The use of molecular imaging combined with genomic techniques to understand the heterogeneity in cancer metastasis

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.

Glioblastoma Recurrence Versus Radiotherapy Injury: Combined Model of Diffusion Kurtosis Imaging and 11C-MET Using PET/MRI May Increase Accuracy of Differentiation

PURPOSE

To evaluate the diagnostic potential of decision-tree model of diffusion kurtosis imaging (DKI) and 11C-methionine (11C-MET) PET, for the differentiation of radiotherapy (RT) injury from glioblastoma recurrence.

METHODS

Eighty-six glioblastoma cases with suspected lesions after RT were retrospectively enrolled. Based on histopathology or follow-up, 48 patients were diagnosed with local glioblastoma recurrence, and 38 patients had RT injury between April 2014 and December 2019. All the patients underwent PET/MRI examinations. Multiple parameters were derived based on the ratio of tumor to normal control (TNR), including SUVmax and SUVmean, mean value of kurtosis and diffusivity (MK, MD) from DKI, and histogram parameters. The diagnostic models were established by decision trees. Receiver operating characteristic analysis was used for evaluating the diagnostic accuracy of each independent parameter and all the diagnostic models.

RESULTS

The intercluster correlations of DKI, PET, and texture parameters were relatively weak, whereas the intracluster correlations were strong. Compared with models of DKI alone (sensitivity =1.00, specificity = 0.70, area under the curve [AUC] = 0.85) and PET alone (sensitivity = 0.83, specificity = 0.90, AUC = 0.89), the combined model demonstrated the best diagnostic accuracy (sensitivity = 1.00, specificity = 0.90, AUC = 0.95).

CONCLUSIONS

Diffusion kurtosis imaging, 11C-MET PET, and histogram parameters provide complementary information about tissue. The decision-tree model combined with these parameters has the potential to further increase diagnostic accuracy for the discrimination between RT injury and glioblastoma recurrence over the standard Response Assessment in Neuro-Oncology criteria. 11C-MET PET/MRI may thus contribute to the management of glioblastoma patients with suspected lesions after RT.

Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome

The aim of this study was to investigate the application of [18F]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [18F]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded for a minimum of 12 months after PET/CT. Radiomics features from each avid lesion in PET and low-dose CT images were extracted. A hybrid descriptive-inferential method and the discriminant analysis (DA) were used for feature selection and for predictive model implementation, respectively. The performance of the features in predicting PD was performed for per-lesion analysis, per-patient analysis, and liver lesions analysis. All lesions were again considered to assess the diagnostic performance of the features in discriminating liver lesions. In predicting PD in the whole group of patients, on PET features radiomics analysis, among per-lesion analysis, only the GLZLM_GLNU feature was selected, while three features were selected from PET/CT images data set. The same features resulted more accurately by associating CT features with PET features (AUROC 65.22%). In per-patient analysis, three features for stand-alone PET images and one feature (i.e., HUKurtosis) for the PET/CT data set were selected. Focusing on liver metastasis, in per-lesion analysis, the same analysis recognized one PET feature (GLZLM_GLNU) from PET images and three features from PET/CT data set. Similarly, in liver lesions per-patient analysis, we found three PET features and a PET/CT feature (HUKurtosis). In discrimination of liver metastasis from the rest of the other lesions, optimal results of stand-alone PET imaging were found for one feature (SUVbwmin; AUROC 88.91%) and two features for merged PET/CT features analysis (AUROC 95.33%). In conclusion, our machine learning model on restaging [18F]FDGPET/CT was demonstrated to be feasible and potentially useful in the predictive evaluation of disease progression in MCC.

Mapping nucleosome and chromatin architectures: A survey of computational methods

With ever-growing genomic sequencing data, the data variabilities and the underlying biases of the sequencing technologies pose significant computational challenges ranging from the need for accurately detecting the nucleosome positioning or chromatin interaction to the need for developing normalization methods to eliminate systematic biases. This review mainly surveys the computational methods for mapping the higher-resolution nucleosome and higher-order chromatin architectures. While a detailed discussion of the underlying algorithms is beyond the scope of our survey, we have discussed the methods and tools that can detect the nucleosomes in the genome, then demonstrated the computational methods for identifying 3D chromatin domains and interactions. We further illustrated computational approaches for integrating multi-omics data with Hi-C data and the advance of single-cell (sc)Hi-C data analysis. Our survey provides a comprehensive and valuable resource for biomedical scientists interested in studying nucleosome organization and chromatin structures as well as for computational scientists who are interested in improving upon them.

Metastases or benign adrenal lesions in patients with histopathological verification of lung cancer: Can CT texture analysis distinguish?

INTRODUCTION

Distant metastases are found in the many of patients with lung cancer at time of diagnosis. Several diagnostic tools are available to distinguish between metastatic spread and benign lesions in the adrenal gland. However, all require additional diagnostic steps after the initial CT. The purpose of this study was to evaluate if texture analysis of CT-abnormal adrenal glands on the initial CT correctly differentiates between malignant and benign lesions in patients with confirmed lung cancer.

MATERIALS AND METHODS

In this retrospective study 160 patients with endoscopic ultrasound-guided biopsy from the left adrenal gland and a contrast-enhanced CT in portal venous phase were assessed with texture analysis. A region of interest encircling the entire adrenal gland was used and from this dataset the slice with the largest cross section of the lesion was analyzed individually.

RESULTS

Several texture parameters showed statistically significantly difference between metastatic and benign lesions but with considerable between-groups overlaps in confidence intervals. Sensitivity and specificity were assessed using ROC-curves, and in univariate binary logistic regression the area under the curve ranged from 36 % (Kurtosis 0.5) to 69 % (Entropy 2.5) compared to 73 % in the best fitting model using multivariate binary logistic regression.

CONCLUSION

In lung cancer patients with abnormal adrenal gland at imaging, adrenal gland texture analyses appear not to have any role in discriminating benign from malignant lesions.

Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients

OBJECTIVE

The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging.

MATERIAL AND METHODS

Ninety-four high-risk PCa patients who underwent restaging Cho-PET/CT were analyzed. Follow-up data were recorded for a minimum of 13 months after the PET/CT scan. PET images were imported in LIFEx toolbox to extract 51 features from each lesion. A statistical system based on correlation matrix and point-biserial-correlation coefficient has been implemented for features reduction and selection, while Discriminant analysis (DA) was used as a method for features classification in a whole sample and sub-groups for primary tumor or local relapse (T), nodal disease (N), and metastatic disease (M).

RESULTS

In the whole group, 2 feature (HISTO_Entropy_log10; HISTO_Energy_Uniformity) results were able to discriminate the occurrence of disease progression at follow-up, obtaining the best performance in DA classification (sensitivity 47.1%, specificity 76.5%, positive predictive value (PPV) 46.7%, and accuracy 67.6%). In the sub-group analysis, the best performance in DA classification for T was obtained by selecting 3 features (SUVmin; SHAPE_Sphericity; GLCM_Correlation) with a sensitivity of 91.6%, specificity 84.1%, PPV 79.1%, and accuracy 87%; for N by selecting 2 features (HISTO = _Energy Uniformity; GLZLM_SZLGE) with a sensitivity of 68.1%, specificity 91.4%, PPV 83%, and accuracy 82.6%; and for M by selecting 2 features (HISTO_Entropy_log10 - HISTO_Entropy_log2) with a sensitivity 64.4%, specificity 74.6%, PPV 40.6%, and accuracy 72.5%.

CONCLUSION

This machine learning model demonstrated to be feasible and useful to select Cho-PET features for T, N, and M with valuable association with high-risk PCa patients' outcomes.

KEY POINTS

• Artificial intelligence applications are feasible and useful to select Cho-PET features. • Our model demonstrated the presence of specific features for T, N, and M with valuable association with high-risk PCa patients' outcomes. • Further prospective studies are necessary to confirm our results and to develop the application of artificial intelligence in PET imaging of PCa.

A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method

Background

Positron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure.

This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[¹¹C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between patients who respond to treatment or not. For these purposes, 31 brain metastases, for predictive evaluation, and 25 ones, for follow-up evaluation after treatment, were delineated using the proposed method. Successively, 11C-MET PET studies and related volumetric segmentations were used to extract 108 features to investigate the potential application of radiomics analysis in patients with brain metastases. A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification.

Results

For predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUV_mean, SUL_peak, SUV_min, SUL_peak prod-surface-area, SUV_mean prod-sphericity, surface mean SUV 3, SUL_peak prod-sphericity, and second angular moment) were selected with optimal performance in discriminant analysis classification (sensitivity 86.28%, specificity 87.75%, and accuracy 86.57%) outperforming the use of all features.

Conclusions

The proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.

Molecular insights and novel approaches for targeting tumor metastasis

In recent years, due to the effective drug delivery and preciseness of tumor sites or microenvironment, the targeted drug delivery approaches have gained ample attention for tumor metastasis therapy. The conventional treatment approaches for metastasis therapy have reported with immense adverse effects because they exhibited maximum probability of killing the carcinogenic cells along with healthy cells. The tumor vasculature, comprising of vasculogenic impressions and angiogenesis, greatly depends upon the growth and metastasis in the tumors. Therefore, various nanocarriers-based delivery approaches for targeting to tumor vasculature have been attempted as efficient and potential approaches for the treatment of tumor metastasis and the associated lesions. Furthermore, the targeted drug delivery approaches have found to be most apt way to overcome from all the limitations and adverse effects associated with the conventional therapies. In this review, various approaches for efficient targeting of pharmacologically active chemotherapeutics against tumor metastasis with the cohesive objectives of prognosis, tracking and therapy are summarized.

Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy

Human cancers exhibit phenotypic diversity that medical imaging can precisely and non-invasively detect. Multiple factors underlying innovations and progresses in the medical imaging field exert diagnostic and therapeutic impacts. The emerging field of radiomics has shown unprecedented ability to use imaging information in guiding clinical decisions. To achieve clinical assessment that exploits radiomic knowledge sources, integration between diverse data types is required. A current gap is the accuracy with which radiomics aligns with clinical endpoints. We propose a novel methodological approach that synergizes data volumes (images), tissue-contextualized information breadth, and network-driven resolution depth. Following the Precision Medicine paradigm, disease monitoring and prognostic assessment are tackled at the individual level by examining medical images acquired from two patients affected by intracranial ependymoma (with and without relapse). The challenge of spatially characterizing intratumor heterogeneity is tackled by a network approach that presents two main advantages: (a) Increased detection in the image domain power from high spatial resolution, (b) Superior accuracy in generating hypotheses underlying clinical decisions.

Advanced modalities of molecular imaging in precision medicine for musculoskeletal malignancies

Musculoskeletal malignancies consist of a heterogenous group of mesenchymal tumors, often with high inter- and intratumoral heterogeneity. The early and accurate diagnosis of these malignancies can have a substantial impact on optimal treatment and quality of life for these patients. Several new applications and techniques have emerged in molecular imaging, including advances in multimodality imaging, the development of novel radiotracers, and advances in image analysis with radiomics and artificial intelligence. This review highlights the recent advances in molecular imaging modalities and the role of non-invasive imaging in evaluating tumor biology in the era of precision medicine.

The beginning of the end for conventional RECIST - novel therapies require novel imaging approaches

Owing to improvements in our understanding of the biological principles of tumour initiation and progression, a wide variety of novel targeted therapies have been developed. Developments in biomedical imaging, however, have not kept pace with these improvements and are still mainly designed to determine lesion size alone, which is reflected in the Response Evaluation Criteria in Solid Tumors (RECIST). Imaging approaches currently used for the evaluation of treatment responses in patients with solid tumours, therefore, often fail to detect successful responses to novel targeted agents and might even falsely suggest disease progression, a scenario known as pseudoprogression. The ability to differentiate between responders and nonresponders early in the course of treatment is essential to allowing the early adjustment of treatment regimens. Various imaging approaches targeting a single dedicated tumour feature, as described in the hallmarks of cancer, have been successful in preclinical investigations, and some have been evaluated in pilot clinical trials. However, these approaches have largely not been implemented in clinical practice. In this Review, we describe current biomedical imaging approaches used to monitor responses to treatment in patients receiving novel targeted therapies, including a summary of the most promising future approaches and how these might improve clinical practice.

A novel approach to monitoring the efficacy of anti-tumor treatments in animal models: combining functional MRI and texture analysis

Background

The aim of this study was to evaluate the early anti-tumor efficiency of different therapeutic agents with a combination of multi-b-value DWI, DCE-MRI and texture analysis.

Methods

Eighteen 4 T1 homograft tumor models were divided into control, paclitaxel monotherapy and paclitaxel and bevacizumab combination therapy groups (n = 6) that underwent multi-b-value DWI, DCE-MRI and texture analysis before and 15 days after treatment.

Results

After treatment, the tumors in the control group were significantly larger than those in the combination group (P = 0.018). In multi-b-value DWI, the ADC_slow obviously increased in the combination group compared to that in the others (P < 0.01). The f increased in the control and paclitaxel groups, but the combination group showed a significant decrease versus the others (P < 0.02). Additionally, in DCE-MRI, the decreasing K^trans showed an evident difference between the combination and control groups (P = 0.003) due to the latter’s increasing K^trans. The intra-group comparisons of tumor texture in pre-, mid- and post-treatments showed that the entropy had all significantly increased in all groups (P < 0.01, SSF = 0–6), though the MPP, mean and SD increased only in the combination group (P_MPP,mean,SD < 0.05, SSF = 4–6). Moreover, the inter-group comparisons revealed that the mean and MPP exhibited significant differences after treatment (P_mean,MPP < 0.05, SSF = 0–3).

Conclusion

All these results suggest some strong correlations among DWI, DCE and texture analysis, which are beneficial for further study and clinical research.

Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis

Background

The aim of this study was to investigate the potential of combined textural feature analysis of contrast-enhanced MRI (CE-MRI) and static O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET) PET for the differentiation between local recurrent brain metastasis and radiation injury since CE-MRI often remains inconclusive.

Methods

Fifty-two patients with new or progressive contrast-enhancing brain lesions on MRI after radiotherapy (predominantly stereotactic radiosurgery) of brain metastases were additionally investigated using FET PET. Based on histology (n = 19) or clinicoradiological follow-up (n = 33), local recurrent brain metastases were diagnosed in 21 patients (40%) and radiation injury in 31 patients (60%). Forty-two textural features were calculated on both unfiltered and filtered CE-MRI and summed FET PET images (20–40 min p.i.), using the software LIFEx. After feature selection, logistic regression models using a maximum of five features to avoid overfitting were calculated for each imaging modality separately and for the combined FET PET/MRI features. The resulting models were validated using cross-validation. Diagnostic accuracies were calculated for each imaging modality separately as well as for the combined model.

Results

For the differentiation between radiation injury and recurrence of brain metastasis, textural features extracted from CE-MRI had a diagnostic accuracy of 81% (sensitivity, 67%; specificity, 90%). FET PET textural features revealed a slightly higher diagnostic accuracy of 83% (sensitivity, 88%; specificity, 75%). However, the highest diagnostic accuracy was obtained when combining CE-MRI and FET PET features (accuracy, 89%; sensitivity, 85%; specificity, 96%).

Conclusions

Our findings suggest that combined FET PET/CE-MRI radiomics using textural feature analysis offers a great potential to contribute significantly to the management of patients with brain metastases.

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Differentiation between brain metastasis recurrence and radiation injury is of high clinical importance.

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Differentiation based on contrast-enhanced conventional MRI is often inconclusive.

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Radiomics and hybrid amino acid PET/MR imaging are increasingly gaining attention in Neuro-Oncology.

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We investigated the potential of combined PET/MRI radiomics analysis using MRI and FET PET in patients with brain metastases.

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Combined PET/MRI radiomics allows the differentiation of brain metastasis recurrence from radiation injury with high accuracy.

Radiologist Engagement as a Potential Barrier to the Clinical Translation of Quantitative Imaging for the Assessment of Tumor Heterogeneity

RATIONALE AND OBJECTIVE

This study aims to identify potential barriers to the clinical implementation of quantitative imaging for the assessment of tumor heterogeneity.

MATERIALS AND METHODS

An 18-month prospective observational study was undertaken in which the clinical implementation of computed tomography texture analysis (CTTA) as a technique for quantifying tumor heterogeneity in patients with non-small cell lung cancer was assessed using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework.

RESULTS

Adopters of the technology comprised five specialists with dual accreditation in radiology and nuclear medicine supervising two trainees. Tumor heterogeneity information was extracted and reported in 190 of 322 eligible cases (59%) and presented at the multidisciplinary team meeting in 124 of 152 patients (82%) for whom CTTA had been performed. The maximum proportion of eligible cases in which heterogeneity information had been extracted and reported in any quarter was 80%, but fell in the latter half of the study. The maximum frequency with which available CTTA results were presented at the multidisciplinary team meeting in any quarter was 92% and was maintained in the latter part of the study. Significant differences in survival were observed for patients categorized using the two reported CTTA values (P = 0.004 and P = 0.0057, respectively).

CONCLUSIONS

Radiologist engagement is a potential barrier to the effective translation of quantitative imaging assessments of tumor heterogeneity into clinical practice and will need to be addressed before tumor heterogeneity information can successfully contribute to clinical decision making in oncology.

Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma

Glioblastoma (GBM) show significant inter- and intra-tumoral heterogeneity, impacting response to treatment and overall survival time of 12-15 months. To study glioblastoma phenotypic heterogeneity, multi-parametric magnetic resonance images (MRI) of 85 glioblastoma patients from The Cancer Genome Atlas were analyzed to characterize tumor-derived spatial habitats for their relationship with outcome (overall survival) and to identify their molecular correlates (i.e., determine associated tumor signaling pathways correlated with imaging-derived habitat measurements). Tumor sub-regions based on four sequences (fluid attenuated inversion recovery, T1-weighted, post-contrast T1-weighted, and T2-weighted) were defined by automated segmentation. From relative intensity of pixels in the 3-dimensional tumor region, "imaging habitats" were identified and analyzed for their association to clinical and genetic data using survival modeling and Dirichlet regression, respectively. Sixteen distinct tumor sub-regions ("spatial imaging habitats") were derived, and those associated with overall survival (denoted "relevant" habitats) in glioblastoma patients were identified. Dirichlet regression implicated each relevant habitat with unique pathway alterations. Relevant habitats also had some pathways and cellular processes in common, including phosphorylation of STAT-1 and natural killer cell activity, consistent with cancer hallmarks. This work revealed clinical relevance of MRI-derived spatial habitats and their relationship with oncogenic molecular mechanisms in patients with GBM. Characterizing the associations between imaging-derived phenotypic measurements with the genomic and molecular characteristics of tumors can enable insights into tumor biology, further enabling the practice of personalized cancer treatment. The analytical framework and workflow demonstrated in this study are inherently scalable to multiple MR sequences.

Advances in Genomic Profiling and Analysis of 3D Chromatin Structure and Interaction

Recent sequence-based profiling technologies such as high-throughput sequencing to detect fragment nucleotide sequence (Hi-C) and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) have revolutionized the field of three-dimensional (3D) chromatin architecture. It is now recognized that human genome functions as folded 3D chromatin units and looping paradigm is the basic principle of gene regulation. To better interpret the 3D data dramatically accumulating in past five years and to gain deep biological insights, huge efforts have been made in developing novel quantitative analysis methods. However, the full understanding of genome regulation requires thorough knowledge in both genomic technologies and their related data analyses. We summarize the recent advances in genomic technologies in identifying the 3D chromatin structure and interaction, and illustrate the quantitative analysis methods to infer functional domains and chromatin interactions, and further elucidate the emerging single-cell Hi-C technique and its computational analysis, and finally discuss the future directions such as advances of 3D chromatin techniques in diseases.

Activatable fluorescence: From small molecule to nanoparticle

Molecular imaging has emerged as an indispensable technology in the development and application of drug delivery systems. Targeted imaging agents report the presence of biomolecules, including therapeutic targets and disease biomarkers, while the biological behaviour of labelled delivery systems can be non-invasively assessed in real time. As an imaging modality, fluorescence offers additional signal specificity and dynamic information due to the inherent responsivity of fluorescence agents to interactions with other optical species and with their environment. Harnessing this responsivity is the basis of activatable fluorescence imaging, where interactions between an engineered fluorescence agent and its biological target induce a fluorogenic response. Small molecule activatable agents are frequently derivatives of common fluorophores designed to chemically react with their target. Macromolecular scale agents are useful for imaging proteins and nucleic acids, although their biological delivery can be difficult. Nanoscale activatable agents combine the responsivity of fluorophores with the unique optical and physical properties of nanomaterials. The molecular imaging application and overall complexity of biological target dictate the most advantageous fluorescence agent size scale and activation strategy.

Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase 18F-FET PET accuracy without dynamic scans

OBJECTIVES

We investigated the potential of textural feature analysis of O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (¹⁸F-FET) PET to differentiate radiation injury from brain metastasis recurrence.

METHODS

Forty-seven patients with contrast-enhancing brain lesions (n = 54) on MRI after radiotherapy of brain metastases underwent dynamic ¹⁸F-FET PET. Tumour-to-brain ratios (TBRs) of ¹⁸F-FET uptake and 62 textural parameters were determined on summed images 20-40 min post-injection. Tracer uptake kinetics, i.e., time-to-peak (TTP) and patterns of time-activity curves (TAC) were evaluated on dynamic PET data from 0-50 min post-injection. Diagnostic accuracy of investigated parameters and combinations thereof to discriminate between brain metastasis recurrence and radiation injury was compared.

RESULTS

Diagnostic accuracy increased from 81 % for TBR_mean alone to 85 % when combined with the textural parameter Coarseness or Short-zone emphasis. The accuracy of TBR_max alone was 83 % and increased to 85 % after combination with the textural parameters Coarseness, Short-zone emphasis, or Correlation. Analysis of TACs resulted in an accuracy of 70 % for kinetic pattern alone and increased to 83 % when combined with TBR_max.

CONCLUSIONS

Textural feature analysis in combination with TBRs may have the potential to increase diagnostic accuracy for discrimination between brain metastasis recurrence and radiation injury, without the need for dynamic ¹⁸F-FET PET scans.

KEY POINTS

• Textural feature analysis provides quantitative information about tumour heterogeneity • Textural features help improve discrimination between brain metastasis recurrence and radiation injury • Textural features might be helpful to further understand tumour heterogeneity • Analysis does not require a more time consuming dynamic PET acquisition.

Omics approaches to individual variation: modeling networks and the virtual patient

Every human is unique. We differ in our genomes, environment, behavior, disease history, and past and current medical treatment—a complex catalog of differences that often leads to variations in the way each of us responds to a particular therapy. We argue here that true personalization of drug therapies will rely on “virtual patient” models based on a detailed characterization of the individual patient by molecular, imaging, and sensor techniques. The models will be based, wherever possible, on the molecular mechanisms of disease processes and drug action but can also expand to hybrid models including statistics/machine learning/artificial intelligence-based elements trained on available data to address therapeutic areas or therapies for which insufficient information on mechanisms is available. Depending on the disease, its mechanisms, and the therapy, virtual patient models can be implemented at a fairly high level of abstraction, with molecular models representing cells, cell types, or organs relevant to the clinical question, interacting not only with each other but also the environment. In the future, “virtual patient/in-silico self” models may not only become a central element of our health care system, reducing otherwise unavoidable mistakes and unnecessary costs, but also act as “guardian angels” accompanying us through life to protect us against dangers and to help us to deal intelligently with our own health and wellness.

Modulation of tissue tropism and biological activity of exosomes and other extracellular vesicles: New nanotools for cancer treatment

Exosomes are naturally secreted nanovesicles that have recently aroused a great interest in the scientific and clinical community for their roles in intercellular communication in almost all physiological and pathological processes. These 30-100nm sized vesicles are released from the cells into the extracellular space and ultimately into biofluids in a tightly regulated way. Their molecular composition reflects their cells of origin, may confer specific cell or tissue tropism and underlines their biological activity. Exosomes and other extracellular vesicles (EVs) carry specific sets of proteins, nucleic acids (DNA, mRNA and regulatory RNAs), lipids and metabolites that represent an appealing source of novel noninvasive markers through biofluid biopsies. Exosome-shuttled molecules maintain their biological activity and are capable of modulating and reprogramming recipient cells. This multi-faceted nature of exosomes hold great promise for improving cancer treatment featuring them as novel diagnostic sensors as well as therapeutic effectors and drug delivery vectors. Natural biological activity including the therapeutic payload and targeting behavior of EVs can be tuned via genetic and chemical engineering. In this review we describe the properties that EVs share with conventional synthetic nanoparticles, including size, liposome-like membrane bilayer with customizable surface, and multifunctional capacity. We also highlight unique characteristics of EVs, which possibly allow them to circumvent some limitations of synthetic nanoparticle systems and facilitate clinical translation. The latter are in particular correlated with their innate stability, ability to cross biological barriers, efficiently deliver bioactive cargos or evade immune recognition. Furthermore, we discuss the potential roles for EVs in diagnostics and theranostics, and highlight the challenges that still need to be overcome before EVs can be applied to routine clinical practice.

"RADIOTRANSCRIPTOMICS": A synergy of imaging and transcriptomics in clinical assessment

Recent advances in quantitative imaging and "omics" technology have generated a wealth of mineable biological "big data". With the push towards a P4 "predictive, preventive, personalized, and participatory" approach to medicine, researchers began integrating complementary tools to further tune existing diagnostic and therapeutic models. The field of radiogenomics has long pioneered such multidisciplinary investigations in neuroscience and oncology, correlating genotypic and phenotypic signatures to study structural and functional changes in relation to altered molecular behavior. Given the innate dynamic nature of complex disorders and the role of environmental and epigenetic factors in pathogenesis, the transcriptome can further elucidate serial modifications undetected at the genome level. We therefore propose "radiotranscriptomics" as a new member of the P4 medicine initiative, combining transcriptome information, including gene expression and isoform variation, and quantitative image annotations.

Targeting tumor metastases: Drug delivery mechanisms and technologies

Primary sites of tumor are the focal triggers of cancers, yet it is the subsequent metastasis events that cause the majority of the morbidity and mortality. Metastatic tumor cells exhibit a phenotype that differs from that of the parent cells, as they represent a resistant, invasive subpopulation of the original tumor, may have acquired additional genetic or epigenetic alterations under exposure to prior chemotherapeutic or radiotherapeutic treatments, and reside in a microenvironment differing from that of its origin. This combination of resistant phenotype and distal location make tracking and treating metastases particularly challenging. In this review, we highlight some of the unique biological traits of metastasis, which in turn, inspire emerging strategies for targeted imaging of metastasized tumors and metastasis-directed delivery of therapeutics.