Accurate Delineation of Glioma Infiltration by Advanced PET/MR Neuro-Imaging (FRONTIER Study): A Diagnostic Study Protocol

Effective extracellular vesicles in glioma: Focusing on effective ncRNA exosomes and immunotherapy methods for treatment

This comprehensive article explores the complex field of glioma treatment, with a focus on the important roles of non-coding RNAsRNAs (ncRNAs) and exosomes, as well as the potential synergies of immunotherapy. The investigation begins by examining the various functions of ncRNAs and their involvement in glioma pathogenesis, progression, and as potential diagnostic biomarkers. Special attention is given to exosomes as carriers of ncRNAs and their intricate dynamics within the tumor microenvironment. The exploration extends to immunotherapy methods, analyzing their mechanisms and clinical implications in the treatment of glioma. By synthesizing these components, the article aims to provide a comprehensive understanding of how ncRNAs, exosomes, and immunotherapy interact, offering valuable insights into the evolving landscape of glioma research and therapeutic strategies.

Full-Stokes polarization laser-induced breakdown spectroscopy detection of infiltrative glioma boundary tissue

The glioma boundary is difficult to identify during surgery due to the infiltrative characteristics of tumor cells. In order to ensure a full resection rate and increase the postoperative survival of patients, it is often necessary to make an expansion range resection, which may have harmful effects on the quality of the patient's survival. A full-Stokes laser-induced breakdown spectroscopy (FSLIBS) theory with a corresponding system is proposed to combine the elemental composition information and polarization information for glioma boundary detection. To verify the elemental content of brain tissues and provide an analytical basis, inductively coupled plasma mass spectrometry (ICP-MS) and LIBS are also applied to analyze the healthy, boundary, and glioma tissues. Totally, 42 fresh tissue samples are analyzed, and the Ca, Na, K elemental lines and CN, C₂ molecular fragmental bands are proved to take an important role in the different tissue identification. The FSLIBS provides complete polarization information and elemental information than conventional LIBS elemental analysis. The Stokes parameter spectra can significantly reduce the under-fitting phenomenon of artificial intelligence identification models. Meanwhile, the FSLIBS spectral features within glioma samples are relatively more stable than boundary and healthy tissues. Other tissues may be affected obviously by individual differences in lesion positions and patients. In the future, the FSLIBS may be used for the precise identification of glioma boundaries based on polarization and elemental characterizing ability.

Identification of a cellular senescence-related-lncRNA (SRlncRNA) signature to predict the overall survival of glioma patients and the tumor immune microenvironment

Background: Gliomas are brain tumors that arise from glial cells, and they are the most common primary intracranial tumors with a poor prognosis. Cellular senescence plays a critical role in cancer, especially in glioma. In this study, we constructed a senescence-related lncRNA (SRlncRNA) signature to assess the prognosis of glioma. Methods: The Cancer Genome Atlas was used to collect SRlncRNA transcriptome profiles and clinical data about glioma. Patients were randomized to training, testing, and whole cohorts. LASSO and Cox regression analyses were employed to construct the SRlncRNA signature, and Kaplan-Meier (K-M) analysis was performed to determine each cohort's survival. Receiver operating characteristic (ROC) curves were applied to verify the accuracy of this signature. Gene set enrichment analysis was used to visualize functional enrichment (GSEA). The CIBERSORT algorithm, ESTIMATE and TIMER databases were utilized to evaluate the differences in the infiltration of 22 types of immune cells and their association with the signature. RT-qPCR and IHC were used to identify the consistency of the signature in tumor tissue. Results: An SRlncRNA signature consisting of six long non-coding RNAs (lncRNAs) was constructed, and patients were divided into high-risk and low-risk groups by the median of their riskscore. The KM analysis showed that the high-risk group had worse overall survival, and the ROC curve confirmed that the riskscore had more accurate predictive power. A multivariate Cox analysis and its scatter plot with clinical characteristics confirmed the riskscore as an independent risk factor for overall survival. GSEA showed that the GO and KEGG pathways were mainly enriched in the immune response to tumor cells, p53 signaling pathway, mTOR signaling pathway, and Wnt signaling pathway. Further validation also yielded significant differences in the risk signature in terms of immune cell infiltration, which may be closely related to prognostic differences, and qRT-PCR and IHC confirmed the consistency of the expression differences in the major lncRNAs with those in the prediction model. Conclusion Our findings indicated that the SRlncRNA signature might be used as a predictive biomarker and that there is a link between it and immune infiltration. This discovery is consistent with the present categorization system and may open new avenues for research and personalized therapy.

Spatial concordance of DNA methylation classification in diffuse glioma

Background

Intratumoral heterogeneity is a hallmark of diffuse gliomas. DNA methylation profiling is an emerging approach in the clinical classification of brain tumors. The goal of this study is to investigate the effects of intratumoral heterogeneity on classification confidence.

Methods

We used neuronavigation to acquire 133 image-guided and spatially separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma (7 IDH-wildtype and 2 IDH-mutant glioblastoma, 6 diffuse astrocytoma, IDH-mutant and 1 oligodendroglioma, IDH-mutant and 1p19q codeleted), which we characterized using DNA methylation arrays. Samples were obtained from regions with and without abnormalities on contrast-enhanced T1-weighted and fluid-attenuated inversion recovery MRI. Methylation profiles were analyzed to devise a 3-dimensional reconstruction of (epi)genetic heterogeneity. Tumor purity was assessed from clonal methylation sites.

Results

Molecular aberrations indicated that tumor was found outside imaging abnormalities, underlining the infiltrative nature of this tumor and the limitations of current routine imaging modalities. We demonstrate that tumor purity is highly variable between samples and explains a substantial part of apparent epigenetic spatial heterogeneity. We observed that DNA methylation subtypes are often, but not always, conserved in space taking tumor purity and prediction accuracy into account.

Conclusion

Our results underscore the infiltrative nature of diffuse gliomas and suggest that DNA methylation subtypes are relatively concordant in this tumor type, although some heterogeneity exists.

Improved detection of diffuse glioma infiltration with imaging combinations: a diagnostic accuracy study

Background

Surgical resection and irradiation of diffuse glioma are guided by standard MRI: T2/fluid attenuated inversion recovery (FLAIR)–weighted MRI for non-enhancing and T1-weighted gadolinium-enhanced (T1G) MRI for enhancing gliomas. Amino acid PET has been suggested as the new standard. Imaging combinations may improve standard MRI and amino acid PET. The aim of the study was to determine the accuracy of imaging combinations to detect glioma infiltration.

Methods

We included 20 consecutive adults with newly diagnosed non-enhancing glioma (7 diffuse astrocytomas, isocitrate dehydrogenase [IDH] mutant; 1 oligodendroglioma, IDH mutant and 1p/19q codeleted; 1 glioblastoma IDH wildtype) or enhancing glioma (glioblastoma, 9 IDH wildtype and 2 IDH mutant). Standardized preoperative imaging (T1-, T2-, FLAIR-weighted, and T1G MRI, perfusion and diffusion MRI, MR spectroscopy and O-(2-[18F]-fluoroethyl)-L-tyrosine ([¹⁸F]FET) PET) was co-localized with multiregion stereotactic biopsies preceding resection. Tumor presence in the biopsies was assessed by 2 neuropathologists. Diagnostic accuracy was determined using receiver operating characteristic analysis.

Results

A total of 174 biopsies were obtained (63 from 9 non-enhancing and 111 from 11 enhancing gliomas), of which 129 contained tumor (50 from non-enhancing and 79 from enhancing gliomas). In enhancing gliomas, the combination of apparent diffusion coefficient (ADC) with [¹⁸F]FET PET (area under the curve [AUC], 95% CI: 0.89, 0.79‒0.99) detected tumor better than T1G MRI (0.56, 0.39‒0.72; P < 0.001) and [¹⁸F]FET PET (0.76, 0.66‒0.86; P = 0.001). In non-enhancing gliomas, no imaging combination detected tumor significantly better than standard MRI. FLAIR-weighted MRI had an AUC of 0.81 (0.65–0.98) compared with 0.69 (0.56–0.81; P = 0.019) for [¹⁸F]FET PET.

Conclusion

Combining ADC and [¹⁸F]FET PET detects glioma infiltration better than standard MRI and [¹⁸F]FET PET in enhancing gliomas, potentially enabling better guidance of local therapy.

GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma

Purpose

There is an annual incidence of 50,000 glioma cases in Europe. The optimal treatment strategy is highly personalised, depending on tumour type, grade, spatial localization, and the degree of tissue infiltration. In research settings, advanced magnetic resonance imaging (MRI) has shown great promise as a tool to inform personalised treatment decisions. However, the use of advanced MRI in clinical practice remains scarce due to the downstream effects of siloed glioma imaging research with limited representation of MRI specialists in established consortia; and the associated lack of available tools and expertise in clinical settings. These shortcomings delay the translation of scientific breakthroughs into novel treatment strategy. As a response we have developed the network “Glioma MR Imaging 2.0” (GliMR) which we present in this article.

Methods

GliMR aims to build a pan-European and multidisciplinary network of experts and accelerate the use of advanced MRI in glioma beyond the current “state-of-the-art” in glioma imaging. The Action Glioma MR Imaging 2.0 (GliMR) was granted funding by the European Cooperation in Science and Technology (COST) in June 2019.

Results

GliMR’s first grant period ran from September 2019 to April 2020, during which several meetings were held and projects were initiated, such as reviewing the current knowledge on advanced MRI; developing a General Data Protection Regulation (GDPR) compliant consent form; and setting up the website.

Conclusion

The Action overcomes the pre-existing limitations of glioma research and is funded until September 2023. New members will be accepted during its entire duration.

Personalized Multimodal Demarcation of Peritumoral Tissue in Glioma

PURPOSE

Gliomas are life-threatening brain tumors, and the extent of surgical resection is one of the strongest influences on survival rate. However, the proper distinction of infiltrated tissue remains elusive. The aim of this study was to use multimodal analyses to demarcate peritumoral tissue (PT) from tumoral (TT) and healthy tissue (HT).

METHODS

A total of 40 patients with histologically confirmed glioma were recruited. We analyzed resting-state functional magnetic resonance imaging (rs-fMRI) using the voxel-based mean blood-oxygen-level-dependent (BOLD) signal and the corresponding structural MRI (s-MRI) alongside RNA sequencing, whole-exome sequencing, and histology results of biopsy samples obtained from PT, HT, and TT.

RESULTS

We demarcated a functionally defined PT area where the mean BOLD signal gradually decreased near the edge of the tumor and extended beyond the TT borders (as defined by s-MRI), which was confirmed on a case-by-case basis. Correspondingly, genetic analyses showed a gene expression pattern and mutational landscape of the PT that were distinct from that seen in HT and TT. The genetic characterization of PT relative to HT and TT converged with the MRI-defined PT zones. This was confirmed in three individual cases after additional histologic analysis. A wider PT was associated with a longer progression-free survival, which suggests PT might act as an intermediate area between TT and HT.

CONCLUSION

Combined multimodal imaging and genetic analyses can allow for an objective demarcation of the PT in glioma and a robust classification of the degree of infiltration of the PT. These findings could help improve both neurosurgical resection and radio-oncologic therapy.

Quantitative parametric maps of O-(2-[18F]fluoroethyl)-L-tyrosine kinetics in diffuse glioma

Quantitative parametric images of O-(2-[¹⁸F]fluoroethyl)-L-tyrosine kinetics in diffuse gliomas could be used to improve glioma grading, tumour delineation or the assessment of the uptake distribution of this positron emission tomography tracer. In this study, several parametric images and tumour-to-normal maps were compared in terms of accuracy of region averages (when compared to results from nonlinear regression of a reversible two-tissue compartment plasma input model) and image noise using 90 min of dynamic scan data acquired in seven patients with diffuse glioma. We included plasma input methods (the basis function implementation of the single-tissue compartment model, spectral analysis and Logan graphical analysis) and reference tissue methods (basis function implementations of the simplified reference tissue model, variations of the multilinear reference tissue model and non-invasive Logan graphical analysis) as well as tumour-to-normal ratio maps at three intervals. (Non-invasive) Logan graphical analysis provided volume of distribution maps and distribution volume ratio maps with the lowest level of noise, while the basis function implementations provided the best accuracy. Tumour-to-normal ratio maps provided better results if later interval times were used, i.e. 60-90 min instead of 20-40 min, leading to lower bias (2.9% vs. 10.8%, respectively) and less noise (12.8% vs. 14.4%).

Direct comparison of [11C] choline and [18F] FET PET to detect glioma infiltration: a diagnostic accuracy study in eight patients

Background

Positron emission tomography (PET) is increasingly used to guide local treatment in glioma. The purpose of this study was a direct comparison of two potential tracers for detecting glioma infiltration, O-(2-[¹⁸F]-fluoroethyl)-l-tyrosine ([¹⁸F] FET) and [¹¹C] choline.

Methods

Eight consecutive patients with newly diagnosed diffuse glioma underwent dynamic [¹¹C] choline and [¹⁸F] FET PET scans. Preceding craniotomy, multiple stereotactic biopsies were obtained from regions inside and outside PET abnormalities. Biopsies were assessed independently for tumour presence by two neuropathologists. Imaging measurements were derived at the biopsy locations from 10 to 40 min [¹¹C] choline and 20–40, 40–60 and 60–90 min [¹⁸F] FET intervals, as standardized uptake value (SUV) and tumour-to-brain ratio (TBR). Diagnostic accuracies of both tracers were compared using receiver operating characteristic analysis and generalized linear mixed modelling with consensus histopathological assessment as reference.

Results

Of the 74 biopsies, 54 (73%) contained tumour. [¹¹C] choline SUV and [¹⁸F] FET SUV and TBR at all intervals were higher in tumour than in normal samples. For [¹⁸F] FET, the diagnostic accuracy of TBR was higher than that of SUV for intervals 40–60 min (area under the curve: 0.88 versus 0.81, p = 0.026) and 60–90 min (0.90 versus 0.81, p = 0.047). The diagnostic accuracy of [¹⁸F] FET TBR 60–90 min was higher than that of [¹¹C] choline SUV 20–40 min (0.87 versus 0.67, p = 0.005).

Conclusions

[¹⁸F] FET was more accurate than [¹¹C] choline for detecting glioma infiltration. Highest accuracy was found for [¹⁸F] FET TBR for the interval 60–90 min post-injection.

Electronic supplementary material

The online version of this article (10.1186/s13550-019-0523-8) contains supplementary material, which is available to authorized users.

Long noncoding RNA UBE2R2-AS1 promotes glioma cell apoptosis via targeting the miR-877-3p/TLR4 axis

Introduction: Brain glioma is the most common type of primary malignancy in the central nervous system (CNS), with high recurrence and mortality rate, especially glioblastoma (GBM). Recent evidence suggests a role for many long noncoding RNAs (lncRNAs) in the pathogenesis, proliferation, apoptosis, metastasis, and chemotherapeutic resistance of cancer cells. Although the functions of some lncRNAs in the occurrence and development of gliomas have been confirmed, detailed mechanisms of action are lacking. Furthermore, the biological roles of many other lncRNAs in glioma have not been reported at all.

Methods: In this study, we identified a novel lncRNA, UBE2R2-AS1, which was dramatically downregulated in glioma compared with normal tissue, by performing microarray detection of six pairs of glioma samples and adjacent normal tissues. In vitro experiments demonstrated that UBE2R2-AS1 regulated glioma cell proliferation, apoptosis, and migration.

Results: UBE2R2-AS1 acted as a competing endogenous RNA (ceRNA) to target Toll-like receptor 4 (TLR4) mRNA by binding to miR-877-3p. Furthermore, lncRNA UBE2R2-AS1 suppressed glioblastoma cell growth, migration, and invasion, as well as promoting cell apoptosis by targeting miR-877-3p/TLR4 directly.

Conclusion: This information regarding UBE2R2-AS1 and its glioma-related molecular mechanisms will aid the future identification of new lncRNA-directed diagnostics and drug-targeting therapies.

Long Noncoding RNA ASB16-AS1 Promotes Proliferation, Migration, and Invasion in Glioma Cells

Glioma is a lethal, malignant intracranial tumor that becomes progressively common. It has been shown that long noncoding RNAs (lncRNAs) serve important roles in numerous diseases such as gliomas. lncRNAs can regulate the expression of targeted genes through various mechanisms. To identify a novel lncRNA that may be critical in glioma, the present study downloaded the RNA expression profiles of 171 glioma tissues and 5 normal tissues from The Cancer Genome Atlas (TCGA) database using the TCGAbiolinks package in R. Then, lncRNAs in the downloaded TCGA data were identified using the HUGO Gene Nomenclature Committee (HGNC). Based on the fragments per kilobase million value, differential expression analysis was conducted using the limma package in R. In addition, receiver operating characteristic (ROC) analysis was performed, and the area under the curve (AUC) was evaluated using the ROCR package in R. A total of 178 lncRNAs corresponding to differentially expressed genes with an AUC >0.85 were selected. Upon identifying the differential lncRNAs, ceRNA networks were constructed with these differential lncRNAs using the starbase database. From these networks, the top 10% hub genes were selected. In addition, the present study randomly selected 4 lncRNAs for quantitative polymerase chain reaction validation in tissue samples. The results revealed that lncRNA ASB16-AS1 exhibited significantly differential expression in tissue samples and was significantly associated with tumor staging and grading. Furthermore, the proliferation, invasion, and migration of U87MG and U251 glioblastoma stem-like cells (U87GS, U251GS) were significantly inhibited upon inhibition of ASB16-AS1, and the expression of key proteins in the EMT signaling pathway was affected by knocking down ASB16-AS1. Overall, the present study revealed that lncRNA ASB16-AS1 improves the proliferation, migration, and invasion of glioma cells.

Quantification of O-(2-[18F]fluoroethyl)-L-tyrosine kinetics in glioma

BACKGROUND

This study identified the optimal tracer kinetic model for quantification of dynamic O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET) positron emission tomography (PET) studies in seven patients with diffuse glioma (four glioblastoma, three lower grade glioma). The performance of more simplified approaches was evaluated by comparison with the optimal compartment model. Additionally, the relationship with cerebral blood flow-determined by [15O]H2O PET-was investigated.

RESULTS

The optimal tracer kinetic model was the reversible two-tissue compartment model. Agreement analysis of binding potential estimates derived from reference tissue input models with the distribution volume ratio (DVR)-1 derived from the plasma input model showed no significant average difference and limits of agreement of - 0.39 and 0.37. Given the range of DVR-1 (- 0.25 to 1.5), these limits are wide. For the simplified methods, the 60-90 min tumour-to-blood ratio to parent plasma concentration yielded the highest correlation with volume of distribution VT as calculated by the plasma input model (r = 0.97). The 60-90 min standardized uptake value (SUV) showed better correlation with VT (r = 0.77) than SUV based on earlier intervals. The 60-90 min SUV ratio to contralateral healthy brain tissue showed moderate agreement with DVR with no significant average difference and limits of agreement of - 0.24 and 0.30. A significant but low correlation was found between VT and CBF in the tumour regions (r = 0.61, p = 0.007).

CONCLUSION

Uptake of [18F]FET was best modelled by a reversible two-tissue compartment model. Reference tissue input models yielded estimates of binding potential which did not correspond well with plasma input-derived DVR-1. In comparison, SUV ratio to contralateral healthy brain tissue showed slightly better performance, if measured at the 60-90 min interval. SUV showed only moderate correlation with VT. VT shows correlation with CBF in tumour.

Long non-coding RNA in glioma: signaling pathways

Glioma is regarded as the most prevalent malignant carcinoma of the central nervous system. Thus, the development of new therapeutic strategies targeting glioma is of significant clinical importance. Long non-coding RNAs (lncRNAs) are functional RNA molecules without a protein-coding function and are reportedly involved in the initiation and progression of glioma. Dysregulation of lncRNAs in glioma is due to activation of several signaling pathways, such as the BRD4-HOTAIR-β-catenin/PDCD4, p53-Hif-H19/IGF2 and CRNDE/mTOR pathways. Furthermore, microRNAs (miRNAs) such as miR-675 also interact with lncRNAs in glioma. Thus, exploring the mechanisms by which lncRNA control processes will be instrumental for devising new effective therapies against glioma.

Comparison among conventional and advanced MRI, 18F-FDG PET/CT, phenotype and genotype in glioblastoma

Glioblastoma (GB) is a highly heterogeneous tumor. In order to identify in vivo the most malignant tumor areas, the extent of tumor infiltration and the sites giving origin to GB stem cells (GSCs), we combined positron emission tomography/computed tomography (PET/CT) and conventional and advanced magnetic resonance imaging (MRI) with histology, immunohistochemistry and molecular genetics. Prior to dura opening and tumor resection, forty-eight biopsy specimens [23 of contrast-enhancing (CE) and 25 of non-contrast enhancing (NE) regions] from 12 GB patients were obtained by a frameless image-guided stereotactic biopsy technique. The highest values of 2-[18F]-fluoro-2-deoxy-D-glucose maximum standardized uptake value (18F-FDG SUVmax), relative cerebral blood volume (rCBV), Choline/Creatine (Cho/Cr), Choline/N-acetylaspartate (Cho/NAA) and Lipids/Lactate (LL) ratio have been observed in the CE region. They corresponded to the most malignant tumor phenotype, to the greatest molecular spectrum and stem cell potential. On the contrary, apparent diffusion coefficient (ADC) and fractional anisotropy (FA) in the CE region were very variable. 18F-FDG SUVmax, Cho/Cr and Cho/NAA ratio resulted the most suitable parameters to detect tumor infiltration. In edematous areas, reactive astrocytes and microglia/macrophages were influencing variables. Combined MRI and 18F-FDG PET/CT allowed to recognize the specific biological significance of the different identified areas of GB.

Network Plasticity and Intraoperative Mapping for Personalized Multimodal Management of Diffuse Low-Grade Gliomas

Gliomas are the most frequent primary brain tumors and include a variety of different histological tumor types and malignancy grades. Recent achievements in terms of molecular and imaging fields have created an unprecedented opportunity to perform a comprehensive interdisciplinary assessment of the glioma pathophysiology, with direct implications in terms of the medical and surgical treatment strategies available for patients. The current paradigm shift considers glioma management in a comprehensive perspective that takes into account the intricate connectivity of the cerebral networks. This allowed significant improvement in the outcome of patients with lesions previously considered inoperable. The current review summarizes the current theoretical framework integrating the adult human brain plasticity and functional reorganization within a dynamic individualized treatment strategy for patients affected by diffuse low-grade gliomas. The concept of neuro-oncology as a brain network surgery has major implications in terms of the clinical management and ensuing outcomes, as indexed by the increased survival and quality of life of patients managed using such an approach.

Evaluation of PET/MRI for Tumor Volume Delineation for Head and Neck Cancer

Introduction

Computed tomography (CT), combined positron emitted tomography and CT (PET/CT), and magnetic resonance imaging (MRI) are commonly used in head and neck radiation planning. Hybrid PET/MRI has garnered attention for potential added value in cancer staging and treatment planning. Herein, we compare PET/MRI vs. planning CT for head and neck cancer gross tumor volume (GTV) delineation.

Material and methods

We prospectively enrolled patients with head and neck cancer treated with definitive chemoradiation to 60–70 Gy using IMRT. We performed pretreatment contrast-enhanced planning CT and gadolinium-enhanced PET/MRI. Primary and nodal volumes were delineated on planning CT (GTV-CT) prospectively before treatment and PET/MRI (GTV-PET/MRI) retrospectively after treatment. GTV-PET/MRI was compared to GTV-CT using separate rigid registrations for each tumor volume. The Dice similarity coefficient (DSC) metric evaluating spatial overlap and modified Hausdorff distance (mHD) evaluating mean orthogonal distance difference were calculated. Minimum dose to 95% of GTVs (D95) was compared.

Results

Eleven patients were evaluable (10 oropharynx, 1 larynx). Nine patients had evaluable primary tumor GTVs and seven patients had evaluable nodal GTVs. Mean primary GTV-CT and GTV-PET/MRI size were 13.2 and 14.3 cc, with mean intersection 8.7 cc, DSC 0.63, and mHD 1.6 mm. D95 was 65.3 Gy for primary GTV-CT vs. 65.2 Gy for primary GTV-PET/MRI. Mean nodal GTV-CT and GTV-PET/MRI size were 19.0 and 23.0 cc, with mean intersection 14.4 cc, DSC 0.69, and mHD 2.3 mm. D95 was 62.3 Gy for both nodal GTV-CT and GTV-PET/MRI.

Conclusion

In this series of patients with head and neck (primarily oropharynx) cancer, PET/MRI and CT-GTVs had similar volumes (though there were individual cases with larger differences) with overall small discrepancies in spatial overlap, small mean orthogonal distance differences, and similar radiation doses.