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

Decoding Intratumoral Heterogeneity of Breast Cancer by Multiparametric In Vivo Imaging: A Translational Study

Differential diagnosis and therapy of heterogeneous breast tumors poses a major clinical challenge. To address the need for a comprehensive, noninvasive strategy to define the molecular and functional profiles of tumors in vivo, we investigated a novel combination of metabolic PET and diffusion-weighted (DW)-MRI in the polyoma virus middle T antigen transgenic mouse model of breast cancer. The implementation of a voxelwise analysis for the clustering of intra- and intertumoral heterogeneity in this model resulted in a multiparametric profile based on [(18)F]Fluorodeoxyglucose ([(18)F]FDG)-PET and DW-MRI, which identified three distinct tumor phenotypes in vivo, including solid acinar, and solid nodular malignancies as well as cystic hyperplasia. To evaluate the feasibility of this approach for clinical use, we examined estrogen receptor-positive and progesterone receptor-positive breast tumors from five patient cases using DW-MRI and [(18)F]FDG-PET in a simultaneous PET/MRI system. The postsurgical in vivo PET/MRI data were correlated to whole-slide histology using the latter traditional diagnostic standard to define phenotype. By this approach, we showed how molecular, structural (microscopic, anatomic), and functional information could be simultaneously obtained noninvasively to identify precancerous and malignant subtypes within heterogeneous tumors. Combined with an automatized analysis, our results suggest that multiparametric molecular and functional imaging may be capable of providing comprehensive tumor profiling for noninvasive cancer diagnostics. Cancer Res; 76(18); 5512-22. ©2016 AACR.

Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art

With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. Radiomics is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.

Acute tumour response to a bispecific Ang-2-VEGF-A antibody: insights from multiparametric MRI and gene expression profiling

BACKGROUND

To assess antivascular effects, and evaluate clinically translatable magnetic resonance imaging (MRI) biomarkers of tumour response in vivo, following treatment with vanucizumab, a bispecific human antibody against angiopoietin-2 (Ang-2) and vascular endothelial growth factor-A (VEGF-A).

METHODS

Colo205 colon cancer xenografts were imaged before and 5 days after treatment with a single 10 mg kg(-1) dose of either vanucizumab, bevacizumab (anti-human VEGF-A), LC06 (anti-murine/human Ang-2) or omalizumab (anti-human IgE control). Volumetric response was assessed using T2-weighted MRI, and diffusion-weighted, dynamic contrast-enhanced (DCE) and susceptibility contrast MRI used to quantify tumour water diffusivity (apparent diffusion coefficient (ADC), × 10(6) mm(2) s(-1)), vascular perfusion/permeability (K(trans), min(-1)) and fractional blood volume (fBV, %) respectively. Pathological correlates were sought, and preliminary gene expression profiling performed.

RESULTS

Treatment with vanucizumab, bevacizumab or LC06 induced a significant (P<0.01) cytolentic response compared with control. There was no significant change in tumour ADC in any treatment group. Uptake of Gd-DTPA was restricted to the tumour periphery in all post-treatment groups. A significant reduction in tumour K(trans) (P<0.05) and fBV (P<0.01) was determined 5 days after treatment with vanucizumab only. This was associated with a significant (P<0.05) reduction in Hoechst 33342 uptake compared with control. Gene expression profiling identified 20 human genes exclusively regulated by vanucizumab, 6 of which are known to be involved in vasculogenesis and angiogenesis.

CONCLUSIONS

Vanucizumab is a promising antitumour and antiangiogenic treatment, whose antivascular activity can be monitored using DCE and susceptibility contrast MRI. Differential gene expression in vanucizumab-treated tumours is regulated by the combined effect of Ang-2 and VEGF-A inhibition.

Assessment of Heterogeneity Difference Between Edge and Core by Using Texture Analysis: Differentiation of Malignant From Inflammatory Pulmonary Nodules and Masses

RATIONALE AND OBJECTIVES

This study aimed to test the hypothesis that the heterogeneity difference between edge and core of lesions by using intensity and entropy features obtained from whole-lesion texture analysis on contrast-enhanced computed tomography (CT) may be useful for differentiation of malignant from inflammatory pulmonary nodules and masses.

MATERIALS AND METHODS

In all, 48 single pulmonary nodules and masses were retrospectively evaluated. All lesions were histologically or clinically confirmed (malignancy: inflammation = 24:20). We utilized a newly introduced texture analysis method based on contrast-enhanced CT (first described by Grove et al.) that automatically divided the whole lesion volume into two regions: edge and core. Mean attenuation value (AV) and entropy of each region and also the whole lesion were evaluated separately. Each texture metric (absolute value for each region, and difference value defined as difference between edge and core) of malignant and inflammatory lesions were compared using Mann-Whitney U test. Individual image parameters were combined by using linear discriminant analysis. Receiver operating characteristic curves were generated to assess each texture metric and their combination for discriminating between the two entities.

RESULTS

Mean AV difference and entropy difference were significantly higher in malignant lesions than in inflammatory lesions (4.71 HU ± 5.06 vs -1.53 HU ± 5.05, P < .001; 0.45 ± 0.23 vs 0.18 ± 0.30, P = .001). Receiver operating characteristic curves for individual mean AV difference and entropy difference provided relatively high values for the area under the curve (0.836 and 0.795, respectively). The combination of mean AV difference, entropy difference, and lesion volume improved the area under the curve to 0.864.

CONCLUSION

Heterogeneity difference between edge and core by using whole-lesion texture analysis on contrast-enhanced CT may be a promising tool for estimating the probability of malignancy in pulmonary nodules and masses.

Positron emission tomography/computerized tomography for tumor response assessment-a review of clinical practices and radiomics studies

Even with recent advances in cancer diagnosis and therapy, treatment outcomes for many cancers remain dismal. Patients often show different response to the same therapy regimen, supporting the development of personalized medicine. 18F-FDG PET/CT has been used routinely in the assessment of tumor response, in prediction of outcomes, and in guiding personalized treatment. These assessments are mainly based on physician's subjective or semi-quantitative evaluation. Recent development in Radiomics provides a promising objective way for tumor response assessment, which uses computerized tools to extract a large number of image features that capture additional information not currently used in clinic that has prognostic value. In this review, we summarized the clinical use of PET/CT and the PET/CT Radiomics studies for tumor response assessment. Finally, we discussed some challenges and future perspectives.

Revisiting the Robustness of PET-Based Textural Features in the Context of Multi-Centric Trials

Purpose

This study aimed to investigate the variability of textural features (TF) as a function of acquisition and reconstruction parameters within the context of multi-centric trials.

Methods

The robustness of 15 selected TFs were studied as a function of the number of iterations, the post-filtering level, input data noise, the reconstruction algorithm and the matrix size. A combination of several reconstruction and acquisition settings was devised to mimic multi-centric conditions. We retrospectively studied data from 26 patients enrolled in a diagnostic study that aimed to evaluate the performance of PET/CT ⁶⁸Ga-DOTANOC in gastro-entero-pancreatic neuroendocrine tumors. Forty-one tumors were extracted and served as the database. The coefficient of variation (COV) or the absolute deviation (for the noise study) was derived and compared statistically with SUVmax and SUVmean results.

Results

The majority of investigated TFs can be used in a multi-centric context when each parameter is considered individually. The impact of voxel size and noise in the input data were predominant as only 4 TFs presented a high/intermediate robustness against SUV-based metrics (Entropy, Homogeneity, RP and ZP). When combining several reconstruction settings to mimic multi-centric conditions, most of the investigated TFs were robust enough against SUVmax except Correlation, Contrast, LGRE, LGZE and LZLGE.

Conclusion

Considering previously published results on either reproducibility or sensitivity against delineation approach and our findings, it is feasible to consider Homogeneity, Entropy, Dissimilarity, HGRE, HGZE and ZP as relevant for being used in multi-centric trials.

SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET

PURPOSE

Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity.

METHODS

The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor-Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basis was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB).

RESULTS

SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14% ± 9% vs 14% ± 7%, p = 0.15) and higher than FLICM (45% ± 18%, p < 0.0001), and improved accuracy with lower CE (14% ± 11%) over both FLICM (29% ± 29%) and FLAB (22% ± 20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17% ± 11% for SPEQTACLE vs 28% ± 22% for FLAB (p = 0.009) and 40% ± 35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15% ± 6% vs 37% ± 14% and 30% ± 17%, p < 0.004).

CONCLUSIONS

SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments.

Radiogenomics: what it is and why it is important

In recent years, a new direction in cancer research has emerged that focuses on the relationship between imaging phenotypes and genomics. This direction is referred to as radiogenomics or imaging genomics. The question that subsequently arises is: What is the practical significance of elucidating this relationship in improving cancer patient outcomes. In this article, I address this question. Although I discuss some limitations of the radiogenomic approach, and describe scenarios in which radiogenomic analysis might not be the best choice, I also argue that radiogenomics will play a significant practical role in cancer research. Specifically, I argue that the significance of radiogenomics is largely related to practical limitations of currently available data that often lack complete characterization of the patients and poor integration of individual datasets. Radiogenomics offers a practical way to leverage limited and incomplete data to generate knowledge that might lead to improved decision making, and as a result, improved patient outcomes.

Monitoring Tumor Response to Carbogen Breathing by Oxygen-Sensitive Magnetic Resonance Parameters to Predict the Outcome of Radiation Therapy: A Preclinical Study

PURPOSE

In an effort to develop noninvasive in vivo methods for mapping tumor oxygenation, magnetic resonance (MR)-derived parameters are being considered, including global R1, water R1, lipids R1, and R2*. R1 is sensitive to dissolved molecular oxygen, whereas R2* is sensitive to blood oxygenation, detecting changes in dHb. This work compares global R1, water R1, lipids R1, and R2* with pO2 assessed by electron paramagnetic resonance (EPR) oximetry, as potential markers of the outcome of radiation therapy (RT).

METHODS AND MATERIALS

R1, R2*, and EPR were performed on rhabdomyosarcoma and 9L-glioma tumor models, under air and carbogen breathing conditions (95% O2, 5% CO2). Because the models demonstrated different radiosensitivity properties toward carbogen, a growth delay (GD) assay was performed on the rhabdomyosarcoma model and a tumor control dose 50% (TCD50) was performed on the 9L-glioma model.

RESULTS

Magnetic resonance imaging oxygen-sensitive parameters detected the positive changes in oxygenation induced by carbogen within tumors. No consistent correlation was seen throughout the study between MR parameters and pO2. Global and lipids R1 were found to be correlated to pO2 in the rhabdomyosarcoma model, whereas R2* was found to be inversely correlated to pO2 in the 9L-glioma model (P=.05 and .03). Carbogen increased the TCD50 of 9L-glioma but did not increase the GD of rhabdomyosarcoma. Only R2* was predictive (P<.05) for the curability of 9L-glioma at 40 Gy, a dose that showed a difference in response to RT between carbogen and air-breathing groups. (18)F-FAZA positron emission tomography imaging has been shown to be a predictive marker under the same conditions.

CONCLUSION

This work illustrates the sensitivity of oxygen-sensitive R1 and R2* parameters to changes in tumor oxygenation. However, R1 parameters showed limitations in terms of predicting the outcome of RT in the tumor models studied, whereas R2* was found to be correlated with the outcome in the responsive model.

Intratumor Heterogeneity of Perfusion and Diffusion in Clear-Cell Renal Cell Carcinoma: Correlation With Tumor Cellularity

BACKGROUND

Magnetic resonance imaging (MRI) has the potential to noninvasively provide information about the tumor microenvironment. A correlation between arterial spin-labeled (ASL) MRI and tumor vasculature has been previously demonstrated; however, its correlation with tumor cellularity is unknown. We sought to assess intratumor heterogeneity of perfusion and diffusion in vivo in clear-cell renal cell carcinoma (ccRCC) using MRI and to correlate these findings with tumor vascularity and cellularity at histopathology.

PATIENTS AND METHODS

Twenty-three ccRCC patients underwent ASL and diffusion-weighted MRI before surgery after signing an informed consent in this prospective institutional review board-approved, HIPAA (Insurance Portability and Accountability Act)-compliant study. Quantitative ASL perfusion and diffusion were measured in 2 areas within the same tumor with high and low perfusion. Microvessel density (MVD) on CD31 and CD34 immunostains and tumor cellularity in anatomically coregistered tissue samples were correlated to MRI measurements (Spearman; P < .05 statistically significant).

RESULTS

ASL perfusion (P < .0001), CD31 MVD (P = .02), CD34 MVD (P = .04), and cellularity (P = .002) from high and low perfusion areas were significantly different across all tumors. There were positive correlations between tumor cellularity and CD31 MVD (ρ = 0.350, P = .021), CD31 and CD34 MVD (ρ = 0.838, P < .0001), ASL perfusion and cellularity (ρ = 0.406, P = .011), and ASL perfusion and CD31 MVD (ρ = 0.468, P = .003), and a negative correlation between tissue diffusion coefficient and cellularity (ρ = -0.316, P = .039).

CONCLUSION

Tumor areas with high ASL perfusion exhibit higher cellularity and MVD compared to areas with low perfusion in the same tumor. A positive correlation between tumor vascularity and cellularity in ccRCC is newly reported. A negative correlation between tumor diffusion and cellularity is confirmed.

Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy

PURPOSE

To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC.

RESULTS

Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65).

CONCLUSION

The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107-1115.

Hypoxia-activated prodrugs: paths forward in the era of personalised medicine

Tumour hypoxia has been pursued as a cancer drug target for over 30 years, most notably using bioreductive (hypoxia-activated) prodrugs that target antineoplastic agents to low-oxygen tumour compartments. Despite compelling evidence linking hypoxia with treatment resistance and adverse prognosis, a number of such prodrugs have recently failed to demonstrate efficacy in pivotal clinical trials; an outcome that demands reflection on the discovery and development of these compounds. In this review, we discuss a clear disconnect between the pathobiology of tumour hypoxia, the pharmacology of hypoxia-activated prodrugs and the manner in which they have been taken into clinical development. Hypoxia-activated prodrugs have been evaluated in the manner of broad-spectrum cytotoxic agents, yet a growing body of evidence suggests that their activity is likely to be dependent on the coincidence of tumour hypoxia, expression of specific prodrug-activating reductases and intrinsic sensitivity of malignant clones to the cytotoxic effector. Hypoxia itself is highly variable between and within individual tumours and is not treatment-limiting in all cancer subtypes. Defining predictive biomarkers for hypoxia-activated prodrugs and overcoming the technical challenges of assaying them in clinical settings will be essential to deploying these agents in the era of personalised cancer medicine.

Decline of Tumor Vascular Function as Assessed by Dynamic Contrast-Enhanced Magnetic Resonance Imaging Is Associated With Poor Responses to Radiation Therapy and Chemotherapy

PURPOSE

To investigate whether changes in the volume transfer coefficient (K(trans)) in a growing tumor could be used as a surrogate marker for predicting tumor responses to radiation therapy (RT) and chemotherapy (CT).

METHODS AND MATERIALS

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was consecutively performed on tumor-bearing mice, and temporal and spatial changes of K(trans) values were measured along with tumor growth. Tumor responses to RT and CT were studied before and after observed changes in K(trans) values with time.

RESULTS

Dynamic changes with an initial increase and subsequent decline in K(trans) values were found to be associated with tumor growth. When each tumor was divided into core and peripheral regions, the K(trans) decline was greater in core, although neither vascular structure or necrosis could be linked to this spatial difference. Tumor responses to RT were worse if applied after the decline of K(trans), and there was less drug distribution and cell death in the tumor core after CT.

CONCLUSION

The K(trans) value in growing tumors, reflecting the changes of tumor microenvironment and vascular function, is strongly associated with tumor responses to RT and CT and could be a potential surrogate marker for predicting the tumor response to these treatments.

Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study

PURPOSE

To develop an intratumor partitioning framework for identifying high-risk subregions from (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer.

METHODS AND MATERIALS

In this institutional review board-approved retrospective study, we analyzed the pretreatment FDG-PET and CT scans of 44 lung cancer patients treated with radiation therapy. A novel, intratumor partitioning method was developed, based on a 2-stage clustering process: first at the patient level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP).

RESULTS

Three spatially distinct subregions were identified within each tumor that were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI of 0.66-0.67. When restricting the analysis to patients with stage III disease (n=32), the same subregion achieved an even higher CI of 0.75 (hazard ratio 3.93, log-rank P=.002) for predicting OS, and a CI of 0.76 (hazard ratio 4.84, log-rank P=.002) for predicting OFP. In comparison, conventional imaging markers, including tumor volume, maximum standardized uptake value, and metabolic tumor volume using threshold of 50% standardized uptake value maximum, were not predictive of OS or OFP, with CI mostly below 0.60 (log-rank P>.05).

CONCLUSION

We propose a robust intratumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.

Development of a nomogram combining clinical staging with (18)F-FDG PET/CT image features in non-small-cell lung cancer stage I-III

PURPOSE

Our goal was to develop a nomogram by exploiting intratumour heterogeneity on CT and PET images from routine (18)F-FDG PET/CT acquisitions to identify patients with the poorest prognosis.

METHODS

This retrospective study included 116 patients with NSCLC stage I, II or III and with staging (18)F-FDG PET/CT imaging. Primary tumour volumes were delineated using the FLAB algorithm and 3D Slicer™ on PET and CT images, respectively. PET and CT heterogeneities were quantified using texture analysis. The reproducibility of the CT features was assessed on a separate test-retest dataset. The stratification power of the PET/CT features was evaluated using the Kaplan-Meier method and the log-rank test. The best standard metric (functional volume) was combined with the least redundant and most prognostic PET/CT heterogeneity features to build the nomogram.

RESULTS

PET entropy and CT zone percentage had the highest complementary values with clinical stage and functional volume. The nomogram improved stratification amongst patients with stage II and III disease, allowing identification of patients with the poorest prognosis (clinical stage III, large tumour volume, high PET heterogeneity and low CT heterogeneity).

CONCLUSION

Intratumour heterogeneity quantified using textural features on both CT and PET images from routine staging (18)F-FDG PET/CT acquisitions can be used to create a nomogram with higher stratification power than staging alone.

Oxygen-Enhanced MRI Accurately Identifies, Quantifies, and Maps Tumor Hypoxia in Preclinical Cancer Models

There is a clinical need for noninvasive biomarkers of tumor hypoxia for prognostic and predictive studies, radiotherapy planning, and therapy monitoring. Oxygen-enhanced MRI (OE-MRI) is an emerging imaging technique for quantifying the spatial distribution and extent of tumor oxygen delivery in vivo. In OE-MRI, the longitudinal relaxation rate of protons (ΔR1) changes in proportion to the concentration of molecular oxygen dissolved in plasma or interstitial tissue fluid. Therefore, well-oxygenated tissues show positive ΔR1. We hypothesized that the fraction of tumor tissue refractory to oxygen challenge (lack of positive ΔR1, termed "Oxy-R fraction") would be a robust biomarker of hypoxia in models with varying vascular and hypoxic features. Here, we demonstrate that OE-MRI signals are accurate, precise, and sensitive to changes in tumor pO2 in highly vascular 786-0 renal cancer xenografts. Furthermore, we show that Oxy-R fraction can quantify the hypoxic fraction in multiple models with differing hypoxic and vascular phenotypes, when used in combination with measurements of tumor perfusion. Finally, Oxy-R fraction can detect dynamic changes in hypoxia induced by the vasomodulator agent hydralazine. In contrast, more conventional biomarkers of hypoxia (derived from blood oxygenation-level dependent MRI and dynamic contrast-enhanced MRI) did not relate to tumor hypoxia consistently. Our results show that the Oxy-R fraction accurately quantifies tumor hypoxia noninvasively and is immediately translatable to the clinic.

Radiomics: Images Are More than Pictures, They Are Data

In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.

Assessment of Tumor Angiogenesis: Dynamic Contrast-enhanced MR Imaging and Beyond

Dynamic contrast-enhanced (DCE) MR imaging is used increasingly often to evaluate tumor angiogenesis and the efficacy of antiangiogenic drugs. In clinical practice DCE-MR imaging applications are largely centered on lesion detection, characterization, and localization. In research, DCE-MR imaging helps inform decision making in early-phase clinical trials by showing efficacy and by selecting dose and schedule. However, the role of these techniques in patient selection is uncertain. Future research is required to optimize existing DCE-MR imaging methods and to fully validate these biomarkers for wider use in patient care and in drug development.

Early effects of low dose bevacizumab treatment assessed by magnetic resonance imaging

Background

Antiangiogenic treatments have been shown to increase blood perfusion and oxygenation in some experimental tumors, and to reduce blood perfusion and induce hypoxia in others. The purpose of this preclinical study was to investigate the potential of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted MRI (DW-MRI) in assessing early effects of low dose bevacizumab treatment, and to investigate intratumor heterogeneity in this effect.

Methods

A-07 and R-18 human melanoma xenografts, showing high and low expression of VEGF-A, respectively, were used as tumor models. Untreated and bevacizumab-treated tumors were subjected to DCE-MRI and DW-MRI before treatment, and twice during a 7-days treatment period. Tumor images of K^trans (the volume transfer constant of Gd-DOTA) and v_e (the fractional distribution volume of Gd-DOTA) were produced by pharmacokinetic analysis of the DCE-MRI data, and tumor images of ADC (the apparent diffusion coefficient) were produced from DW-MRI data.

Results

Untreated A-07 tumors showed higher K^trans, v_e, and ADC values than untreated R-18 tumors. Untreated tumors showed radial heterogeneity in K^trans, i.e., K^trans was low in central tumor regions and increased gradually towards the tumor periphery. After the treatment, bevacizumab-treated A-07 tumors showed lower K^trans values than untreated A-07 tumors. Peripherial tumor regions showed substantial reductions in K^trans, whereas little or no effect was seen in central regions. Consequently, the treatment altered the radial heterogeneity in K^trans. In R-18 tumors, significant changes in K^trans were not observed. Treatment induced changes in tumor size, v_e, and ADC were not seen in any of the tumor lines.

Conclusions

Early effects of low dose bevacizumab treatment may be highly heterogeneous within tumors and can be detected with DCE-MRI.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1918-1) contains supplementary material, which is available to authorized users.

Association Between Computed Tomographic Features and Kirsten Rat Sarcoma Viral Oncogene Mutations in Patients With Stage I Lung Adenocarcinoma and Their Prognostic Value

BACKGROUND

We investigated the association between computed tomographic (CT) features and Kirsten rat sarcoma viral oncogene (KRAS) mutations in patients with stage I lung adenocarcinoma and their prognostic value.

PATIENTS AND METHODS

A total of 79 patients with pathologic stage I lung adenocarcinoma, available KRAS mutational status, preoperative CT images available, and survival data were included in the present study. Seven CT features, including spiculation, concavity, ground-glass opacity, bubble-like lucency, air bronchogram, pleural retraction, and pleural attachment, were evaluated. The association among the clinical characteristics, CT features, and mutational status was analyzed using Student's t test, the χ(2) test or Fisher's exact test, and logistic regression. The association among CT features, mutational status, and overall survival was analyzed using Kaplan-Meier survival curves with the log-rank test and Cox proportional hazard regression.

RESULTS

The prevalence of KRAS mutations was 41.77%. Spiculation was significantly associated with the presence of KRAS mutations (odds ratio, 2.99; 95% confidence interval [CI], 1.16-7.68). Although KRAS mutational status was not significantly associated with overall survival, the presence of pleural attachment was associated with an increased risk of death (hazard ratio, 2.46; 95% CI, 1.09-5.53). When analyzing KRAS mutational status and pleural attachment combined, patients with wild-type KRAS and no pleural attachment had significantly better survival than did those with wild-type KRAS and pleural attachment (P = .014).

CONCLUSION

These data suggest that spiculation is associated with KRAS mutations and pleural attachment is associated with overall survival in patients with stage I lung adenocarcinoma. Combining the analysis of KRAS mutational status and CT features could better predict survival.

Improved accuracy and precision of tracer kinetic parameters by joint fitting to variable flip angle and dynamic contrast enhanced MRI data

PURPOSE

To improve the accuracy and precision of tracer kinetic model parameter estimates for use in dynamic contrast enhanced (DCE) MRI studies of solid tumors.

THEORY

Quantitative DCE-MRI requires an estimate of precontrast T1 , which is obtained prior to fitting a tracer kinetic model. As T1 mapping and tracer kinetic signal models are both a function of precontrast T1 it was hypothesized that its joint estimation would improve the accuracy and precision of both precontrast T1 and tracer kinetic model parameters.

METHODS

Accuracy and/or precision of two-compartment exchange model (2CXM) parameters were evaluated for standard and joint fitting methods in well-controlled synthetic data and for 36 bladder cancer patients. Methods were compared under a number of experimental conditions.

RESULTS

In synthetic data, joint estimation led to statistically significant improvements in the accuracy of estimated parameters in 30 of 42 conditions (improvements between 1.8% and 49%). Reduced accuracy was observed in 7 of the remaining 12 conditions. Significant improvements in precision were observed in 35 of 42 conditions (between 4.7% and 50%). In clinical data, significant improvements in precision were observed in 18 of 21 conditions (between 4.6% and 38%).

CONCLUSION

Accuracy and precision of DCE-MRI parameter estimates are improved when signal models are fit jointly rather than sequentially. Magn Reson Med 76:1270-1281, 2016. © 2015 Wiley Periodicals, Inc.

Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images

PURPOSE

To develop and independently validate prognostic imaging biomarkers for predicting survival in patients with glioblastoma on the basis of multiregion quantitative image analysis.

MATERIALS AND METHODS

This retrospective study was approved by the local institutional review board, and informed consent was waived. A total of 79 patients from two independent cohorts were included. The discovery and validation cohorts consisted of 46 and 33 patients with glioblastoma from the Cancer Imaging Archive (TCIA) and the local institution, respectively. Preoperative T1-weighted contrast material-enhanced and T2-weighted fluid-attenuation inversion recovery magnetic resonance (MR) images were analyzed. For each patient, we semiautomatically delineated the tumor and performed automated intratumor segmentation, dividing the tumor into spatially distinct subregions that demonstrate coherent intensity patterns across multiparametric MR imaging. Within each subregion and for the entire tumor, we extracted quantitative imaging features, including those that fully capture the differential contrast of multimodality MR imaging. A multivariate sparse Cox regression model was trained by using TCIA data and tested on the validation cohort.

RESULTS

The optimal prognostic model identified five imaging biomarkers that quantified tumor surface area and intensity distributions of the tumor and its subregions. In the validation cohort, our prognostic model achieved a concordance index of 0.67 and significant stratification of overall survival by using the log-rank test (P = .018), which outperformed conventional prognostic factors, such as age (concordance index, 0.57; P = .389) and tumor volume (concordance index, 0.59; P = .409).

CONCLUSION

The multiregion analysis presented here establishes a general strategy to effectively characterize intratumor heterogeneity manifested at multimodality imaging and has the potential to reveal useful prognostic imaging biomarkers in glioblastoma.

Multiparametric imaging of patient and tumour heterogeneity in non-small-cell lung cancer: quantification of tumour hypoxia, metabolism and perfusion

Purpose

Multiple imaging techniques are nowadays available for clinical in-vivo visualization of tumour biology. FDG PET/CT identifies increased tumour metabolism, hypoxia PET visualizes tumour oxygenation and dynamic contrast-enhanced (DCE) CT characterizes vasculature and morphology. We explored the relationships among these biological features in patients with non-small-cell lung cancer (NSCLC) at both the patient level and the tumour subvolume level.

Methods

A group of 14 NSCLC patients from two ongoing clinical trials (NCT01024829 and NCT01210378) were scanned using FDG PET/CT, HX4 PET/CT and DCE CT prior to chemoradiotherapy. Standardized uptake values (SUV) in the primary tumour were calculated for the FDG and hypoxia HX4 PET/CT scans. For hypoxia imaging, the hypoxic volume, fraction and tumour-to-blood ratio (TBR) were also defined. Blood flow and blood volume were obtained from DCE CT imaging. A tumour subvolume analysis was used to quantify the spatial overlap between subvolumes.

Results

At the patient level, negative correlations were observed between blood flow and the hypoxia parameters (TBR >1.2): hypoxic volume (−0.65, p = 0.014), hypoxic fraction (−0.60, p = 0.025) and TBR (−0.56, p = 0.042). At the tumour subvolume level, hypoxic and metabolically active subvolumes showed an overlap of 53 ± 36 %. Overlap between hypoxic sub-volumes and those with high blood flow and blood volume was smaller: 15 ± 17 % and 28 ± 28 %, respectively. Half of the patients showed a spatial mismatch (overlap <5 %) between increased blood flow and hypoxia.

Conclusion

The biological imaging features defined in NSCLC tumours showed large interpatient and intratumour variability. There was overlap between hypoxic and metabolically active subvolumes in the majority of tumours, there was spatial mismatch between regions with high blood flow and those with increased hypoxia.

Electronic supplementary material

The online version of this article (doi:10.1007/s00259-015-3169-4) contains supplementary material, which is available to authorized users.

Toward understanding and exploiting tumor heterogeneity

The extent of tumor heterogeneity is an emerging theme that researchers are only beginning to understand. How genetic and epigenetic heterogeneity affects tumor evolution and clinical progression is unknown. The precise nature of the environmental factors that influence this heterogeneity is also yet to be characterized. Nature Medicine, Nature Biotechnology and the Volkswagen Foundation organized a meeting focused on identifying the obstacles that need to be overcome to advance translational research in and tumor heterogeneity. Once these key questions were established, the attendees devised potential solutions. Their ideas are presented here.

Cluster analysis of quantitative parametric maps from DCE-MRI: application in evaluating heterogeneity of tumor response to antiangiogenic treatment

PURPOSE

The objective of this study was to compare a clustering approach to conventional analysis methods for assessing changes in pharmacokinetic parameters obtained from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) during antiangiogenic treatment in a breast cancer model.

MATERIALS AND METHODS

BALB/c mice bearing established transplantable her2+ tumors were treated with a DNA-based antiangiogenic vaccine or with an empty plasmid (untreated group). DCE-MRI was carried out by administering a dose of 0.05 mmol/kg of Gadocoletic acid trisodium salt, a Gd-based blood pool contrast agent (CA) at 1T. Changes in pharmacokinetic estimates (K(trans) and vp) in a nine-day interval were compared between treated and untreated groups on a voxel-by-voxel analysis. The tumor response to therapy was assessed by a clustering approach and compared with conventional summary statistics, with sub-regions analysis and with histogram analysis.

RESULTS

Both the K(trans) and vp estimates, following blood-pool CA injection, showed marked and spatial heterogeneous changes with antiangiogenic treatment. Averaged values for the whole tumor region, as well as from the rim/core sub-regions analysis were unable to assess the antiangiogenic response. Histogram analysis resulted in significant changes only in the vp estimates (p<0.05). The proposed clustering approach depicted marked changes in both the K(trans) and vp estimates, with significant spatial heterogeneity in vp maps in response to treatment (p<0.05), provided that DCE-MRI data are properly clustered in three or four sub-regions.

CONCLUSIONS

This study demonstrated the value of cluster analysis applied to pharmacokinetic DCE-MRI parametric maps for assessing tumor response to antiangiogenic therapy.