Reproducibility of 2D and 3D fractal analysis techniques for the assessment of spatial heterogeneity of regional blood flow in rectal cancer

Perfusion Computed Tomography in Rectal Carcinoma: Influence of Optimization of the Patlak Range on Calculation of Equivalent Blood Volume and Flow Extraction

PURPOSE

The aim of the study is to assess the influence of manual adjustment of the Patlak range in computed tomography (CT) perfusion analysis of rectal carcinoma compared with default range of the perfusion software.

METHODS

This study was approved by the institutional review board and informed consent was obtained. Twenty-one patients (12 male, 9 female; mean age ± SD, 59 ± 11 years) with rectal cancer were included and underwent perfusion CT before preoperative chemoradiotherapy. Equivalent blood volume (BV) and flow-extraction (FE) were calculated using the Patlak plot model. Two perfusion sets were calculated per patient, a perfusion set using the default setting as provided by the software (dBV, dFE) and an optimized perfusion set after manual adaption of the Patlak range (aBV, aFE), which was limited to the intravascular space clearance of contrast to the extravascular space. Perfusion values calculated with both methods were compared for significance in differences using the Wilcoxon test. A P value of 0.05 or less was defined as statistically significant.

RESULTS

Adjustment of the Patlak range statistically significantly influenced BV and FE calculation. Median dBV was 23.2 mL/100 mL (interquartile range [IQR], 12.1 mL/100 mL), whereas median aBV was 20.3 mL/100 mL (IQR, 10.9 mL/100 mL). The difference in BV was statistically significant ( P = 0.021). Median dFE was 8.3 mL/min/100 mL (IQR, 4.7 mL/min/100 mL), whereas median aFE was 15.4 mL/min/100 mL (IQR, 5.8 mL/min/100 mL). The difference in FE was statistically significant ( P < 0.001).

CONCLUSIONS

Our findings indicate that in perfusion CT of rectal carcinoma, adjustment of the Patlak range may significantly influence BV and FE compared with default setting of the software. This may contribute to standardization in the use of this technique for functional imaging of rectal cancer.

Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis

Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics, and spatial transcriptomics allow recording of patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multiscale dynamics of its evolution. Here, we review the latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed burgeoning growth in the recent past in terms of mapping heterogeneity within tumor cell types as well as the stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor and a more systematic investigation of the implications of heterogeneity for patient outcomes.

Quantification of Structural Heterogeneity Using Fractal Analysis of Contrast-Enhanced CT Image to Predict Survival in Gastric Cancer Patients

BACKGROUND

Malignant tumor essentially implies structural heterogeneity. Fractal analysis of medical imaging has a potential to quantify this structural heterogeneity in the tumor AIMS: The purpose of this study is to quantify this structural abnormality in the tumor applying fractal analysis to contrast-enhanced computed tomography (CE-CT) image and to evaluate its biomarker value for predicting survival of surgically treated gastric cancer patients.

METHODS

A total of 108 gastric cancer patients (77 men and 31 women; mean age: 69.1 years), who received curative surgery without any neoadjuvant therapy, were retrospectively investigated. Portal-phase CE-CT images were analyzed with use of a plug-in tool for ImageJ (NIH, Bethesda, USA), and the fractal dimension (FD) in the tumor was calculated using a differential box-counting method to quantify structural heterogeneity in the tumor. Tumor FD was compared with clinicopathologic features and disease-specific survival (DSS).

RESULTS

High FD value of the tumor significantly associated with high T stage and high pathological stage (P = 0.009, 0.007, respectively). In Kaplan-Meier analysis, patients with higher FD tumors (FD > 0.9746) showed a significantly worse DSS (P = 0.009, log rank). Multivariate analysis demonstrated that tumor FD, T stage, and N stage were independent prognostic factors for DSS. In subset analysis of lymph-node positive gastric cancers, only tumor FD was an independent prognostic factor for DSS.

CONCLUSION

CT fractal analysis can be a useful biomarker for gastric cancer patients, reflecting survival and clinicopathologic features.

A Role for FDG PET Radiomics in Personalized Medicine?

Radiomics describes the extraction of multiple features from medical images, including molecular imaging modalities, that with bioinformatic approaches, provide additional clinically relevant information that may be invisible to the human eye. This information may complement standard radiological interpretation with data that may better characterize a disease or that may provide predictive or prognostic information. Progressing from predefined image features, often describing heterogeneity of voxel intensities within a volume of interest, there is increasing use of machine learning to classify disease characteristics and deep learning methods based on artificial neural networks that can learn features without a priori definition and without the need for preprocessing of images. There have been advances in standardization and harmonization of methods to a level that should support multicenter studies. However, in this relatively early phase of research in the field, there are limited aspects that have been adopted into routine practice. Most of the reports in the molecular imaging field describe radiomic approaches in cancer using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET). In this review, we will describe radiomics in molecular imaging and summarize the pertinent literature in lung cancer where reports are most prevalent and mature.

Texture Analysis of Ultrasound Images to Differentiate Simple Fibroadenomas From Complex Fibroadenomas and Benign Phyllodes Tumors

OBJECTIVES

American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions can be distinguished from BI-RADS 3 lesions with main ultrasound (US) findings such as a well-defined contour, round/oval shape, and parallel orientation with a homogeneous echo pattern. Breast Imaging Reporting and Data System 4A solid masses might be diagnosed as simple fibroadenomas (SFAs), complex fibroadenomas (CFAs), or benign phyllodes tumors (BPTs). Complex fibroadenomas have an increased risk of invasive cancer development than SFAs, and BPTs have a risk of borderline-malignant phyllodes tumor transformation; both of them are surgically treated, whereas follow-up procedures are applied in SFAs. It is essential to differentiate SFAs from CFAs and BPTs. Grayscale features of these lesions include a prominent overlap. Texture analyses in breast lesions have contributions in benign-malignant lesion differentiation. In this study, we aimed to use texture analysis of US images to differentiate these benign lesions.

METHODS

Grayscale US features of lesions (32 SFAs, 31 CFAs, and 32 BPTs) were classified according to the BI-RADS. Texture analysis of US images with LIFEx software (http://www.lifexsoft.org) was performed retrospectively. First- and second-order histogram parameters were evaluated.

RESULTS

In grayscale US, the shape, orientation, and posterior acoustic characteristics had statistical significance (P < .05). In the statistical analysis, skewness, kurtosis, excess kurtosis, gray-level co-occurrence matrix (GLCM)-energy, GLCM-entropy log 2, and GLCM-entropy log 10 revealed significant differences among all 3 groups (P < .05).

CONCLUSIONS

As grayscale US features show prominent intersections, and treatment options differ, correct diagnosis is essential in SFAs, CFAs, and BPTs. In this study, we concluded that texture analysis of US images can discriminate SFAs from CFAs and BPTs. Texture analyses of US images is a potential candidate diagnostic tool for these lesions, and accurate diagnoses will preclude patients from undergoing unnecessary biopsies.

The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges

Recently, radiomic texture quantification of tumors has received much attention from radiologists, scientists, and stakeholders because several results have shown the feasibility of using the technique to diagnose and manage oncological conditions. In patients with hepatocellular carcinoma, radiomics has been applied in all stages of tumor evaluation, including diagnosis and characterization of the genotypic behavior of the tumor, monitoring of treatment responses and prediction of various clinical endpoints. It is also useful in selecting suitable candidates for specific treatment strategies. However, the clinical validation of hepatocellular carcinoma radiomics is limited by challenges in imaging protocol and data acquisition parameters, challenges in segmentation techniques, dimensionality reduction, and modeling methods. Identification of the best segmentation and optimal modeling methods, as well as texture features most stable to imaging protocol variability would go a long way in harmonizing HCC radiomics for personalized patient care. This article reviews the process of HCC radiomics, its clinical applications, associated challenges, and current optimization strategies.

Distinguishing Lymphomatous and Cancerous Lymph Nodes in 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography by Radiomics Analysis

Background. The National Comprehensive Cancer Network guidelines recommend excisional biopsies for the diagnosis of lymphomas. However, resection biopsies in all patients who are suspected of having malignant lymph nodes may cause unnecessary injury and increase medical costs. We investigated the usefulness of 18F-fluorodeoxyglucose positron emission/computed tomography- (18F-FDG-PET/CT-) based radiomics analysis for differentiating between lymphomatous lymph nodes (LLNs) and cancerous lymph nodes (CLNs). Methods. Using texture analysis, radiomic parameters from the 18F-FDG-PET/CT images of 492 lymph nodes (373 lymphomatous lymph nodes and 119 cancerous lymph nodes) were extracted with the LIFEx package. Predictive models were generated from the six parameters with the largest area under the receiver operating characteristics curve (AUC) in PET or CT images in the training set (70% of the data), using binary logistic regression. These models were applied to the test set to calculate predictive variables, including the combination of PET and CT predictive variables (PREcombination). The AUC, sensitivity, specificity, and accuracy were used to compare the differentiating ability of the predictive variables. Results. Compared with the pathological diagnosis of the patient’s primary tumor, the AUC, sensitivity, specificity, and accuracy of PREcombination in differentiating between LLNs and CLNs were 0.95, 91.67%, 94.29%, and 92.96%, respectively. Moreover, PREcombination could effectively distinguish LLNs caused by various lymphoma subtypes (Hodgkin’s lymphoma and non-Hodgkin’s lymphoma) from CLNs, with the AUC, sensitivity, specificity, and accuracy being 0.85 and 0.90, 77.78% and 77.14%, 97.22% and 88.89%, and 90.74% and 83.10%, respectively. Conclusions. Radiomics analysis of 18F-FDG-PET/CT images may provide a noninvasive, effective method to distinguish LLN and CLN and inform the choice between fine-needle aspiration and excision biopsy for sampling suspected lymphomatous lymph nodes.

Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade

OBJECTIVE

To assess whether 3-dimensional (3D) fractal dimension (FD) and lacunarity features from MRI can predict the meningioma grade.

METHODS

This retrospective study included 131 patients with meningiomas (98 low-grade, 33 high-grade) who underwent preoperative MRI with post-contrast T1-weighted imaging. The 3D FD and lacunarity parameters from the enhancing portion of the tumor were extracted by box-counting algorithms. Inter-rater reliability was assessed with the intraclass correlation coefficient (ICC). Additionally, conventional imaging features such as location, heterogeneous enhancement, capsular enhancement, and necrosis were assessed. Independent clinical and imaging risk factors for meningioma grade were investigated using multivariable logistic regression. The discriminative value of the prediction model with and without fractal features was evaluated. The relationship of fractal parameters with the mitosis count and Ki-67 labeling index was also assessed.

RESULTS

The inter-reader reliability was excellent, with ICCs of 0.99 for FD and 0.97 for lacunarity. High-grade meningiomas had higher FD (p < 0.001) and higher lacunarity (p = 0.007) than low-grade meningiomas. In the multivariable logistic regression, the diagnostic performance of the model with clinical and conventional imaging features increased with 3D fractal features for predicting the meningioma grade, with AUCs of 0.78 and 0.84, respectively. The 3D FD showed significant correlations with both mitosis count and Ki-67 labeling index, and lacunarity showed a significant correlation with the Ki-67 labeling index (all p values < 0.05).

CONCLUSION

The 3D FD and lacunarity are higher in high-grade meningiomas and fractal analysis may be a useful imaging biomarker for predicting the meningioma grade.

KEY POINTS

• Fractal dimension (FD) and lacunarity are the two parameters used in fractal analysis to describe the complexity of a subject and may aid in predicting meningioma grade. • High-grade meningiomas had a higher fractal dimension and higher lacunarity than low-grade meningiomas, suggesting higher complexity and higher rotational variance. • The discriminative value of the predictive model using clinical and conventional imaging features improved when combined with 3D fractal features for predicting the meningioma grade.

Relationship between anastomotic leakage and CT value of the mesorectum in laparoscopic anterior resection for rectal cancer

OBJECTIVE

This study aims to indicate whether the CT value of the mesorectum could be correlated with the incidence of anastomotic leakage (AL) in laparoscopic surgery for rectal cancer.

METHODS

The study subjects included 173 patients who underwent laparoscopic anterior resection (LAR) for rectal cancer from September 2005 to 2016 in our institution as well as reliable contrast-enhanced CT preoperatively. Univariate and multivariate analyses were performed to determine the correlation between surgical outcomes, including AL and CT value of the mesorectum.

RESULTS

AL was observed in 30 (17.3%) patients. Amongst short-term surgical outcomes, overall complication showed significant correlation with the CT value of the mesorectum (P = 0.003). In addition, AL was the only factor, which significantly correlated with the CT value of the mesorectum (P = 0.017). By plotting receiver operating characteristic curve, -75 HU was the threshold of the CT value of the mesorectum for predicting AL with an area under the curve of 0.772. Categorized into two groups as per the threshold, low group showed significantly higher incidence of AL (OR, 2.738; 95% CI, 1.105-6.788; P = 0.030) as well as whole complications (OR, 4.431; 95%CI, 1.912-10.266; P = 0.001).

CONCLUSION

The CT value of the mesorectum may be a helpful preoperative radiological biomarker to predict AL after LAR for rectal cancer.

Comparison of Diagnostic Performance of Two-Dimensional and Three-Dimensional Fractal Dimension and Lacunarity Analyses for Predicting the Meningioma Grade

BACKGROUND

To compare the diagnostic performance of two-dimensional (2D) and three-dimensional (3D) fractal dimension (FD) and lacunarity features from MRI for predicting the meningioma grade.

METHODS

This retrospective study included 123 meningioma patients [90 World Health Organization (WHO) grade I, 33 WHO grade II/III] with preoperative MRI including post-contrast T1-weighted imaging. The 2D and 3D FD and lacunarity parameters from the contrast-enhancing portion of the tumor were calculated. Reproducibility was assessed with the intraclass correlation coefficient. Multivariable logistic regression analysis using 2D or 3D fractal features was performed to predict the meningioma grade. The diagnostic ability of the 2D and 3D fractal models were compared.

RESULTS

The reproducibility between observers was excellent, with intraclass correlation coefficients of 0.97, 0.95, 0.98, and 0.96 for 2D FD, 2D lacunarity, 3D FD, and 3D lacunarity, respectively. WHO grade II/III meningiomas had a higher 2D and 3D FD (p=0.003 and p<0.001, respectively) and higher 2D and 3D lacunarity (p=0.002 and p=0.006, respectively) than WHO grade I meningiomas. The 2D fractal model showed an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.690 [95% confidence interval (CI) 0.581-0.799], 72.4%, 75.8%, and 64.4%, respectively. The 3D fractal model showed an AUC, accuracy, sensitivity, and specificity of 0.813 (95% CI 0.733-0.878), 82.9%, 81.8%, and 70.0%, respectively. The 3D fractal model exhibited significantly better diagnostic performance than the 2D fractal model (p<0.001).

CONCLUSION

The 3D fractal analysis proved superiority in diagnostic performance to 2D fractal analysis in grading meningioma.

Biofabrication strategies for creating microvascular complexity

Design and fabrication of effective biomimetic vasculatures constitutes a relevant and yet unsolved challenge, lying at the heart of tissue repair and regeneration strategies. Even if cell growth is achieved in 3D tissue scaffolds or advanced implants, tissue viability inevitably requires vascularization, as diffusion can only transport nutrients and eliminate debris within a few hundred microns. This engineered vasculature may need to mimic the intricate branching geometry of native microvasculature, referred to herein as vascular complexity, to efficiently deliver blood and recreate critical interactions between the vascular and perivascular cells as well as parenchymal tissues. This review first describes the importance of vascular complexity in labs- and organs-on-chips, the biomechanical and biochemical signals needed to create and maintain a complex vasculature, and the limitations of current 2D, 2.5D, and 3D culture systems in recreating vascular complexity. We then critically review available strategies for design and biofabrication of complex vasculatures in cell culture platforms, labs- and organs-on-chips, and tissue engineering scaffolds, highlighting their advantages and disadvantages. Finally, challenges and future directions are outlined with the hope of inspiring researchers to create the reliable, efficient and sustainable tools needed for design and biofabrication of complex vasculatures.

Variability and Reproducibility of 3rd-generation dual-source dynamic volume perfusion CT Parameters in Comparison to MR-perfusion Parameters in Rectal Cancer

To compare in patients with untreated rectal cancer quantitative perfusion parameters calculated from 3^rd-generation dual-source dynamic volume perfusion CT (dVPCT) with 3-Tesla-MR-perfusion with regard to data variability and tumour differentiation. In MR-perfusion, plasma flow (PF), plasma volume (PV) and mean transit time (MTT) were assessed in two measurements (M1 and M2) by the same reader. In dVPCT, blood flow (BF), blood volume (BV), MTT and permeability (PERM) were assessed respectively. CT dose values were calculated. 20 patients (60 ± 13 years) were analysed. Intra-individual and intra-reader variability of duplicate MR-perfusion measurements was higher compared to duplicate dVPCT measurements. dVPCT-derived BF, BV and PERM could differentiate between tumour and normal rectal wall (significance level for M1 and M2, respectively, regarding BF: p < 0.0001*/0.0001*; BV: p < 0.0001*/0.0001*; MTT: p = 0.93/0.39; PERM: p < 0.0001*/0.0001*), with MR-perfusion this was true for PF and PV (p-values M1/M2 for PF: p = 0.04*/0.01*; PV: p = 0.002*/0.003*; MTT: p = 0.70/0.27*). Mean effective dose of CT-staging incl. dVPCT was 29 ± 6 mSv (20 ± 5 mSv for dVPCT alone). In conclusion, dVPCT has a lower data variability than MR-perfusion while both dVPCT and MR-perfusion could differentiate tumour tissue from normal rectal wall. With 3^rd-generation dual-source CT dVPCT could be included in a standard CT-staging without exceeding national dose reference values.

Primary Rectal Cancer: Repeatability of Global and Local-Regional MR Imaging Texture Features

Purpose To assess the day-to-day repeatability of global and local-regional magnetic resonance (MR) imaging texture features derived from primary rectal cancer. Materials and Methods After ethical approval and patient informed consent were obtained, two pretreatment T2-weighted axial MR imaging studies performed prospectively with the same imaging unit on 2 consecutive days in 14 patients with rectal cancer (11 men [mean age, 61.7 years], three women [mean age, 70.0 years]) were analyzed to extract (a) global first-order statistical histogram and model-based fractal features reflecting the whole-tumor voxel intensity histogram distribution and repeating patterns, respectively, without spatial information and (b) local-regional second-order and high-order statistical texture features reflecting the intensity and spatial interrelationships between adjacent in-plane or multiplanar voxels or regions, respectively. Repeatability was assessed for 46 texture features, and mean difference, 95% limits of agreement, within-subject coefficient of variation (wCV), and repeatability coefficient (r) were recorded. Results Repeatability was better for global parameters than for most local-regional parameters. In particular, histogram mean, median, and entropy, fractal dimension mean and standard deviation, and second-order entropy, homogeneity, difference entropy, and inverse difference moment demonstrated good repeatability, with narrow limits of agreement and wCVs of 10% or lower. Repeatability was poorest for the following high-order gray-level run-length (GLRL) gray-level zone size matrix (GLZSM) and neighborhood gray-tone difference matrix (NGTDM) parameters: GLRL intensity variability, GLZSM short-zone emphasis, GLZSM intensity nonuniformity, GLZSM intensity variability, GLZSM size zone variability, and NGTDM complexity, demonstrating wider agreement limits and wCVs of 50% or greater. Conclusion MR imaging repeatability is better for global texture parameters than for local-regional texture parameters, indicating that global texture parameters should be sufficiently robust for clinical practice. Online supplemental material is available for this article.

Perfusion CT measurements predict tumor response in rectal carcinoma

PURPOSE

To evaluate the capacity of perfusion CT imaging to distinguish between complete and incomplete responders after neoadjuvant chemoradiation therapy for rectal carcinoma, with particular attention to segmentation technique.

MATERIALS AND METHODS

17 patients were evaluated in this prospective IRB-approved study. For each patient, a perfusion CT acquisition was obtained prior to the initiation of chemoradiation, at 1-2 weeks after the start of chemoradiation, and at 12 weeks after the start of chemoradiation therapy. From each dataset, three perfusion parameters were measured, each in two different ways: a region of interest incorporating only "hot spots" of greatest enhancement and whole-tumor measurements.

RESULTS

In univariate analysis, blood volume and permeability differed significantly between responders and non-responders. In logistic regression analysis evaluating predictors of the "complete response" outcome, only two predictors were retained as statistically significant: peak hot spot blood volume 1-2 weeks into therapy (OR 10.25, p = 0.0026) and hot spot permeability decline at 12 weeks after the initiation of therapy (OR 5.62, p = 0.03). The overall likelihood ratio test for this model supported the conclusion that hot spot blood volume and hot spot permeability decline were significant predictors of the complete pathologic response outcome (p < 0.0001).

CONCLUSION

In this pilot study, peak tumor blood volume and decline in tumor permeability, when measured in "hot spots" of greatest enhancement, were strong predictors of complete therapeutic response in rectal cancer after neoadjuvant therapy.

PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology

Imaging with positron emission tomography (PET)/computed tomography (CT) is crucial in the management of cancer because of its value in tumor staging, response assessment, restaging, prognosis and treatment responsiveness prediction. In the last years, interest has grown in texture analysis which provides an "in-vivo" lesion characterization, and predictive information in several malignances including NSCLC; however several drawbacks and limitations affect these studies, especially because of lack of standardization in features calculation, definitions and methodology reporting. The present paper provides a comprehensive review of literature describing the state-of-the-art of FDG-PET/CT texture analysis in NSCLC, suggesting a proposal for harmonization of methodology.

Assessing Tumor Response to Treatment in Patients with Lung Cancer Using Dynamic Contrast-Enhanced CT

The aim of this study was to provide an overview of the literature available on dynamic contrast-enhanced computed tomography (DCE-CT) as a tool to evaluate treatment response in patients with lung cancer. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning treatment response in patients with lung cancer assessed with DCE-CT were included. To assess the validity of each study we implemented Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). The initial search yielded 651 publications, and 16 articles were included in this study. The articles were divided into groups of treatment. In studies where patients were treated with systemic chemotherapy with or without anti-angiogenic drugs, four out of the seven studies found a significant decrease in permeability after treatment. Four out of five studies that measured blood flow post anti-angiogenic treatments found that blood flow was significantly decreased. DCE-CT may be a useful tool in assessing treatment response in patients with lung cancer. It seems that particularly permeability and blood flow are important perfusion values for predicting treatment outcome. However, the heterogeneity in scan protocols, scan parameters, and time between scans makes it difficult to compare the included studies.

Three-dimensional ultrasound molecular imaging of angiogenesis in colon cancer using a clinical matrix array ultrasound transducer

OBJECTIVES

We sought to assess the feasibility and reproducibility of 3-dimensional ultrasound molecular imaging (USMI) of vascular endothelial growth factor receptor 2 (VEGFR2) expression in tumor angiogenesis using a clinical matrix array transducer and a clinical grade VEGFR2-targeted contrast agent in a murine model of human colon cancer.

MATERIALS AND METHODS

Animal studies were approved by the Institutional Administrative Panel on Laboratory Animal Care. Mice with human colon cancer xenografts (n = 33) were imaged with a clinical ultrasound system and transducer (Philips iU22; X6-1) after intravenous injection of either clinical grade VEGFR2-targeted microbubbles or nontargeted control microbubbles. Nineteen mice were scanned twice to assess imaging reproducibility. Fourteen mice were scanned both before and 24 hours after treatment with either bevacizumab (n = 7) or saline only (n = 7). Three-dimensional USMI data sets were retrospectively reconstructed into multiple consecutive 1-mm-thick USMI data sets to simulate 2-dimensional imaging. Vascular VEGFR2 expression was assessed ex vivo using immunofluorescence.

RESULTS

Three-dimensional USMI was highly reproducible using both VEGFR2-targeted microbubbles and nontargeted control microbubbles (intraclass correlation coefficient, 0.83). The VEGFR2-targeted USMI signal significantly (P = 0.02) decreased by 57% after antiangiogenic treatment compared with the control group, which correlated well with ex vivo VEGFR2 expression on immunofluorescence (ρ = 0.93, P = 0.003). If only central 1-mm tumor planes were analyzed to assess antiangiogenic treatment response, the USMI signal change was significantly (P = 0.006) overestimated by an average of 27% (range, 2%-73%) compared with 3-dimensional USMI.

CONCLUSIONS

Three-dimensional USMI is feasible and highly reproducible and allows accurate assessment and monitoring of VEGFR2 expression in tumor angiogenesis in a murine model of human colon cancer.

Imaging Tumor Response and Tumoral Heterogeneity in Non-Small Cell Lung Cancer Treated With Antiangiogenic Therapy: Comparison of the Prognostic Ability of RECIST 1.1, an Alternate Method (Crabb), and Image Heterogeneity Analysis

PURPOSE

We aimed to assess computed tomography (CT) intratumoral heterogeneity changes, and compared the prognostic ability of the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, an alternate response method (Crabb), and CT heterogeneity in non-small cell lung cancer treated with chemotherapy with and without bevacizumab.

MATERIALS AND METHODS

Forty patients treated with chemotherapy (group C) or chemotherapy and bevacizumab (group BC) underwent contrast-enhanced CT at baseline and after 1, 3, and 6 cycles of chemotherapy. Radiologic response was assessed using RECIST 1.1 and an alternate method. CT heterogeneity analysis generating global and locoregional parameters depicting tumor image spatial intensity characteristics was performed. Heterogeneity parameters between the 2 groups were compared using the Mann-Whitney U test. Associations between heterogeneity parameters and radiologic response with overall survival were assessed using Cox regression.

RESULTS

Global and locoregional heterogeneity parameters changed with treatment, with increased tumor heterogeneity in group BC. Entropy [group C: median -0.2% (interquartile range -2.2, 1.7) vs. group BC: 0.7% (-0.7, 3.5), P=0.10] and busyness [-27.7% (-62.2, -5.0) vs. -11.5% (-29.1, 92.4), P=0.10] showed a greater reduction in group C, whereas uniformity [1.9% (-8.0, 9.8) vs. -5.0% (-13.9, 5.6), P=0.10] showed a relative increase after 1 cycle but did not reach statistical significance. Two (9%) and 1 (6%) additional responders were identified using the alternate method compared with RECIST in group C and group BC, respectively. Heterogeneity parameters were not significant prognostic factors.

CONCLUSIONS

The alternate response method described by Crabb identified more responders compared with RECIST. However, both criteria and baseline imaging heterogeneity parameters were not prognostic of survival.

Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome

Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as CT density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death, and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks using PET, MRI, and other emerging molecular imaging techniques. These methods can establish whether one tumor is more or less heterogeneous than another and can identify subregions with differing biology. In this article, we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, instead of being developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care.

Fractal analysis of contrast-enhanced CT images to predict survival of patients with hepatocellular carcinoma treated with sunitinib

BACKGROUND

Intratumoral heterogeneity is a well-recognized feature of malignancy.

AIMS

To assess the heterogeneity of tumor using fractal analysis of contrast-enhanced computed tomography (CE-CT) images for predicting survival of hepatocellular carcinoma (HCC) patients treated with sunitinib.

METHODS

The patient cohort comprised 23 patients (19 men, 4 women; mean age 61.5 years) with HCC who underwent CE-CT at baseline and after one cycle of sunitinib. Arterial-phase (AP) and portal-phase (PP) CE-CT images were analyzed using a plugin software for ImageJ (NIH, Bethesda, MD). A differential box-counting method was employed to calculate the fractal dimension (FD) of the tumor. Tumor FD, density, and size were compared with survival.

RESULTS

Median progression-free survival (PFS) was 4.43 months. Patients were grouped into a favorable PFS (PFS >4.43 months; 9 patients) and an unfavorable PFS group (PFS ≤ 4.43; 13 patients). The baseline FD on both the AP and PP images was lower in the favorable PFS group than in the unfavorable PFS group (both P = 0.03). There was a significant difference in the change of the FD on the AP image between the favorable and unfavorable PFS groups (P = 0.02). Tumor density and size showed no significant correlations with PFS. In the Kaplan-Meier analysis, patients with tumors showing lower FD on the AP image at baseline showed longer PFS (P = 0.002). Patients with tumors showing a greater reduction in the FD on the PP image after one cycle of the therapy showed longer overall survival (P = 0.002).

CONCLUSION

The FD of the tumor on CE-CT images may be a useful biomarker for HCC patients treated with sunitinib.

Monitoring the effects of anti-angiogenesis on the radiation sensitivity of pancreatic cancer xenografts using dynamic contrast-enhanced computed tomography

PURPOSE

To image the intratumor vascular physiological status of pancreatic tumors xenografts and their response to anti-angiogenic therapy using dynamic contrast-enhanced computed tomography (DCE-CT), and to identify parameters of vascular physiology associated with tumor x-ray sensitivity after anti-angiogenic therapy.

METHODS AND MATERIALS

Nude mice bearing human BxPC-3 pancreatic tumor xenografts were treated with 5 Gy of radiation therapy (RT), either a low dose (40 mg/kg) or a high dose (150 mg/kg) of DC101, the anti-VEGF receptor-2 anti-angiogenesis antibody, or with combination of low or high dose DC101 and 5 Gy RT (DC101-plus-RT). DCE-CT scans were longitudinally acquired over a 3-week period post-DC101 treatment. Parametric maps of tumor perfusion and fractional plasma volume (Fp) were calculated and their averaged values and histogram distributions evaluated and compared to controls, from which a more homogeneous physiological window was observed 1-week post-DC101. Mice receiving a combination of DC101-plus-RT(5 Gy) were imaged baseline before receiving DC101 and 1 week after DC101 (before RT). Changes in perfusion and Fp were compared with alternation in tumor growth delay for RT and DC101-plus-RT (5 Gy)-treated tumors.

RESULTS

Pretreatment with low or high doses of DC101 before RT significantly delayed tumor growth by an average 7.9 days compared to RT alone (P ≤ .01). The increase in tumor growth delay for the DC101-plus-RT-treated tumors was strongly associated with changes in tumor perfusion (ΔP>-15%) compared to RT treated tumors alone (P=.01). In addition, further analysis revealed a trend linking the tumor's increased growth delay to its tumor volume-to-DC101 dose ratio.

CONCLUSIONS

DCE-CT is capable of monitoring changes in intratumor physiological parameter of tumor perfusion in response to anti-angiogenic therapy of a pancreatic human tumor xenograft that was associated with enhanced radiation response.

Perfusion CT imaging of colorectal cancer

Imaging plays an important role in the assessment of colorectal cancer, including diagnosis, staging, selection of treatment, assessment of treatment response, surveillance and investigation of suspected disease relapse. Anatomical imaging remains the mainstay for size measurement and structural evaluation; however, functional imaging techniques may provide additional insights into the tumour microenvironment. With dynamic contrast-enhanced CT techniques, iodinated contrast agent kinetics may inform on regional tumour perfusion, shunting and microvascular function and provide a surrogate measure of tumour hypoxia and angiogenesis. In colorectal cancer, this may be relevant for clinical practice in terms of tumour phenotyping, prognostication, selection of individualized treatment and therapy response assessment.

FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules

PURPOSE

The present study aimed to determine whether fractal analysis of morphological complexity and intratumoral heterogeneity of FDG uptake can help to differentiate malignant from benign pulmonary nodules.

MATERIALS AND METHODS

We retrospectively analyzed data from 54 patients with suspected non-small cell lung cancer (NSCLC) who were examined by FDG PET/CT. Pathological assessments of biopsy specimens confirmed 35 and 19 nodules as NSCLC and inflammatory lesions, respectively. The morphological fractal dimension (m-FD), maximum standardized uptake value (SUV(max)) and density fractal dimension (d-FD) of target nodules were calculated from CT and PET images. Fractal dimension is a quantitative index of morphological complexity and tracer uptake heterogeneity; higher values indicate increased complexity and heterogeneity.

RESULTS

The m-FD, SUV(max) and d-FD significantly differed between malignant and benign pulmonary nodules (p<0.05). Although the diagnostic ability was better for d-FD than m-FD and SUV(max), the difference did not reach statistical significance. Tumor size correlated significantly with SUV(max) (r=0.51, p<0.05), but not with either m-FD or d-FD. Furthermore, m-FD combined with either SUV(max) or d-FD improved diagnostic accuracy to 92.6% and 94.4%, respectively.

CONCLUSION

The d-FD of intratumoral heterogeneity of FDG uptake can help to differentially diagnose malignant and benign pulmonary nodules. The SUV(max) and d-FD obtained from FDG-PET images provide different types of information that are equally useful for differential diagnoses. Furthermore, the morphological complexity determined by CT combined with heterogeneous FDG uptake determined by PET improved diagnostic accuracy.

Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review

OBJECTIVES

To provide an overview of recent research in fractal analysis of tissue perfusion imaging, using standard radiological and nuclear medicine imaging techniques including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) and to discuss implications for different fields of application.

METHODS

A systematic review of fractal analysis for tissue perfusion imaging was performed by searching the databases MEDLINE (via PubMed), EMBASE (via Ovid) and ISI Web of Science.

RESULTS

Thirty-seven eligible studies were identified. Fractal analysis was performed on perfusion imaging of tumours, lung, myocardium, kidney, skeletal muscle and cerebral diseases. Clinically, different aspects of tumour perfusion and cerebral diseases were successfully evaluated including detection and classification. In physiological settings, it was shown that perfusion under different conditions and in various organs can be properly described using fractal analysis.

CONCLUSIONS

Fractal analysis is a suitable method for quantifying heterogeneity from radiological and nuclear medicine perfusion images under a variety of conditions and in different organs. Further research is required to exploit physiologically proven fractal behaviour in the clinical setting.

KEY POINTS

• Fractal analysis of perfusion images can be successfully performed. • Tumour, pulmonary, myocardial, renal, skeletal muscle and cerebral perfusion have already been examined. • Clinical applications of fractal analysis include tumour and brain perfusion assessment. • Fractal analysis is a suitable method for quantifying perfusion heterogeneity. • Fractal analysis requires further research concerning the development of clinical applications.

Robust estimation of fractal measures for characterizing the structural complexity of the human brain: optimization and reproducibility

High-resolution isotropic three-dimensional reconstructions of human brain gray and white matter structures can be characterized to quantify aspects of their shape, volume and topological complexity. In particular, methods based on fractal analysis have been applied in neuroimaging studies to quantify the structural complexity of the brain in both healthy and impaired conditions. The usefulness of such measures for characterizing individual differences in brain structure critically depends on their within-subject reproducibility in order to allow the robust detection of between-subject differences. This study analyzes key analytic parameters of three fractal-based methods that rely on the box-counting algorithm with the aim to maximize within-subject reproducibility of the fractal characterizations of different brain objects, including the pial surface, the cortical ribbon volume, the white matter volume and the gray matter/white matter boundary. Two separate datasets originating from different imaging centers were analyzed, comprising 50 subjects with three and 24 subjects with four successive scanning sessions per subject, respectively. The reproducibility of fractal measures was statistically assessed by computing their intra-class correlations. Results reveal differences between different fractal estimators and allow the identification of several parameters that are critical for high reproducibility. Highest reproducibility with intra-class correlations in the range of 0.9-0.95 is achieved with the correlation dimension. Further analyses of the fractal dimensions of parcellated cortical and subcortical gray matter regions suggest robustly estimated and region-specific patterns of individual variability. These results are valuable for defining appropriate parameter configurations when studying changes in fractal descriptors of human brain structure, for instance in studies of neurological diseases that do not allow repeated measurements or for disease-course longitudinal studies.

Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

Background

Tumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images

Methods

Image texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods.

Results

Early evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice.

Conclusion

This review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging.

Teaching Points

• Tumor spatial heterogeneity is an important prognostic factor.

• Image texture analysis is an approach of quantifying heterogeneity.

• Different methods can be applied, including statistical-, model-, and transform-based methods.

• Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.