Functional Parameters Derived from Magnetic Resonance Imaging Reflect Vascular Morphology in Preclinical Tumors and in Human Liver Metastases

Application of nanotechnology in reversing therapeutic resistance and controlling metastasis of colorectal cancer

Colorectal cancer (CRC) is the most common digestive malignancy across the world. Its first-line treatments applied in the routine clinical setting include surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy. However, resistance to therapy has been identified as the major clinical challenge that fails the treatment method, leading to recurrence and distant metastasis. An increasing number of studies have been attempting to explore the underlying mechanisms of the resistance of CRC cells to different therapies, which can be summarized into two aspects: (1) The intrinsic characters and adapted alterations of CRC cells before and during treatment that regulate the drug metabolism, drug transport, drug target, and the activation of signaling pathways; and (2) the suppressive features of the tumor microenvironment (TME). To combat the issue of therapeutic resistance, effective strategies are warranted with a focus on the restoration of CRC cells’ sensitivity to specific treatments as well as reprogramming impressive TME into stimulatory conditions. To date, nanotechnology seems promising with scope for improvement of drug mobility, treatment efficacy, and reduction of systemic toxicity. The instinctive advantages offered by nanomaterials enable the diversity of loading cargoes to increase drug concentration and targeting specificity, as well as offer a platform for trying the combination of different treatments to eventually prevent tumor recurrence, metastasis, and reversion of therapy resistance. The present review intends to summarize the known mechanisms of CRC resistance to chemotherapy, radiotherapy, immunotherapy, and targeted therapy, as well as the process of metastasis. We have also emphasized the recent application of nanomaterials in combating therapeutic resistance and preventing metastasis either by combining with other treatment approaches or alone. In summary, nanomedicine is an emerging technology with potential for CRC treatment; hence, efforts should be devoted to targeting cancer cells for the restoration of therapeutic sensitivity as well as reprogramming the TME. It is believed that the combined strategy will be beneficial to achieve synergistic outcomes contributing to control and management of CRC in the future.

Functional biomarkers derived from computed tomography and magnetic resonance imaging differentiate PDAC subgroups and reveal gemcitabine-induced hypo-vascularization

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is a molecularly heterogeneous tumor entity with no clinically established imaging biomarkers. We hypothesize that tumor morphology and physiology, including vascularity and perfusion, show variations that can be detected by differences in contrast agent (CA) accumulation measured non-invasively. This work seeks to establish imaging biomarkers for tumor stratification and therapy response monitoring in PDAC, based on this hypothesis.

METHODS AND MATERIALS

Regional CA accumulation in PDAC was correlated with tumor vascularization, stroma content, and tumor cellularity in murine and human subjects. Changes in CA distribution in response to gemcitabine (GEM) were monitored longitudinally with computed tomography (CT) Hounsfield Units ratio (HUr) of tumor to the aorta or with magnetic resonance imaging (MRI) ΔR₁ area under the curve at 60 s tumor-to-muscle ratio (AUC60r). Tissue analyses were performed on co-registered samples, including endothelial cell proliferation and cisplatin tissue deposition as a surrogate of chemotherapy delivery.

RESULTS

Tumor cell poor, stroma-rich regions exhibited high CA accumulation both in human (meanHUr 0.64 vs. 0.34, p < 0.001) and mouse PDAC (meanAUC60r 2.0 vs. 1.1, p < 0.001). Compared to the baseline, in vivo CA accumulation decreased specifically in response to GEM treatment in a subset of human (HUr -18%) and mouse (AUC60r -36%) tumors. Ex vivo analyses of mPDAC showed reduced cisplatin delivery (GEM: 0.92 ± 0.5 mg/g, vs. vehicle: 3.1 ± 1.5 mg/g, p = 0.004) and diminished endothelial cell proliferation (GEM: 22.3% vs. vehicle: 30.9%, p = 0.002) upon GEM administration.

CONCLUSION

In PDAC, CA accumulation, which is related to tumor vascularization and perfusion, inversely correlates with tumor cellularity. The standard of care GEM treatment results in decreased CA accumulation, which impedes drug delivery. Further investigation is warranted into potentially detrimental effects of GEM in combinatorial therapy regimens.

Multiscale topology characterizes dynamic tumor vascular networks

Advances in imaging techniques enable high-resolution three-dimensional (3D) visualization of vascular networks over time and reveal abnormal structural features such as twists and loops, and their quantification is an active area of research. Here, we showcase how topological data analysis, the mathematical field that studies the "shape" of data, can characterize the geometric, spatial, and temporal organization of vascular networks. We propose two topological lenses to study vasculature, which capture inherent multiscale features and vessel connectivity, and surpass the single-scale analysis of existing methods. We analyze images collected using intravital and ultramicroscopy modalities and quantify spatiotemporal variation of twists, loops, and avascular regions (voids) in 3D vascular networks. This topological approach validates and quantifies known qualitative trends such as dynamic changes in tortuosity and loops in response to antibodies that modulate vessel sprouting; furthermore, it quantifies the effect of radiotherapy on vessel architecture.

Multiscale topology characterizes dynamic tumor vascular networks

Advances in imaging techniques enable high-resolution three-dimensional (3D) visualization of vascular networks over time and reveal abnormal structural features such as twists and loops, and their quantification is an active area of research. Here, we showcase how topological data analysis, the mathematical field that studies the "shape" of data, can characterize the geometric, spatial, and temporal organization of vascular networks. We propose two topological lenses to study vasculature, which capture inherent multiscale features and vessel connectivity, and surpass the single-scale analysis of existing methods. We analyze images collected using intravital and ultramicroscopy modalities and quantify spatiotemporal variation of twists, loops, and avascular regions (voids) in 3D vascular networks. This topological approach validates and quantifies known qualitative trends such as dynamic changes in tortuosity and loops in response to antibodies that modulate vessel sprouting; furthermore, it quantifies the effect of radiotherapy on vessel architecture.

An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors

This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast–enhanced MRI (DCE-MRI)–derived perfusion parameters. The clinical data and preoperative DCE–MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate the performance of the prediction models. A predictive model was built based on multi-class classification, which detected lymphoma, thymoma, and thymic carcinoma with sensitivity of 52.9%, 74.2%, and 92.8%, respectively. In addition, two predictive models were built based on binary classification for distinguishing Hodgkin from non-Hodgkin lymphoma and for distinguishing invasive from noninvasive thymoma, with sensitivity of 75% and 71.4%, respectively. In addition to two perfusion parameters (efflux rate constant from tissue extravascular extracellular space into the blood plasma, and extravascular extracellular space volume per unit volume of tissue), age and tumor volume were also essential parameters for predicting PMT subtypes. In conclusion, our machine learning–based predictive model, constructed with clinical data and perfusion parameters, may represent a useful tool for differential diagnosis of PMT subtypes.

Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis

Angiogenesis is the process by which blood vessels form from pre-existing vessels. It plays a key role in many biological processes, including embryonic development and wound healing, and contributes to many diseases including cancer and rheumatoid arthritis. The structure of the resulting vessel networks determines their ability to deliver nutrients and remove waste products from biological tissues. Here we simulate the Anderson-Chaplain model of angiogenesis at different parameter values and quantify the vessel architectures of the resulting synthetic data. Specifically, we propose a topological data analysis (TDA) pipeline for systematic analysis of the model. TDA is a vibrant and relatively new field of computational mathematics for studying the shape of data. We compute topological and standard descriptors of model simulations generated by different parameter values. We show that TDA of model simulation data stratifies parameter space into regions with similar vessel morphology. The methodologies proposed here are widely applicable to other synthetic and experimental data including wound healing, development, and plant biology.

Preclinical Applications of Multi-Platform Imaging in Animal Models of Cancer

In animal models of cancer, oncologic imaging has evolved from a simple assessment of tumor location and size to sophisticated multimodality exploration of molecular, physiologic, genetic, immunologic, and biochemical events at microscopic to macroscopic levels, performed noninvasively and sometimes in real time. Here, we briefly review animal imaging technology and molecular imaging probes together with selected applications from recent literature. Fast and sensitive optical imaging is primarily used to track luciferase-expressing tumor cells, image molecular targets with fluorescence probes, and to report on metabolic and physiologic phenotypes using smart switchable luminescent probes. MicroPET/single-photon emission CT have proven to be two of the most translational modalities for molecular and metabolic imaging of cancers: immuno-PET is a promising and rapidly evolving area of imaging research. Sophisticated MRI techniques provide high-resolution images of small metastases, tumor inflammation, perfusion, oxygenation, and acidity. Disseminated tumors to the bone and lung are easily detected by microCT, while ultrasound provides real-time visualization of tumor vasculature and perfusion. Recently available photoacoustic imaging provides real-time evaluation of vascular patency, oxygenation, and nanoparticle distributions. New hybrid instruments, such as PET-MRI, promise more convenient combination of the capabilities of each modality, enabling enhanced research efficacy and throughput.

Identifying response in colorectal liver metastases treated with bevacizumab: development of RECIST by combining contrast-enhanced and diffusion-weighted MRI

OBJECTIVES

Response evaluation criteria in solid tumors (RECIST) often fail to identify clinically meaningful response to bevacizumab-containing therapy in colorectal liver metastasis (CRLM). This study aimed to develop RECIST by combining contrast-enhanced and diffusion-weighted magnetic resonance imaging (MRI).

METHODS

A total of 126 patients with CRLM who underwent hepatic resection after bevacizumab-containing chemotherapy were split into initial analyses cohort (N = 42, with 76 indexed liver metastases) and validation cohort (N = 84). In lesion-based analyses, percentage decrease of arterial enhancement area and percentage increase of apparent diffusion coefficient (ADC) value from baseline to post-chemotherapy were measured. Their optimal cutoff values for distinguishing pathology-confirmed major and minor response were determined. Then, the developed RECIST (D-RECIST) was established by combining functional and size-based items. Survival relevance of D-RECIST and RECIST was examined in the validation cohort.

RESULTS

Percentage decrease of arterial enhancement area and increase of ADC value significantly differed between lesions of pathologic major or minor response, with optimal cutoffs of approximately 33% and 19%, respectively. Patients defined as responders by D-RECIST had a significantly longer median disease-free survival (DFS) than non-responders (p = 0.021; 12.9 versus 8.6 months). No significant difference was observed with RECIST (p = 0.524). In a Cox regression model, D-RECIST- but not RECIST-defined responses independently predicted the DFS (p = 0.034 and 0.811).

CONCLUSIONS

D-RECIST-defined responses provided significant prognostic information, and thus may serve as a better response evaluation approach than RECIST in CRLM treated with bevacizumab-containing therapy.

KEY POINTS

• Changes in arterial enhancement area and apparent diffusion coefficient value are associated with pathological response in colorectal liver metastases treated with bevacizumab. • The MRI-based response criteria developed by combining size-based and functional features can provide significant prognostic information.

Deep learning-assisted magnetic resonance imaging prediction of tumor response to chemotherapy in patients with colorectal liver metastases

Accurate evaluation of tumor response to preoperative chemotherapy is crucial for assigning appropriate patients with colorectal liver metastases (CRLM) to surgery or conservative therapy. However, there is no well-recognized method for predicting pathological response before surgery. Our study constructed and validated a deep learning algorithm using prechemotherapy and postchemotherapy magnetic resonance imaging (MRI) to predict pathological response in CRLM. CRLM patients from center one who had ≤5 lesions and were scheduled to receive preoperative chemotherapy followed by liver resection between January 2013 and November 2016, were included prospectively and chronologically divided into a training cohort (80% of patients) and a testing cohort (20% of patients). Patients from center two were included January 2017 and December 2018 as an external validation cohort. MRI-based models were constructed to discriminate according to pathology tumor regression grade (TRG) between the response (TRG1/2) and nonresponse (TRG3/4/5) groups at the lesion level. From center one, 155 patients (328 lesions) were included; chronologically, 101 (264 lesions) in the training cohort and 54 (64 lesions) in the testing cohort. The model achieved better accuracy (0.875 vs 0.578) and AUC (0.849 vs 0.615) than RECIST for discriminating response; it also distinguished the survival outcomes after hepatectomy better than the RECIST criteria. Evaluations of the external validation cohort (25 patients, 61 lesions) also showed good ability with an AUC of 0.833. In conclusion, the MRI-based deep learning model provided accurate prediction of pathological tumor response to preoperative chemotherapy in patients with CRLM and may inform individualized treatment.

DCE-MRI and Quantitative Histology Reveal Enhanced Vessel Maturation but Impaired Perfusion and Increased Hypoxia in Bevacizumab-Treated Cervical Carcinoma

PURPOSE

This study had a dual purpose: to investigate (1) whether bevacizumab can change the microvasculature and oxygenation of cervical carcinomas and (2) whether any changes can be detected with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

METHODS AND MATERIALS

Two patient-derived xenograft models of cervical cancer (BK-12 and HL-16) were included in the study. Immunostained histologic preparations from untreated and bevacizumab-treated tumors were analyzed with respect to microvascular density, vessel pericyte coverage, and tumor hypoxia using CD31, α-SMA, and pimonidazole as markers, respectively. DCE-MRI was performed at 7.05 T, and parametric images of K^trans and v_e were derived from the data using the Tofts pharmacokinetic model.

RESULTS

The tumors of both models showed decreased microvascular density, increased vessel pericyte coverage, and increased vessel maturation after bevacizumab treatment. Bevacizumab-treated tumors were more hypoxic and had lower K^trans values than untreated tumors in the BK-12 model, whereas bevacizumab-treated and untreated HL-16 tumors had similar hypoxic fractions and similar K^trans values. Significant correlations were found between median K^trans and hypoxic fraction, and the data for untreated and bevacizumab-treated tumors were well fitted by the same curve in both tumor models.

CONCLUSIONS

Bevacizumab-treated tumors show less abnormal microvessels than untreated tumors do, but because of treatment-induced vessel pruning, the overall function of the microvasculature might be impaired after bevacizumab treatment, resulting in increased tumor hypoxia. DCE-MRI has great potential for monitoring bevacizumab-induced changes in tumor hypoxia in cervical carcinoma.

Tumor enhancement of colorectal liver metastases on preoperative gadobutrol-enhanced MRI at 5 minutes post-contrast injection is associated with overall survival post-hepatectomy

Previous studies have demonstrated that target tumor enhancement (TTE) of colorectal cancer liver metastases (CRCLM) on 10-minute delayed phase gadobutrol-enhanced MRI is associated with overall survival post-hepatectomy. The purpose of this study was to determine whether TTE of CRCLM on preoperative gadobutrol-enhanced MRI measured at 5-minute delayed phase is also associated with overall survival. We performed a single-institution, REB-approved, retrospective study of 121 patients with CRCLM who had received a clinical gadobutrol-enhanced MRI after treatment with chemotherapy and prior to liver surgery between January 1, 2006 and December 31, 2012. The TTE of the colorectal liver metastases (CRLM) on 5-minute delayed phase was determined. Kaplan-Meier and Cox-regression survival analyses were used in order to determine the association between TTE on 5-minute delayed phase and overall survival, after adjusting for known prognostic variables. TTE of chemotherapy-treated CRLM on gadobutrol-enhanced MRI at 5-minute post-contrast injection is associated with overall survival post-hepatectomy. On Kaplan-Meier survival analysis, there was a significant difference in overall survival between strong and weak TTE groups (log-rank P=0.009) with 74.4% survival at 36 months in the strong TTE group compared to only 44.6% in the weak TTE group. On Cox-regression analysis, the adjusted hazard ratio of death for patients with low TTE was 0.40 (95% CI: 0.18-0.90, P=0.026), after adjusting for known prognostic variables. This study provides preliminary evidence that tumor enhancement of CRLM at 5 minutes post-contrast injection on gadobutrol-enhanced MRI may provide preoperative prognostic information. This may be helpful for risk stratification of patients for surgery.