Autonomous control of an ultrasound probe for intra-operative ultrasonography using vision-based shape sensing of pneumatically attachable flexible rails.

PURPOSE: In robotic-assisted minimally invasive surgery, surgeons often use intra-operative ultrasound to visualise endophytic structures and localise resection margins. This must be performed by a highly skilled surgeon. Automating this subtask may reduce the cognitive load for the surgeon and improve patient outcomes. METHODS: We demonstrate vision-based shape sensing of the pneumatically attachable flexible (PAF) rail by using colour-dependent image segmentation. The shape-sensing framework is evaluated on known curves ranging from r = 30 to r = 110 mm, replicating curvatures in a human kidney. The shape sensing is then used to inform path planning of a collaborative robot arm paired with an intra-operative ultrasound probe. We execute 15 autonomous ultrasound scans of a tumour-embedded kidney phantom and retrieve viable ultrasound images, as well as seven freehand ultrasound scans for comparison. RESULTS: The vision-based sensor is shown to have comparable sensing accuracy with FBGS-based systems. We find the RMSE of the vision-based shape sensing of the PAF rail compared with ground truth to be 0.4975 ± 0.4169 mm. The ultrasound images acquired by the robot and by the human were evaluated by two independent clinicians. The median score across all criteria for both readers was '3-good' for human and '4-very good' for robot. CONCLUSION: We have proposed a framework for autonomous intra-operative US scanning using vision-based shape sensing to inform path planning. Ultrasound images were evaluated by clinicians for sharpness of image, clarity of structures visible, and contrast of solid and fluid areas. Clinicians evaluated that robot-acquired images were superior to human-acquired images in all metrics. Future work will translate the framework to a da Vinci surgical robot.

First-in-human in-vivo depiction of paraganglioma metabolism by hyperpolarised 13C-magnetic resonance.

Phaeochromocytomas (PCC) and paragangliomas (PGL), cumulatively referred to as PPGLs, are neuroendocrine tumours arising from neural crest-derived cells in the sympathetic and parasympathetic nervous systems. Predicting future tumour behaviour and the likelihood of metastatic disease remains problematic as genotype-phenotype correlations are limited, the disease has variable penetrance and, to date, no reliable molecular, cellular or histological markers have emerged. Tumour metabolism quantification can be considered as a method to delineating tumour aggressiveness by utilising hyperpolarised 13 C-MR (HP-MR). The technique may provide an opportunity to non-invasively characterise disease behaviour. Here, we present the first instance of the analysis of PPGL metabolism via HP-MR in a single case.

Quantification of Prostate Cancer Metabolism Using 3D Multiecho bSSFP and Hyperpolarized [1-13 C] Pyruvate: Metabolism Differs Between Tumors of the Same Gleason Grade.

BACKGROUND: Three-dimensional (3D) multiecho balanced steady-state free precession (ME-bSSFP) has previously been demonstrated in preclinical hyperpolarized (HP) 13 C-MRI in vivo experiments, and it may be suitable for clinical metabolic imaging of prostate cancer (PCa). PURPOSE: To validate a signal simulation framework for the use of sequence parameter optimization. To demonstrate the feasibility of ME-bSSFP for HP 13 C-MRI in patients. To evaluate the metabolism in PCa measured by ME-bSSFP. STUDY TYPE: Retrospective single-center cohort study. PHANTOMS/POPULATION: Phantoms containing aqueous solutions of [1-13 C] lactate (2.3 M) and [13 C] urea (8 M). Eight patients (mean age 67 ± 6 years) with biopsy-confirmed Gleason 3 + 4 (n = 7) and 4 + 3 (n = 1) PCa. FIELD STRENGTH/SEQUENCES: 1 H MRI at 3 T with T2 -weighted turbo spin-echo sequence used for spatial localization and spoiled dual gradient-echo sequence used for B0 -field measurement. ME-bSSFP sequence for 13 C MR spectroscopic imaging with retrospective multipoint IDEAL metabolite separation. ASSESSMENT: The primary endpoint was the analysis of pyruvate-to-lactate conversion in PCa and healthy prostate regions of interest (ROIs) using model-free area under the curve (AUC) ratios and a one-directional kinetic model (kP ). The secondary objectives were to investigate the correlation between simulated and experimental ME-bSSFP metabolite signals for HP 13 C-MRI parameter optimization. STATISTICAL TESTS: Pearson correlation coefficients with 95% confidence intervals and paired t-tests. The level of statistical significance was set at P < 0.05. RESULTS: Strong correlations between simulated and empirical ME-bSSFP signals were found (r > 0.96). Therefore, the simulation framework was used for sequence optimization. Whole prostate metabolic HP 13 C-MRI, observing the conversion of pyruvate into lactate, with a temporal resolution of 6 seconds was demonstrated using ME-bSSFP. Both assessed metrics resulted in significant differences between PCa (mean ± SD) (AUC = 0.33 ± 012, kP  = 0.038 ± 0.014) and healthy (AUC = 0.15 ± 0.10, kP  = 0.011 ± 0.007) ROIs. DATA CONCLUSION: Metabolic HP 13 C-MRI in the prostate using ME-bSSFP allows for differentiation between aggressive PCa and healthy tissue. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Histo-MRI map study protocol: a prospective cohort study mapping MRI to histology for biomarker validation and prediction of prostate cancer.

INTRODUCTION: Multiparametric MRI (mpMRI) is now widely used to risk stratify men with a suspicion of prostate cancer and identify suspicious regions for biopsy. However, the technique has modest specificity and a high false-positive rate, especially in men with mpMRI scored as indeterminate (3/5) or likely (4/5) to have clinically significant cancer (csPCa) (Gleason ≥3+4). Advanced MRI techniques have emerged which seek to improve this characterisation and could predict biopsy results non-invasively. Before these techniques are translated clinically, robust histological and clinical validation is required. METHODS AND ANALYSIS: This study aims to clinically validate two advanced MRI techniques in a prospectively recruited cohort of men suspected of prostate cancer. Histological analysis of men undergoing biopsy or prostatectomy will be used for biological validation of biomarkers derived from Vascular and Extracellular Restricted Diffusion for Cytometry in Tumours and Luminal Water imaging. In particular, prostatectomy specimens will be processed using three-dimension printed patient-specific moulds to allow for accurate MRI and histology mapping. The index tests will be compared with the histological reference standard to derive false positive rate and true positive rate for men with mpMRI scores which are indeterminate (3/5) or likely (4/5) to have clinically significant prostate cancer (csPCa). Histopathological validation from both biopsy and prostatectomy samples will provide the best ground truth in validating promising MRI techniques which could predict biopsy results and help avoid unnecessary biopsies in men suspected of prostate cancer. ETHICS AND DISSEMINATION: Ethical approval was granted by the London-Queen Square Research Ethics Committee (19/LO/1803) on 23 January 2020. Results from the study will be presented at conferences and submitted to peer-reviewed journals for publication. Results will also be available on ClinicalTrials.gov. TRIAL REGISTRATION NUMBER: NCT04792138.

A reproducible dynamic phantom for sequence testing in hyperpolarised 13C-magnetic resonance.

OBJECTIVE: To develop a phantom system which can be integrated with an automated injection system, eliminating the experimental variability that arises with manual injection; for the purposes of pulse sequence testing and metric derivation in hyperpolarised 13C-MR. METHODS: The custom dynamic phantom was machined from Ultem and filled with a nicotinamide adenine dinucleotide and lactate dehydrogenase mixture dissolved in phosphate buffered saline. Hyperpolarised [1-13C]-pyruvate was then injected into the phantom (n = 8) via an automated syringe pump and the conversion of pyruvate to lactate monitored through a 13C imaging sequence. RESULTS: The phantom showed low coefficient of variation for the lactate to pyruvate peak signal heights (11.6%) and dynamic area-under curve ratios (11.0%). The variance for the lactate dehydrogenase enzyme rate constant (kP) was also seen to be low at 15.6%. CONCLUSION: The dynamic phantom demonstrates high reproducibility for quantification of 13C-hyperpolarised MR-derived metrics. Establishing such a phantom is needed to facilitate development of hyperpolarsed 13C-MR pulse sequenced; and moreover, to enable multisite hyperpolarised 13C-MR clinical trials where assessment of metric variability across sites is critical. ADVANCES IN KNOWLEDGE: The dynamic phantom developed during the course of this study will be a useful tool in testing new pulse sequences and standardisation in future hyperpolarised work.

Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly.

Imaging plays a fundamental role in all aspects of the cancer management pathway. However, conventional imaging techniques are largely reliant on morphological and size descriptors that have well-known limitations, particularly when considering targeted-therapy response monitoring. Thus, new imaging methods have been developed to characterise cancer and are now routinely implemented, such as diffusion-weighted imaging, dynamic contrast enhancement, positron emission technology (PET) and magnetic resonance spectroscopy. However, despite the improvement these techniques have enabled, limitations still remain. Novel imaging methods are now emerging, intent on further interrogating cancers. These techniques are at different stages of maturity along the biomarker pathway and aim to further evaluate the cancer microstructure (vascular, extracellular and restricted diffusion for cytometry in tumours) magnetic resonance imaging (MRI), luminal water fraction imaging] as well as the metabolic alterations associated with cancers (novel PET tracers, hyperpolarised MRI). Finally, the use of machine learning has shown powerful potential applications. By using prostate cancer as an exemplar, this Review aims to showcase these potentially potent imaging techniques and what stage we are at in their application to conventional clinical practice.

Natural history of prostate cancer on active surveillance: stratification by MRI using the PRECISE recommendations in a UK cohort.

OBJECTIVES: The PRECISE recommendations for magnetic resonance imaging (MRI) in patients on active surveillance (AS) for prostate cancer (PCa) include repeated measurement of each lesion, and attribution of a PRECISE radiological progression score for the likelihood of clinically significant change over time. We aimed to compare the PRECISE score with clinical progression in patients who are managed using an MRI-led AS protocol. METHODS: A total of 553 patients on AS for low- and intermediate-risk PCa (up to Gleason score 3 + 4) who had two or more MRI scans performed between December 2005 and January 2020 were included. Overall, 2161 scans were retrospectively re-reported by a dedicated radiologist to give a PI-RADS v2 score for each scan and assess the PRECISE score for each follow-up scan. Clinical progression was defined by histological progression to ≥ Gleason score 4 + 3 (Gleason Grade Group 3) and/or initiation of active treatment. Progression-free survival was assessed using Kaplan-Meier curves and log-rank test was used to assess differences between curves. RESULTS: Overall, 165/553 (30%) patients experienced the primary outcome of clinical progression (median follow-up, 74.5 months; interquartile ranges, 53-98). Of all patients, 313/553 (57%) did not show radiological progression on MRI (PRECISE 1-3), of which 296/313 (95%) had also no clinical progression. Of the remaining 240/553 patients (43%) with radiological progression on MRI (PRECISE 4-5), 146/240 (61%) experienced clinical progression (p < 0.0001). Patients with radiological progression on MRI (PRECISE 4-5) showed a trend to an increase in PSA density. CONCLUSIONS: Patients without radiological progression on MRI (PRECISE 1-3) during AS had a very low likelihood of clinical progression and many could avoid routine re-biopsy. KEY POINTS: • Patients without radiological progression on MRI (PRECISE 1-3) during AS had a very low likelihood of clinical progression and many could avoid routine re-biopsy. • Clinical progression was almost always detectable in patients with radiological progression on MRI (PRECISE 4-5) during AS. • Patients with radiological progression on MRI (PRECISE 4-5) during AS showed a trend to an increase in PSA density.

PET-CT imaging features that differentiate between positive and negative EWSR1 translocation in Ewing sarcoma.

OBJECTIVES: Ewing sarcoma breakpoint region 1 (EWSR1) translocation-negative tumors represent a minor portion of small round cell tumors consistent with Ewing sarcoma morphology. The purpose of this study was to differentiate EWSR1 translocation-positive tumors from EWSR1 translocation-negative tumors using PET-computed tomography features. MATERIALS AND METHODS: In this retrospective study 27, Ewing sarcoma patients (December 2011 to November 2016) were divided into two groups, EWSR1 translocation-positive and EWSR1 translocation-negative based on cytogenetic analysis. Pretreatment standardized uptake value maximum (SUVmax) and Hounsfield Units (HU) were measured in the primary tumor in the axial slice with the largest tumor diameter.The associations between SUVmax, HU and the presence of EWSR1 translocation were evaluated. Receiver operating characteristic curve analysis was used to determine cut-off levels of SUVmax and HU suggestive of EWSR1-negative tumors. RESULTS: Twenty-one patients were classified as EWSR1-positive and six as EWSR1-negative. Eighteen had SUVmax and 21 had HU measurements. EWSR1-negative tumors had significantly higher SUVmax values (P = 0.003) and significantly lower HU values (P = 0.008). Receiver operating characteristic curve analysis showed that SUVmax had diagnostic ability to discriminate between EWSR1-negative and EWSR1-positive tumors (area under the curve = 0.964, P = 0.006). A SUVmax of at least 10 had a sensitivity of 100% and specificity of 85.7% for EWSR1-negative tumors. HU had lower diagnostic ability than SUVmax (area under the curve = 0.881, P = 0.012). A HU up to 57 had a sensitivity of 81.3% and specificity of 80.0% for EWSR1-negative tumors. CONCLUSION: Higher SUVmax and lower HU may differentiate between EWSR1-positive and EWSR1-negative tumors. This may reflect EWSR1-negative tumor aggressiveness.