A multi-parametric PET/MRI study of breast cancer: Evaluation of DCE-MRI pharmacokinetic models and correlation with diffusion and functional parameters

The Clinical Added Value of Breast Cancer Imaging Using Hybrid PET/MR Imaging

Dedicated MR imaging is highly performant for the evaluation of the primary lesion and should regularly be added to whole-body PET/MR imaging for the initial staging. PET/MR imaging is highly sensitive for the detection of nodal involvement and could be combined with the high specificity of axillary second look ultrasound for the confirmation of the N staging. For M staging, with the exception of lung lesions, PET/MR imaging is superior to PET/computed tomography, at half the radiation dose. The predictive value of multiparametric imaging with PET/MR imaging holds promise to improve through radiomics and artificial intelligence.

Dynamic 2-deoxy-D-glucose-enhanced multispectral optoacoustic tomography for assessing metabolism and vascular hemodynamics of breast cancer

Clinical tools for measuring tumor vascular hemodynamics, such as dynamic contrast-enhanced MRI, are clinically important to assess tumor properties. Here we explored the use of multispectral optoacoustic tomography (MSOT), which has a high spatial and temporal resolution, to measure the intratumoral pharmacokinetics of a near-infrared-dye-labeled 2-Deoxyglucose, 2-DG-800, in orthotropic 2-LMP breast tumors in mice. As uptake of 2-DG-800 is dependent on both vascular properties, and glucose transporter activity - a widely-used surrogate for metabolism, we evaluate hemodynamics of 2-DG-MP by fitting the dynamic MSOT signal of 2-DG-800 into two-compartment models including the extended Tofts model (ETM) and reference region model (RRM). We showed that dynamic 2-DG-enhanced MSOT (DGE-MSOT) is powerful in acquiring hemodynamic rate constants, including Ktrans and Kep, via systemically injecting a low dose of 2-DG-800 (0.5 µmol/kg b.w.). In our study, both ETM and RRM are efficient in deriving hemodynamic parameters in the tumor. Area-under-curve (AUC) values (which correlate to metabolism), and Ktrans and Kep values, can effectively distinguish tumor from muscle. Hemodynamic parameters also demonstrated correlations to hemoglobin, oxyhemoglobin, and blood oxygen level (SO2) measurements by spectral unmixing of the MSOT data. Together, our study for the first time demonstrated the capability of DGE-MSOT in assessing vascular hemodynamics of tumors.

Clinical advances in PET-MRI for breast cancer

Imaging is paramount for the early detection and clinical staging of breast cancer, as well as to inform management decisions and direct therapy. PET-MRI is a quantitative hybrid imaging technology that combines metabolic and functional PET data with anatomical detail and functional perfusion information from MRI. The clinical applicability of PET-MRI for breast cancer is an active area of research. In this Review, we discuss the rationale and summarise the clinical evidence for the use of PET-MRI in the diagnosis, staging, prognosis, tumour phenotyping, and assessment of treatment response in breast cancer. The continued development and approval of targeted radiopharmaceuticals, together with radiomics and automated analysis tools, will further expand the opportunity for PET-MRI to provide added value for breast cancer imaging and patient care.

Vascularity and Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging

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

Application of Community Detection Algorithm to Investigate the Correlation between Imaging Biomarkers of Tumor Metabolism, Hypoxia, Cellularity, and Perfusion for Precision Radiotherapy in Head and Neck Squamous Cell Carcinomas

The present study aimed to investigate the correlation at pre-treatment (TX) between quantitative metrics derived from multimodality imaging (MMI), including 18F-FDG-PET/CT, 18F-FMISO-PET/CT, DW- and DCE-MRI, using a community detection algorithm (CDA) in head and neck squamous cell carcinoma (HNSCC) patients. Twenty-three HNSCC patients with 27 metastatic lymph nodes underwent a total of 69 MMI exams at pre-TX. Correlations among quantitative metrics derived from FDG-PET/CT (SUL), FMSIO-PET/CT (K1, k3, TBR, and DV), DW-MRI (ADC, IVIM [D, D*, and f]), and FXR DCE-MRI [Ktrans, ve, and τi]) were investigated using the CDA based on a "spin-glass model" coupled with the Spearman's rank, ρ, analysis. Mean MRI T2 weighted tumor volumes and SULmean values were moderately positively correlated (ρ = 0.48, p = 0.01). ADC and D exhibited a moderate negative correlation with SULmean (ρ ≤ -0.42, p < 0.03 for both). K1 and Ktrans were positively correlated (ρ = 0.48, p = 0.01). In contrast, Ktrans and k3max were negatively correlated (ρ = -0.41, p = 0.03). CDA revealed four communities for 16 metrics interconnected with 33 edges in the network. DV, Ktrans, and K1 had 8, 7, and 6 edges in the network, respectively. After validation in a larger population, the CDA approach may aid in identifying useful biomarkers for developing individual patient care in HNSCC.

PET/MRI for Staging the Axilla in Breast Cancer: Current Evidence and the Rationale for SNB vs. PET/MRI Trials

Simple Summary

PET/MRI is a relatively new, hybrid imaging tool that allows practitioners to obtain both a local and systemic staging in breast cancer patients in a single exam. To date, the available evidence is not sufficient to determine the role of PET/MRI in breast cancer management. The aims of this paper are to provide an overview of the current literature on PET/MRI in breast cancer, and to illustrate two ongoing trials aimed at defining the eventual role of PET/MRI in axillary staging in two different settings: patients with early breast cancer and patients with positive axillary nodes that are candidates for primary systemic therapy. In both cases, findings from PET/MRI will be compared with the final pathology and could be helpful to better tailor axillary surgery in the future.

Abstract

Axillary surgery in breast cancer (BC) is no longer a therapeutic procedure but has become a purely staging procedure. The progressive improvement in imaging techniques has paved the way to the hypothesis that prognostic information on nodal status deriving from surgery could be obtained with an accurate diagnostic exam. Positron emission tomography/magnetic resonance imaging (PET/MRI) is a relatively new imaging tool and its role in breast cancer patients is still under investigation. We reviewed the available literature on PET/MRI in BC patients. This overview showed that PET/MRI yields a high diagnostic performance for the primary tumor and distant lesions of liver, brain and bone. In particular, the results of PET/MRI in staging the axilla are promising. This provided the rationale for two prospective comparative trials between axillary surgery and PET/MRI that could lead to a further de-escalation of surgical treatment of BC. • SNB vs. PET/MRI 1 trial compares PET/MRI and axillary surgery in staging the axilla of BC patients undergoing primary systemic therapy (PST). • SNB vs. PET/MRI 2 trial compares PET/MRI and sentinel node biopsy (SNB) in staging the axilla of early BC patients who are candidates for upfront surgery. Finally, these ongoing studies will help clarify the role of PET/MRI in BC and establish whether it represents a useful diagnostic tool that could guide, or ideally replace, axillary surgery in the future.

Breast Cancer Image Classification: A Review

BACKGROUND

Breast cancer represents uncontrolled breast cell growth. Breast cancer is the most diagnosed cancer in women worldwide. Early detection of breast cancer improves the chances of survival and increases treatment options. There are various methods for screening breast cancer, such as mammogram, ultrasound, computed tomography and Magnetic Resonance Imaging (MRI). MRI is gaining prominence as an alternative screening tool for early detection and breast cancer diagnosis. Nevertheless, MRI can hardly be examined without the use of a Computer-Aided Diagnosis (CAD) framework, due to the vast amount of data.

OBJECTIVE

This paper aims to cover the approaches used in the CAD system for the detection of breast cancer.

METHODS

In this paper, the methods used in CAD systems are categories into two classes: the conventional approach and artificial intelligence (AI) approach.

RESULTS

The conventional approach covers the basic steps of image processing, such as preprocessing, segmentation, feature extraction and classification. The AI approach covers the various convolutional and deep learning networks used for diagnosis.

CONCLUSION

This review discusses some of the core concepts used in breast cancer and presents a comprehensive review of efforts in the past to address this problem.

Semi-automatic segmentation from intrinsically-registered 18F-FDG-PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE-MRI

Objectives

To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images.

Materials and methods

Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE–MRI images (manual DCE) and using GMM with corresponding PET images (GMM–PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement.

Results

No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM–PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM–PET for untreated and treated lesions. The mean Dice score for GMM–PET was 0.770 and 0.649 for untreated and treated lesions, respectively.

Discussion

Using PET/MRI, tumor area and mean ADC value estimated with a GMM–PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.

Preclinical Molecular Imaging for Precision Medicine in Breast Cancer Mouse Models

Precision and personalized medicine is gaining importance in modern clinical medicine, as it aims to improve diagnostic precision and to reduce consequent therapeutic failures. In this regard, prior to use in human trials, animal models can help evaluate novel imaging approaches and therapeutic strategies and can help discover new biomarkers. Breast cancer is the most common malignancy in women worldwide, accounting for 25% of cases of all cancers and is responsible for approximately 500,000 deaths per year. Thus, it is important to identify accurate biomarkers for precise stratification of affected patients and for early detection of responsiveness to the selected therapeutic protocol. This review aims to summarize the latest advancements in preclinical molecular imaging in breast cancer mouse models. Positron emission tomography (PET) imaging remains one of the most common preclinical techniques used to evaluate biomarker expression in vivo, whereas magnetic resonance imaging (MRI), particularly diffusion-weighted (DW) sequences, has been demonstrated as capable of distinguishing responders from nonresponders for both conventional and innovative chemo- and immune-therapies with high sensitivity and in a noninvasive manner. The ability to customize therapies is desirable, as this will enable early detection of diseases and tailoring of treatments to individual patient profiles. Animal models remain irreplaceable in the effort to understand the molecular mechanisms and patterns of oncologic diseases.

Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models

Purpose

The purpose of this study is to investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test.

Methods

DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV).

Results

The model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (< 0.1%). In analysing the reliability of K^trans, when considering regions with a CV < 20%, ≈ 25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable.

Conclusions

The majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data.

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics

INTRODUCTION

The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes.

OBJECTIVE

The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.

Circulating miRNAs in Untreated Breast Cancer: An Exploratory Multimodality Morpho-Functional Study

The aim of this study was to identify new disease-related circulating miRNAs with high diagnostic accuracy for breast cancer (BC) and to correlate their deregulation with the morpho-functional characteristics of the tumour, as assessed in vivo by positron emission tomography/magnetic resonance (PET/MR) imaging. A total of 77 untreated female BC patients underwent same-day PET/MR and blood collection, and 78 healthy donors were recruited as negative controls. The expression profile of 84 human miRNAs was screened by using miRNA PCR arrays and validated by real-time PCR. The validated miRNAs were correlated with the quantitative imaging parameters extracted from the primary BC samples. Circulating miR-125b-5p and miR-143-3p were upregulated in BC plasma and able to discriminate BC patients from healthy subjects (miR-125-5p area under the receiver operating characteristic ROC curve (AUC) = 0.85 and miR-143-3p AUC = 0.80). Circulating CA15-3, a soluble form of the transmembrane glycoprotein Mucin 1 (MUC-1) that is upregulated in epithelial cancer cells of different origins, was combined with miR-125b-5p and improved the diagnostic accuracy from 70% (CA15-3 alone) to 89% (CA15-3 plus miR-125b-5p). MiR-143-3p showed a strong and significant correlation with the stage of the disease, apparent diffusion coefficient (ADCmean), reverse efflux volume transfer constant (Kepmean) and maximum standardized uptake value (SUVmax), and it might represent a biomarker of tumour aggressiveness. Similarly, miR-125b-5p was correlated with stage and grade 2 but inversely correlated with the forward volume transfer constant (Ktransmean) and proliferation index (Ki67), suggesting a potential role as a biomarker of a relatively more favourable prognosis. In situ hybridization (ISH) experiments revealed that miR-143-3p was expressed in endothelial tumour cells, miR-125-5p in cancer-associated fibroblasts, and neither in epithelial tumour cells. Our results suggested that miR-125-5p and miR-143-3p are potential biomarkers for the risk stratification of BC, and Kaplan-Maier plots confirmed this hypothesis. In addition, the combined use of miR-125-b-5p and CA15-3 enhanced the diagnostic accuracy up to 89%. This is the first study that correlates circulating miRNAs with in vivo quantified tumour biology through PET/MR biomarkers. This integration elucidates the link between the plasmatic increase in these two potential circulating biomarkers and the biology of untreated BC. In conclusion, while miR-143-3b and miR-125b-5p provide valuable information for prognosis, a combination of miR-125b-5p with the tumour marker CA15-3 improves sensitivity for BC detection, which warrants consideration by further validation studies.

NMR shutter-speed elucidates apparent population inversion of 1 H2 O signals due to active transmembrane water cycling

Purpose

The desire to quantitatively discriminate the extra‐ and intracellular tissue ¹H₂O MR signals has gone hand‐in‐hand with the continual, historic increase in MRI instrument magnetic field strength [B₀]. However, recent studies have indicated extremely valuable, novel metabolic information can be readily accessible at ultra–low B₀. The two signals can be distinguished, and the homeostatic activity of the cell membrane sodium/potassium pump (Na⁺,K⁺,ATPase) detected. The mechanism allowing ¹H₂O MRI to do this is the newly discovered active transmembrane water cycling (AWC) phenomenon, which we found using paramagnetic extracellular contrast agents at clinical B₀ values. AWC is important because Na⁺,K⁺,ATPase can be considered biology’s most vital enzyme, and its in vivo steady‐state activity has not before been measurable, let alone amenable to mapping with high spatial resolution. Recent reports indicate AWC correlates with neuronal firing rate, with malignant tumor metastatic potential, and inversely with cellular reducing equivalent fraction. We wish to systematize the ways AWC can be precisely measured.

Methods

We present a theoretical longitudinal relaxation analysis of considerable scope: it spans the low‐ and high–field situations.

Results

We show the NMR shutter‐speed organizing principle is pivotal in understanding how trans–membrane steady–state water exchange kinetics are manifest throughout the range. Our findings illuminate an aspect, apparent population inversion, which is crucial in understanding ultra‐low field results.

Conclusions

Without an appreciation of apparent population inversion, significant misinterpretations of future data are likely. These could have unfortunate diagnostic consequences.