AUC (95%CI) of 0.82 (0.71,0.93) (P < 0.05).

DATA CONCLUSION

FTV and TV measured at 1 minute after injection can predict response to NAST in TNBC.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: 4.

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.

Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2-3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.

Feasibility of Velocity-Selective Arterial Spin Labeling in Breast Cancer Patients for Noncontrast-Enhanced Perfusion Imaging

Background

Dynamic contrast‐enhanced (DCE) MRI is the most sensitive method for detection of breast cancer. However, due to high costs and retention of intravenously injected gadolinium‐based contrast agent, screening with DCE‐MRI is only recommended for patients who are at high risk for developing breast cancer. Thus, a noncontrast‐enhanced alternative to DCE is desirable.

Purpose

To investigate whether velocity selective arterial spin labeling (VS‐ASL) can be used to identify increased perfusion and vascularity within breast lesions compared to surrounding tissue.

Study Type

Prospective.

Population

Eight breast cancer patients.

Field Strength/Sequence

A 3 T; VS‐ASL with multislice single‐shot gradient‐echo echo‐planar‐imaging readout.

Assessment

VS‐ASL scans were independently assessed by three radiologists, with 3–25 years of experience in breast radiology. Scans were scored on lesion visibility and artifacts, based on a 3‐point Likert scale. A score of 1 corresponded to “lesions being distinguishable from background” (lesion visibility), and “no or few artifacts visible, artifacts can be distinguished from blood signal” (artifact score). A distinction was made between mass and nonmass lesions (based on BI‐RADS lexicon), as assessed in the standard clinical exam.

Statistical Tests

Intra‐class correlation coefficient (ICC) for interobserver agreement.

Results

The ICC was 0.77 for lesion visibility and 0.84 for the artifact score. Overall, mass lesions had a mean score of 1.27 on lesion visibility and 1.53 on the artifact score. Nonmass lesions had a mean score of 2.11 on lesion visibility and 2.11 on the artifact score.

Data Conclusion

We have demonstrated the technical feasibility of bilateral whole‐breast perfusion imaging using VS‐ASL in breast cancer patients.

Evidence Level

1

Technical Efficacy

Stage 1

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.

Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer

While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response.

Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics

The overall goal of this study is to employ quantitative magnetic resonance imaging (MRI) data to constrain a patient-specific, computational fluid dynamics (CFD) model of blood flow and interstitial transport in breast cancer. We develop image processing methodologies to generate tumor-related vasculature-interstitium geometry and realistic material properties, using dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) data. These data are used to constrain CFD simulations for determining the tumor-associated blood supply and interstitial transport characteristics unique to each patient. We then perform a proof-of-principle statistical comparison between these hemodynamic characteristics in 11 malignant and 5 benign lesions from 12 patients. Significant differences between groups (i.e., malignant versus benign) were observed for the median of tumor-associated interstitial flow velocity ( P = 0.028 ), and the ranges of tumor-associated blood pressure (P = 0.016) and vascular extraction rate (P = 0.040). The implication is that malignant lesions tend to have larger magnitude of interstitial flow velocity, and higher heterogeneity in blood pressure and vascular extraction rate. Multivariable logistic models based on combinations of these hemodynamic data achieved excellent differentiation between malignant and benign lesions with an area under the receiver operator characteristic curve of 1.0, sensitivity of 1.0, and specificity of 1.0. This image-based model system is a fundamentally new way to map flow and pressure fields related to breast tumors using only non-invasive, clinically available imaging data and established laws of fluid mechanics. Furthermore, the results provide preliminary evidence for this methodology's utility for the quantitative characterization of breast cancer.

Multiparametric Analysis of Longitudinal Quantitative MRI data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer

This study identifies physiological tumor habitats from quantitative magnetic resonance imaging (MRI) data and evaluates their alterations in response to therapy. Two models of breast cancer (BT-474 and MDA-MB-231) were imaged longitudinally with diffusion-weighted MRI and dynamic contrast-enhanced MRI to quantify tumor cellularity and vascularity, respectively, during treatment with trastuzumab or albumin-bound paclitaxel. Tumors were stained for anti-CD31, anti-Ki-67, and H&E. Imaging and histology data were clustered to identify tumor habitats and percent tumor volume (MRI) or area (histology) of each habitat was quantified. Histological habitats were correlated with MRI habitats. Clustering of both the MRI and histology data yielded three clusters: high-vascularity high-cellularity (HV-HC), low-vascularity high-cellularity (LV-HC), and low-vascularity low-cellularity (LV-LC). At day 4, BT-474 tumors treated with trastuzumab showed a decrease in LV-HC (p = 0.03) and increase in HV-HC (p = 0.03) percent tumor volume compared to control. MDA-MB-231 tumors treated with low-dose albumin-bound paclitaxel showed a longitudinal decrease in LV-HC percent tumor volume at day 3 (p = 0.01). Positive correlations were found between histological and imaging-derived habitats: HV-HC (BT-474: p = 0.03), LV-HC (MDA-MB-231: p = 0.04), LV-LC (BT-474: p = 0.04; MDA-MB-231: p < 0.01). Physiologically distinct tumor habitats associated with therapeutic response were identified with MRI and histology data in preclinical models of breast cancer.

Medical physics challenges in clinical MR-guided radiotherapy

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

A hybrid model of tumor growth and angiogenesis: In silico experiments

Tumor associated angiogenesis is the development of new blood vessels in response to proteins secreted by tumor cells. These new blood vessels allow tumors to continue to grow beyond what the pre-existing vasculature could support. Here, we construct a mathematical model to simulate tumor angiogenesis by considering each endothelial cell as an agent, and allowing the vascular endothelial growth factor (VEGF) and nutrient fields to impact the dynamics and phenotypic transitions of each tumor and endothelial cell. The phenotypes of the endothelial cells (i.e., tip, stalk, and phalanx cells) are selected by the local VEGF field, and govern the migration and growth of vessel sprouts at the cellular level. Over time, these vessels grow and migrate to the tumor, forming anastomotic loops to supply nutrients, while interacting with the tumor through mechanical forces and the consumption of VEGF. The model is able to capture collapsing and breaking of vessels caused by tumor-endothelial cell interactions. This is accomplished through modeling the physical interaction between the vasculature and the tumor, resulting in vessel occlusion and tumor heterogeneity over time due to the stages of response in angiogenesis. Key parameters are identified through a sensitivity analysis based on the Sobol method, establishing which parameters should be the focus of subsequent experimental efforts. During the avascular phase (i.e., before angiogenesis is triggered), the nutrient consumption rate, followed by the rate of nutrient diffusion, yield the greatest influence on the number and distribution of tumor cells. Similarly, the consumption and diffusion of VEGF yield the greatest influence on the endothelial and tumor cell numbers during angiogenesis. In summary, we present a hybrid mathematical approach that characterizes vascular changes via an agent-based model, while treating nutrient and VEGF changes through a continuum model. The model describes the physical interaction between a tumor and the surrounding blood vessels, explicitly allowing the forces of the growing tumor to influence the nutrient delivery of the vasculature.

The additional utility of ultrafast MRI on conventional DCE-MRI in evaluating preoperative MRI of breast cancer patients

PURPOSE

To investigate whether the additional use of ultrafast MRI can improve the diagnostic performance of conventional dynamic contrast-enhanced MRI (DCE-MRI) in evaluating MRI-detected lesions in breast cancer patients.

METHODS

This retrospective study enrolled 101 consecutive breast cancer patients with 202 breast lesions (62 benign and 140 malignant) who underwent preoperative DCE-MRI with ultrafast imaging (9 image sets with 6.5-second temporal resolution). Two reviewers assessed the BI-RADS categories of breast lesions using conventional DCE-MRI and assessed the following parameters using the ultrafast MRI: initial enhancement phase, maximum relative enhancement, slope, and maximum slope (slope_max) on the kinetic curve. Interobserver agreement was analyzed between the two reviewers. The ultrafast MRI parameters were compared between benign and malignant tumors, and cut-off values were determined. For 97 additional MRI-detected lesions, the BI-RADS category was re-assessed using cut-off values, and the diagnostic performance was compared between the conventional DCE-MRI and the combined conventional and ultrafast DCE-MRI.

RESULTS

All ultrafast MRI parameters differed significantly between malignant and benign tumors (p < 0.001). Initial enhancement phase by reviewer and slope_max were the top two parameters showing significant differences between benign and malignant tumors with high reliability. With the use of cut-off values for initial enhancement phase (≤phase 2) and slope_max (>9.8%/sec), the specificity of conventional DCE-MRI was significantly increased (29.4% vs 64.7%, p < 0.001) without significant loss of sensitivity (100% vs 88.2%, p = 0.157) in evaluating masses.

CONCLUSIONS

The additional use of ultrafast MRI can improve the specificity of conventional DCE-MRI when evaluating MRI-detected masses in breast cancer patients.

Tumor Vessel Normalization: A Window to Enhancing Cancer Immunotherapy

Hostile microenvironment produced by abnormal blood vessels, which is characterized by hypoxia, low pH value and increasing interstitial fluid pressure, would facilitate tumor progression, metastasis, immunosuppression and anticancer treatments resistance. These abnormalities are the result of the imbalance of pro-angiogenic and anti-angiogenic factors (such as VEGF and angiopoietin 2, ANG2). Prudent use of anti-angiogenesis drugs would normalize these aberrant tumor vessels, resulting in a transient window of vessel normalization. In addition, use of cancer immunotherapy including immune checkpoint blockers when vessel normalization is achieved brings better outcomes. In this review, we sum up the advances in the field of understanding and application of the concept of tumor vessels normalization window to treat cancer. Moreover, we also outline some challenges and opportunities ahead to optimize the combination of anti-angiogenic agents and immunotherapy, leading to improve patients' outcomes.

New parameters of ultrafast dynamic contrast-enhanced breast MRI using compressed sensing

BACKGROUND

Ultrafast dynamic contrast-enhanced (UF-DCE) breast MRI is considered a promising method of accelerated breast MRI. However, the value of new kinetic parameters derived from UF-DCE need clinical evaluation.

PURPOSE

To evaluate the diagnostic performance of the maximum slope (MS), time to enhancement (TTE), and time interval between arterial and venous visualization (AVI) derived from UF-DCE MRI using compressed sensing (CS).

STUDY TYPE

Retrospective.

POPULATION

Seventy-five patients with histologically proven breast lesions. The total number of analyzed lesions was 90 (61 malignant and 29 benign).

FIELD STRENGTH/SEQUENCE

3T MRI with UF-DCE MRI based on the 3D gradient-echo volumetric interpolated breath-hold examination (VIBE) sequence using incoherent k-space sampling combined with a CS reconstruction followed by conventional DCE MRI.

ASSESSMENT

The diagnostic performance of the MS, TTE, AVI, and conventional kinetic analysis was analyzed and compared with histology.

STATISTICAL TESTS

Wilcoxon rank sum test, receiver operating characteristic analysis.

RESULTS

The MS was larger and the TTE and AVI were smaller for malignant lesions compared with benign lesions: MS: 29.3%/s and 18.4%/s (P < 0.001), TTE: 7.0 and 12.0 seconds (P < 0.001), AVI: 2.7 and 4.4 frames (P = 0.006) for malignant and benign lesions. The discriminating power of the MS (area under the curve [AUC], 0.76) was slightly better than that of conventional kinetic analysis (AUC, 0.69) and comparable to that of the TTE and AVI (AUC, 0.78 and 0.76 for TTE and AVI, respectively). Invasive lobular carcinoma had smaller MS (21.8%/s) among malignant lesions (29.3%/s).

DATA CONCLUSION

The MS, TTE, and AVI can be used to evaluate breast lesions with clinical performance equivalent to that of conventional kinetic analysis. These parameters vary among histologies.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:164-174.