Search

Experimental evidence supports an association between heterogeneity in tumor perfusion and response to chemotherapy/radiotherapy, disease progression and malignancy. Therefore, changes in tumor perfusion may be used to assess early effects of tumor treatment. However, evaluating changes in tumor perfusion during treatment is complicated by extensive changes in tumor type, size, shape and appearance. Therefore, this study assesses the regional heterogeneity of tumors by dynamic contrast-enhanced MRI (DCE-MRI) and evaluates changes in response to isolated limb perfusion (ILP) with tumor necrosis factor alpha and melphalan. Data were acquired in an experimental cancer model, using a macromolecular contrast medium, albumin-(Gd-DTPA)45. Small fragments of BN 175 (a soft-tissue sarcoma) were implanted in eight brown Norway rats. MRI of five drug-treated and three sham-treated rats was performed at baseline and 1 h after ILP intervention. Properly co-registered baseline and follow-up DCE-MRI were used to estimate the volume transfer constant (K(trans) ) pharmacokinetic maps. The regional heterogeneity was estimated in 16 tumor sectors and presented in cumulative map-volume histograms. On average, ILP-treated tumors showed a decrease in regional heterogeneity on the histograms. This study shows that heterogenic changes in regional tumor perfusion, estimated using DCE-MRI pharmacokinetic maps, can be measured and used to assess the short-term effects of a potentially curative treatment on the tumor microvasculature in an experimental soft-tissue sarcoma model.

One of the challenges during surgery is to make a proper distinction between vital tissues, structures and organs, both at the surface of the exposed area and beneath it. Currently, identification of different tissue types relies either on the skills and anatomical knowledge of the surgeon (e.g., nerves, blood vessels, different tissue types), or on the results of off-line histopathological analysis on biopsies for assessment of cancer-free margins. These approaches are observer-dependent, or, as in the case of off-line histopathological analysis, also time consuming. A quick, non-invasive, and more observer-independent intraoperative tissue recognition method, applicable both ex-vivo and in-vivo, would be of great assistance during surgical procedures. Hyper-Spectral Imaging (HSI) has a lot of potential for such applications. It combines high spatial resolution with spectral information at numerous wavelengths that cannot be separated by human eye in the visible, and also beyond the visible spectral range. This technique enables enhanced discrimination between different tissue types based on their specific spectral signatures. We will describe the approach to HSI which is being developed within van't Hoff Medical Shared Research Program at TNO in collaboration with multiple clinical, academic and industrial partners. We study and analyze wide-band reflectance spectra of different tissues types collected through a fiber-based probe, both in-vivo during open surgery procedures and ex-vivo on freshly excised tissues. These data are used to identify spectral signatures of different tissue types. Based on this information, we develop data processing techniques to identify and distinguish the target tissues. We also further develop this approach towards a HSI setup for tissue imaging. The first step with such a setup is testing on inorganic phantoms and animal tissues. At a later stage the setup will be employed for ex-vivo human tissue analysis, both at the surface and up to several millimeters beneath the surface, with the final goal of in vivo intraoperative tissue recognition and tissue-specific contrast-enhanced imaging

PURPOSE: Tumour heterogeneity could be a valuable biomarker for differentiation, grading, response monitoring and outcome prediction. Many quantification techniques have been described, however in clinical practice these methods are scarcely used. The aim of this study is to evaluate the performance of the described methods and to identify the bottlenecks for the implementation in clinical practice. METHOD AND MATERIALS: We searched OVID, EMBASE, and Cochrane CENTRAL up to 24 March 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (FandT). RESULTS:From 6908 potentially relevant publications, 183 studies were included. The number of studies has been increasing steadily since 2009. Generally, 60 % studies use NSM,49% use SGLM, 11 % use FA, and 28% use FandT. Differential diagnosis, grading or outcome prediction was the goal in 86% studies, 36% studies were based on MRI, and 88% studies were conducted retrospectively. Tumours in the breast and brain together cover 49% of the studies. No relation was found between the discriminative power and the quantification methods used, or between the discriminative power and the imaging modality. The reported AUC ranged from 0.5 to 1 with a median of 0.89. A negative correlation was found between the AUC and the number of features estimated per tumour, which is presumably caused by overfitting in small datasets. In only 53.4% of the classification studies, the use of cross-validation was reported. None of the publications report the use of an external validation set to test their findings. Retrospective analyses were conducted in 60% of the studies without a clear description of the inclusion criteria. Only 12% of the studies had a prospective study design. Almost none of the papers evaluated the incremental value of the heterogeneity biomarker on top of clinical established markers. CONCLUSION:To enable the translation of imaging biomarkers from the research stage to clinical practice, research should focus more on prospective studies, use external datasets for validation, and focus on the added value of the proposed heterogeneity biomarker on top of the clinical established markers. CLINICAL RELEVANCE/APPLICATION:Heterogeneity has the potential of a valuable biomarker.

Distinguishing critical tissues can be challenging in (para-)thyroid surgery. As a first step towards intraoperative image contrast enhancement, we collected and analyzed spectral signatures (350-1830nm) of thyroid, parathyroid and recurrent laryngeal nerve in 10 patients.

BACKGROUND: During colorectal surgical dissections, recognizing essential anatomy is crucial to prevent iatrogenic injury. This study builds forth upon previously identified new inherent anatomical spectral contrasts in fresh human colonic samples. AIM: Assessment of optical spectrometry for discriminating critical tissues (colon, adipose tissue, artery, vein and ureter) in colorectal surgery. PROJECT DESCRIPTION: Acquisition of in vivo AND ex vivo wide-band diffuse reflectance spectra (350-1830nm) during colorectal surgery, analysis of all separate tissue spectra using principal component analysis (PCA) and comparing tissue categories. PRELIMINARY RESULTS: In 6 consecutive patients 156 in vivo spectra (32 tissue spots) and 118 ex vivo spectra (24 tissue spots) were collected. PCA derived parameters were used to differentiate between tissue types. Distinctive spectral contrast features (partly within wavelengths invisible to the naked human eye) could indeed be identified for all tissue types. Visualizing these invisible contrasts may enhance surgical imaging (either during open or endoscopic surgery).

INTRODUCTION: In thyroid and parathyroid surgery iatrogenic injury of the parathyroid glands or the recurrent laryngeal nerve (RLN) is a possible complication that needs to be prevented. The visible contrast between thyroid and parathyroid tissue is delicate to observe. The aim of this pilot study was to collect in vivo spectral reflectance-signatures of critical tissue types encountered during thyroid and parathyroid surgery, and to assess the presence of useful spectral distinctive features that might be applied for future devices enabling intraoperative tissue-specific image contrast enhancement. METHODS: Wide-band spectra (350-1830nm, 1nm resolution) were collected in vivo during thyroid and parathyroid surgery. Subjected to tissue type accessibility, on average 2 tissue types per patient were measured. For each tissue type, 5 spectra were recorded per site, covering 1-2 sites per tissue type. Mean tissue spectra were calculated for all measured tissue types. After visually comparing these mean spectra, two spectral signature features for all individual measured sites were extracted: 1. Slope within the 650-700nm range; 2. Amplitude gradient between dominant local reflectance minimum and maximum within 1350-1830nm. RESULTS: In 10 consecutive patients 158 in vivo spectra were recorded on 32 tissue sites. Based on the mean diffuse reflectance spectra for thyroid, parathyroid and RLN, the estimated features for the three tissue types were plotted. Significance was tested using a paired Student's t-test and by applying a Holm-Bonferoni correction (criterion set to p

Three different DCE-MRI quantification methods: model-free-based, compartment-model-based and principal component analysis, are compared by evaluating parameter maps for histological defined volumes of vital and non-vital tumor tissue. To obtain an accurate spatial correspondence between histology and DCE-MRI, a two-step registration process was used involving dense histological sampling, a reference plane and an intermediate ex vivo MRI. Results show that the model-free parameter washout and the second principal component score can adequately separate vital from non-vital tumor tissue, with an accuracy of respectively 99.2% and 99.7%. The other model-free parameters and the compartment-model-based Ktrans show some overlap in values between vital and non-vital tissue. The first, third and fourth pc-score have limited discriminative power.

Background: Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is complicated by deformations during pathological processing, and differences in scale and information content. Methodology/Principal Findings: This study proposes a methodology for establishing an accurate 3D relation between histological sections and high resolution in vivo MRI tumor data. The key features of the methodology are: 1) standardized acquisition and processing, 2) use of an intermediate ex vivo MRI, 3) use of a reference cutting plane, 4) dense histological sampling, 5) elastic registration, and 6) use of complete 3D data sets. Five rat pancreatic tumors imaged by T2*-w MRI were used to evaluate the proposed methodology. The registration accuracy was assessed by root mean squared (RMS) distances between manually annotated landmark points in both modalities. After elastic registration the average RMS distance decreased from 1.4 to 0.7 mm. The intermediate ex vivo MRI and the reference cutting plane shared by all three 3D images (in vivo MRI, ex vivo MRI, and 3D histology data) were found to be crucial for the accurate co-registration between the 3D histological data set and in vivo MRI. The MR intensity in necrotic regions, as manually annotated in 3D histology, was significantly different from other histologically confirmed regions (i.e., viable and hemorrhagic). However, the viable and the hemorrhagic regions showed a large overlap in T2*-w MRI signal intensity. Conclusions: The established 3D correspondence between tumor histology and in vivo MRI enables extraction of MRI characteristics for histologically confirmed regions. The proposed methodology allows the creation of a tumor database of spatially registered multi-spectral MR images and multi-stained 3D histology.