Ioannis Lavdas

CXCR4-Targeted and MMP-Responsive Iron Oxide Nanoparticles for Enhanced Magnetic Resonance Imaging†

MRI offers high spatial resolution with excellent tissue penetration but it has limited sensitivity and the commonly administered contrast agents lack specificity. In this study, two sets of iron oxide nanoparticles (IONPs) were synthesized that were designed to selectively undergo copper-free click conjugation upon sensing of matrix metalloproteinase (MMP) enzymes, thereby leading to a self-assembled superparamagnetic nanocluster network with T2 signal enhancement properties. For this purpose, IONPs with bioorthogonal azide and alkyne surfaces masked by polyethylene glycol (PEG) layers tethered to CXCR4-targeted peptide ligands were synthesized and characterized. The IONPs were tested in vitro and T2 signal enhancements of around 160 % were measured when the IONPs were incubated with cells expressing MMP2/9 and CXCR4. Simultaneous systemic administration of the bioorthogonal IONPs in tumor-bearing mice demonstrated the signal-enhancing ability of these ‘smart’ self-assembling nanomaterials.

A phantom for diffusion-weighted MRI (DW-MRI)

To develop tissue‐equivalent diffusivity materials and build a spherical diffusion phantom which mimics the conditions typically found in biological tissues. Also, to assess the reproducibility of ADC measurements from a whole‐body diffusion protocol.

Temporal and Spatial Evolution of Therapy-Induced Tumor Apoptosis Detected by Caspase-3–Selective Molecular Imaging

Purpose: Induction of apoptosis in tumors is considered a desired goal of anticancer therapy. We investigated whether the dynamic temporal and spatial evolution of apoptosis in response to cytotoxic and mechanism-based therapeutics could be detected noninvasively by the caspase-3 radiotracer [18F]ICMT-11 and positron emission tomography (PET). Experimental Design: The effects of a single dose of the alkylating agent cyclophosphamide (CPA or 4-hydroperoxycyclophosphamide), or the mechanism-based small molecule SMAC mimetic birinapant on caspase-3 activation was assessed in vitro and by [18F]ICMT-11–PET in mice bearing 38C13 B-cell lymphoma, HCT116 colon carcinoma, or MDA-MB-231 breast adenocarcinoma tumors. Ex vivo analysis of caspase-3 was compared to the in vivo PET imaging data. Results: Drug treatment increased the mean [18F]ICMT-11 tumor uptake with a peak at 24 hours for CPA (40 mg/kg; AUC40–60: 8.04 ± 1.33 and 16.05 ± 3.35 %ID/mL × min at baseline and 24 hours, respectively) and 6 hours for birinapant (15 mg/kg; AUC40–60: 20.29 ± 0.82 and 31.07 ± 5.66 %ID/mL × min, at baseline and 6 hours, respectively). Voxel-based spatiotemporal analysis of tumor-intrinsic heterogeneity suggested that discrete pockets of caspase-3 activation could be detected by [18F]ICMT-11. Increased tumor [18F]ICMT-11 uptake was associated with caspase-3 activation measured ex vivo, and early radiotracer uptake predicted apoptosis, distinct from the glucose metabolism with [18F]fluorodeoxyglucose-PET, which depicted continuous loss of cell viability. Conclusion: The proapoptotic effects of CPA and birinapant resulted in a time-dependent increase in [18F]ICMT-11 uptake detected by PET. [18F]ICMT-11–PET holds promise as a noninvasive pharmacodynamic biomarker of caspase-3–associated apoptosis in tumors. Clin Cancer Res; 19(14); 3914–24. ©2013 AACR.

Comparison between diffusion-weighted MRI (DW-MRI) at 1.5 and 3 tesla: A phantom study

To compare DW‐MRI between 1.5 and 3 Tesla (T) in terms of image quality, apparent diffusion coefficient (ADC), reproducibility, lesion‐to‐background contrast and signal‐to‐noise ratio (SNR), using a test object.

RGD-targeted MnO nanoparticles as T1 contrast agents for cancer imaging – the effect of PEG length in vivo

As magnetic resonance imaging (MRI) contrast agents, T1 Gd3+ chelates are generally the preferred option for radiologists over T2 iron oxide nanoparticles. The main reason for the popularity of T1 agents is the easier interpretation of T1-weighted MR images. However, the chemical versatility of nanoparticulate platforms makes them ideal candidates for the next generation of targeted MRI contrast agents. In this context, we present herein the design and preparation of a nanoparticulate contrast agent based on MnO, which presents T1 contrast enhancement properties as well as nanoparticle formulation. Functionalization of MnO nanoparticles with the extensively studied RGD peptide was used to target tumours over-expressing the αvβ3 integrin. PEG (polyethylene glycol) molecules were used to increase the blood half-life of the nanoparticles in vivo, and the effect of different PEG lengths on the final contrast on MR images was investigated.

Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth

When integrating computational tools, such as automatic segmentation, into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data and, in particular, to detect when an automatic method fails. However, this is difficult to achieve due to the absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross validation, because validation data are often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared with a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA, we take the predicted segmentation from a new image to train a reverse classifier, which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as a part of large-scale image analysis studies.

Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi‐atlas (MA) approach

Purpose: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. Methods: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi‐atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross‐validation experiments were run on 34 artifact‐free subjects. We report three overlap and three surface distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average surface distance (ASD), root‐mean‐square surface distance (RMSSD), and Hausdorff distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a ‘per‐organ’ basis. A Mann–Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a ‘per‐organ’ basis, when using different imaging combinations as input for training. Results: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean surface distance metrics for all the segmented structures were: CFs: ASD = 13.5 ± 11.3 mm, RMSSD = 34.6 ± 37.6 mm and HD = 185.7 ± 194.0 mm, CNNs; ASD = 5.48 ± 4.84 mm, RMSSD = 17.0 ± 13.3 mm and HD = 199.0 ± 101.2 mm, MA: ASD = 4.22 ± 2.42 mm, RMSSD = 6.13 ± 2.55 mm, and HD = 38.9 ± 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. Conclusions: Three state‐of‐the‐art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance.

Small Organ Segmentation in Whole-Body MRI Using a Two-Stage FCN and Weighting Schemes.

Accurate and robust segmentation of small organs in whole-body MRI is difficult due to anatomical variation and class imbalance. Recent deep network based approaches have demonstrated promising performance on abdominal multi-organ segmentations. However, the performance on small organs is still suboptimal as these occupy only small regions of the whole-body volumes with unclear boundaries and variable shapes. A coarse-to-fine, hierarchical strategy is a common approach to alleviate this problem, however, this might miss useful contextual information. We propose a two-stage approach with weighting schemes based on auto-context and spatial atlas priors. Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.

Clinical translation of [18F]ICMT-11 for measuring chemotherapy-induced caspase 3/7 activation in breast and lung cancer

BackgroundEffective anticancer therapy is thought to involve induction of tumour cell death through apoptosis and/or necrosis. [18F]ICMT-11, an isatin sulfonamide caspase-3/7-specific radiotracer, has been developed for PET imaging and shown to have favourable dosimetry, safety, and biodistribution. We report the translation of [18F]ICMT-11 PET to measure chemotherapy-induced caspase-3/7 activation in breast and lung cancer patients receiving first-line therapy.ResultsBreast tumour SUVmax of [18F]ICMT-11 was low at baseline and unchanged following therapy. Measurement of M30/M60 cytokeratin-18 cleavage products showed that therapy was predominantly not apoptosis in nature. While increases in caspase-3 staining on breast histology were seen, post-treatment caspase-3 positivity values were only approximately 1%; this low level of caspase-3 could have limited sensitive detection by [18F]ICMT-11-PET. Fourteen out of 15 breast cancer patients responded to first–line chemotherapy (complete or partial response); one patient had stable disease. Four patients showed increases in regions of high tumour [18F]ICMT-11 intensity on voxel-wise analysis of tumour data (classed as PADS); response was not exclusive to patients with this phenotype. In patients with lung cancer, multi-parametric [18F]ICMT-11 PET and MRI (diffusion-weighted- and dynamic contrast enhanced-MRI) showed that PET changes were concordant with cell death in the absence of significant perfusion changes.ConclusionThis study highlights the potential use of [18F]ICMT-11 PET as a promising candidate for non-invasive imaging of caspase3/7 activation, and the difficulties encountered in assessing early-treatment responses. We summarize that tumour response could occur in the absence of predominant chemotherapy-induced caspase-3/7 activation measured non-invasively across entire tumour lesions in patients with breast and lung cancer.

Histogram analysis of apparent diffusion coefficient from whole-body diffusion-weighted MRI to predict early response to chemotherapy in patients with metastatic colorectal cancer: preliminary results

AIM To evaluate apparent diffusion coefficient (ADC) histogram analysis parameters, acquired from whole-body diffusion-weighted magnetic resonance imaging (DW-MRI), as very early predictors of response to chemotherapy in patients with metastatic colorectal cancer (mCRC). MATERIALS AND METHODS This was a single-institution prospective study, approved by the West Midlands-South Birmingham research ethics committee. All patients gave fully informed consent prior to imaging. Sixteen patients with histologically confirmed mCRC were enrolled to the study and 11 were successfully scanned with whole-body DW-MRI before (baseline) and 10.8±2.7 days after commencing chemotherapy (follow-up). Therapy response was assessed by RECIST 1.1. Mean ADC and histogram parameters (skewness, kurtosis, 25th, 50th, and 75th percentiles) were compared between progressors and non-progressors at baseline and follow-up. Receiver operating characteristics (ROC) analysis was performed for the statistically significant parameters. Data from metastases were also compared to normative tissue data acquired from healthy volunteers. RESULTS Three patients had progressive disease (progressors) and eight had partial response/stable disease (non-progressors). Mean, 25th, 50th, and 75th percentiles were significantly lower for progressors at baseline (p=0.012, 0.012, 0.012 and 0.025 respectively) with areas under the ROC curves (AUC)=0.58, 0.50, 0.58 and 0.63, respectively. Skewness and kurtosis were significantly lower for non-progressors at follow-up (p=0.001 and 0.003 respectively) with AUC=0.67 and 0.79 respectively. CONCLUSION ADC histogram analysis shows potential in discriminating progressive from non-progressive disease in patients with mCRC, who underwent whole-body DW-MRI. The technique can potentially be tested as a response assessment methodology in larger trials.