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Our congratulations go to Dr Richard Colling from the Nuffield Department of Surgical Sciences (NDS) who has been elected to a Junior Research Fellowship (JRF) at Kellogg College in Oxford.
Deep brain stimulation of the pedunculopontine nucleus is a promising surgical procedure for the treatment of Parkinsonian gait and balance dysfunction. It has, however, produced mixed clinical results that are poorly understood. We used tractography with the aim to rationalise this heterogeneity. A cohort of eight patients with postural instability and gait disturbance (Parkinson's disease subtype) underwent pre-operative structural and diffusion MRI, then progressed to deep brain stimulation targeting the pedunculopontine nucleus. Pre-operative and follow-up assessments were carried out using the Gait and Falls Questionnaire, and Freezing of Gait Questionnaire. Probabilistic diffusion tensor tractography was carried out between the stimulating electrodes and both cortical and cerebellar regions of a priori interest. Cortical surface reconstructions were carried out to measure cortical thickness in relevant areas. Structural connectivity between stimulating electrode and precentral gyrus (r = 0.81, p = 0.01), Brodmann areas 1 (r = 0.78, p = 0.02) and 2 (r = 0.76, p = 0.03) were correlated with clinical improvement. A negative correlation was also observed for the superior cerebellar peduncle (r = -0.76, p = 0.03). Lower cortical thickness of the left parietal lobe and bilateral premotor cortices were associated with greater pre-operative severity of symptoms. Both motor and sensory structural connectivity of the stimulated surgical target characterises the clinical benefit, or lack thereof, from surgery. In what is a challenging region of brainstem to effectively target, these results provide insights into how this can be better achieved. The mechanisms of action are likely to have both motor and sensory components, commensurate with the probable nature of the underlying dysfunction.
Testicular cancer is the most common cancer in men aged from 15 to 34 years. Lymphovascular invasion refers to the presence of tumours within endothelial-lined lymphatic or vascular channels, and has been shown to have prognostic significance in testicular germ cell tumours. In non-seminomatous tumours, lymphovascular invasion is the most powerful prognostic factor for stage 1 disease. For the pathologist, searching multiple slides for lymphovascular invasion can be highly time-consuming. The aim of this retrospective study was to develop and assess an artificial intelligence algorithm that can identify areas suspicious for lymphovascular invasion in histological digital whole slide images. Areas of possible lymphovascular invasion were annotated in a total of 184 whole slide images of haematoxylin and eosin (H&E) stained tissue from 19 patients with testicular germ cell tumours, including a mixture of seminoma and non-seminomatous cases. Following consensus review by specialist uropathologists, we trained a deep learning classifier for automatic segmentation of areas suspicious for lymphovascular invasion. The classifier identified 34 areas within a validation set of 118 whole slide images from 10 patients, each of which was reviewed by three expert pathologists to form a majority consensus. The precision was 0.68 for areas which were considered to be appropriate to flag, and 0.56 for areas considered to be definite lymphovascular invasion. An artificial intelligence tool which highlights areas of possible lymphovascular invasion to reporting pathologists, who then make a final judgement on its presence or absence, has been demonstrated as feasible in this proof-of-concept study. Further development is required before clinical deployment.
A functional vascular system is indispensable for drug delivery and fundamental for responsiveness of the tumour microenvironment to such medication. At the same time, the progression of a tumour is defined by the interactions of the cancer cells with their surrounding environment, including neovessels, and the vascular network continues to be the major route for the dissemination of tumour cells in cancer, facilitating metastasis. So how can this apparent conflict be reconciled? Vessel normalisation—in which redundant structures are pruned and the abnormal vasculature is stabilised and remodelled—is generally considered to be beneficial in the course of anti-cancer treatments. A causality between normalised vasculature and improved response to medication and treatment is observed. For this reason, it is important to discern the consequence of vessel normalisation on the tumour microenvironment and to modulate the vasculature advantageously. This article will highlight the challenges of controlled neovascular remodelling and outline how vascular normalisation can shape disease management.
Treating to target in psoriatic arthritis: assessing real-world outcomes and optimising therapeutic strategy for adults with psoriatic arthritis-study protocol for the MONITOR-PsA study, a trials within cohorts study design.
BACKGROUND: The Tight Control of psoriatic arthritis (TICOPA) trial confirmed improved clinical outcomes with a treat to target (T2T) strategy in psoriatic arthritis (PsA). This consisted of 4-weekly review and escalation of 'step up' therapy (single disease modifying therapy (DMARD), combination DMARDs and then biologics) based on remission criteria. Based on this, a T2T approach is supported by European PsA treatment recommendations. However, it is not commonly implemented in routine care primarily due to feasibility and cost concerns. In the TICOPA trial, the same treatment regime was used for all participants regardless of their disease profile. Despite the recognition of PsA as a highly heterogeneous condition, no studies have tailored which drugs are used depending on disease severity. The cohort will establish real world outcomes for the T2T approach in PsA and also form the basis of a trials within cohorts (TWiCs) design to test alternative therapeutic approaches within embedded clinical trials providing an evidence base for treatment strategy in PsA. METHODS: The Multicentre Observational Initiative in Treat to target Outcomes in Psoriatic Arthritis (MONITOR-PsA) cohort will apply a T2T approach within routine care. It will recruit newly diagnosed adult patients with PsA starting systemic therapies. The cohort is observational allowing routine therapeutic care within NHS clinics but a T2T approach will be supported when monitoring treatment within the cohort. Eligible participants will be adults (≥18 years) with active PsA with ≥ 1 tender or swollen joints or enthesis who have not previously had treatment with DMARDs for articular disease. DISCUSSION: This study is the first TWiC designed to support a fully powered randomised drug trial. The results from the observational cohort will be compared with those observed in the TICOPA trial investigating the clinical effectiveness and health care costs of the pragmatic T2T approach. Nested trials will provide definitive RCT evidence establishing the optimal management of PsA within the T2T approach. The TWiCs design allows robust generalizability to routine healthcare, avoids disappointment bias, aids recruitment and in future will allow assessment of longer-term outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03531073 . Retrospectively registered on 21 May 2018.
Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.
Importance: An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer). Objectives: To conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation. Evidence Review: A search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed. Findings: A total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I. Conclusions and Relevance: This systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs.
Large-Volume Hyperthermia for Safe and Cost-Effective Targeted Drug Delivery Using a Clinical Ultrasound-Guided Focused Ultrasound Device.
Lyso-thermosensitive liposomes (LTSLs) are specifically designed to release chemotherapy agents under conditions of mild hyperthermia. Preclinical studies have indicated that magnetic resonance (MR)-guided focused ultrasound (FUS) systems can generate well-controlled volumetric hyperthermia using real-time thermometry. However, high-throughput clinical translation of these approaches for drug delivery is challenging, not least because of the significant cost overhead of MR guidance and the much larger volumes that need to be heated clinically. Using an ultrasound-guided extracorporeal clinical FUS device (Chongqing HAIFU, JC200) with thermistors in a non-perfused ex vivo bovine liver tissue model with ribs, we present an optimised strategy for rapidly inducing (5-15 min) and sustaining (>30 min) mild hyperthermia (ΔT <+4°C) in large tissue volumes (≤92 cm3). We describe successful clinical translation in a first-in-human clinical trial of targeted drug delivery of LTSLs (TARDOX: a phase I study to investigate drug release from thermosensitive liposomes in liver tumours), in which targeted tumour hyperthermia resulted in localised chemo-ablation. The heating strategy is potentially applicable to other indications and ultrasound-guided FUS devices.
Triggered release and targeted drug delivery of potent anti-cancer agents using hyperthermia-mediated focused-ultrasound (FUS) is gaining momentum in the clinical setting. In early phase studies, tissue biopsy samples may be harvested to assess drug delivery efficacy and demonstrate lack of instantaneous cell death due to FUS exposure. We present an optimised tissue cell recovery method and a cell viability assay, compatible with intra-cellular doxorubicin. Flow cytometry was used to determine levels of cell death with suspensions comprised of: (i) HT29 cell line exposed to hyperthermia (30 min at 47 °C) and/or doxorubicin, or ex-vivo bovine liver tissue exposed to (ii) hyperthermia (up to 2 h at 45 °C), or (iii) ablative high intensity FUS (HIFU). Flow cytometric analysis revealed maximal cell death in HT29 receiving both heat and doxorubicin insults and increases in both cell granularity (p < 0.01) and cell death (p < 0.01) in cells recovered from ex-vivo liver tissue exposed to hyperthermia and high pressures of HIFU (8.2 MPa peak-to-peak free-field at 1 MHz) relative to controls. Ex-vivo results were validated with microscopy using pan-cytokeratin stain. This rapid, sensitive and highly quantitative cell-viability method is applicable to the small masses of liver tissue typically recovered from a standard core biopsy (5-20 mg) and may be applied to tissues of other histological origins including immunostaining.
Social distancing enhanced automated optimal design of physical spaces in the wake of the COVID-19 pandemic
As the COVID-19 pandemic unfolds, manually enhanced ad-hoc solutions have helped the physical space designers and decision makers to cope with the dynamic nature of space planning. Due to the unpredictable nature by which the pandemic is unfolding, the standard operating procedures also change, and the protocols for physical interaction require continuous reconsideration. Consequently, the development of an appropriate technological solution to address the current challenge of reconfiguring common physical environments with prescribed physical distancing measures is much needed. To do this, we propose a design optimization methodology which takes the dimensions, as well as the constraints and other necessary requirements of a given physical space to yield optimal redesign solutions on the go. The methodology we propose here utilizes the solution to the well-known mathematical circle packing problem, which we define as a constrained mathematical optimization problem. The resulting optimization problem is solved subject to a given set of parameters and constraints – corresponding to the requirements on the social distancing criteria between people and the imposed constraints on the physical spaces such as the position of doors, windows, walkways and the variables related to the indoor airflow pattern. Thus, given the dimensions of a physical space and other essential requirements, the solution resulting from the automated optimization algorithm can suggest an optimal set of redesign solutions from which a user can pick the most feasible option. We demonstrate our automated optimal design methodology by way of a number of practical examples, and we discuss how this framework can be further taken forward as a design platform that can be implemented practically.
Recognizing the Dying Patient, When Less Could be More: A Diagnostic Framework for Shared Decision-Making at the End of Life.
Background: Recognizing dying patients is crucial to produce outcomes that are satisfactory to patients, their families, and clinicians. Aim: Earlier discussion of and shared decision-making around dying to improve these outcomes. Design: In this study, we interviewed 16 senior clinicians to develop summaries of palliative care in 4 key specialties: Cardiology, Vascular Surgery, Emergency General Surgery, and Intensive Care. Setting: Oxford University Hospitals. Results: Based on themes common to our 4 clinical areas, we developed a novel diagnostic framework to support shared palliative decision-making that can be summarized as follows: 1) Is the acute pathology reversible? 2) What is the patient's physiological reserve? 3) What is important to the patient? Will they be fit enough for discharge for a reasonable length of time? Conclusions: We believe that education using this framework in the medical school and postgraduate curricula would significantly improve recognition of dying patients. This would serve to stimulate earlier conversations, more shared decision-making, and ultimately better outcomes in palliative care and patient experience.
Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerised Tomography Images Acquired During the Aneurysm Surveillance Period.
OBJECTIVE: We investigated the utility of geometric features for future abdominal aortic aneurysms (AAA) growth prediction. BACKGROUND: Novel methods for growth prediction of AAA are recognised as a research priority. Geometric feature has been applied to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth. METHODS: Computerised tomography (CT) scans from patients with infra-renal AAAs were analysed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI) and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients. RESULTS: There was no correlation between the geometric measurements and patients' demographic features. APD (spearman r = 0.25, p < 0.05), UI (spearman r = 0.38, p < 0.001) and RC (Spearman r = -0.53, p < 0.001) significantly correlated with annual AAA growth. Using APD, UI and RC as three input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/year) or fast growth (>5 mm/year) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases. CONCLUSIONS: Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway.
A Deep Learning Pipeline to Automate High-Resolution Arterial Segmentation with or without Intravenous Contrast.
BACKGROUND: Existing methods to reconstruct vascular structures from a computerized tomography (CT) angiogram rely on contrast injection to enhance the radio-density within the vessel lumen. However, pathological changes in the vasculature may be present that prevent accurate reconstruction. In aortic aneurysmal disease, a thrombus adherent to the aortic wall within the expanding aneurysmal sac is present in > 90% of cases. These deformations prevent the automatic extraction of vital clinical information by existing image reconstruction methods. AIM: In this study, a deep learning architecture consisting of a modified U-Net with attention-gating was implemented to establish a high-throughput and automated segmentation pipeline of pathological blood vessels in CT images acquired with or without the use of a contrast agent. METHODS AND RESULTS: Seventy-Five patients with paired non-contrast and contrast-enhanced CT images were randomly selected from an ongoing study (Ethics Ref 13/SC/0250), manually annotated and used for model training and evaluation. Data augmentation was implemented to diversify the training data set in a ratio of 10:1. The performance of our Attention-based U-Net in extracting both the inner (blood flow) lumen and the wall structure of the aortic aneurysm from CT angiograms (CTA) was compared against a generic 3-D U-Net and displayed superior results. Implementation of this network within the aortic segmentation pipeline for both contrast and non-contrast CT images has allowed for accurate and efficient extraction of the morphological and pathological features of the entire aortic volume. CONCLUSION: This extraction method can be used to standardize aneurysmal disease management and sets the foundation for complex geometric and morphological analysis. Furthermore, this pipeline can be extended to other vascular pathologies.
Purpose of Review: This article aims to review published outcomes associated with full-thickness vascularized abdominal wall transplantation, with particular emphasis on advances in the field in the last 3 years. Recent Findings: Forty-six full-thickness vascularized abdominal wall transplants have been performed in 44 patients worldwide. Approximately 35% of abdominal wall transplant recipients will experience at least one episode of acute rejection in the first year after transplant, compared with rejection rates of 87.8% and 72.7% for hand and face transplant respectively. Recent evidence suggests that combining a skin containing abdominal wall transplant with an intestinal transplant does not appear to increase sensitization or de novo donor-specific antibody formation. Summary: Published data suggests that abdominal wall transplantation is an effective safe solution to achieve primary closure of the abdomen after intestinal or multivisceral transplant. However, better data is needed to confirm observations made and to determine long-term outcomes, requiring standardized data collection and reporting and collaboration between the small number of active transplant centres around the world.
Drugs that inhibit insulin-like growth factor 1 (IGFI) receptor IGFIR were encouraging in early trials, but predictive biomarkers were lacking and the drugs provided insufficient benefit in unselected patients. In this study, we used genetic screening and downstream validation to identify the WNT pathway element DVL3 as a mediator of resistance to IGFIR inhibition. Sensitivity to IGFIR inhibition was enhanced specifically in vitro and in vivo by genetic or pharmacologic blockade of DVL3. In breast and prostate cancer cells, sensitization tracked with enhanced MEK-ERK activation and relied upon MEK activity and DVL3 expression. Mechanistic investigations showed that DVL3 is present in an adaptor complex that links IGFIR to RAS, which includes Shc, growth factor receptor-bound-2 (Grb2), son-of-sevenless (SOS), and the tumor suppressor DAB2. Dual DVL and DAB2 blockade synergized in activating ERKs and sensitizing cells to IGFIR inhibition, suggesting a nonredundant role for DVL3 in the Shc-Grb2-SOS complex. Clinically, tumors that responded to IGFIR inhibition contained relatively lower levels of DVL3 protein than resistant tumors, and DVL3 levels in tumors correlated inversely with progression-free survival in patients treated with IGFIR antibodies. Because IGFIR does not contain activating mutations analogous to EGFR variants associated with response to EGFR inhibitors, we suggest that IGF signaling achieves an equivalent integration at the postreceptor level through adaptor protein complexes, influencing cellular dependence on the IGF axis and identifying a patient population with potential to benefit from IGFIR inhibition.
Histone H3K36 trimethylation (H3K36me3) is frequently lost in multiple cancer types, identifying it as an important therapeutic target. Here we identify a synthetic lethal interaction in which H3K36me3-deficient cancers are acutely sensitive to WEE1 inhibition. We show that RRM2, a ribonucleotide reductase subunit, is the target of this synthetic lethal interaction. RRM2 is regulated by two pathways here: first, H3K36me3 facilitates RRM2 expression through transcription initiation factor recruitment; second, WEE1 inhibition degrades RRM2 through untimely CDK activation. Therefore, WEE1 inhibition in H3K36me3-deficient cells results in RRM2 reduction, critical dNTP depletion, S-phase arrest, and apoptosis. Accordingly, this synthetic lethality is suppressed by increasing RRM2 expression or inhibiting RRM2 degradation. Finally, we demonstrate that WEE1 inhibitor AZD1775 regresses H3K36me3-deficient tumor xenografts.
BACKGROUND: Activated type 1 insulin-like growth factor receptors (IGF-1Rs) undergo internalisation and nuclear translocation, promoting cell survival. We previously reported that IGF-1R inhibition delays DNA damage repair, sensitising prostate cancer cells to ionising radiation. Here we tested the clinical relevance of these findings. METHODS: We assessed associations between IGF-1R and clinical outcomes by immunohistochemistry in diagnostic biopsies of 136 men treated with 55-70 Gy external beam radiotherapy for prostate cancer, comparing results with publicly available transcriptional data in surgically treated patients. RESULTS: Following radiotherapy, overall recurrence-free survival was shorter in patients whose tumours contained high total, cytoplasmic and internalised (nuclear/cytoplasmic) IGF-1R. High total IGF-1R associated with high primary Gleason grade and risk of metastasis, and cytoplasmic and internalised IGF-1R with biochemical recurrence, which includes patients experiencing local recurrence within the radiation field indicating radioresistance. In multivariate analysis, cytoplasmic, internalised and total IGF-1R were independently associated with risk of overall recurrence, and cytoplasmic IGF-1R was an independent predictor of biochemical recurrence post radiotherapy. Insulin-like growth factor receptors expression did not associate with biochemical recurrence after radical prostatectomy. CONCLUSIONS: These data reveal increased risk of post-radiotherapy recurrence in men whose prostate cancers contain high levels of total or cytoplasmic IGF-1R.
TOPK modulates tumour-specific radiosensitivity and correlates with recurrence after prostate radiotherapy.
BACKGROUND: Tumour-specific radiosensitising treatments may enhance the efficacy of radiotherapy without exacerbating side effects. In this study we determined the radiation response following depletion or inhibition of TOPK, a mitogen-activated protein kinase kinase family Ser/Thr protein kinase that is upregulated in many cancers. METHODS: Radiation response was studied in a wide range of cancer cell lines and normal cells using colony formation assays. The effect on cell cycle progression was assessed and the relationship between TOPK expression and therapeutic efficacy was studied in a cohort of 128 prostate cancer patients treated with radical radiotherapy. RESULTS: TOPK knockdown did not alter radiation response in normal tissues, but significantly enhanced radiosensitivity in cancer cells. This result was recapitulated in TOPK knockout cells and with the TOPK inhibitor, OTS964. TOPK depletion altered the G1/S transition and G2/M arrest in response to radiation. Furthermore, TOPK depletion increased chromosomal aberrations, multinucleation and apoptotic cell death after irradiation. These results suggest a possible role for TOPK in the radiation-induced DNA damage checkpoints. These findings have clinical relevance, as elevated TOPK protein expression was associated with poorer clinical outcomes in prostate cancer patients treated with radical radiotherapy. CONCLUSIONS: This study demonstrates that TOPK disruption may cause tumour-specific radiosensitisation in multiple different tumour types.