A study of the Intratumoural Vascular Network by Fractal Analysis, Percolation Theroy and Syntactic Structure Analysis : An Investigation of Possible Image Analysis Parameters Applied to Histological Sections in Hypoxia and Angiogenesis Related Research

Abstract The purpose of this thesis is the investigation of the intratumoural network through image analysis of histological sections. Tumour vasculature is characterized by complexity, irregularities and poorly regulated growth. Fractal analysis has been used to establish that tumour vasculature has a different network architecture from that of the normal arterio-venous system or the capillary network. The vasculature is responsible for the transportation of oxygen to tumour cells, however its many pathological features results in, among others, the presence of hypoxic regions. Hypoxia is a challenge to the treatment of cancer, both through its indirect biological effects, such as a reduced progression through the cell cycle, but also through direct chemical effects. In particular, the oxygen effect reduces the effectiveness of radiation therapy. Furthermore, the network morphology relates to many other parameters as well, such as the angiogenic and the metastatic capability of the cancer. This raises the possibility of using image analysis, and fractal analysis in particular, to quantify different aspects of the network morphology. The study limits itself to parameters which may be obtained from digitized images of histological sections with endothelial-specific staining. The investigated parameters are primarily obtained through fractal analysis and syntactic structure analysis. A few more parameters, such as the number of vessels, the size of the vessels, the total vascular area, and cumulative histograms of distances to the nearest vessel, were obtained directly from the images. The investigated parameters depend on both the number of vessels in the image, and the distribution of the vessels. Two particular areas have been emphasized, the first is the identification of how strongly the parameters relate to the vessel distribution, and the second is the implementation of fractal analysis on vascular cross sections. Four different CD34-stained immunohistological sections have been analysed. They were obtained from malignant carcinomas of the breast and exhibited qualitatively different vascular patterns. A routine has been developed to segment out the vessels from the background staining before the image analysis. The investigated fractal dimensions include the Box Counting dimension, the Sandbox dimension, the Correlation dimension, the Mass dimension and the Fourier dimension. These have been applied to images processed in three different ways. The first contained the entire vessel lumens, the second only the outer vessel wall perimeter and the last only the vessels’ geometric centre of mass. In addition fractal analysis has been performed on Gabriels’s Graph and the Euclidean Minimum Spanning Tree, both of which belong to the Syntactic Structure Analysis graphs. The different methods and images provided both different dimensions and different curve shapes. Some of the curves did not have any meaningful power-law scaling regions at all, however, most of them did. The Sandbox dimension in general and the mass centre images in particular, have been considered the most promising of these methods. Although it may be argued that the term dimension does not, in any meaningful way, relate to most of the parameters obtained through these methods, they do most certainly appear capable of differentiating various vessel distributions from each other. In addition to the fractal analysis methods, all other investigated methods have been applied to the four cases as well. In order to identify the relationship between the parameters and the number of vessels in a particular image, two simulations have been performed. The first simulation generated the images through a uniform random distribution probability (10.000 images), while the second used a three-dimensional invasion percolation cluster to generate the vessel positions (15.560 images). The mean and standard deviations of the results at each vessel count have been investigated. Large standard deviations have been interpreted as a strong dependency on the vessel distribution. The slope of the mean, on the other hand, shows the parameters dependency on the number of vessels. The sizes of the standard deviations are considered relative to the slopes. Most of the analysis parameters showed large variations for low vascular densities. A subset of the parameters had large variations even at very high vascular densities. In conclusion, most of the investigated parameters appear to be promising candidates for further studies. Fractal analysis may be applied to vascular cross-sections. It is, however, important to rigorously specify how the analysis is performed, as a large number of possible results may be acquired through these methods. In particular the Sandbox dimensions of the mass centre images, Gabriel’s Graph, and the Euclidean Minimum Spanning Tree, at large sandbox diameters, are recommended for further study, with the possible additon of the EMST dimension at small diameters, as this require no extra computation time. At this point in time it is not recommended to exclude any of the SSA-parameters from further studies. The next adviceable step would be to perform a correlation study, comparing these parameters to other data of clinical value, related to treatment, diagnosis or prognosis

Correlations between [⁶⁸Ga]Ga-DOTA-TOC Uptake and Absorbed Dose from [¹⁷⁷Lu]Lu-DOTA-TATE

Purpose: The aim of this paper was to investigate correlations between pre- therapeutic [68Ga]Ga-DOTA-TOC uptake and absorbed dose to tumours from therapy with [177Lu]Lu-DOTA-TATE. Methods: This retrospective study included 301 tumours from 54 GEP-NET patients. The tumours were segmented on pre-therapeutic [68Ga]Ga-DOTA-TOC PET/CT, and post-therapy [177Lu]Lu-DOTA-TATE SPECT/CT images, using a fixed 40% threshold. The SPECT/CT images were used for absorbed dose calculations by assuming a linear build-up from time zero to day one, and mono-exponential wash-out after that. Both SUVmean and SUVmax were measured from the PET images. A linear absorbed-dose prediction model was formed with SUVmean as the independent variable, and the accuracy was tested with a split 70–30 training-test set. Results: Mean SUVmean and SUVmax from [68Ga]Ga-DOTA-TOC PET was 24.0 (3.6–84.4) and 41.0 (6.7–146.5), and the mean absorbed dose from [177Lu]Lu-DOTA-TATE was 26.9 Gy (2.4–101.9). A linear relationship between SUVmean and [177Lu]Lu-DOTA-TATE activity concentration at 24 h post injection was found (R2 = 0.44, p 68Ga]Ga-DOTA-TOC SUVs and absorbed doses from [177Lu]Lu-DOTA-TATE. Depending on the required accuracy, [68Ga]Ga-DOTA-TOC PET imaging may estimate the [177Lu]Lu-DOTA-TATE uptake. However, there could be a high variance between predicted and actual absorbed doses

Optimization of Q.Clear reconstruction for dynamic 18F PET imaging

Abstract Background Q.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optimal β-factor for accurate quantitation of dynamic PET scans. Methods A Flangeless Esser PET Phantom with eight hollow spheres (4–25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different β-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CVRC) and root-mean-square error (RMSERC) were evaluated for the phantom data. Two male patients with recurrent glioblastoma were scanned on the same scanner using 18F-PSMA-1007. Using an irreversible two-tissue compartment model, the area under curve (AUC) and the net influx rate Ki were calculated to assess the impact of different β-factors on the pharmacokinetic analysis of clinical PET brain data. Results In general, RC and CVRC decreased with increasing β-factor in the phantom data. For small spheres (< 10 mm), and in particular for short acquisition times, low β-factors resulted in high variability and an overestimation of measured activity. Increasing the β-factor improves the variability, however at a cost of underestimating the measured activity. For the clinical data, AUC decreased and Ki increased with increased β-factor; a change in β-factor from 300 to 1000 resulted in a 25.5% increase in the Ki. Conclusion In a complex dynamic dataset with variable acquisition times, the optimal β-factor provides a balance between accuracy and precision. Based on our results, we suggest a β-factor of 300–500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher β-factors. Trial registration Clinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO

Combining radioiodine and external beam radiation therapy: the potential of integrated treatment planning for differentiated thyroid cancer

Combining radioiodine and external beam radiation therapy: the potential of integrated treatment planning for differentiated thyroid cance

The effect of new formulas for lean body mass on lean- body-mass-normalized SUV in oncologic 18F-FDG PET/CT

Due to better precision and intercompatibility, the use of lean body mass (LBM) as mass estimate in the calculation of standardized uptake values (SUV) has become more common in research and clinical studies today. Thus, the equations deciding this quantity have to be verified in order to choose the ones that best represents the actual body composition. Methods - LBM was calculated for 44 patients examined with 18F-FDG PET/CT scans by means of James’ and Janmahasatians’ sex specific predictive equations and the results validated using a CT based method. The latter method makes use of the eyes-to-thighs CT from the PET/CT aquisition protocol and segments the voxels according to Hounsfield Units. Intraclass correlation coefficients (ICC) and Bland-Altman plots have been used to assess agreement between the various methods. Results - A mean difference of 6.3kg (-15.1 kg to 2.5 kg LOA) between LBMjames and LBMCT1 was found. This is higher than the observed mean difference of 3.8kg (-12.5 kg to 4.9 kg LOA) between LBMjan and LBMCT1. In addition, LBMjan had higher ICC with LBMCT1 of rI = 0.87 (rL = 0.60, rU = 0.94) than LBMjames with rI = 0.77 (rL = 0.11, rU = 0.91). Thus, we obtained better agreement between and LBMjan and LBMCT1. Although there were exceptions, the overall effect on SUL values was that SULjames values were greater than SULjan values. Conclusion - From our results, we have verified the reliability of the LBMjan suggested formulas with a CT derived reference standard. Compared with the more traditional and available set of equations LBMjames, the LBMjan formulas tend to yield better agreement

Comparison of Intravoxel incoherent motion imaging and multiecho dynamic contrast-based MRI in rectal cancer

Background Dynamic contrast‐based MRI and intravoxel incoherent motion imaging (IVIM) MRI are both methods showing promise as diagnostic and prognostic tools in rectal cancer. Both methods aim at measuring perfusion‐related parameters, but the relationship between them is unclear. Purpose To investigate the relationship between perfusion‐ and permeability‐related parameters obtained by IVIM‐MRI, T1‐weighted dynamic contrast‐enhanced (DCE)‐MRI and T2*‐weighted dynamic susceptibility contrast (DSC)‐MRI. Study Type Prospective. Subjects In all, 94 patients with histologically confirmed rectal cancer. Field Strength/Sequence Subjects underwent pretreatment 1.5T clinical procedure MRI, and in addition a study‐specific diffusion‐weighted sequence (b = 0, 25, 50, 100, 500, 1000, 1300 s/mm2) and a multiecho dynamic contrast‐based echo‐planer imaging sequence. Assessment Median tumor values were obtained from IVIM (perfusion fraction [f], pseudodiffusion [D*], diffusion [D]), from the extended Tofts model applied to DCE data (Ktrans, kep, vp, ve) and from model free deconvolution of DSC (blood flow [BF] and area under curve). A subgroup of the excised tumors underwent immunohistochemistry with quantification of microvessel density and vessel size. Statistical Test Spearman's rank correlation test. Results D* was correlated with BF (rs = 0.47, P < 0.001), and f was negatively correlated with kep (rs = –0.31, P = 0.002). BF was correlated with Ktrans (rs = 0.29, P = 0.004), but this correlation varied extensively when separating tumors into groups of low (rs = 0.62, P < 0.001) and high (rs = –0.06, P = 0.68) BF. Ktrans was negatively correlated with vessel size (rs = –0.82, P = 0.004) in the subgroup of tumors with high BF. Data Conclusion We found an association between D* from IVIM and BF estimated from DSC‐MRI. The relationship between IVIM and DCE‐MRI was less clear. Comparing parameters from DSC‐MRI and DCE‐MRI highlights the importance of the underlying biology for the interpretation of these parameters. Level of Evidence: 2 Technical Efficacy: Stage

Comparison of Intravoxel incoherent motion imaging and multiecho dynamic contrast-based MRI in rectal cancer

Background Dynamic contrast‐based MRI and intravoxel incoherent motion imaging (IVIM) MRI are both methods showing promise as diagnostic and prognostic tools in rectal cancer. Both methods aim at measuring perfusion‐related parameters, but the relationship between them is unclear. Purpose To investigate the relationship between perfusion‐ and permeability‐related parameters obtained by IVIM‐MRI, T1‐weighted dynamic contrast‐enhanced (DCE)‐MRI and T2*‐weighted dynamic susceptibility contrast (DSC)‐MRI. Study Type Prospective. Subjects In all, 94 patients with histologically confirmed rectal cancer. Field Strength/Sequence Subjects underwent pretreatment 1.5T clinical procedure MRI, and in addition a study‐specific diffusion‐weighted sequence (b = 0, 25, 50, 100, 500, 1000, 1300 s/mm2) and a multiecho dynamic contrast‐based echo‐planer imaging sequence. Assessment Median tumor values were obtained from IVIM (perfusion fraction [f], pseudodiffusion [D*], diffusion [D]), from the extended Tofts model applied to DCE data (Ktrans, kep, vp, ve) and from model free deconvolution of DSC (blood flow [BF] and area under curve). A subgroup of the excised tumors underwent immunohistochemistry with quantification of microvessel density and vessel size. Statistical Test Spearman's rank correlation test. Results D* was correlated with BF (rs = 0.47, P < 0.001), and f was negatively correlated with kep (rs = –0.31, P = 0.002). BF was correlated with Ktrans (rs = 0.29, P = 0.004), but this correlation varied extensively when separating tumors into groups of low (rs = 0.62, P < 0.001) and high (rs = –0.06, P = 0.68) BF. Ktrans was negatively correlated with vessel size (rs = –0.82, P = 0.004) in the subgroup of tumors with high BF. Data Conclusion We found an association between D* from IVIM and BF estimated from DSC‐MRI. The relationship between IVIM and DCE‐MRI was less clear. Comparing parameters from DSC‐MRI and DCE‐MRI highlights the importance of the underlying biology for the interpretation of these parameters. Level of Evidence: 2 Technical Efficacy: Stage

Comparison of Intravoxel incoherent motion imaging and multiecho dynamic contrast-based MRI in rectal cancer

Background Dynamic contrast‐based MRI and intravoxel incoherent motion imaging (IVIM) MRI are both methods showing promise as diagnostic and prognostic tools in rectal cancer. Both methods aim at measuring perfusion‐related parameters, but the relationship between them is unclear. Purpose To investigate the relationship between perfusion‐ and permeability‐related parameters obtained by IVIM‐MRI, T1‐weighted dynamic contrast‐enhanced (DCE)‐MRI and T2*‐weighted dynamic susceptibility contrast (DSC)‐MRI. Study Type Prospective. Subjects In all, 94 patients with histologically confirmed rectal cancer. Field Strength/Sequence Subjects underwent pretreatment 1.5T clinical procedure MRI, and in addition a study‐specific diffusion‐weighted sequence (b = 0, 25, 50, 100, 500, 1000, 1300 s/mm2) and a multiecho dynamic contrast‐based echo‐planer imaging sequence. Assessment Median tumor values were obtained from IVIM (perfusion fraction [f], pseudodiffusion [D*], diffusion [D]), from the extended Tofts model applied to DCE data (Ktrans, kep, vp, ve) and from model free deconvolution of DSC (blood flow [BF] and area under curve). A subgroup of the excised tumors underwent immunohistochemistry with quantification of microvessel density and vessel size. Statistical Test Spearman's rank correlation test. Results D* was correlated with BF (rs = 0.47, P < 0.001), and f was negatively correlated with kep (rs = –0.31, P = 0.002). BF was correlated with Ktrans (rs = 0.29, P = 0.004), but this correlation varied extensively when separating tumors into groups of low (rs = 0.62, P < 0.001) and high (rs = –0.06, P = 0.68) BF. Ktrans was negatively correlated with vessel size (rs = –0.82, P = 0.004) in the subgroup of tumors with high BF. Data Conclusion We found an association between D* from IVIM and BF estimated from DSC‐MRI. The relationship between IVIM and DCE‐MRI was less clear. Comparing parameters from DSC‐MRI and DCE‐MRI highlights the importance of the underlying biology for the interpretation of these parameters. Level of Evidence: 2 Technical Efficacy: Stage

The Clinical Impact of Mean Vessel Size and Solidity in Breast Carcinoma Patients

<div><p>Angiogenesis quantification, through vessel counting or area estimation in the most vascular part of the tumour, has been found to be of prognostic value across a range of carcinomas, breast cancer included. We have applied computer image analysis to quantify vascular properties pertaining to size, shape and spatial distributions in photographed fields of CD34 stained sections. Aided by a pilot (98 cases), seven parameters were selected and validated on a separate set from 293 breast cancer patients.</p><p>Two new prognostic markers were identified through continuous Cox regression with endpoints Breast Cancer Specific Survival and Distant Disease Free Survival: The average size of the vessels as measured by their perimeter (p = 0.003 and 0.004, respectively), and the average complexity of the vessel shapes measured by their solidity (p = 0.004 and 0.004). The Hazard ratios for the corresponding median-dichotomized markers were 2.28 (p = 0.005) and 1.89 (p = 0.016) for the mean perimeter and 1.80 (p = 0.041) and 1.55 (p = 0.095) for the shape complexity. The markers were associated with poor histologic type, high grade, necrosis, HR negativity, inflammation, and p53 expression (vessel size only).</p><p>Both markers were found to strongly influence the prognostic properties of vascular invasion (VI) and disseminated tumour cells in the bone marrow. The latter being prognostic only in cases with large vessels (p = 0.004 and 0.043) or low complexity (p = 0.018 and 0.024), but not in the small or complex vessel groups (p>0.47). VI was significant in all groups, but showed greater hazard ratios for small and low complexity vessels (6.54–11.2) versus large and high complexity vessels (2.64–3.06).</p><p>We find that not only the overall amount of produced vasculature in angiogenic hot-spots is of prognostic significance, but also the morphological appearance of the generated vessels, <i>i.e.</i> the size and shape of vessels in the studied hot spots.</p></div