Imaging oligometastatic cancer before local treatment

The term oligometastases is in common clinical use, but remains poorly defined. As novel treatment strategies widen the therapeutic window for patients defined as having oligometastatic cancer, improved biomarkers to reliably define patients who benefit from these treatments are needed. Multimodal imaging should be optimized to comprehensively assess the metastatic sites, disease burden and response to neoadjuvant treatment in each disease setting. These features will likely remain important prognostic biomarkers, and are critical in planning multidisciplinary treatment. There are opportunities to extract additional phenotypic information from conventional imaging, while novel imaging techniques can also image specific aspects of tumour biology. Imaging can both characterise and localise the phenotypic heterogeneity of multiple tumour sites. Novel approaches to existing imaging datasets, and correlation with tumour biology, will be important in realizing the potential of imaging to guide treatment in the oligometastatic setting. This article discusses the current status and future directions of imaging in patients with extracranial oligometastases

GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications.

Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though computationally efficient, rule out their application to intrinsically more complex organ motions, such as sliding interfaces. We propose regularization of motion based on supervoxels, which provides an integrated discontinuity preserving prior for motions, such as sliding. More precisely, we replace Gaussian smoothing by fast, structure-preserving, guided filtering to provide efficient, locally adaptive regularization of the estimated displacement field. We illustrate the approach by applying it to estimate sliding motions at lung and liver interfaces on challenging four-dimensional computed tomography (CT) and dynamic contrast-enhanced magnetic resonance imaging datasets. The results show that guided filter-based regularization improves the accuracy of lung and liver motion correction as compared to Gaussian smoothing. Furthermore, our framework achieves state-of-the-art results on a publicly available CT liver dataset

Chronic Obstructive Pulmonary Disease: Lobar Analysis with Hyperpolarized 129Xe MR Imaging.

Purpose To compare lobar ventilation and apparent diffusion coefficient (ADC) values obtained with hyperpolarized xenon 129 ((129)Xe) magnetic resonance (MR) imaging to quantitative computed tomography (CT) metrics on a lobar basis and pulmonary function test (PFT) results on a whole-lung basis in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods The study was approved by the National Research Ethics Service Committee; written informed consent was obtained from all patients. Twenty-two patients with COPD (Global Initiative for Chronic Obstructive Lung Disease stage II-IV) underwent hyperpolarized (129)Xe MR imaging at 1.5 T, quantitative CT, and PFTs. Whole-lung and lobar (129)Xe MR imaging parameters were obtained by using automated segmentation of multisection hyperpolarized (129)Xe MR ventilation images and hyperpolarized (129)Xe MR diffusion-weighted images after coregistration to CT scans. Whole-lung and lobar quantitative CT-derived metrics for emphysema and bronchial wall thickness were calculated. Pearson correlation coefficients were used to evaluate the relationship between imaging measures and PFT results. Results Percentage ventilated volume and average ADC at lobar (129)Xe MR imaging showed correlation with percentage emphysema at lobar quantitative CT (r = -0.32, P < .001 and r = 0.75, P < .0001, respectively). The average ADC at whole-lung (129)Xe MR imaging showed moderate correlation with PFT results (percentage predicted transfer factor of the lung for carbon monoxide [Tlco]: r = -0.61, P < .005) and percentage predicted functional residual capacity (r = 0.47, P < .05). Whole-lung quantitative CT percentage emphysema also showed statistically significant correlation with percentage predicted Tlco (r = -0.65, P < .005). Conclusion Lobar ventilation and ADC values obtained from hyperpolarized (129)Xe MR imaging demonstrated correlation with quantitative CT percentage emphysema on a lobar basis and with PFT results on a whole-lung basis. (©) RSNA, 2016

Review of hyperpolarized pulmonary functional 129Xe MR for long-COVID

The respiratory consequences of acute COVID-19 infection and related symptoms tend to resolve 4 weeks post-infection. However, for some patients, new, recurrent, or persisting symptoms remain beyond the acute phase and persist for months, post-infection. The symptoms that remain have been referred to as long-COVID. A number of research sites employed 129Xe magnetic resonance imaging (MRI) during the pandemic and evaluated patients post-infection, months after hospitalization or home-based care as a way to better understand the consequences of infection on 129Xe MR gas-exchange and ventilation imaging. A systematic review and comprehensive search were employed using MEDLINE via PubMed (April 2023) using the National Library of Medicine's Medical Subject Headings and key words: post-COVID-19, MRI, 129Xe, long-COVID, COVID pneumonia, and post-acute COVID-19 syndrome. Fifteen peer-reviewed manuscripts were identified including four editorials, a single letter to the editor, one review article, and nine original research manuscripts (2020–2023). MRI and MR spectroscopy results are summarized from these prospective, controlled studies, which involved small sample sizes ranging from 9 to 76 participants. Key findings included: 1) 129Xe MRI gas-exchange and ventilation abnormalities, 3 months post-COVID-19 infection, and 2) a combination of MRI gas-exchange and ventilation abnormalities alongside persistent symptoms in patients hospitalized and not hospitalized for COVID-19, 1-year post-infection. The persistence of respiratory symptoms and 129Xe MRI abnormalities in the context of normal or nearly normal pulmonary function test results and chest computed tomography (CT) was consistent. Longitudinal improvements were observed in long-term follow-up of long-COVID patients but mean 129Xe gas-exchange, ventilation heterogeneity values and symptoms remained abnormal, 1-year post-infection. Pulmonary functional MRI using inhaled hyperpolarized 129Xe gas has played a role in detecting gas-exchange and ventilation abnormalities providing complementary information that may help develop our understanding of the root causes of long-COVID

Tumour subregion analysis of colorectal liver metastases using semi-automated clustering based on DCE-MRI: Comparison with histological subregions and impact on pharmacokinetic parameter analysis.

PURPOSE: To use a novel segmentation methodology based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to define tumour subregions of liver metastases from colorectal cancer (CRC), to compare these with histology, and to use these to compare extracted pharmacokinetic (PK) parameters between tumour subregions. MATERIALS AND METHODS: This ethically-approved prospective study recruited patients with CRC and ≥1 hepatic metastases scheduled for hepatic resection. Patients underwent DCE-MRI pre-metastasectomy. Histological sections of resection specimens were spatially matched to DCE-MRI acquisitions and used to define histological subregions of viable and non-viable tumour. A semi-automated voxel-wise image segmentation algorithm based on the DCE-MRI contrast-uptake curves was used to define imaging subregions of viable and non-viable tumour. Overlap of histologically-defined and imaging subregions was compared using the Dice similarity coefficient (DSC). DCE-MRI PK parameters were compared for the whole tumour and histology-defined and imaging-derived subregions. RESULTS: Fourteen patients were included in the analysis. Direct histological comparison with imaging was possible in nine patients. Mean DSC for viable tumour subregions defined by imaging and histology was 0.738 (range 0.540-0.930). There were significant differences between Ktrans and kep for viable and non-viable subregions (p < 0.001) and between whole lesions and viable subregions (p < 0.001). CONCLUSION: We demonstrate good concordance of viable tumour segmentation based on pre-operative DCE-MRI with a post-operative histological gold-standard. This can be used to extract viable tumour-specific values from quantitative image analysis, and could improve treatment response assessment in clinical practice

Multi-observer concordance and accuracy of the British Thoracic Society scale and other visual assessment qualitative criteria for solid pulmonary nodule assessment using FDG PET-CT

AIM: To compare the interobserver reliability and diagnostic accuracy of the British Thoracic Society (BTS) scale and other visual assessment criteria in the context of 2-[ 18F]-fluoro-2-deoxy- d-glucose (FDG) positron-emission tomography (PET)-computed tomography (CT) evaluation of solid pulmonary nodules (SPNs). MATERIALS AND METHODS: Fifty patients who underwent FDG PET-CT for assessment of a SPN were identified. Seven reporters with varied experience at four centres graded FDG uptake visually using the British Thoracic Society (BTS) four-point scale. Five reporters also scored SPNs according to three- and five-point visual assessment scales and using semi-quantitative assessment (maximum standardised uptake value [SUV max]). Interobserver reliability was assessed with the intra-class correlation coefficient (ICC) and weighted Cohen's kappa (κ). Diagnostic performance was evaluated by receiver operator characteristic (ROC) analysis. RESULTS: Good interobserver reliability was demonstrated with the BTS scale (ICC=0.78, 95% confidence interval [CI]: 0.69–0.85) and five-point scale (ICC=0.78, 95 CI 0.68–0.86), whilst the three-point scale demonstrated moderate reliability (ICC=0.70, 95% CI: 0.59–0.80). Almost perfect agreement was achieved between two consultants (κ=0.85), and substantial agreement between two other consultants (κ=0.78) using the BTS scale. ROC curves for the BTS and five-point scales demonstrated equivalent accuracy (BTS area under the ROC curve [AUC]=0.768; five-point AUC=0.768). SUV max was no more accurate compared to the BTS scale (SUV max AUC=0.794; BTS AUC=0.768, p=0.43). CONCLUSIONS: The BTS scale can be applied reliably by reporters with varied levels of PET-CT reporting experience, across different centres and has a diagnostic performance that is not surpassed by alternative scales

Dynamic contrast-enhanced CT compared with positron emission tomography CT to characterise solitary pulmonary nodules : the SPUtNIk diagnostic accuracy study and economic modelling

Background Current pathways recommend positron emission tomography–computerised tomography for the characterisation of solitary pulmonary nodules. Dynamic contrast-enhanced computerised tomography may be a more cost-effective approach. Objectives To determine the diagnostic performances of dynamic contrast-enhanced computerised tomography and positron emission tomography–computerised tomography in the NHS for solitary pulmonary nodules. Systematic reviews and a health economic evaluation contributed to the decision-analytic modelling to assess the likely costs and health outcomes resulting from incorporation of dynamic contrast-enhanced computerised tomography into management strategies. Design Multicentre comparative accuracy trial. Setting Secondary or tertiary outpatient settings at 16 hospitals in the UK. Participants Participants with solitary pulmonary nodules of ≥ 8 mm and of ≤ 30 mm in size with no malignancy in the previous 2 years were included. Interventions Baseline positron emission tomography–computerised tomography and dynamic contrast-enhanced computer tomography with 2 years’ follow-up. Main outcome measures Primary outcome measures were sensitivity, specificity and diagnostic accuracy for positron emission tomography–computerised tomography and dynamic contrast-enhanced computerised tomography. Incremental cost-effectiveness ratios compared management strategies that used dynamic contrast-enhanced computerised tomography with management strategies that did not use dynamic contrast-enhanced computerised tomography. Results A total of 380 patients were recruited (median age 69 years). Of 312 patients with matched dynamic contrast-enhanced computer tomography and positron emission tomography–computerised tomography examinations, 191 (61%) were cancer patients. The sensitivity, specificity and diagnostic accuracy for positron emission tomography–computerised tomography and dynamic contrast-enhanced computer tomography were 72.8% (95% confidence interval 66.1% to 78.6%), 81.8% (95% confidence interval 74.0% to 87.7%), 76.3% (95% confidence interval 71.3% to 80.7%) and 95.3% (95% confidence interval 91.3% to 97.5%), 29.8% (95% confidence interval 22.3% to 38.4%) and 69.9% (95% confidence interval 64.6% to 74.7%), respectively. Exploratory modelling showed that maximum standardised uptake values had the best diagnostic accuracy, with an area under the curve of 0.87, which increased to 0.90 if combined with dynamic contrast-enhanced computerised tomography peak enhancement. The economic analysis showed that, over 24 months, dynamic contrast-enhanced computerised tomography was less costly (£3305, 95% confidence interval £2952 to £3746) than positron emission tomography–computerised tomography (£4013, 95% confidence interval £3673 to £4498) or a strategy combining the two tests (£4058, 95% confidence interval £3702 to £4547). Positron emission tomography–computerised tomography led to more patients with malignant nodules being correctly managed, 0.44 on average (95% confidence interval 0.39 to 0.49), compared with 0.40 (95% confidence interval 0.35 to 0.45); using both tests further increased this (0.47, 95% confidence interval 0.42 to 0.51). Limitations The high prevalence of malignancy in nodules observed in this trial, compared with that observed in nodules identified within screening programmes, limits the generalisation of the current results to nodules identified by screening. Conclusions Findings from this research indicate that positron emission tomography–computerised tomography is more accurate than dynamic contrast-enhanced computerised tomography for the characterisation of solitary pulmonary nodules. A combination of maximum standardised uptake value and peak enhancement had the highest accuracy with a small increase in costs. Findings from this research also indicate that a combined positron emission tomography–dynamic contrast-enhanced computerised tomography approach with a slightly higher willingness to pay to avoid missing small cancers or to avoid a ‘watch and wait’ policy may be an approach to consider. Future work Integration of the dynamic contrast-enhanced component into the positron emission tomography–computerised tomography examination and the feasibility of dynamic contrast-enhanced computerised tomography at lung screening for the characterisation of solitary pulmonary nodules should be explored, together with a lower radiation dose protocol. Study registration This study is registered as PROSPERO CRD42018112215 and CRD42019124299, and the trial is registered as ISRCTN30784948 and ClinicalTrials.gov NCT02013063

Comparative Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography and Positron Emission Tomography in the Characterisation of Solitary Pulmonary Nodules

Abstract Introduction: Dynamic contrast-enhanced computed tomography (DCE-CT) and Positron Emission Tomography/Computed Tomography (PET/CT) have a high reported accuracy for the diagnosis of malignancy in solitary pulmonary nodules. The aim of this study was to compare the accuracy and cost-effectiveness of these. Methods: In this prospective multicentre trial, 380 participants with a solitary pulmonary nodule (8-30mm) and no recent history of malignancy underwent DCE-CT and PET/CT. All patients underwent either biopsy with histological diagnosis or completed CT follow-up. Primary outcome measures were sensitivity, specificity, and overall diagnostic accuracy for PET/CT and DCE-CT. Costs and cost-effectiveness were estimated from a healthcare provider perspective using a decision-model. Results: 312 participants (47% female, 68.1±9.0 years) completed the study, with 61% rate of malignancy at 2 years. The sensitivity, specificity, positive predictive value and negative predictive values for DCE-CT were 95.3% [95% CI 91.3;97.5], 29.8% [95% CI 22.3;38.4], 68.2% [95% CI 62.4%;73.5%] and 80.0% [95% CI 66.2;89.1] respectively, and for PET/CT were 79.1% [95% CI 72.7;84.2], 81.8% [95% CI 74.0;87.7], 87.3%[95% CI 81.5;91.5) and 71·2% [95% CI 63.2;78.1]. The area under the receiver operator characteristic curve (AUROC) for DCE-CT and PET/CT was 0.62 [95%CI 0.58;0.67] and 0.80 [95%CI 0.76;0.85] respectively (p<0.001). Combined results significantly increased diagnostic accuracy over PET/CT alone (AUROC=0.90 [95%CI 0.86;0.93], p<0.001). DCE-CT was preferred when the willingness to pay per incremental cost per correctly treated malignancy was below £9000. Above £15500 a combined approach was preferred. Conclusions: PET/CT has a superior diagnostic accuracy to DCE-CT for the diagnosis of solitary pulmonary nodules. Combining both techniques improves the diagnostic accuracy over either test alone and could be cost-effective. (Clinical trials.gov - NCT02013063)

Lobar ventilation in patients with COPD assessed with the full-scale airway network flow model and xenon-enhanced dual-energy CT

Background: The full-scale airway network (FAN) flow model shows excellent agreement with limited functional imaging data but requires further validation prior to clinical use. Purpose: To validate the ventilation distributions computed with the FAN flow model with xenon ventilation from xenon-enhanced dual-energy (DE) CT in participants with chronic obstructive pulmonary disease (COPD). Materials and Methods: In this prospective study, the FAN model extracted structural data from xenon-enhanced DE CT images of men with COPD scanned between June 2012 and July 2013 to compute gas ventilation dynamics. The ventilation distributions on the middle cross-section plane, percentage lobar ventilation, and ventilation heterogeneity quantified by the coefficient of variation (CV) were compared between xenon-enhanced DE CT imaging and the FAN model. The relationship between the ventilation parameters with the densitometry and pulmonary function test results was demonstrated. The agreements and correlations between the parameters were measured using the concordance correlation coefficient and the Pearson correlation coefficient. Results: Twenty-two men with COPD (mean age, 67 years 6 7 [standard deviation]) were evaluated. The percentage lobar ventilation computed with FAN showed a strong positive correlation with xenon-enhanced DE CT data (r = 0.7, P,.001). Ninety-five percent of lobar ventilation CV differences lay within 95% confidence intervals. Correlations of the percentage lobar ventilation were negative for percentage emphysema (xenon-enhanced DE CT: r = -0.38, P,.001; FAN: r = -0.23, P =.02) but were positive for percentage normal tissue volume (xenon-enhanced DE CT: r = 0.78, P,.001; FAN: r = 0.45, P,.001). Lung CVs of FAN revealed negative correlations with the spirometry results (CVFAN vs percentage predicted forced expiratory volume in 1 second: r = -0.75, P,.001; CVFAN vs ratio of forced expiratory volume in 1 second to forced vital capacity: r = -0.67, P,.001). Conclusion: The full-scale airway network modeled lobar ventilation in patients with chronic obstructive pulmonary disease correlated with the xenon-enhanced dual-energy CT imaging data. © RSNA, 2020Engineering and Physical Sciences Research Council, EPSRC: A16466; Cancer Research UK, CRUK: C5255; NIHR Oxford Biomedical Research Centre, OxBRCSupported by the Cancer Research UK (grant C5255), Engineering and Physical Sciences Research Council (grant A16466), and NIHR Oxford Biomedical Research Centre