Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer

Diagnosis and treatment status of inoperable locally advanced breast cancer and the application value of inorganic nanomaterials

Locally advanced breast cancer (LABC) is a heterogeneous group of breast cancer that accounts for 10-30% of breast cancer cases. Despite the ongoing development of current treatment methods, LABC remains a severe and complex public health concern around the world, thus prompting the urgent requirement for innovative diagnosis and treatment strategies. The primary treatment challenges are inoperable clinical status and ineffective local control methods. With the rapid advancement of nanotechnology, inorganic nanoparticles (INPs) exhibit a potential application prospect in diagnosing and treating breast cancer. Due to the unique inherent characteristics of INPs, different functions can be performed via appropriate modifications and constructions, thus making them suitable for different imaging technology strategies and treatment schemes. INPs can improve the efficacy of conventional local radiotherapy treatment. In the face of inoperable LABC, INPs have proposed new local therapeutic methods and fostered the evolution of novel strategies such as photothermal and photodynamic therapy, magnetothermal therapy, sonodynamic therapy, and multifunctional inorganic nanoplatform. This article reviews the advances of INPs in local accurate imaging and breast cancer treatment and offers insights to overcome the existing clinical difficulties in LABC management.

Magnetic Resonance Perfusion Imaging for Breast Cancer

Breast cancer is the most frequently diagnosed cancer among women worldwide, carrying a significant socioeconomic burden. Breast cancer is a heterogeneous disease with 4 major subtypes identified. Each subtype has unique prognostic factors, risks, treatment responses, and survival rates. Advances in targeted therapies have considerably improved the 5-year survival rates for primary breast cancer patients largely due to widespread screening programs that enable early detection and timely treatment. Imaging techniques are indispensable in diagnosing and managing breast cancer. While mammography is the primary screening tool, MRI plays a significant role when mammography results are inconclusive or in patients with dense breast tissue. MRI has become standard in breast cancer imaging, providing detailed anatomic and functional data, including tumor perfusion and cellularity. A key characteristic of breast tumors is angiogenesis, a biological process that promotes tumor development and growth. Increased angiogenesis in tumors generally indicates poor prognosis and increased risk of metastasis. Dynamic contrast-enhanced (DCE) MRI measures tumor perfusion and serves as an in vivo metric for angiogenesis. DCE-MRI has become the cornerstone of breast MRI, boasting a high negative-predictive value of 89% to 99%, although its specificity can vary. This review presents a thorough overview of magnetic resonance (MR) perfusion imaging in breast cancer, focusing on the role of DCE-MRI in clinical applications and exploring emerging MR perfusion imaging techniques.

Breast Fibroglandular Tissue Segmentation for Automated BPE Quantification With Iterative Cycle-Consistent Semi-Supervised Learning

Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast cancer diagnosis and prognosis. However, the emerging deep learning-based breast fibroglandular tissue segmentation, a crucial step in automated BPE quantification, often suffers from limited training samples with accurate annotations. To address this challenge, we propose a novel iterative cycle-consistent semi-supervised framework to leverage segmentation performance by using a large amount of paired pre-/post-contrast images without annotations. Specifically, we design the reconstruction network, cascaded with the segmentation network, to learn a mapping from the pre-contrast images and segmentation predictions to the post-contrast images. Thus, we can implicitly use the reconstruction task to explore the inter-relationship between these two-phase images, which in return guides the segmentation task. Moreover, the reconstructed post-contrast images across multiple auto-context modeling-based iterations can be viewed as new augmentations, facilitating cycle-consistent constraints across each segmentation output. Extensive experiments on two datasets with various data distributions show great segmentation and BPE quantification accuracy compared with other state-of-the-art semi-supervised methods. Importantly, our method achieves 11.80 times of quantification accuracy improvement along with 10 times faster, compared with clinical physicians, demonstrating its potential for automated BPE quantification. The code is available at https://github.com/ZhangJD-ong/Iterative-Cycle-consistent-Semi-supervised-Learning-for-fibroglandular-tissue-segmentation.

Tumour status prediction by means of carbon-ion beam irradiation: comparison of washout rates between in-beam PET and DCE-MRI in rats

Objective.Tumour response to radiation therapy appears as changes in tumour vascular condition. There are several methods for analysing tumour blood circulatory changes one of which is dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), but there is no method that can observe the tumour vascular condition and physiological changes at the site of radiation therapy. Positron emission tomography (PET) has been applied for treatment verification in charged particle therapy, which is based on the detection of positron emitters produced through nuclear fragmentation reactions in a patient's body. However, the produced positron emitters are washed out biologically depending on the tumour vascular condition. This means that measuring the biological washout rate may allow evaluation of the tumour radiation response, in a similar manner to DCE-MRI. Therefore, this study compared the washout rates in rats between in-beam PET during12C ion beam irradiation and DCE-MRI.Approach.Different vascular conditions of the tumour model were prepared for six nude rats. The tumour of each nude rat was irradiated by a12C ion beam with simultaneous in-beam PET measurement. In 10-12 h, the DCE-MRI experiment was performed for the same six nude rats. The biological washout rate of the produced positron emitters (k2,1st) and the MRI contrast agent (k2a) were derived using the single tissue compartment model.Main results.A linear correlation was observed betweenk2,1standk2a, and they were inversely related to fractional necrotic volume.Significance.This is the first animal study which confirmed the biological washout rate of in-beam PET correlates closely with tumour vascular condition measured with the MRI contrast agent administrated intravenously.

A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.

Exploring the Potential of Sensing for Breast Cancer Detection

Breast cancer is a generalized global problem. Biomarkers are the active substances that have been considered as the signature of the existence and evolution of cancer. Early screening of different biomarkers associated with breast cancer can help doctors to design a treatment plan. However, each screening technique for breast cancer has some limitations. In most cases, a single technique can detect a single biomarker at a specific time. In this study, we address different types of biomarkers associated with breast cancer. This review article presents a detailed picture of different techniques and each technique’s associated mechanism, sensitivity, limit of detection, and linear range for breast cancer detection at early stages. The limitations of existing approaches require researchers to modify and develop new methods to identify cancer biomarkers at early stages.

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.

Noninvasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound

OBJECTIVES

To explore an optimal model to predict the response of patients with axillary lymph node (ALN) positive breast cancer to neoadjuvant chemotherapy (NAC) with machine learning using clinical and ultrasound-based radiomic features.

METHODS

In this study, 1014 patients with ALN-positive breast cancer confirmed by histological examination and received preoperative NAC in the Affiliated Hospital of Qingdao University (QUH) and Qingdao Municipal Hospital (QMH) were included. Finally, 444 participants from QUH were divided into the training cohort (n = 310) and validation cohort (n = 134) based on the date of ultrasound examination. 81 participants from QMH were used to evaluate the external generalizability of our prediction models. A total of 1032 radiomic features of each ALN ultrasound image were extracted and used to establish the prediction models. The clinical model, radiomics model, and radiomics nomogram with clinical factors (RNWCF) were built. The performance of the models was assessed with respect to discrimination and clinical usefulness.

RESULTS

Although the radiomics model did not show better predictive efficacy than the clinical model, the RNWCF showed favorable predictive efficacy in the training cohort (AUC, 0.855; 95% CI 0.817-0.893), the validation cohort (AUC, 0.882; 95% CI 0.834-0.928), and the external test cohort (AUC, 0.858; 95% CI 0.782-0.921) compared with the clinical factor model and radiomics model.

CONCLUSIONS

The RNWCF, a noninvasive, preoperative prediction tool that incorporates a combination of clinical and radiomics features, showed favorable predictive efficacy for the response of node-positive breast cancer to NAC. Therefore, the RNWCF could serve as a potential noninvasive approach to assist personalized treatment strategies, guide ALN management, avoiding unnecessary ALND.

Surgical Planning after Neoadjuvant Treatment in Breast Cancer: A Multimodality Imaging-Based Approach Focused on MRI

Neoadjuvant chemotherapy (NACT) today represents a cornerstone in the treatment of locally advanced breast cancer and highly chemo-sensitive tumors at early stages, increasing the possibilities of performing more conservative treatments and improving long term outcomes. Imaging has a fundamental role in the staging and prediction of the response to NACT, thus aiding surgical planning and avoiding overtreatment. In this review, we first examine and compare the role of conventional and advanced imaging techniques in preoperative T Staging after NACT and in the evaluation of lymph node involvement. In the second part, we analyze the different surgical approaches, discussing the role of axillary surgery, as well as the possibility of non-operative management after-NACT, which has been the subject of recent trials. Finally, we focus on emerging techniques that will change the diagnostic assessment of breast cancer in the near future.

Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI

Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann-Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p < 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p < 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients.

Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients' pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the validation, respectively. The model achieved an AUC of 0.86 ± 0.03 when evaluated in the independent testing group of 32 patients. In an additional prospective blinded testing group of 48 patients, the model achieved an AUC of 0.83 ± 0.02. These results demonstrated that DL based on multiparametric MRI can potentially differentiate TNBC patients with pCR or non-pCR in the breast early during NAST.

Tumor-Attentive Segmentation-Guided GAN for Synthesizing Breast Contrast-Enhanced MRI Without Contrast Agents

OBJECTIVE

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive imaging technique critical for breast cancer diagnosis. However, the administration of contrast agents poses a potential risk. This can be avoided if contrast-enhanced MRI can be obtained without using contrast agents. Thus, we aimed to generate T1-weighted contrast-enhanced MRI (ceT1) images from pre-contrast T1 weighted MRI (preT1) images in the breast.

METHODS

We proposed a generative adversarial network to synthesize ceT1 from preT1 breast images that adopted a local discriminator and segmentation task network to focus specifically on the tumor region in addition to the whole breast. The segmentation network performed a related task of segmentation of the tumor region, which allowed important tumor-related information to be enhanced. In addition, edge maps were included to provide explicit shape and structural information. Our approach was evaluated and compared with other methods in the local (n = 306) and external validation (n = 140) cohorts. Four evaluation metrics of normalized mean squared error (NRMSE), Pearson cross-correlation coefficients (CC), peak signal-to-noise ratio (PSNR), and structural similarity index map (SSIM) for the whole breast and tumor region were measured. An ablation study was performed to evaluate the incremental benefits of various components in our approach.

RESULTS

Our approach performed the best with an NRMSE 25.65, PSNR 54.80 dB, SSIM 0.91, and CC 0.88 on average, in the local test set.

CONCLUSION

Performance gains were replicated in the validation cohort.

SIGNIFICANCE

We hope that our method will help patients avoid potentially harmful contrast agents. Clinical and Translational Impact Statement-Contrast agents are necessary to obtain DCE-MRI which is essential in breast cancer diagnosis. However, administration of contrast agents may cause side effects such as nephrogenic systemic fibrosis and risk of toxic residue deposits. Our approach can generate DCE-MRI without contrast agents using a generative deep neural network. Thus, our approach could help patients avoid potentially harmful contrast agents resulting in an improved diagnosis and treatment workflow for breast cancer.

A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer

PURPOSE

To develop a nomogram based on radiomics signature and deep-learning signature for predicting the axillary lymph node (ALN) metastasis in breast cancer.

METHODS

A total of 151 patients were assigned to a training cohort (n = 106) and a test cohort (n = 45) in this study. Radiomics features were extracted from DCE-MRI images, and deep-learning features were extracted by VGG-16 algorithm. Seven machine learning models were built using the selected features to evaluate the predictive value of radiomics or deep-learning features for the ALN metastasis in breast cancer. A nomogram was then constructed based on the multivariate logistic regression model incorporating radiomics signature, deep-learning signature, and clinical risk factors.

RESULTS

Five radiomics features and two deep-learning features were selected for machine learning model construction. In the test cohort, the AUC was above 0.80 for most of the radiomics models except DecisionTree and ExtraTrees. In addition, the K-nearest neighbor (KNN), XGBoost, and LightGBM models using deep-learning features had AUCs above 0.80 in the test cohort. The nomogram, which incorporated the radiomics signature, deep-learning signature, and MRI-reported LN status, showed good calibration and performance with the AUC of 0.90 (0.85-0.96) in the training cohort and 0.90 (0.80-0.99) in the test cohort. The DCA showed that the nomogram could offer more net benefit than radiomics signature or deep-learning signature.

CONCLUSIONS

Both radiomics and deep-learning features are diagnostic for predicting ALN metastasis in breast cancer. The nomogram incorporating radiomics and deep-learning signatures can achieve better prediction performance than every signature used alone.

CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI

Simple Summary

Breast cancer represents the most frequent cancer in women in the world. The state of the axillary lymph node is considered an independent prognostic factor and is currently evaluated only with invasive methods. Deep learning approaches, especially the ones based on convolutional neural networks, offer a valid, non-invasive alternative, allowing extraction of large amounts of the quantitative data that are used to build predictive models. The aim of our work is to evaluate the influence of the peritumoral parenchyma through different bounding box techniques on the prediction of the axillary lymph node in breast cancer patients using a deep learning artificial intelligence approach.

Abstract

Background: The axillary lymph node status (ALNS) is one of the most important prognostic factors in breast cancer (BC) patients, and it is currently evaluated by invasive procedures. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), highlights the physiological and morphological characteristics of primary tumor tissue. Deep learning approaches (DL), such as convolutional neural networks (CNNs), are able to autonomously learn the set of features directly from images for a specific task. Materials and Methods: A total of 155 malignant BC lesions evaluated via DCE-MRI were included in the study. For each patient’s clinical data, the tumor histological and MRI characteristics and axillary lymph node status (ALNS) were assessed. LNS was considered to be the final label and dichotomized (LN+ (27 patients) vs. LN− (128 patients)). Based on the concept that peritumoral tissue contains valuable information about tumor aggressiveness, in this work, we analyze the contributions of six different tumor bounding options to predict the LNS using a CNN. These bounding boxes include a single fixed-size box (SFB), a single variable-size box (SVB), a single isotropic-size box (SIB), a single lesion variable-size box (SLVB), a single lesion isotropic-size box (SLIB), and a two-dimensional slice (2DS) option. According to the characteristics of the volumes considered as inputs, three different CNNs were investigated: the SFB-NET (for the SFB), the VB-NET (for the SVB, SIB, SLVB, and SLIB), and the 2DS-NET (for the 2DS). All the experiments were run in 10-fold cross-validation. The performance of each CNN was evaluated in terms of accuracy, sensitivity, specificity, the area under the ROC curve (AUC), and Cohen’s kappa coefficient (K). Results: The best accuracy and AUC are obtained by the 2DS-NET (78.63% and 77.86%, respectively). The 2DS-NET also showed the highest specificity, whilst the highest sensibility was attained by the VB-NET based on the SVB and SIB as bounding options. Conclusion: We have demonstrated that a selective inclusion of the DCE-MRI’s peritumoral tissue increases accuracy in the lymph node status prediction in BC patients using CNNs as a DL approach.

MRI background parenchymal enhancement, breast density and breast cancer risk factors: A cross-sectional study in pre- and post-menopausal women

Breast tissue enhances on contrast MRI and is called background parenchymal enhancement (BPE). Having high BPE has been associated with an increased risk of breast cancer. We examined the relationship between BPE and the amount of fibroglandular tissue on MRI (MRI-FGT) and breast cancer risk factors. This was a cross-sectional study of 415 women without breast cancer undergoing contrast-enhanced breast MRI at Memorial Sloan Kettering Cancer Center. All women completed a questionnaire assessing exposures at the time of MRI. Prevalence ratios (PR) and 95% confidence intervals (CI) describing the relationship between breast cancer risk factors and BPE and MRI-FGT were generated using modified Poisson regression. In multivariable-adjusted models a positive association between body mass index (BMI) and BPE was observed, with a 5-unit increase in BMI associated with a 14% and 44% increase in prevalence of high BPE in pre- and post-menopausal women, respectively. Conversely, a strong inverse relationship between BMI and MRI-FGT was observed in both pre- (PR = 0.66, 95% CI 0.57, 0.76) and post-menopausal (PR = 0.66, 95% CI 0.56, 0.78) women. Use of preventive medication (e.g., tamoxifen) was associated with having low BPE, while no association was observed for MRI-FGT. BPE is an imaging marker available from standard contrast-enhanced MRI, that is influenced by endogenous and exogenous hormonal exposures in both pre- and post-menopausal women.

Physics-informed neural networks for myocardial perfusion MRI quantification

Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, as they are physiologically plausible and resolve directly for blood flow and microvascular function. However, the reliability of model fitting is limited by the low signal-to-noise ratio, temporal resolution, and acquisition length. This may result in inaccurate parameter estimates. This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters. These neural networks can be trained to fit the observed perfusion MR data while respecting the underlying physical conservation laws described by a multi-compartment exchange model. Here, we provide a framework for the implementation of PINNs in myocardial perfusion MR. The approach is validated both in silico and in vivo. In the in silico study, an overall decrease in mean-squared error with the ground-truth parameters was observed compared to a standard non-linear least squares fitting approach. The in vivo study demonstrates that the method produces parameter values comparable to those previously found in literature, as well as providing parameter maps which match the clinical diagnosis of patients.

A Radiomics Nomogram for Distinguishing Benign From Malignant Round-Like Breast Tumors

Objective

The objective of this study is to develop a radiomics nomogram for the presurgical distinction of benign and malignant round-like solid tumors.

Methods

This retrospective trial enrolled patients with round-like tumors who had received preoperative digital mammography (DM) no sooner than 20 days prior to surgery. Breast tumors were segmented manually on DM images in order to extract radiomic features. Four machine learning classification models were constructed, and their corresponding areas under the receiver operating characteristic (ROC) curves (AUCs) for differential tumor diagnosis were calculated. The optimal classifier was then selected for the validation set. After this, predictive machine learning models that employed radiomic features and/or patient features were applied for tumor assessment. The models' AUC, accuracy, negative (NPV) and positive (PPV) predictive values, sensitivity, and specificity were then derived.

Results

In total 129 cases with benign and malignant tumors confirmed by pathological analysis were enrolled in the study, including 91 and 38 in the training and test sets, respectively. The DM images yielded 1,370 features per patient. For the machine learning models, the Least Absolute Shrinkage and Selection Operator for Gradient Boosting Classifier turned out to be the optimal classifier (AUC=0.87, 95% CI 0.76-0.99), and ROC curves for the radiomics nomogram and the DM-only model were statistically different (P<0.001). The radiomics nomogram achieved an AUC of 0.90 (95% CI 0.80-1.00) in the test cohort and was statistically higher than the DM-based model (AUC=0.67, 95% CI 0.51-0.84). The radiomics nomogram was highly efficient in detecting malignancy, with accuracy, sensitivity, specificity, PPV, and NPV in the validation set of 0.868, 0.950, 0.778, 0.826, and 0.933, respectively.

Conclusions

This radiomics nomogram that combines radiomics signatures and clinical characteristics represents a noninvasive, cost-efficient presurgical prediction technique.

Assessment of Suspected Breast Lesions in Early-Stage Triple-Negative Breast Cancer during Follow-Up after Breast-Conserving Surgery Using Multiparametric MRI

Background

The local recurrence rate of triple-negative breast cancer (TNBC) can be as high as 12%.The standard treatment for early-stage TNBC is breast-conserving surgery (BCS), followed by postoperative radiotherapy with or without chemotherapy. However, detection of the local recurrence of the disease after radiotherapy is a major issue.

Objective

The aim of this study was at investigating the role of dynamic and functional magnetic resonance imaging (MRI) during follow-up after BCS and radiotherapy with/without chemotherapy to differentiate between locoregional recurrence and postoperative fibrosis. Patients and Methods. This prospective study was conducted at the oncology, radiology, and pathology departments, Tanta University. It involved 50 patients with early-stage TNBC who were treated with BCS, followed by radiotherapy with/without chemotherapy. The suspected lesions were evaluated during the follow-up period by sonomammography. All patients were subjected to MRI, including conventional sequences, diffusion-weighted imaging (DWI), and dynamic postcontrast study.

Results

Ten cases were confirmed as recurrent malignant lesions. After contrast administration, they all exhibited irregular T1 hypodense lesions of variable morphology with diffusion restriction and positive enhancement. Eight cases displayed a type III curve, while two showed a type II curve. Histopathological assessment was consistent with the MRI findings in all eight cases. The combination of the data produced by DWI-MRI and dynamic contrast-enhanced (DCE) MRI resulted in 100%sensitivity, 92.5% specificity, 90.9% positive predictive value, 100% negative predictive value, and 98% accuracy.

Conclusion

Combination of DWI-MRI and DCE-MRI could have high diagnostic value for evaluating postoperative changes in patients with TNBC after BCS, followed by radiotherapy with/without chemotherapy. Trial Registrations. No trial to be registered.

Predictive Value of Multiparametric MRI for Response to Single-Cycle Induction Chemo-Immunotherapy in Locally Advanced Head and Neck Squamous Cell Carcinoma

Objectives

To assess the predictive value of multiparametric MRI for treatment response evaluation of induction chemo-immunotherapy in locally advanced head and neck squamous cell carcinoma.

Methods

Twenty-two patients with locally advanced, histologically confirmed head and neck squamous cell carcinoma who were enrolled in the prospective multicenter phase II CheckRad-CD8 trial were included in the current analysis. In this unplanned secondary single-center analysis, all patients who received contrast-enhanced MRI at baseline and in week 4 after single-cycle induction therapy with cisplatin/docetaxel combined with the immune checkpoint inhibitors tremelimumab and durvalumab were included. In week 4, endoscopy with representative re-biopsy was performed to assess tumor response. All lesions were segmented in the baseline and restaging multiparametric MRI, including the primary tumor and lymph node metastases. The volume of interest of the respective lesions was volumetrically measured, and time-resolved mean intensities of the golden-angle radial sparse parallel-volume-interpolated gradient-echo perfusion (GRASP-VIBE) sequence were extracted. Additional quantitative parameters including the T1 ratio, short-TI inversion recovery ratio, apparent diffusion coefficient, and dynamic contrast-enhanced (DCE) values were measured. A model based on parallel random forests incorporating the MRI parameters from the baseline MRI was used to predict tumor response to therapy. Receiver operating characteristic (ROC) curves were used to evaluate the prognostic performance.

Results

Fifteen patients (68.2%) showed pathologic complete response in the re-biopsy, while seven patients had a residual tumor (31.8%). In all patients, the MRI-based primary tumor volume was significantly lower after treatment. The baseline DCE parameters of time to peak and wash-out were significantly different between the pathologic complete response group and the residual tumor group (p < 0.05). The developed model, based on parallel random forests and DCE parameters, was able to predict therapy response with a sensitivity of 78.7% (95% CI 71.24–84.93) and a specificity of 78.6% (95% CI 67.13–87.48). The model had an area under the ROC curve of 0.866 (95% CI 0.819–0.914).

Conclusions

DCE parameters indicated treatment response at follow-up, and a random forest machine learning algorithm based on DCE parameters was able to predict treatment response to induction chemo-immunotherapy.

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.

The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study

Simple Summary

Breast cancer is the most common cancer in women worldwide. Currently the use of MR is mandatory in staging phase. The standard protocol includes T2-weighted sequences for morphology and signal analysis, T1-weighted images for adding information (i.e., ematic or adipous components), diffusion-weighted sequences which provide information on tissue cellularity, and dynamic post-contrast sequences useful for detecting and locating lesions. Although not considered among the main prognostic factors in current guidelines, tumor-associated edema provides useful information on tumor aggressiveness, and has been shown to be associated with the main histological tumor characteristics. With this work, entitled “The Impact of Tumor Edema on T2-weighted 3T-MRI Invasive Breast Cancer Histological Characterization: a Pilot Radiomics Study”, we want to demonstrate that radiomics edema, based on algorithms that allow the extraction of imaging features not visible to the human eye, can further increase the accuracy in the prediction of histological factors compared to the use of traditional information only.

Abstract

Background: to evaluate the contribution of edema associated with histological features to the prediction of breast cancer (BC) prognosis using T2-weighted MRI radiomics. Methods: 160 patients who underwent staging 3T-MRI from January 2015 to January 2019, with 164 histologically proven invasive BC lesions, were retrospectively reviewed. Patient data (age, menopausal status, family history, hormone therapy), tumor MRI-features (location, margins, enhancement) and histological features (histological type, grading, ER, PgR, HER2, Ki-67 index) were collected. Of the 160 MRI exams, 120 were considered eligible, corresponding to 127 lesions. T2-MRI were used to identify edema, which was classified in four groups: peritumoral, pre-pectoral, subcutaneous, or diffuse. A semi-automatic segmentation of the edema was performed for each lesion, using 3D Slicer open-source software. Main radiomics features were extracted and selected using a wrapper selection method. A Random Forest type classifier was trained to measure the performance of predicting histological factors using semantic features (patient data and MRI features) alone and semantic features associated with edema radiomics features. Results: edema was absent in 37 lesions and present in 127 (62 peritumoral, 26 pre-pectoral, 16 subcutaneous, 23 diffuse). The AUC-classifier obtained by associating edema radiomics with semantic features was always higher compared to the AUC-classifier obtained from semantic features alone, for all five histological classes prediction (0.645 vs. 0.520 for histological type, 0.789 vs. 0.590 for grading, 0.487 vs. 0.466 for ER, 0.659 vs. 0.546 for PgR, and 0.62 vs. 0.573 for Ki67). Conclusions: radiomic features extracted from tumor edema contribute significantly to predicting tumor histology, increasing the accuracy obtained from the combination of patient clinical characteristics and breast imaging data.

Current State of Breast Cancer Diagnosis, Treatment, and Theranostics

Breast cancer is one of the leading causes of cancer-related morbidity and mortality in women worldwide. Early diagnosis and effective treatment of all types of cancers are crucial for a positive prognosis. Patients with small tumor sizes at the time of their diagnosis have a significantly higher survival rate and a significantly reduced probability of the cancer being fatal. Therefore, many novel technologies are being developed for early detection of primary tumors, as well as distant metastases and recurrent disease, for effective breast cancer management. Theranostics has emerged as a new paradigm for the simultaneous diagnosis, imaging, and treatment of cancers. It has the potential to provide timely and improved patient care via personalized therapy. In nanotheranostics, cell-specific targeting moieties, imaging agents, and therapeutic agents can be embedded within a single formulation for effective treatment. In this review, we will highlight the different diagnosis techniques and treatment strategies for breast cancer management and explore recent advances in breast cancer theranostics. Our main focus will be to summarize recent trends and technologies in breast cancer diagnosis and treatment as reported in recent research papers and patents and discuss future perspectives for effective breast cancer therapy.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Three-dimensional breast tumor segmentation on DCE-MRI with a multilabel attention-guided joint-phase-learning network

Accurate breast and tumor segmentations from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is vital in breast disease diagnosis. Here, we propose a novel attention-guided joint-phase-learning network for multilabel segmentation including the breast and tumors simultaneously and automatically. Instead of common multichannel inputs, our novel network consists of five separated streams designed for extracting comprehensive features for each DCE-MRI phase to fully use the dynamic intensity of enhanced images. A new time-signal intensity map was designed based on the DCE-MRI pixel-by-pixel values and added as an additional stream to reflect breast tumor dynamic variations. The multiple streams were fused in a fully connected layer to integrate the comprehensive tumor information. Weighted-loss was applied to the multilabel strategy to highlight breast tumor segmentation. In addition, the net applies the self-attention module with grid-based attention coefficients based on a global feature vector to emphasize breast regions and suppress irrelevant non-breast tissue features. We trained our method on 144 DCE-MRI datasets acquired from Philips and achieved mean Dice coefficients of 0.92 and 0.86 for breast and tumor segmentations that were superior to common networks with multichannel structures. The model was extended to an independent test set with 59 cases from two different MRI machines and achieved a Dice coefficient of 0.83 for breast tumor segmentation, which illustrates the robustness of our framework. The automatically generated masks can improve the accuracy and time of diagnosis of malignant and benign breast tumors. Qualitative comparisons illustrate that the proposed method has high precision and generalizability.

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.

Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution

Time-resolved MR angiography (tMRA) has been widely used for dynamic contrast enhanced MRI (DCE-MRI) due to its highly accelerated acquisition. In tMRA, the periphery of the k -space data are sparsely sampled so that neighbouring frames can be merged to construct one temporal frame. However, this view-sharing scheme fundamentally limits the temporal resolution, and it is not possible to change the view-sharing number to achieve different spatio-temporal resolution trade-offs. Although many deep learning approaches have been recently proposed for MR reconstruction from sparse samples, the existing approaches usually require matched fully sampled k -space reference data for supervised training, which is not suitable for tMRA due to the lack of high spatio-temporal resolution ground-truth images. To address this problem, here we propose a novel unpaired training scheme for deep learning using optimal transport driven cycle-consistent generative adversarial network (cycleGAN). In contrast to the conventional cycleGAN with two pairs of generator and discriminator, the new architecture requires just a single pair of generator and discriminator, which makes the training much simpler but still improves the performance. Reconstruction results using in vivo tMRA and simulation data set confirm that the proposed method can immediately generate high quality reconstruction results at various choices of view-sharing numbers, allowing us to exploit better trade-off between spatial and temporal resolution in time-resolved MR angiography.

Molecular MR Contrast Agents

Molecular magnetic resonance (MR) imaging utilizes molecular probes to provide added biochemical or cellular information to what can already be achieved with anatomical and functional MR imaging. This review provides an overview of molecular MR and focuses specifically on molecular MR contrast agents that provide contrast by shortening the T1 time. We describe the requirements for a successful molecular MR contrast agent and the challenges for clinical translation. The review highlights work from the last 5 years and places an emphasis on new contrast agents that have been validated in multiple preclinical models. Applications of molecular MR include imaging of inflammation, fibrosis, fibrogenesis, thromboembolic disease, and cancers. Molecular MR is positioned to move beyond detection of disease to the quantitative staging of disease and measurement of treatment response.

Hypoxia and perfusion in breast cancer: simultaneous assessment using PET/MR imaging

OBJECTIVES

Hypoxia is associated with poor prognosis and treatment resistance in breast cancer. However, the temporally variant nature of hypoxia can complicate interpretation of imaging findings. We explored the relationship between hypoxia and vascular function in breast tumours through combined 18F-fluoromisonidazole (18 F-FMISO) PET/MRI, with simultaneous assessment circumventing the effect of temporal variation in hypoxia and perfusion.

METHODS

Women with histologically confirmed, primary breast cancer underwent a simultaneous 18F-FMISO-PET/MR examination. Tumour hypoxia was assessed using influx rate constant Ki and hypoxic fractions (%HF), while parameters of vascular function (Ktrans, kep, ve, vp) and cellularity (ADC) were derived from dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI, respectively. Additional correlates included histological subtype, grade and size. Relationships between imaging variables were assessed using Pearson correlation (r).

RESULTS

Twenty-nine women with 32 lesions were assessed. Hypoxic fractions > 1% were observed in 6/32 (19%) cancers, while 18/32 (56%) tumours showed a %HF of zero. The presence of hypoxia in lesions was independent of histological subtype or grade. Mean tumour Ktrans correlated negatively with Ki (r = - 0.38, p = 0.04) and %HF (r = - 0.33, p = 0.04), though parametric maps exhibited intratumoural heterogeneity with hypoxic regions colocalising with both hypo- and hyperperfused areas. No correlation was observed between ADC and DCE-MRI or PET parameters. %HF correlated positively with lesion size (r = 0.63, p = 0.001).

CONCLUSION

Hypoxia measured by 18F-FMISO-PET correlated negatively with Ktrans from DCE-MRI, supporting the hypothesis of perfusion-driven hypoxia in breast cancer. Intratumoural hypoxia-perfusion relationships were heterogeneous, suggesting that combined assessment may be needed for disease characterisation, which could be achieved using simultaneous multimodality imaging.

KEY POINTS

• At the tumour level, hypoxia measured by 18F-FMISO-PET was negatively correlated with perfusion measured by DCE-MRI, which supports the hypothesis of perfusion-driven hypoxia in breast cancer. • No associations were observed between 18F-FMISO-PET parameters and tumour histology or grade, but tumour hypoxic fractions increased with lesion size. • Intratumoural hypoxia-perfusion relationships were heterogeneous, suggesting that the combined hypoxia-perfusion status of tumours may need to be considered for disease characterisation, which can be achieved via simultaneous multimodality imaging as reported here.

Application of digital mammography-based radiomics in the differentiation of benign and malignant round-like breast tumors and the prediction of molecular subtypes

BACKGROUND

This study aimed to investigate the diagnostic performance of radiomic features based on digital mammography (DM) in the differential diagnosis of benign and malignant round-like (round and oval) solid tumors with circumscribed or obscured margins but without suspicious malignant or benign macrocalcifications and to investigate whether quantitative radiomic features can distinguish triple-negative breast cancer (TNBC) from non-TNBC (NTNBC).

METHODS

This retrospective study included 112 patients with round-like tumors who underwent DM within 20 days preoperatively. Breast masses were segmented manually on the DM images, then radiomic features were extracted. The predictive models were used to distinguish between benign and malignant tumors and to predict TNBC in invasive ductal carcinoma. The receiver operating characteristic curves (ROCs) for these models were obtained for initial DM characteristics, radiomic features to predict malignant tumors and TNBC. The decision curve was obtained to evaluate the clinical usefulness of the model for the prediction of benign or malignant tumors.

RESULTS

The study cohort included 79 patients with pathologically confirmed malignant masses and 33 patients with benign (training cohort: n=79; testing cohort: n=33). A total of 396 features were extracted from the DM images for each patient. The radiomics model for the prediction of malignant tumors achieved an area under the receiver operating characteristic curve (AUC) of 0.88 [95% confidence interval (CI), 0.76-1.00] in the testing cohort; the radiomics model for the prediction of TNBC achieved an AUC of 0.84 (95% CI, 0.73-0.96). In contrast, DM characteristics alone poorly predicted malignant tumors, with the density achieving an AUC 0.69 (95% CI, 0.59-0.79); there was no significant difference in DM characteristics between TNBC and NTNBC (P>0.05, all). The decision curve showed the good clinical usefulness of the model for the prediction of malignant tumors.

CONCLUSIONS

This study showed that DM-based radiomics can accurately discriminate between benign and malignant round-like tumors with circumscribed or obscured margins but without suspicious malignant or benign macrocalcifications. Additionally, it can be used to predict TNBC in invasive ductal carcinoma. DM-based radiomics can aid radiologists in mammogram reading, clinical diagnosis and decision-making.

Evaluation of metronomic chemotherapy response using diffusion and dynamic contrast-enhanced MRI

PURPOSE

To investigate the feasibility of using diffusion MRI (dMRI) and dynamic contrast-enhanced (DCE) MRI to evaluate the treatment response of metronomic chemotherapy (MCT) in the 4T1 mammary tumor model of locally advanced breast cancer.

METHODS

Twelve Balb/c mice with metastatic breast cancer were divided into treated and untreated (control) groups. The treated group (n = 6) received five treatments of anti-metabolite agent 5-Fluorouracil (5FU) in the span of two weeks. dMRI and DCE-MRI were acquired for both treated and control groups before and after MCT. Immunohistochemically staining and measurements were performed after the post-MRI measurements for comparison.

RESULTS

The control mice had significantly (p<0.005) larger tumors than the MCT treated mice. The DCE-MRI analysis showed a decrease in contrast enhancement for the control group, whereas the MCT mice had a more stable enhancement between the pre-chemo and post-chemo time points. This confirms the antiangiogenic effects of 5FU treatment. Comparing amplitude of enhancement revealed a significantly (p<0.05) higher enhancement in the MCT tumors than in the controls. Moreover, the MCT uptake rate was significantly (p<0.001) slower than the controls. dMRI analysis showed the MCT ADC values were significantly larger than the control group at the post-scan time point.

CONCLUSION

dMRI and DCE-MRI can be used as potential biomarkers for assessing the treatment response of MCT. The MRI and pathology observations suggested that in addition to the cytotoxic effect of cell kills, the MCT with a cytotoxic drug, 5FU, induced changes in the tumor vasculature similar to the anti-angiogenic effect.

Diffusion-weighted imaging versus dynamic contrast-enhanced MRI: a new horizon for characterisation of suspicious breast lesions

AIM

To investigate the value of new diffusion-weighted imaging applications in the characterisation of suspicious breast lesions with emphasis on diffusion tensor imaging (DTI) and fibre tractography (FT).

MATERIALS AND METHODS

The present prospective study included 40 female patients with suspicious breast lesions according to sono-mammography American College of Radiologists' classification. All patients underwent conventional magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), DTI, and FT after meeting the inclusion criteria. Dynamic contrast-enhanced MRI was performed as the reference standard for radiological evaluation. The study was conducted between August 2018 and January 2019. The final diagnosis was confirmed histopathologically.

RESULTS

Malignant lesions were diagnosed in 32/40 (80%) patients and 8/40 (20%) patients had benign lesions. Apparent diffusion coefficient (ADC) values of malignant lesions were statistically lower than that of benign lesions (p<0.001) using a cut-off value of 0.99±0.07×10^-3 mm²/s. Fractional anisotropy (FA) values were lower in malignant lesions than in benign lesions with a cut-off value for malignancy of 0.19±0.05 and were statistically significant (p<0.005). The sensitivity, specificity, and accuracy of combined DTI and FT were similar to DCE MRI (p<0.001).

CONCLUSION

DTI and FT are new applications of DWI. They show promising results for the evaluation of suspicious breast masses and can distinguish between benign and malignant breast lesions with statistical significance approaching contrast-enhanced MRI, which is considered the imaging reference standard for characterising breast lesions.

Effect of IV Administration of a Gadolinium-Based Contrast Agent on Breast Diffusion-Tensor Imaging

OBJECTIVE. The purpose of this study was to quantify changes in diffusion-tensor imaging (DTI) parameters before and after IV administration of a gadolinium-based contrast agent (GBCA) and explore the influence of those parameters on breast cancer diagnosis. SUBJECTS AND METHODS. A prospective cohort of 26 women with BI-RADS categories 0, 4, 5, or 6 underwent 3-T breast MRI with sequential DTI before GBCA administration and immediately after. Quantitative image analysis using dedicated DTI software yielded parametric DTI maps of each directional diffusion coefficient (DDC), mean diffusivity, and maximal anisotropy of the lesions and normal tissue. The color maps were evaluated and the lesion DTI parameters were compared before and after GBCA administration using appropriate statistical tests. RESULTS. Of the cohort, 58% had cancer (13 infiltrating ductal carcinoma, two ductal carcinoma in situ) and 42% had benign or normal results. All breast cancers were visually detected in the DDC λ₁ maps before and after GBCA administration. Mean cancer size derived from λ₁ maps before GBCA administration was 15.3 mm (range, 3.3-72.3 mm), and was not statistically significantly different from the size derived after GBCA administration of 17.3 mm (range, 3.9-71.0 mm). After GBCA administration, the cancers exhibited statistically significantly lower DDCs, mean diffusivity, and b0 intensity (p < 0.05), and no change in maximal anisotropy compared with before GBCA administration, whereas these parameters in normal and benign lesions did not change significantly after GBCA administration. The mean AUC values before and after GBCA administration, ranging from 0.735 to 0.985 and from 0.867 to 0.990, respectively, were not statistically significantly different for all parameters aside from λ₃. CONCLUSION. Diagnostic accuracy using DTI was equivalent before and after GBCA administration, despite a change in the values of the DTI parameters. However, the limitations in standardization of contrast enhancement implies that unenhanced diffusion measurements should be preferred.

Feasibility of contrast-enhanced MRI derived textural features to predict overall survival in locally advanced breast cancer

BACKGROUND

The prognosis for women with locally advanced breast cancer (LABC) is poor and there is a need for better treatment stratification. Gray-level co-occurrence matrix (GLCM) texture analysis of magnetic resonance (MR) images has been shown to predict pathological response and could become useful in stratifying patients to more targeted treatments.

PURPOSE

To evaluate the ability of GLCM textural features obtained before neoadjuvant chemotherapy to predict overall survival (OS) seven years after diagnosis of patients with LABC.

MATERIAL AND METHODS

This retrospective study includes data from 55 patients with LABC. GLCM textural features were extracted from segmented tumors in pre-treatment dynamic contrast-enhanced 3-T MR images. Prediction of OS by GLCM textural features was assessed and compared to predictions using traditional clinical variables.

RESULTS

Linear mixed-effect models showed significant differences in five GLCM features (f₁, f₂, f₅, f₁₀, f₁₁) between survivors and non-survivors. Using discriminant analysis for prediction of survival, GLCM features from 2 min post-contrast images achieved a classification accuracy of 73% (P < 0.001), whereas traditional prognostic factors resulted in a classification accuracy of 67% (P = 0.005). Using a combination of both yielded the highest classification accuracy (78%, P < 0.001). Median values for features f₁, f₂, f₁₀, and f₁₁ provided significantly different survival curves in Kaplan-Meier analysis.

CONCLUSION

This study shows a clear association between textural features from post-contrast images obtained before neoadjuvant chemotherapy and OS seven years after diagnosis. Further studies in larger cohorts should be undertaken to investigate how this prognostic information can be used to benefit treatment stratification.

Evaluating the Relationship Between Dynamic Contrast-Enhanced MRI (DCE-MRI) Parameters and Pathological Characteristics in Breast Cancer

BACKGROUND

Dynamic contrast-enhanced MRI (DCE-MRI) is used to evaluate tumor microvasculature. However, studies demonstrating an association between perfusion parameters derived from DCE-MRI and histopathologic characteristics are limited to a small set of histopathologic factors, and the results are inconsistent.

PURPOSE

To evaluate the relationship between DCE-MRI perfusion parameters and common histopathologic tumor characteristics used to predict angiogenesis and determine prognosis in breast cancer.

STUDY TYPE

Retrospective.

POPULATION

In all, 105 breast cancer patients with invasive ductal carcinoma (122 lesions).

FIELD STRENGTH/SEQUENCE

3.0T, turbo spin-echo (TSE) T₁ -weighted, fat-suppressed T₂ -weighted, TSE T₂ -weighted, and dynamic unenhanced and contrast-enhanced 3D T₁ high-resolution isotropic volume examination.

ASSESSMENT

One reviewer obtained perfusion parameters (K^trans , k_ep , v_e , and v_p ) of each breast cancer from DCE MRI using the extended Tofts model with a fixed baseline T₁ value and a population-based arterial input function. The relationship between DCE-MRI perfusion parameters and histopathologic tumor characteristics used to predict angiogenesis and determine prognosis was evaluated.

STATISTICAL TESTS

Student's t-test, Mann-Whitney U-test, analysis of variance (ANOVA), and Kruskal-Wallis test were used.

RESULTS

Triple-negative breast cancers exhibited higher K^trans and k_ep than luminal cancers (P < 0.05). Estrogen receptor (ER)-negative tumors showed higher K^trans than ER-positive tumors (P < 0.05). Progesterone receptor (PR)-negative tumors presented higher v_e than PR-positive tumors (P < 0.05). Tumors with higher Ki-67 showed higher k_ep than tumors with lower Ki-67 (P < 0.05). P53-positive tumors exhibited higher K^trans and k_ep than p53-negative tumors (P < 0.05). Higher histologic grade tumors (grade II/III) presented higher K^trans , k_ep , v_p (P < 0.05) than grade I tumors. Tumors with LVSI presented higher K^trans and k_ep than tumors without LVSI (P < 0.05).

DATA CONCLUSION

Breast cancer presenting higher K^trans and k_ep on DCE-MRI was associated with poor prognostic histopathologic factors. Therefore, pretreatment DCE-MRI perfusion parameters may be useful imaging biomarkers for the evaluation of tumor prognosis and angiogenesis.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Preoperative tumor size measurement in breast cancer patients: which threshold is appropriate on computer-aided detection for breast MRI?

Background

Computer-aided detection (CAD) can detect breast lesions by using an enhancement threshold. Threshold means the percentage of increased signal intensity in post-contrast imaging compared to precontrast imaging. If the pixel value of the enhanced tumor increases above the set threshold, CAD provides the size of the tumor, which is calculated differently depending on the set threshold. Therefore, CAD requires the accurate setting of thresholds. We aimed to compare the diagnostic accuracy of tumor size measurement using MRI and CAD with 3 most commonly used thresholds and to identify which threshold is appropriate on CAD in breast cancer patients.

Methods

A total of 130 patients with breast cancers (80 invasive cancers and 50 ductal carcinoma in situ [DCIS]) who underwent preoperative MRI with CAD and surgical treatment were included. Tumor size was manually measured on first contrast-enhanced MRI and acquired by CAD using 3 different thresholds (30, 50, and 100%) for each tumor. Tumor size measurements using MRI and CAD were compared with pathological sizes using Spearman correlation analysis. For comparison of size discrepancy between imaging and pathology, concordance was defined as estimation of size by imaging within 5 mm of the pathological size. Concordance rates were compared using Chi-square test.

Results

For both invasive cancers and DCIS, correlation coefficient rho (r) between tumor size on imaging and pathology was highest at CAD with 30% threshold, followed by MRI, CAD with 50% threshold, and CAD with 100% threshold (all p <  0.05). For invasive cancers, the concordance rate of 72.5% at CAD with 30% threshold showed no difference with that of 62.5% at MRI (p = 0.213). For DCIS, the concordance rate of 30.0% at CAD with 30% threshold showed no difference with that of 36.0% at MRI (p = 0.699). Compared to MRI, higher risk of underestimation was noted when using CAD with 50% or 100% threshold for invasive cancers and when using CAD with 100% threshold for DCIS.

Conclusion

For CAD analysis, 30% threshold is the most appropriate threshold whose accuracy is comparable to manual measurement on MRI for tumor size measurement. However, clinicians should be aware of the higher risk of underestimation when using CAD with 50% threshold for tumor staging in invasive cancers.

CAD and AI for breast cancer-recent development and challenges

Computer-aided diagnosis (CAD) has been a popular area of research and development in the past few decades. In CAD, machine learning methods and multidisciplinary knowledge and techniques are used to analyze the patient information and the results can be used to assist clinicians in their decision making process. CAD may analyze imaging information alone or in combination with other clinical data. It may provide the analyzed information directly to the clinician or correlate the analyzed results with the likelihood of certain diseases based on statistical modeling of the past cases in the population. CAD systems can be developed to provide decision support for many applications in the patient care processes, such as lesion detection, characterization, cancer staging, treatment planning and response assessment, recurrence and prognosis prediction. The new state-of-the-art machine learning technique, known as deep learning (DL), has revolutionized speech and text recognition as well as computer vision. The potential of major breakthrough by DL in medical image analysis and other CAD applications for patient care has brought about unprecedented excitement of applying CAD, or artificial intelligence (AI), to medicine in general and to radiology in particular. In this paper, we will provide an overview of the recent developments of CAD using DL in breast imaging and discuss some challenges and practical issues that may impact the advancement of artificial intelligence and its integration into clinical workflow.

Toward precise arterial input functions derived from DCE-MRI through a novel extracorporeal circulation approach in mice

PURPOSE

Dynamic contrast-enhanced MRI can be used in pharmacokinetic models to quantify functional parameters such as perfusion and permeability. However, precise quantification in preclinical models is challenged by the difficulties to dynamically measure the true arterial blood contrast agent concentration. We propose a novel approach toward a precise and experimentally feasible method to derive the arterial input function from DCE-MRI in mice.

METHODS

Arterial blood was surgically shunted from the femoral artery to the tail vein and led through an extracorporeal circulation that resided on the head of brain tumor-bearing mice inside the FOV of a 9.4T MRI scanner. Dynamic 3D-FLASH scanning was performed after injection of gadobutrol with an effective resolution of 0.175 × 0.175 × 1 mm and a temporal resolution of 4 seconds. Pharmacokinetic modeling was performed using the extended Tofts and two-compartment exchange model.

RESULTS

Arterial input functions measured inside the extracorporeal circulation showed little noise, small interindividual variance, and typical curve shapes. Ex vivo and mass spectrometry validation measurements documented the influence of shunt flow velocity and hematocrit on estimation of contrast agent concentrations. Modeling of tumors and muscles allowed fitting of the recorded dynamic concentrations, resulting in quantitative plausible parameters.

CONCLUSION

The extracorporeal circulation allows deriving the contrast agent dynamics in arterial blood with high robustness and at acceptable experimental effort from DCE-MRI, previously not achievable in mice. It sets the basis for quantitative precise pharmacokinetic modeling in small animals to enhance the translatability of preclinical DCE-MRI measurements to patients.

Combination of an ultrafast TWIST-VIBE Dixon sequence protocol and diffusion-weighted imaging into an accurate easily applicable classification tool for masses in breast MRI

OBJECTIVES

This study aimed to develop a tool for the classification of masses in breast MRI, based on ultrafast TWIST-VIBE Dixon (TVD) dynamic sequences combined with DWI. TVD sequences allow to abbreviate breast MRI protocols, but provide kinetic information only on the contrast wash-in, and because of the lack of the wash-out kinetics, their diagnostic value might be hampered. A special focus of this study was thus to maintain high diagnostic accuracy in lesion classification.

MATERIALS AND METHODS

Sixty-one patients who received breast MRI between 02/2014 and 04/2015 were included, with 83 reported lesions (60 malignant). Our institute's standard breast MRI protocol was complemented by an ultrafast TVD sequence. ADC and peak enhancement of the TVD sequences were integrated into a generalised linear model (GLM) for malignancy prediction. For comparison, a second GLM was calculated using ADC and conventional DCE curve type. The resulting GLMs were evaluated for standard diagnostic parameters. For easy application of the GLMs, nomograms were created.

RESULTS

The GLM based on peak enhancement of the TVD and ADC was as equally accurate as the GLM based on conventional DCE and ADC, with no significant differences (sensitivity, 93.3%/93.3%; specificity, 91.3%/87.0%; PPV, 96.6%/94.9%; NPV, 84.0%/83.3%; all, p ≥ 0.315).

CONCLUSIONS

This study presents a method to integrate ultrafast TVD sequences into a breast MRI protocol, allowing a reduction of the examination time while maintaining diagnostic accuracy. A GLM based on the combination of TVD-derived peak enhancement and ADC provides high diagnostic accuracy, and can be easily applied using a nomogram.

KEY POINTS

• Ultrafast TWIST-VIBE Dixon sequence protocols in combination with diffusion-weighted imaging allow to shorten breast MRI examinations, while diagnostic accuracy is maintained. • Integrating peak enhancement from the TWIST-VIBE Dixon sequence and the apparent diffusion coefficient into a generalised linear model provides a comprehensible image evaluation approach. • This approach is further facilitated by nomograms.

The Application of Radiomics in Breast MRI: A Review

Breast cancer has been a worldwide burden of women's health. Although concerns have been raised for early diagnosis and timely treatment, the efforts are still needed for precision medicine and individualized treatment. Radiomics is a new technology with immense potential to obtain mineable data to provide rich information about the diagnosis and prognosis of breast cancer. In our study, we introduced the workflow and application of radiomics as well as its outlook and challenges based on published studies. Radiomics has the potential ability to differentiate between malignant and benign breast lesions, predict axillary lymph node status, molecular subtypes of breast cancer, tumor response to chemotherapy, and survival outcomes. Our study aimed to help clinicians and radiologists to know the basic information of radiomics and encourage cooperation with scientists to mine data for better application in clinical practice.

Advances in Diffusion and Perfusion MRI for Quantitative Cancer Imaging

PURPOSE OF REVIEW

This article is to review recent technical developments and their clinical applications in cancer imaging quantitative measurement of cellular and vascular properties of the tumors.

RECENT FINDINGS

Rapid development of fast Magnetic Resonance Imaging (MRI) technologies over last decade brought new opportunities in quantitative MRI methods to measure both cellular and vascular properties of tumors simultaneously.

SUMMARY

Diffusion MRI (dMRI) and dynamic contrast enhanced (DCE)-MRI have become widely used to assess the tissue structural and vascular properties, respectively. However, the ultimate potential of these advanced imaging modalities has not been fully exploited. The dependency of dMRI on the diffusion weighting gradient strength and diffusion time can be utilized to measure tumor perfusion, cellular structure, and cellular membrane permeability. Similarly, DCE-MRI can be used to measure vascular and cellular membrane permeability along with cellular compartment volume fractions. To facilitate the understanding of these potentially important methods for quantitative cancer imaging, we discuss the basic concepts and recent developments, as well as future directions for further development.

Fast MRI breast cancer screening - Ready for prime time

OBJECTIVE

The manuscript discusses landmark studies using abbreviated MRI for breast cancer screening. This includes abbreviated dynamic contrast enhanced MRI and diffusion weighted imaging. Our institutional experience with abbreviated MR protocol for breast cancer screening is also described.

CONCLUSION

Abbreviated MRI protocols were found to demonstrate value for screening of breast cancer. It has been shown that abbreviated protocol MRI provides similar diagnostic sensitivities to full protocol MRI for breast cancer in women with increased lifetime risk. Our institutional abbreviated MRI protocol for breast cancer offers improved time and workflow efficiencies and has the potential to increase the number of breast cancers detected and the detection of pathologically relevant invasive breast cancer at earlier stages.

Horizons of nanotechnology applications in female specific cancers

Female-specific cancers are the most common cancers in women worldwide. Early detection methods remain unavailable for most of these cancers, signifying that most of them are diagnosed at later stages. Furthermore, current treatment options for most female-specific cancers are surgery, radiation and chemotherapy. Although important milestones in molecularly targeted approaches have been achieved lately, current therapeutic strategies for female-specific cancers remain limited, ineffective and plagued by the emergence of chemoresistance, which aggravates prognosis. Recently, the application of nanotechnology to the medical field has allowed the development of novel nano-based approaches for the management and treatment of cancers, including female-specific cancers. These approaches promise to improve patient survival rates by reducing side effects, enabling selective delivery of drugs to tumor tissues and enhancing the uptake of therapeutic compounds, thus increasing anti-tumor activity. In this review, we focus on the application of nano-based technologies to the design of novel and innovative diagnostic and therapeutic strategies in the context of female-specific cancers, highlighting their potential uses and limitations.

In vivo MR spectroscopy for breast cancer diagnosis

Breast cancer is a significant health concern in females, worldwide. In vivo proton (1H) MR spectroscopy (MRS) has evolved as a non-invasive tool for diagnosis and for biochemical characterization of breast cancer. Water-to-fat ratio, fat and water fractions and choline containing compounds (tCho) have been identified as diagnostic biomarkers of malignancy. Detection of tCho in normal breast tissue of volunteers and in lactating females limits the use of tCho as a diagnostic marker. Technological developments like high-field scanners, multi channel coils, pulse sequences with water and fat suppression facilitated easy detection of tCho. Also, quantification of tCho and its cut-off for objective assessment of malignancy have been reported. Meta-analysis of in vivo 1H MRS studies have documented the pooled sensitivities and the specificities in the range of 71-74% and 78-88%, respectively. Inclusion of MRS has been shown to enhance the diagnostic specificity of MRI, however, detection of tCho in small sized lesions (≤1 cm) is challenging even at high magnetic fields. Potential of MRS in monitoring the effect of chemotherapy in breast cancer has also been reported. This review briefly presents the potential clinical role of in vivo 1H MRS in the diagnosis of breast cancer, its current status and future developments.

Magnetic Resonance Angiography Shows Increased Arterial Blood Supply Associated with Murine Mammary Cancer

Breast cancer is a major cause of morbidity and mortality in Western women. Tumor neoangiogenesis, the formation of new blood vessels from pre-existing ones, may be used as a prognostic marker for cancer progression. Clinical practice uses dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) to detect cancers based on increased blood flow and capillary permeability. However, DCE-MRI requires repeated injections of contrast media. Therefore we explored the use of noninvasive time-of-flight (TOF) MR angiography for serial studies of mouse mammary glands to measure the number and size of arteries feeding mammary glands with and without cancer. Virgin female C3(1) SV40 TAg mice (n=9), aged 18-20 weeks, were imaged on a 9.4 Tesla small animal scanner. Multislice T₂-weighted (T2W) images and TOF-MRI angiograms were acquired over inguinal mouse mammary glands. The data were analyzed to determine tumor burden in each mammary gland and the volume of arteries feeding each mammary gland. After in vivo MRI, inguinal mammary glands were excised and fixed in formalin for histology. TOF angiography detected arteries with a diameter as small as 0.1 mm feeding the mammary glands. A significant correlation (r=0.79; p< 0.0001) was found between tumor volume and the arterial blood volume measured in mammary glands. Mammary arterial blood volumes ranging from 0.08 mm³ to 3.81 mm³ were measured. Tumors and blood vessels found on in vivo T2W and TOF images, respectively, were confirmed with ex vivo histological images. These results demonstrate increased recruitment of arteries to mammary glands with cancer, likely associated with neoangiogenesis. Neoangiogenesis may be detected by TOF angiography without injection of contrast agents. This would be very useful in mouse models where repeat placement of I.V. lines is challenging. In addition, analogous methods could be tested in humans to evaluate the vasculature of suspicious lesions without using contrast agents.

A nomogram for individual prediction of vascular invasion in primary breast cancer

OBJECTIVES

To explore the feasibility of preoperative prediction of vascular invasion (VI) in breast cancer patients using nomogram based on multiparametric MRI and pathological reports.

METHODS

We retrospectively collected 200 patients with confirmed breast cancer between January 2016 and January 2018. All patients underwent MRI examinations before the surgery. VI was identified by postoperative pathology. The 200 patients were randomly divided into training (n = 100) and validation datasets (n = 100) at a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors most associated with VI of breast cancer. A nomogram was constructed to calculate the area under the curve (AUC) of receiver operating characteristics, sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV). We bootstrapped the data for 2000 times without setting the random seed to obtain corrected results.

RESULTS

VI was observed in 79 patients (39.5%). LASSO selected 10 predictors associated with VI. In the training dataset, the AUC for nomogram was 0.94 (95% confidence interval [CI]: 0.89-0.99, the sensitivity was 78.9% (95%CI: 72.4%-89.1%), the specificity was 95.3% (95%CI: 89.1%-100.0%), the accuracy was 86.0% (95%CI: 82.0%-92.0%), the PPV was 95.7% (95%CI: 90.0%-100.0%), and the NPV was 77.4% (95%CI: 67.8%-87.0%). In the validation dataset, the AUC for nomogram was 0.89 (95%CI: 0.83-0.95), the sensitivity was 70.3% (95%CI: 60.7%-79.2%), the specificity was 88.9% (95%CI: 80.0%-97.1%), the accuracy was 77.0% (95%CI: 70.0%-83.0%), the PPV was 91.8% (95%CI: 85.3%-98.0%), and the NPV was 62.7% (95%CI: 51.7%-74.0%). The nomogram calibration curve shows good agreement between the predicted probability and the actual probability.

CONCLUSION

The proposed nomogram could be used to predict VI in breast cancer patients, which was helpful for clinical decision-making.

Patient-based 4D digital breast phantom for perfusion contrast-enhanced breast CT imaging

Purpose

The purpose of this study was to develop a realistic patient‐based 4D digital breast phantom including time‐varying contrast enhancement for simulation of dedicated breast CT perfusion imaging.

Methods

A 3D static phantom is first created by segmenting a breast CT image from a healthy patient into skin, fibroglandular tissue, adipose tissue, and vasculature. For the creation of abnormal cases, a breast lesion model was developed and can be added to the phantom. After defining the necessary perfusion parameters for each tissue (e.g., arterial input function for vasculature, blood volume and blood flow for the other normal tissues) based on contrast‐enhanced dynamic breast MRI data, the corresponding time‐enhancement curves are computed for each voxel in the phantom, according to tissue type. These curves are calculated by convolution between the arterial input function and a shifted exponential function. This exponential depends on the perfusion parameters associated with each tissue voxel, and, to incorporate normal biological variability, a uniform random distribution is used to vary the perfusion parameters on a voxel‐basis. Finally, a 4D array is produced by sampling the continuous time‐enhancement curves at the desired sampling rate. Beside modeling different enhancement dynamics according to the given input perfusion parameters, the phantom also includes the possibility to realistically simulate different spatial enhancement patterns for the breast parenchyma, taking into account the arterial sources supplying the breast. Finally, different patterns of contrast medium uptake can also be simulated for the tumor models (homogeneous and rim enhancement).

Results

As an example, a typical 4D phantom has dimensions of 426 × 421 × 260 × 559 (x, y, z, t), with a voxel size of 273 μm and a sampling time of 1 s. The characteristics of the tumor model can be modified at will to evaluate perfusion in different types of breast lesions. Results show the expected enhancement of tissues, consistent with the given input parameters. Moreover, the tumor models evaluated in this work show different enhancement dynamics according to the tumor type (defined by different input perfusion parameters), and also present a higher enhancement compared to the other healthy tissues, as expected.

Conclusions

The proposed digital phantom can model the breast tissue perfusion during 4D breast CT image acquisition, displaying the different enhancement dynamics that could be found in a real patient breast. This phantom can be used during the development of dynamic contrast‐enhanced dedicated breast CT imaging, for optimization of image acquisition, image reconstruction, and image analysis. This modality could provide functional information of the breast, resulting in detection, diagnosis, and treatment improvements of breast cancer with breast CT.

Accuracy and precision of quantitative DCE-MRI parameters: How should one estimate contrast concentration?

INTRODUCTION

Pharmacokinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) data are sensitive to acquisition and post-processing techniques, which makes it difficult to compare results obtained using different methods. In particular, one of the most important factors affecting estimation of model parameters is how to convert MRI signal intensities to contrast agent concentration. The purpose of our study was to quantitatively compare a linear signal-to-concentration conversion (LC) as an approximation and a non-linear conversion (NLC) based on the MRI signal equation, in terms of the accuracy and precision of the pharmacokinetic parameters in T₁-weighted DCE-MRI.

MATERIALS AND METHODS

Numerical simulation studies were conducted to compare LC and NLC in terms of the accuracy and precision in contrast kinetic parameter estimation, and to evaluate their dependency on flip angle (FA), pre-contrast T₁ (T₁₀) and arterial input function (AIF). In addition, the effect of the conversion method on the diagnostic accuracy was evaluated with 36 breast lesions (19 benign and 17 malignant).

RESULTS

The transfer rate (K^trans) estimated using LC and measured AIF (mAIF) were up to 38% higher than the true K^trans values, while the LC K^trans estimates with the presumed AIF (pAIF) were up to 7% lower than the true K^trans values, when FA = 45°. When using a small FA, such as 12°, the LC K^trans with pAIF had least sensitivity to the error in T₁₀ compared to the K^trans estimated using LC with mAIF, and NLC with pAIF or mAIF. The breast DCE-MRI study showed that both LC and NLC K^trans were significantly different (p < 0.05) between the malignant and benign lesions. The effect size between benign and malignant values as measured by Cohen's d was 1.06 for LC K^trans and 1.02 for NLC K^trans.

CONCLUSION

The present study results show that, when precontrast T₁ measurement is not available and a low FA is used for DCE-MRI, the uncertainty in the contrast kinetic parameter estimation can be reduced by using the LC method with pAIF, without compromising the diagnostic accuracy.