Emerging Techniques in Breast MRI

As indicated throughout this chapter, there is a constant effort to move to more sensitive, specific, and quantitative methods for characterizing breast tissue via magnetic resonance imaging (MRI). In the present chapter, we focus on six emerging techniques that seek to quantitatively interrogate the physiological and biochemical properties of the breast. At the physiological scale, we present an overview of ultrafast dynamic contrast-enhanced MRI and magnetic resonance elastography which provide remarkable insights into the vascular and mechanical properties of tissue, respectively. Moving to the biochemical scale, magnetization transfer, chemical exchange saturation transfer, and spectroscopy (both “conventional” and hyperpolarized) methods all provide unique, noninvasive, insights into tumor metabolism. Given the breadth and depth of information that can be obtained in a single MRI session, methods of data synthesis and interpretation must also be developed. Thus, we conclude the chapter with an introduction to two very different, though complementary, methods of data analysis: (1) radiomics and habitat imaging, and (2) mechanism-based mathematical modeling

Multiparametric MRI radiomics fusion for predicting the response and shrinkage pattern to neoadjuvant chemotherapy in breast cancer

PurposeDuring neoadjuvant chemotherapy (NACT), breast tumor morphological and vascular characteristics are usually changed. This study aimed to evaluate the tumor shrinkage pattern and response to NACT by preoperative multiparametric magnetic resonance imaging (MRI), including dynamic contrast-enhanced MRI (DCE-MRI), diffuse weighted imaging (DWI) and T2 weighted imaging (T2WI).MethodIn this retrospective analysis, female patients with unilateral unifocal primary breast cancer were included for predicting tumor pathologic/clinical response to NACT (n=216, development set, n=151 and validation set, n=65) and for discriminating the tumor concentric shrinkage (CS) pattern from the others (n=193; development set, n=135 and validation set, n=58). Radiomic features (n=102) of first-order statistical, morphological and textural features were calculated on tumors from the multiparametric MRI. Single- and multiparametric image-based features were assessed separately and were further combined to feed into a random forest-based predictive model. The predictive model was trained in the testing set and assessed on the testing dataset with an area under the curve (AUC). Molecular subtype information and radiomic features were fused to enhance the predictive performance.ResultsThe DCE-MRI-based model showed higher performance (AUCs of 0.919, 0.830 and 0.825 for tumor pathologic response, clinical response and tumor shrinkage patterns, respectively) than either the T2WI or the ADC image-based model. An increased prediction performance was achieved by a model with multiparametric MRI radiomic feature fusion.ConclusionsAll these results demonstrated that multiparametric MRI features and their information fusion could be of important clinical value for the preoperative prediction of treatment response and shrinkage pattern

Radiomics and imaging genomics in precision medicine

“Radiomics,” a field of study in which high-throughput data is extracted and large amounts of advanced quantitative imaging features are analyzed from medical images, and “imaging genomics,” the field of study of high-throughput methods of associating imaging features with genomic data, has gathered academic interest. However, a radiomics and imaging genomics approach in the oncology world is still in its very early stages and many problems remain to be solved. In this review, we will look through the steps of radiomics and imaging genomics in oncology, specifically addressing potential applications in each organ and focusing on technical issues

PET-MR Imaging of Hypoxia and Vascularity in Breast Cancer

Breast cancer is the most common cancer in the UK and in women globally. Imaging methods like mammography, ultrasound (US) and magnetic resonance imaging (MRI) play an important role in the diagnosis and management of breast cancer; they are generally utilised to provide anatomical or structural description of tumours in the clinical setting. It is widely accepted that the tumour microenvironment influences the phenotype, progression and treatment of breast cancer. This gave the impetus to move beyond tumour visualization in images to radiomics in order to provide additional disease characterisation and early biomarkers of tumour response. Due to their ability to assess physiological processes in vivo, positron emission tomography (PET) and MRI can provide non-invasive characterisation of the tumour microenvironment, including perfusion, vascular permeability, cellularity and hypoxia, which is associated with poor clinical outcome and metastasis. Clinical imaging studies in breast tumours have hitherto assessed tumour physiological parameters separately, with only few directly comparing data from these modalities. To this end, hybrid PET-MRI represents an attractive option as it can allow examination of functional processes and features of tumours simultaneously, while also conferring methodological advantages to the way imaging information is combined. The main aim of this thesis is to provide a better understanding of breast cancer pathophysiology using simultaneous PET and multi-parametric MRI. In particular, this work aims to explore relationships between imaging biomarkers of tumour vascularity measured by dynamic contrast-enhanced (DCE) MRI, cellularity using diffusion-weighted imaging (DWI) and hypoxic status using 18F-fluoromisonidazole (18F-FMISO) PET. Correlations between functional PET-MRI parameters and immunohistochemical (IHC) biomarkers of hypoxia and vascularity as well as MRI morphological tumour descriptors are also presented. The thesis concludes with an investigation of the utility of MRI markers of perfusion and surrogate markers of hypoxia to quantitatively monitor and predict pathological response in patients undergoing neoadjuvant chemotherapy (NACT) and provides projections for future work

Computational Imaging Biomarkers For Precision Medicine: Characterizing Heterogeneity In Breast Cancer

In the United States, 1 in 8 women are diagnosed with breast cancer. Breast tumor heterogeneity is well-established, with intratumor heterogeneity manifesting spatially and temporally. Increased heterogeneity is associated with adverse clinical outcomes. Current critical disease treatment decisions are made on the basis of biomarkers acquired from tissue samples, largely under sampling the heterogeneous disease burden. In order to drive precision medicine treatment strategies for cancer, personalized biomarkers are needed to truly characterize intratumor heterogeneity. Medical imaging can provide anon-invasive, whole tumor sampling of disease burden at the time of diagnosis and allows for longitudinal monitoring of disease progression. The studies outlined in this thesis introduce analytical tools developed through computer vision, bioinformatics, and machine learning and use diagnostic and longitudinal clinical images of breast cancer to develop computational imaging biomarkers characterizing intratumor heterogeneity. Intrinsic imaging phenotypes of spatial heterogeneity, identified in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) images at the time of diagnosis, were identified and validated, demonstrating improved prognostic value over conventional histopathologic biomarkers when predicting 10-year recurrence free survival. Intrinsic phenotypes of longitudinal change in spatial heterogeneity in response to neoadjuvant treatment, identified in DCE-MRI were identified and leveraged as prognostic and predictive biomarkers, demonstrating augmented prognostic value when added to conventional histopathologic and personalized molecular biomarkers. To better characterize 4-D spatial and temporal heterogeneity, illuminated through dynamic positron emission tomography imaging, a novel 4-D segmentation algorithm was developed to identify spatially constrained, functionally discrete intratumor sub-regions. Quantifying the identified sub-regions through a novel imaging signature demonstrated the prognostic value of characterizing intratumor heterogeneity when predicting recurrence free survival, demonstrating prognostic improvement over established histopathologic biomarkers and conventional kinetic model derived parameters. Collectively, the studies in this thesis demonstrate the value of leveraging computational imaging biomarkers to characterize intratumor heterogeneity. Such biomarkers have the potential to be utilized towards precision medicine for cancer care

Machine Learning Strategies to Analyze Quantitative Ultrasound Multi-Parametric Images for Prediction of Therapy Response in Breast Cancer Patients

In this thesis project, two novel machine learning strategies were investigated to predict tumor response to neoadjuvant chemotherapy (NAC) at pre-treatment using quantitative ultrasound (QUS) multi-parametric images. The ultrasound data for analytical development and evaluation of the methodologies investigated in this project were acquired from 181 patients diagnosed with locally advanced breast cancer (LABC) and planned for NAC followed by surgery. The QUS multi-parametric images were generated using spectral analyses on the raw ultrasound radiofrequency (RF) data acquired before starting the NAC. In the first machine learning approach investigated in this project, distinct intra-tumor regions were identified within the parametric maps using a hidden Markov random field (HMRF) and its expectation-maximization (EM) algorithm. Several hand-crafted features characterizing the tumor, intra-tumor regions, and the tumor margin were extracted from different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker for response prediction. Evaluation results on an independent test set indicated that the developed biomarker using the characteristics of intra-tumor regions and tumor margin in conjunction with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patients at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In the second machine learning approach investigated in this project, two deep convolutional neural network (DCNN) architectures including the residual network (ResNet) and residual attention network (RAN) were explored for extracting optimal feature maps from the parametric images, with a fully connected network for response prediction. Results demonstrated that the developed model with the RAN architecture to extract feature maps from the expanded parametric images of the tumor core and margin had a superior performance with an accuracy of 0.88 and an AUC of 0.86 on the independent test set. Also, survival analysis demonstrated a statistically significant difference between survival curves of the two response cohorts identified at pre-treatment based on both the conventional machine learning method and the deep learning model. Obtained results in this study demonstrated a great promise of QUS multi-parametric imaging integrated with both unsupervised learning methods in identifying distinct breast cancer intra-tumor regions and traditional classification techniques, as well as deep convolutional neural networks in predicting tumor response to NAC prior to start of treatment

Machine Learning Strategies to Analyze Quantitative Ultrasound Multi-Parametric Images for Prediction of Therapy Response in Breast Cancer Patients

In this thesis project, two novel machine learning strategies were investigated to predict tumor response to neoadjuvant chemotherapy (NAC) at pre-treatment using quantitative ultrasound (QUS) multi-parametric images. The ultrasound data for analytical development and evaluation of the methodologies investigated in this project were acquired from 181 patients diagnosed with locally advanced breast cancer (LABC) and planned for NAC followed by surgery. The QUS multi-parametric images were generated using spectral analyses on the raw ultrasound radiofrequency (RF) data acquired before starting the NAC. In the first machine learning approach investigated in this project, distinct intra-tumor regions were identified within the parametric maps using a hidden Markov random field (HMRF) and its expectation-maximization (EM) algorithm. Several hand-crafted features characterizing the tumor, intra-tumor regions, and the tumor margin were extracted from different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker for response prediction. Evaluation results on an independent test set indicated that the developed biomarker using the characteristics of intra-tumor regions and tumor margin in conjunction with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patients at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In the second machine learning approach investigated in this project, two deep convolutional neural network (DCNN) architectures including the residual network (ResNet) and residual attention network (RAN) were explored for extracting optimal feature maps from the parametric images, with a fully connected network for response prediction. Results demonstrated that the developed model with the RAN architecture to extract feature maps from the expanded parametric images of the tumor core and margin had a superior performance with an accuracy of 0.88 and an AUC of 0.86 on the independent test set. Also, survival analysis demonstrated a statistically significant difference between survival curves of the two response cohorts identified at pre-treatment based on both the conventional machine learning method and the deep learning model. Obtained results in this study demonstrated a great promise of QUS multi-parametric imaging integrated with both unsupervised learning methods in identifying distinct breast cancer intra-tumor regions and traditional classification techniques, as well as deep convolutional neural networks in predicting tumor response to NAC prior to start of treatment

A Novel Ultrasound Elastography Technique for Evaluating Tumor Response to Neoadjuvant Chemotherapy in Patients with Locally Advanced Breast Cancer

Breast cancer is the second most diagnosed cancer in women, estimated to affect 1 in 8 women during their lifetime. About 10% to 20% of new breast cancer cases are diagnosed with locally advanced breast cancer (LABC). LABC tumors are usually larger than 5 cm and/or attached to the skin or chest wall. It has been reported that when such cases are treated with surgery alone, metastasis and mortality rates are high, especially where skin involvement or attachment to the chest wall is extensive. As such, efficient treatment for this kind of breast cancer includes neoadjuvant chemotherapy (NAC) to shrink the tumor and detach it from the chest wall followed by surgery. Several studies have shown that there is a strong correlation between response to NAC and improved treatment outcomes, including survival rate. Unfortunately, 30% to 40% of patients do not respond to chemotherapy, hence losing critical treatment time and resources. Predicting a patient’s response at the early stages of treatment can help physicians make informed decisions about whether to continue the treatment or use an alternative treatment if a poor response is predicted. Such early and accurate response prediction can shorten the wasted time and reduce resources dedicated to patients while they endure significant side effects. Therefore, it is important to identify this group of non-responder patients as early as possible so that they can be prescribed alternative treatments. Current methods for evaluating LABC response to NAC are based on changes in tumor dimensions using physical examinations or standard anatomical imaging. Such changes may take several months to be detectable. Studies have shown that there is a correlation between LABC response to NAC and tumor softening. In other words, in contrast to responder patients where tumor stiffness generally decreases in response to NAC, in non-responder patients the stiffness of the tumor increases or does not change significantly. As such, a reliable and widely available breast elastography technique can have a major impact on the effective treatment of LABC patients. In this study, we first develop a tissue-mechanics-based method for improving the accuracy of ultrasound elastography. This method consists of 3 steps that are applied to the displacement fields generated from conventional motion-tracking methods. These three steps include: smoothing the displacement fields using Laplacian filtering, enforcing tissue incompressibility equation to refine the displacement fields, and finally enforcing tissue compatibility equation to refine the strain fields. The method was promising through validation using in silico, phantom, and in vivo studies. A huge improvement of this method compared to other motion-tracking methods is its ability in generating lateral displacement with high accuracy. This becomes especially important when the displacement and strain fields are used as inputs to an inverse-problem framework for calculating the stiffness characteristics of tissue, for example, Young’s modulus. We then use this enhanced ultrasound elastography technique to assess the response of LABC patients to NAC based on monitoring the stiffness of their tumors throughout the chemotherapy course. Our results show that this method is effective in predicting patients’ responses accurately as early as 1 week after NAC initiation