Quantification Of Heterogeneity Research Articles

Quantification of atrial electrical heterogeneity via atrial electromechanical coupling.

Quantification of atrial electrical heterogeneity via atrial electromechanical coupling.

Evaluation of PET texture features with heterogeneous phantoms: complementarity and effect of motion and segmentation method

A major source of error in quantitative PET/CT scans of lung cancer tumors is respiratory motion. Regarding the variability of PET texture features (TF), the impact of respiratory motion has not been properly studied with experimental phantoms. The primary aim of this work was to evaluate the current use of PET texture analysis for heterogeneity characterization in lesions affected by respiratory motion. Twenty-eight heterogeneous lesions were simulated by a mixture of alginate and 18F-fluoro-2-deoxy-D-glucose (FDG). Sixteen respiratory patterns were applied. Firstly, the TF response for different heterogeneous phantoms and its robustness with respect to the segmentation method were calculated. Secondly, the variability for TF derived from PET image with (gated, G-) and without (ungated, U-) motion compensation was analyzed. Finally, TF complementarity was assessed. In the comparison of TF derived from the ideal contour with respect to TF derived from 40%-threshold and adaptive-threshold PET contours, 7/8 TF showed strong linear correlation (LC) (p < 0.001, r > 0.75), despite a significant volume underestimation. Independence of lesion movement (LC in 100% of the combined pairs of movements, p < 0.05) was obtained for 1/8 TF with U-image (width of the volume-activity histogram, WH) and 4/8 TF with G-image (WH and energy (ENG), local-homogeneity (LH) and entropy (ENT), derived from the co-ocurrence matrix). Their variability in terms of the coefficient of variance () resulted in (WH) = 0.18 on the U-image and (WH) = 0.24, (ENG) = 0.15, (LH) = 0.07 and (ENT) = 0.06 on the G-image. Apart from WH (r > 0.9, p < 0.001), not one of these TF has shown LC with Cmax. Complementarity was observed for the TF pairs: ENG–LH, CONT (contrast)–ENT and LH–ENT. In conclusion, the effect of respiratory motion should be taken into account when the heterogeneity of lung cancer is quantified on PET/CT images. Despite inaccurate volume delineation, TF derived from 40% and COA contours could be reliable for their prognostic use. The TF that exhibited simultaneous added value and independence of lesion movement were ENG and ENT computed from the G-image. Their use is therefore recommended for heterogeneity quantification of lesions affected by respiratory motion.

Spectral Clustering Predicts Tumor Tissue Heterogeneity Using Dynamic 18F-FDG PET: A Complement to the Standard Compartmental Modeling Approach

In this study, we described and validated an unsupervised segmentation algorithm for the assessment of tumor heterogeneity using dynamic 18F-FDG PET. The aim of our study was to objectively evaluate the proposed method and make comparisons with compartmental modeling parametric maps and SUV segmentations using simulations of clinically relevant tumor tissue types. Methods: An irreversible 2-tissue-compartmental model was implemented to simulate clinical and preclinical 18F-FDG PET time-activity curves using population-based arterial input functions (80 clinical and 12 preclinical) and the kinetic parameter values of 3 tumor tissue types. The simulated time-activity curves were corrupted with different levels of noise and used to calculate the tissue-type misclassification errors of spectral clustering (SC), parametric maps, and SUV segmentation. The utility of the inverse noise variance- and Laplacian score-derived frame weighting schemes before SC was also investigated. Finally, the SC scheme with the best results was tested on a dynamic 18F-FDG measurement of a mouse bearing subcutaneous colon cancer and validated using histology. Results: In the preclinical setup, the inverse noise variance-weighted SC exhibited the lowest misclassification errors (8.09%-28.53%) at all noise levels in contrast to the Laplacian score-weighted SC (16.12%-31.23%), unweighted SC (25.73%-40.03%), parametric maps (28.02%-61.45%), and SUV (45.49%-45.63%) segmentation. The classification efficacy of both weighted SC schemes in the clinical case was comparable to the unweighted SC. When applied to the dynamic 18F-FDG measurement of colon cancer, the proposed algorithm accurately identified densely vascularized regions from the rest of the tumor. In addition, the segmented regions and clusterwise average time-activity curves showed excellent correlation with the tumor histology. Conclusion: The promising results of SC mark its position as a robust tool for quantification of tumor heterogeneity using dynamic PET studies. Because SC tumor segmentation is based on the intrinsic structure of the underlying data, it can be easily applied to other cancer types as well.

Identification of low variability textural features for heterogeneity quantification of 18F-FDG PET/CT imaging

ObjectiveTo identify those textural features that are insensitive to both technical and biological factors in order to standardise heterogeneity studies on 18F-FDG PET imaging. Materials and methodsTwo different studies were performed. First, nineteen series from a cylindrical phantom filled with different 18F-FDG activity concentration were acquired and reconstructed using three different protocols. Seventy-two texture features were calculated inside a circular region of interest. The variability of each feature was obtained. Second, the data for 15 patients showing non-pathological liver were acquired. Anatomical and physiological features such as patient's weight, height, body mass index, metabolic active volume, blood glucose level, SUV and SUV standard deviation were also recorded. A liver covering region of interest was delineated and low variability textural features calculated in each patient. Finally, a multivariate Spearman's correlation analysis between biological factors and texture features was performed. ResultsOnly eight texture features analysed show small variability (<5%) with activity concentration and reconstruction protocol making them suitable for heterogeneity quantification. On the other hand, there is a high statistically significant correlation between MAV and entropy (P<0.05). Entropy feature is, indeed, correlated (P<0.05) with all patient parameters, except body mass index. ConclusionsThe textural features that are correlated with neither technical nor biological factors are run percentage, short-zone emphasis and intensity, making them suitable for quantifying functional changes or classifying patients. Other textural features are correlated with technical and biological factors and are, therefore, a source of errors if used for this purpose.

Assessing forest fragmentation in north-western Himalaya: a case study from Ranikhet forest range, Uttarakhand, India

The northwestern Himalaya harbors high levels of biodiversity due to its unique topography, climatic conditions and heterogeneity. Forest fragmentation is one of the major threats causing a decline in biodiversity in the Himalayan region. We assesses forest fragmentation and changes in land use land cover (LULC) patterns using multi-temporal satellite data over a time span of four decades (1976–2013). Fragmentation analysis using the Landscape Fragmentation Tool (LFT) reveals a decrease in core and edge areas by 14 and 2.3 %, respectively; while an increase in non-forest, patch area and perforation area by 2.1, 0.4, and 14 %, respectively. The LULC dynamics show that the areas under dense forest and scrub forest have decreased by 2.8 % and 1.9 %, respectively; and there is an increase in open forest, crop land and fallow land area by 2.6, 1.7 and 2.1 %, respectively. The quantification of landscape heterogeneity is undertaken with the help of landscape metrics computed using FRAGSTATS at class and landscape level, showing signs of increased fragmentation. Our study provides baseline database that can support the future biodiversity conservation and sustainable forest management initiatives.

Quantifying cluster dynamics to improve EMMS drag law and radial heterogeneity description in coupling with gas-solid two-fluid method

Quantification of multiscale heterogeneity is the key to the simulation of gas-solid two-phase flow. The energy-minimization multiscale (EMMS) model can be used at different scales to describe local and global heterogeneities as well as steady-state fluid dynamics of gas-solid fluidization. However, the same cluster diameter correlation based on energy dissipation was used in all cases, which may give a smaller cluster diameter than single particle diameter at low solids fluxes. In this paper, a description of the solids mass transfer between the dilute and dense phases in heterogeneous gas-solid flow is proposed to replace the correlation of cluster diameter to avoid some problems associated with the empirical calculation of the cluster diameter. This cluster description can facilitate the prediction of the drag coefficient at the scale of numerical cells in computational fluid dynamics (CFD) at extremely low solids fluxes and the radial heterogeneity in large-scale circulating fluidized bed (CFB) risers with downward particle velocities near the wall. It is integrated with two-fluid model (TFM) to simulate the fluid dynamics in an industrial-scale CFB riser accurately, which is also accelerated significantly by using the predicted radial voidage distribution as the initial condition of CFD.

Quantification of heterogeneity in lung disease with image-based pulmonary function testing

Computed tomography (CT) and spirometry are the mainstays of clinical pulmonary assessment. Spirometry is effort dependent and only provides a single global measure that is insensitive for regional disease, and as such, poor for capturing the early onset of lung disease, especially patchy disease such as cystic fibrosis lung disease. CT sensitively measures change in structure associated with advanced lung disease. However, obstructions in the peripheral airways and early onset of lung stiffening are often difficult to detect. Furthermore, CT imaging poses a radiation risk, particularly for young children, and dose reduction tends to result in reduced resolution. Here, we apply a series of lung tissue motion analyses, to achieve regional pulmonary function assessment in β-ENaC-overexpressing mice, a well-established model of lung disease. The expiratory time constants of regional airflows in the segmented airway tree were quantified as a measure of regional lung function. Our results showed marked heterogeneous lung function in β-ENaC-Tg mice compared to wild-type littermate controls; identified locations of airway obstruction, and quantified regions of bimodal airway resistance demonstrating lung compensation. These results demonstrate the applicability of regional lung function derived from lung motion as an effective alternative respiratory diagnostic tool.

Abstract 2710: Model evolution technique as a novel concept for characterization of tumor heterogeneity in dynamic contrast enhanced MRI studies

Abstract Introduction: Many studies have shown that tumor vascular network and the assortment of tumorous cells inside and on the periphery of solid tumors are spatially heterogeneous. Variation in cell packing density (VCPD), hypoxia, acidosis, and elevated interstitial fluid pressure (IFP) are main characteristic features of solid tumors. Elevated IFP and VCPD in solid tumors can be generally relevant to the pathological structures at the cellular level that is fundamental to understanding the chance of response to treatment and recurrence. Therefore, non-invasive quantification of tumor heterogeneity for the same types of tumors can play an important role in diagnosis and treatment planning. Hypothesis: In this pilot study, using Nested Model (NM) selection technique, Model Evolution (ME) concept is framed and introduced to quantify the evolutions of 3 different physiologically NM that are derived from standard Tofts model, throughout the course of Dynamic Contrast Enhanced (DCE) Magnetic Resonance Imaging (MRI) experiment. Using ME technique for pharmacokinetic (PK) modeling and DCE-MRI data analysis, a heterogeneity measure is formulated and introduced based on the evolutionary profile of the estimated extra-cellular extra-vascular (ve) volume. We hypothesized that the ME profiles in the course of DCE-MRI experiment, highly depend on the inward diffusion and outward convection of contrast agent concentration and contain abundant information for describing the compartmentalization and heterogeneity levels of solid tumors. Material and Methods: 24 athymic Nude rats with U251n rat tumor model of cerebral tumor were studied. Look-Locker T1 mapping and DCE-MRI experiments (Dual Gradient Echo, 150 image sets at 4.0 sec intervals over 10 min: matrix = 128×64, five 2.0 mm slices, NE = 2, NA = 1, TE/TE/TR = 2.0/4.0/40ms with bolus intravenous injection of the Magnevist at 0.25 mmol/kg) acquired at 7T field strengths. In each animal, in-vivo measurement of tumor IFP was done right after the DCE-MRI experiment using a wick-in-needle technique. The ME technique was applied on DCE-MR data of 24 U251n rat tumors to characterize the heterogeneity of each tumor and then the results were compared to their known in vivo measure of IFPs. Results and Conclusions: Results of this pilot study clearly attest that the evolutionary profile of ve can be used to characterize the heterogeneity level of solid tumors. The ME results imply that as the slop of the evolutionary profile increases, the IFP of tumor increases. Also, the latency of the profile during the course of MR experiment can reliably explain the tumor compartmentalization and their elevated IFP. This pilot study confirms that the ME concept can make a paradigm shift in non-invasive quantification of tumor heterogeneity from DCE-MRI studies Citation Format: Hassan Bagher-Ebadian, Azimeh NV Dehkordi, Rasha Alamgharibi, David Nathanson, Hamid Soltanian-Zadeh, Arbab S. Ali, Stephen Brown, Meser M. Ali, Tom Mikkelsen, James R. Ewing. Model evolution technique as a novel concept for characterization of tumor heterogeneity in dynamic contrast enhanced MRI studies. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2710.

Abstract SY38-03: Quantifying tumor heterogeneity in the microenvironment

Abstract Cancer has long been understood as a somatic evolutionary process, but many details of tumor progression remain elusive. Here, I will talk about computational methods to infer evolutionary pathways leading to tumour heterogeneity. Using a study in ovarian cancer I will describe how summary statistics of phylogenetic quantification of genetic heterogeneity can predict patient survival. Then, I will present probabilistic frameworks to reconstruct intra-tumor evolution from data obtained by bulk bisulfite sequencing of mixed tumor samples or single cell genomes. Our approaches jointly estimates the number and composition of clones in the sample as well as the most likely tree connecting them. I will finish with an outlook how to put cancer genomes in a tissue context by using image analysis and deconvolution of gene expression data. Citation Format: Florian Markowetz. Quantifying tumor heterogeneity in the microenvironment. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr SY38-03.

Development of X-ray micro-focus computed tomography to image and quantify biofilms in central venous catheter models in vitro.

Bacterial infections of central venous catheters (CVCs) cause much morbidity and mortality, and are usually diagnosed by concordant culture of blood and catheter tip. However, studies suggest that culture often fails to detect biofilm bacteria. This study optimizes X-ray micro-focus computed tomography (X-ray µCT) for the quantification and determination of distribution and heterogeneity of biofilms in in vitro CVC model systems.Bacterial culture and scanning electron microscopy (SEM) were used to detect Staphylococcus epidermidis ATCC 35984 biofilms grown on catheters in vitro in both flow and static biofilm models. Alongside this, X-ray µCT techniques were developed in order to detect biofilms inside CVCs. Various contrast agent stains were evaluated using energy-dispersive X-ray spectroscopy (EDS) to further optimize these methods. Catheter material and biofilm were segmented using a semi-automated matlab script and quantified using the Avizo Fire software package. X-ray µCT was capable of distinguishing between the degree of biofilm formation across different segments of a CVC flow model. EDS screening of single- and dual-compound contrast stains identified 10 nm gold and silver nitrate as the optimum contrast agent for X-ray µCT. This optimized method was then demonstrated to be capable of quantifying biofilms in an in vitro static biofilm formation model, with a strong correlation between biofilm detection via SEM and culture. X-ray µCT has good potential as a direct, non-invasive, non-destructive technology to image biofilms in CVCs, as well as other in vivo medical components in which biofilms accumulate in concealed areas.

18F-FDG PET/CT heterogeneity quantification through textural features in the era of harmonisation programs: a focus on lung cancer.

Quantification of tumour heterogeneity in PET images has recently gained interest, but has been shown to be dependent on image reconstruction. This study aimed to evaluate the impact of the EANM/EARL accreditation program on selected 18F-FDG heterogeneity metrics. To carry out our study, we prospectively analysed 71 tumours in 60 biopsy-proven lung cancer patient acquisitions reconstructed with unfiltered point spread function (PSF) positron emission tomography (PET) images (optimised for diagnostic purposes), PSF-reconstructed images with a 7-mm Gaussian filter (PSF7) chosen to meet European Association of Nuclear Medicine (EANM) 1.0 harmonising standards, and EANM Research Ltd. (EARL)-compliant ordered subset expectation maximisation (OSEM) images. Delineation was performed with fuzzy locally adaptive Bayesian (FLAB) algorithm on PSF images and reported on PSF7 and OSEM ones, and with a 50% standardised uptake values (SUV)max threshold (SUVmax50%) applied independently to each image. Robust and repeatable heterogeneity metrics including 1st-order [area under the curve of the cumulative histogram (CHAUC)], 2nd-order (entropy, correlation, and dissimilarity), and 3rd-order [high-intensity larger area emphasis (HILAE) and zone percentage (ZP)] textural features (TF) were statistically compared. Volumes obtained with SUVmax50% were significantly smaller than FLAB-derived ones, and were significantly smaller in PSF images compared to OSEM and PSF7 images. PSF-reconstructed images showed significantly higher SUVmax and SUVmean values, as well as heterogeneity for CHAUC, dissimilarity, correlation, and HILAE, and a wider range of heterogeneity values than OSEM images for most of the metrics considered, especially when analysing larger tumours. Histological subtypes had no impact on TF distribution. No significant difference was observed between any of the considered metrics (SUV or heterogeneity features) that we extracted from OSEM and PSF7 reconstructions. Furthermore, the distributions of TF for OSEM and PSF7 reconstructions according to tumour volumes were similar for all ranges of volumes. PSF reconstruction with Gaussian filtering chosen to meet harmonising standards resulted in similar SUV values and heterogeneity information as compared to OSEM images, which validates its use within the harmonisation strategy context. However, unfiltered PSF-reconstructed images also showed higher heterogeneity according to some metrics, as well as a wider range of heterogeneity values than OSEM images for most of the metrics considered, especially when analysing larger tumours. This suggests that, whenever available, unfiltered PSF images should also be exploited to obtain the most discriminative quantitative heterogeneity features.

The Impact of Optimal Respiratory Gating and Image Noise on Evaluation of Intratumor Heterogeneity on 18F-FDG PET Imaging of Lung Cancer.

We used an optimal-respiratory-gating algorithm on the list-mode data of 60 lung cancer patients who underwent 18F-FDG PET. The images were reconstructed using a duty cycle of 35% (percentage of the total acquired PET data). In addition, nongated images of varying statistical quality (using 35% and 100% of the PET data) were reconstructed to investigate the effects of image noise. Several global image-derived indices and textural parameters (entropy, high-intensity emphasis, zone percentage, and dissimilarity) that have been associated with patient outcome were calculated. The clinical impact of optimal respiratory gating and image noise on assessment of intratumor heterogeneity was evaluated using Cox regression models, with overall survival as the outcome measure. The threshold for statistical significance was adjusted for multiple comparisons using Bonferroni correction. In the lower lung lobes, respiratory motion significantly affected quantification of intratumor heterogeneity for all textural parameters (P < 0.007) except entropy (P > 0.007). The mean increase in entropy, dissimilarity, zone percentage, and high-intensity emphasis was 1.3% ± 1.5% (P = 0.02), 11.6% ± 11.8% (P = 0.006), 2.3% ± 2.2% (P = 0.002), and 16.8% ± 17.2% (P = 0.006), respectively. No significant differences were observed for lesions in the upper lung lobes (P > 0.007). Differences in the statistical quality of the PET images affected the textural parameters less than respiratory motion, with no significant difference observed. The median follow-up time was 35 mo (range, 7-39 mo). In multivariate analysis for overall survival, total lesion glycolysis and high-intensity emphasis were the two most relevant image-derived indices and were considered to be independent significant covariates for the model regardless of the image type considered. The tested textural parameters are robust in the presence of respiratory motion artifacts and varying levels of image noise.

Characterization of PET/CT images using texture analysis: the past, the present… any future?

After seminal papers over the period 2009-2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.

Single-cell sequencing reveals karyotype heterogeneity in murine and human malignancies.

BackgroundChromosome instability leads to aneuploidy, a state in which cells have abnormal numbers of chromosomes, and is found in two out of three cancers. In a chromosomal instable p53 deficient mouse model with accelerated lymphomagenesis, we previously observed whole chromosome copy number changes affecting all lymphoma cells. This suggests that chromosome instability is somehow suppressed in the aneuploid lymphomas or that selection for frequently lost/gained chromosomes out-competes the CIN-imposed mis-segregation.ResultsTo distinguish between these explanations and to examine karyotype dynamics in chromosome instable lymphoma, we use a newly developed single-cell whole genome sequencing (scWGS) platform that provides a complete and unbiased overview of copy number variations (CNV) in individual cells. To analyse these scWGS data, we develop AneuFinder, which allows annotation of copy number changes in a fully automated fashion and quantification of CNV heterogeneity between cells. Single-cell sequencing and AneuFinder analysis reveals high levels of copy number heterogeneity in chromosome instability-driven murine T-cell lymphoma samples, indicating ongoing chromosome instability. Application of this technology to human B cell leukaemias reveals different levels of karyotype heterogeneity in these cancers.ConclusionOur data show that even though aneuploid tumours select for particular and recurring chromosome combinations, single-cell analysis using AneuFinder reveals copy number heterogeneity. This suggests ongoing chromosome instability that other platforms fail to detect. As chromosome instability might drive tumour evolution, karyotype analysis using single-cell sequencing technology could become an essential tool for cancer treatment stratification.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0971-7) contains supplementary material, which is available to authorized users.

Identification of low variability textural features for heterogeneity quantification of 18F-FDG PET/CT imaging.

To identify those textural features that are insensitive to both technical and biological factors in order to standardise heterogeneity studies on 18F-FDG PET imaging. Two different studies were performed. First, nineteen series from a cylindrical phantom filled with different 18F-FDG activity concentration were acquired and reconstructed using three different protocols. Seventy-two texture features were calculated inside a circular region of interest. The variability of each feature was obtained. Second, the data for 15 patients showing non-pathological liver were acquired. Anatomical and physiological features such as patient's weight, height, body mass index, metabolic active volume, blood glucose level, SUV and SUV standard deviation were also recorded. A liver covering region of interest was delineated and low variability textural features calculated in each patient. Finally, a multivariate Spearman's correlation analysis between biological factors and texture features was performed. Only eight texture features analysed show small variability (<5%) with activity concentration and reconstruction protocol making them suitable for heterogeneity quantification. On the other hand, there is a high statistically significant correlation between MAV and entropy (P<0.05). Entropy feature is, indeed, correlated (P<0.05) with all patient parameters, except body mass index. The textural features that are correlated with neither technical nor biological factors are run percentage, short-zone emphasis and intensity, making them suitable for quantifying functional changes or classifying patients. Other textural features are correlated with technical and biological factors and are, therefore, a source of errors if used for this purpose.

Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis.

Purpose To identify quantitative imaging biomarkers at fluorine 18 ((18)F) positron emission tomography (PET) for predicting distant metastasis in patients with early-stage non-small cell lung cancer (NSCLC). Materials and Methods In this institutional review board-approved HIPAA-compliant retrospective study, the pretreatment (18)F fluorodeoxyglucose PET images in 101 patients treated with stereotactic ablative radiation therapy from 2005 to 2013 were analyzed. Data for 70 patients who were treated before 2011 were used for discovery purposes, while data from the remaining 31 patients were used for independent validation. Quantitative PET imaging characteristics including statistical, histogram-related, morphologic, and texture features were analyzed, from which 35 nonredundant and robust features were further evaluated. Cox proportional hazards regression model coupled with the least absolute shrinkage and selection operator was used to predict distant metastasis. Whether histologic type provided complementary value to imaging by combining both in a single prognostic model was also assessed. Results The optimal prognostic model included two image features that allowed quantification of intratumor heterogeneity and peak standardized uptake value. In the independent validation cohort, this model showed a concordance index of 0.71, which was higher than those of the maximum standardized uptake value and tumor volume, with concordance indexes of 0.67 and 0.64, respectively. The prognostic model also allowed separation of groups with low and high risk for developing distant metastasis (hazard ratio, 4.8; P = .0498, log-rank test), which compared favorably with maximum standardized uptake value and tumor volume (hazard ratio, 1.5 and 2.0, respectively; P = .73 and 0.54, log-rank test, respectively). When combined with histologic types, the prognostic power was further improved (hazard ratio, 6.9; P = .0289, log-rank test; and concordance index, 0.80). Conclusion PET imaging characteristics associated with distant metastasis that could potentially help practitioners to tailor appropriate therapy for individual patients with early-stage NSCLC were identified. (©) RSNA, 2016 Online supplemental material is available for this article.

Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity.

Recent large-scale genome analyses of human tissue samples have uncovered a high degree of genetic alterations and tumour heterogeneity in most tumour entities, independent of morphological phenotypes and histopathological characteristics. Assessment of genetic copy-number variation (CNV) and tumour heterogeneity by fluorescence in situ hybridization (ISH) provides additional tissue morphology at single-cell resolution, but it is labour intensive with limited throughput and high inter-observer variability. We present an integrative method combining bright-field dual-colour chromogenic and silver ISH assays with an image-based computational workflow (ISHProfiler), for accurate detection of molecular signals, high-throughput evaluation of CNV, expressive visualization of multi-level heterogeneity (cellular, inter- and intra-tumour heterogeneity), and objective quantification of heterogeneous genetic deletions (PTEN) and amplifications (19q12, HER2) in diverse human tumours (prostate, endometrial, ovarian and gastric), using various tissue sizes and different scanners, with unprecedented throughput and reproducibility.

SP-0607: PET/CT heterogeneity quantification through texture analysis: potential role for prognostic and predictive models

SP-0607: PET/CT heterogeneity quantification through texture analysis: potential role for prognostic and predictive models

Abstract IA20: Quantifying patterns of tumor evolution

Abstract Cancer has long been understood as a somatic evolutionary process, but many details of tumor progression remain elusive. Here, I will talk about computational methods to infer evolutionary pathways leading to tumor heterogeneity. I will start by discussing how to quantify intra-patient heterogeneity by rigorous analysis of copy-number profiles from multiple patient samples. Using a study in ovarian cancer I will describe how summary statistics of phylogenetic quantification of genetic heterogeneity can predict patient survival. Then, I will present a probabilistic framework to reconstruct intra-tumor evolution from data obtained by bulk bisulfite sequencing of mixed tumor samples or single cell genomes. Our approach jointly estimates the number and composition of clones in the sample as well as the most likely tree connecting them. I will finish by describing ongoing work to reconstruct patterns of clonal evolution from single cell sequencing data. Citation Format: Florian Markowetz. Quantifying patterns of tumor evolution. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Metastasis; 2015 Nov 30-Dec 3; Austin, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(7 Suppl):Abstract nr IA20.

Quantification of HER2 heterogeneity in breast cancer–implications for identification of sub-dominant clones for personalised treatment

Breast cancer is a heterogeneous disease, at both an inter- and intra-tumoural level. Appreciating heterogeneity through the application of biomarkers and molecular signatures adds complexity to tumour taxonomy but is key to personalising diagnosis, treatment and prognosis. The extent to which heterogeneity exists, and its interpretation remains a challenge to pathologists. Using HER2 as an exemplar, we have developed a simple reproducible heterogeneity index. Cell-to-cell HER2 heterogeneity was extensive in a proportion of both reported ‘amplified’ and ‘non-amplified’ cases. The highest levels of heterogeneity objectively identified occurred in borderline categories and higher ratio non-amplified cases. A case with particularly striking heterogeneity was analysed further with an array of biomarkers in order to assign a molecular diagnosis. Broad biological complexity was evident. In essence, interpretation, depending on the area of tumour sampled, could have been one of three distinct phenotypes, each of which would infer different therapeutic interventions. Therefore, we recommend that heterogeneity is assessed and taken into account when determining treatment options.