In most patients with small-cell lung cancer (SCLC)-a metastatic, aggressive disease-the condition is initially chemosensitive but then relapses with acquired chemoresistance. In a minority of patients, however, relapse occurs within 3 months of initial treatment; in these cases, disease is defined as chemorefractory. The molecular mechanisms that differentiate chemosensitive from chemorefractory disease are currently unknown. To identify genetic features that distinguish chemosensitive from chemorefractory disease, we examined copy-number aberrations (CNAs) in circulating tumor cells (CTCs) from pretreatment SCLC blood samples. After analysis of 88 CTCs isolated from 13 patients (training set), we generated a CNA-based classifier that we validated in 18 additional patients (testing set, 112 CTC samples) and in six SCLC patient-derived CTC explant tumors. The classifier correctly assigned 83.3% of the cases as chemorefractory or chemosensitive. Furthermore, a significant difference was observed in progression-free survival (PFS) (Kaplan-Meier P value = 0.0166) between patients designated as chemorefractory or chemosensitive by using the baseline CNA classifier. Notably, CTC CNA profiles obtained at relapse from five patients with initially chemosensitive disease did not switch to a chemorefractory CNA profile, which suggests that the genetic basis for initial chemoresistance differs from that underlying acquired chemoresistance.

Background The clinical significance of circulating tumour cells (CTCs) in limited-stage small-cell lung cancer (LS-SCLC) is not well defined. We report a planned exploratory analysis of the prevalence and prognostic value of CTCs in LS-SCLC patients enrolled within the phase III randomised CONVERT (concurrent once-daily versus twice-daily chemoradiotherapy) trial. Patients and methods Baseline blood samples were enumerated for CTCs using CellSearch in 75 patients with LS-SCLC who were enrolled in the CONVERT trial and randomised between twice- and once-daily concurrent chemoradiation. Standard statistical methods were used for correlations of CTCs with clinical factors. Log-rank test and Cox regression analyses were applied to establish the associations of 2, 15 and 50 CTC thresholds with progression-free survival (PFS) and overall survival (OS). An optimal CTC count threshold for LS-SCLC was established. Results CTCs were detected in 60% (45/75) of patients (range 0–3750). CTC count thresholds of 2, 15 and 50 CTCs all significantly correlate with PFS and OS. An optimal CTC count threshold in LS-SCLC was established at 15 CTCs, defining ‘favourable’ and ‘unfavourable’ prognostic risk groups. The median OS in <15 versus ≥15 CTCs was 26.7 versus 5.9 m ( P = 0.001). The presence of ≥15 CTCs at baseline independently predicted ≤1 year survival in 70% and ≤2 years survival in 100% of patients. Conclusion We report the prognostic value of baseline CTC count in an exclusive LS-SCLC population at thresholds of 2, 15 and 50 CTCs. Specific to LS-SCLC, ≥15 CTCs was associated with worse PFS and OS independent of all other factors and predicted ≤2 years survival. These results may improve disease stratification in future clinical trial designs and aid clinical decision making. Trial registration ClinicalTrials.gov identifier: NCT00433563.

Introduction: We assessed the clinical validity of circulating tumour cell (CTC) quantification for prognostication of patients with advanced non-small cell lung cancer (NSCLC) by undertaking a pooled analysis of individual patient data. Methods: Nine European NSCLC CTC centres were asked to provide reported/unreported pseudo-anonymised data for patients with advanced NSCLC who participated in CellSearch CTC studies from January 2003 to March 2017. We used Cox regression models, stratified by centres, to establish the association between CTC count and survival. We assessed the added value of CTCs to prognostic clinicopathological models using likelihood ratio (LR) statistics and c-indices. Results: Seven out of nine eligible centres provided data for 550 patients with prognostic information for overall survival.

Objectives1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs.MethodsThis study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge.ResultsPrimary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods.ConclusionData-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs.

Objective: Antipsychotics are licensed for psychosis and are also prescribed for behavior control. This study aims to examine characteristics and outcomes of children prescribed antipsychotics.Methods: A cohort study using general practice and hospital records linked with education records for 1,488,936 children living in Wales between 1999 and 2015. The characteristics of the children who were prescribed antipsychotics are presented using descriptive statistics and outcomes such as respiratory illness, diabetes, and injury were analyzed using multilevel logistic regression and the prior event rate ratio (PERR).Results: Children with intellectual difficulty/autism were more likely to be prescribed antipsychotics (2.8% have been prescribed an antipsychotic [75% with autism] compared with 0.15% of children without intellectual difficulty). Those with intellectual disabilities/autism were prescribed antipsychotics at a younger age and for a longer period. Antipsychotic use was associated with a higher rate of respiratory illness for all (PERR of hospital admission: 1.55 [95% CI: 1.51–1.598] or increase in rate of 2 per 100 per year in those treated), and for those with intellectual difficulty/autism, there was a higher rate of injury and hospitalized depression. However, among those without intellectual difficulty/autism, there were lower rates of depression (PERR: 0.55 [95% CI: 0.51–0.59]).Conclusions: This work shows real-world use of antipsychotics and provides information on the rate of possible adverse events in children treated. Antipsychotics are predominantly used for those with intellectual difficulty/autism rather than those with a psychotic diagnosis. There is evidence that rates of respiratory disease, epilepsy, and diabetes are also higher postantipsychotic use for all. In those with intellectual difficulty/autism, hospital-admitted depression and injury are higher postantipsychotic use. The use of antipsychotics for behavioral management is likely to have increased cost implications to the healthcare system.

Small cell lung cancer (SCLC) accounts for 15% of lung cancer diagnosed worldwide. It is aggressive and characterised by early metastatic spread with rapid development of chemo resistance such that less than 5% of patients diagnosed survive 5 years. Surgery is rarely performed and failure to identify new effective treatments has been attributed in a large part to lack of good quality tumour biopsies available for translational research. Liquid biopsies provide a minimally invasive alternative to traditional tumour biopsy.Circulating tumour cells (CTCs) are abundant in SCLC and can be enriched and isolated from a venous blood sample. In recent years progress has been made into the molecular characterisation of CTCs and their use to form tumour xenografts in mice for preclinical studies. This review will discuss the current status of the clinical utility of CTCs in patients with SCLC, highlighting their potential application to treatment decision making, drug development in clinical trials and preclinical testing.

Magnetic resonance guided focused ultrasound surgery (MRgFUS) has become an attractive, non-invasive treatment for benign and malignant tumours, and offers specific benefits for poorly accessible locations in the liver. However, the presence of the ribcage and the occurrence of liver motion due to respiration limit the applicability MRgFUS. Several techniques are being developed to address these issues or to decrease treatment times in other ways. However, the potential benefit of such improvements has not been quantified. In this research, the detailed workflow of current MRgFUS procedures was determined qualitatively and quantitatively by using observation studies on uterine MRgFUS interventions, and the bottlenecks in MRgFUS were identified. A validated simulation model based on discrete events simulation was developed to quantitatively predict the effect of new technological developments on the intervention duration of MRgFUS on the liver. During the observation studies, the duration and occurrence frequencies of all actions and decisions in the MRgFUS workflow were registered, as were the occurrence frequencies of motion detections and intervention halts. The observation results show that current MRgFUS uterine interventions take on average 213min. Organ motion was detected on average 2.9 times per intervention, of which on average 1.0 actually caused a need for rework. Nevertheless, these motion occurrences and the actions required to continue after their detection consumed on average 11% and up to 29% of the total intervention duration. The simulation results suggest that, depending on the motion occurrence frequency, the addition of new technology to automate currently manual MRgFUS tasks and motion compensation could potentially reduce the intervention durations by 98.4% (from 256h 5min to 4h 4min) in the case of 90% motion occurrence, and with 24% (from 5h 19min to 4h 2min) in the case of no motion. In conclusion, new tools were developed to predict how intervention durations will be affected by future workflow changes and by the introduction of new technology.

This study presents a framework for workflow optimisation of multimodal image-guided procedures (MIGP) based on discrete event simulation (DES). A case of a combined X-Ray and magnetic resonance image-guided transarterial chemoembolisation (TACE) is presented to illustrate the application of this method. We used a ranking and selection optimisation algorithm to measure the performance of a number of proposed alternatives to improve a current scenario. A DES model was implemented with detail data collected from 59 TACE procedures and durations of magnetic resonance imaging (MRI) diagnostic procedures usually performed in a common MRI suite. Fourteen alternatives were proposed and assessed to minimise the waiting times and improve workflow. Data analysis observed an average of 20.68 (7.68) min of waiting between angiography and MRI for TACE patients in 71.19% of the cases. Following the optimisation analysis, an alternative was identified to reduce waiting times in angiography suite up to 48.74%. The model helped to understand and detect 'bottlenecks' during multimodal TACE procedures, identifying a better alternative to the current workflow and reducing waiting times. Simulation-based workflow analysis provides a cost-effective way to face some of the challenges of introducing MIGP in clinical radiology, highligthed in this study.