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Background: Recent advances in targeted therapies have raised expectations that the clinical application of biomarkers would improve patient’s health outcomes and potentially save costs. However, the cost-effectiveness of biomarkers remains unclear irrespective of the cost-effectiveness of corresponding therapies. It is thus important to determine whether biomarkers for targeted therapies provide good value for money. This study systematically reviews economic evaluations of biomarkers for targeted therapies in metastatic colorectal cancer (mCRC) and assesses the cost-effectiveness of predictive biomarkers in mCRC. Methods: A literature search was performed using Medline, Embase, EconLit, NHSEED. Papers published from 2000 until June 2018 were searched. All economic evaluations assessing biomarker-guided therapies with companion diagnostics in mCRC were searched. To make studies more comparable, cost-effectiveness results were synthesized as per biomarker tests and corresponding therapies. Methodological quality was assessed using the Quality of Health Economic Studies (QHES) instrument. Results: Forty-six studies were included in this review. Of these, 17 studies evaluated the intrinsic value of cancer biomarkers, whereas the remaining studies focused on assessing the cost-effectiveness of corresponding drugs. Most studies indicated favourable cost-effectiveness of biomarkers for targeted therapies in mCRC. Some studies reported that biomarkers were cost-effective, while their corresponding therapies were not cost-effective. A considerable number of economic evaluations were conducted in pre-defined genetic populations and thus, often failed to fully capture the biomarker’s clinical and economic values. The average QHES score was 73.6. Conclusion: Cancer biomarkers for targeted therapies in mCRC were mostly found to be cost-effective; otherwise, they at least improved the cost-effectiveness of targeted therapies by saving some costs. However, this did not necessarily make their corresponding therapies cost-effective. While companion biomarkers reduced therapy costs, the savings were not sufficient to make the corresponding agents cost-effective. Evaluation of biomarkers was often restricted to the cost of tests and did not consider their clinical values or biomarker prevalence. [ABSTRACT FROM AUTHOR]

Many different physiological processes affect the growth of malignant lesions and their response to therapy. Each of these processes is spatially and genetically heterogeneous; dynamically evolving in time; controlled by many other physiological processes, and intrinsically random and unpredictable. The objective of this paper is to show that all of these properties of cancer physiology can be treated in a unified, mathematically rigorous way via the theory of random processes. We treat each physiological process as a random function of position and time within a tumor, defining the joint statistics of such functions via the infinite-dimensional characteristic functional. The theory is illustrated by analyzing several models of drug delivery and response of a tumor to therapy. To apply the methodology to precision cancer therapy, we use maximum-likelihood estimation with Emission Computed Tomography (ECT) data to estimate unknown patient-specific physiological parameters, ultimately demonstrating how to predict the probability of tumor control for an individual patient undergoing a proposed therapeutic regimen. [ABSTRACT FROM AUTHOR]

A closer association has been found between the microRNA-146a rs2910164 polymorphism and the risk of head and neck carcinoma in some molecular epidemiological studies. Recently two meta-analyses were performed to explore the relationship between miRNA-146a polymorphisms and the susceptibility of squamous cell carcinoma of the head and neck (SCCHN); however, they yielded conflicting results in susceptibility regarding ethnic variations. Hence, the present study was performed to explain the relationship between the miRNA-146a rs2910164 polymorphism and the risk of SCCHN development of Chinese patients. We retrieved databases and screened eligible papers up to March 10, 2017 and then we extracted the essential data. The subgroup analyses were also performed based on the tumor site, region, and genotyping means. Crude odds ratios (OR) at 95% confidence intervals (CI) were chosen to describe the strength of the association. As a result, 6 publications were included in our study which involved 8 independent case-control studies. A significant association was found between miR-146a rs2910164 polymorphisms and the risk of SCCHN in Chinese patients according to the overall data [CC+CG vs. GG: OR = 1.13; 95%CI = 1.00–1.29; CC vs. GG: OR = 1.19; 95%CI = 1.03–1.38]. According to the subgroup analysis based on tumor site, the risk of cancer was significantly increased among laryngeal cancer (dominant model: OR = 1.76, 95%CI = 1.26~2.46, P = 0.001; homozygote model: OR = 1.83, 95%CI = 1.25~2.67, P = 0.002) and nasopharyngeal carcinoma (homozygote model: OR = 1.41, 95%CI = 1.05~1.90, P = 0.022). In summary, variant alleles of miR-146a rs2910164 alleles may have an association with the increased risk of SCCHN in Chinese patients, and these associations differed based on tumor site. Further studies including a larger sample size will be necessary to clarify these results. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer is one of the leading causes of cancer deaths in both sexes in the world. Improvement of existing therapy modalities and implementing new ones in order to improve survival of patients with colorectal cancer represents a great challenge for medicine. The aim of this paper was to assess the impact that adding bevacizumab to chemotherapy has on survival in patients with metastatic colorectal cancer, compared to the use of chemotherapy alone. Methods: Hazard ratios (HRs) with their 95% confidence intervals (CI) were determined from the studies and pooled. Two-sided p values were reported and considered to indicate statistical significance if less than 0.05. Results: A total of 12 studies that meet the inclusion criteria were identified in the literature search, 3 phase II studies and 9 phase III studies. Based on the random effects meta-analysis, a statistically significant improvement was identified for both overall survival (HR = 0.84; 95% CI: 0.74–0.94; p = 0.003) and progression free survival (HR = 0.64; 95% CI: 0.55–0.73; p<0.00001) in patients with metastatic colorectal cancer when bevacizumab was added to chemotherapy, compared to chemotherapy treatment alone. Conclusion: The findings of this meta analysis confirm the benefit of adding bevacizumab to chemotherapy in terms of survival and progression free survival, but the magnitude of this effect is not consistent throughout the included studies. This suggests the need for further research of interaction of bevacizumab with chemotherapeutic agents as well as recognition of patients’ characteristics important for the treatment selection criteria. [ABSTRACT FROM AUTHOR]

Colorectal Cancer (CRC) is becoming a major threat to people’s life in China. Screening methods adopted by many other countries as effective counter-cancer methods have not been explicitly explored for people there. Thus, we present a Markov model with detailed precancerous adenoma states and then evaluate various screening strategies in this paper. Different from current researches, our model considers the population’s heterogeneous risk of developing adenomas and observation-based screening strategies. Furthermore, we also give a new cost-effectiveness metric. After calibrating, the model is simulated using the Monte Carlo method. Numerical results show that there are threshold values of compliance rates below which strategy with every ten-year colonoscopy becomes the most cost-effective method; otherwise, an observation-based screening strategy is the most cost-effective. We also find that strategy with single colonoscopy for adenoma-free individuals and every three-year colonoscopy for those with adenoma is recommended when the observation-based strategy is not considered. Our findings give an explicit and complete instruction in CRC screening protocol in average-risk Chinese. [ABSTRACT FROM AUTHOR]

Background: Low-dose aspirin has been shown to reduce the incidence of cancer, but its role in the treatment of cancer is uncertain. Objectives: We conducted a systematic search of the scientific literature on aspirin taken by patients following a diagnosis of cancer, together with appropriate meta-analyses. Methods: Searches were completed in Medline and Embase in December 2015 using a pre-defined search strategy. References and abstracts of all the selected papers were scanned and expert colleagues were contacted for additional studies. Two reviewers applied pre-determined eligibility criteria (cross-sectional, cohort and controlled studies, and aspirin taken after a diagnosis of cancer), assessed study quality and extracted data on cancer cause-specific deaths, overall mortality and incidence of metastases. Random effects meta-analyses and planned sub-group analyses were completed separately for observational and experimental studies. Heterogeneity and publication bias were assessed in sensitivity analyses and appropriate omissions made. Papers were examined for any reference to bleeding and authors of the papers were contacted and questioned. Results: Five reports of randomised trials were identified, together with forty two observational studies: sixteen on colorectal cancer, ten on breast and ten on prostate cancer mortality. Pooling of eleven observational reports of the effect of aspirin on cause-specific mortality from colon cancer, after the omission of one report identified on the basis of sensitivity analyses, gave a hazard ratio (HR) of 0.76 (95% CI 0.66, 0.88) with reduced heterogeneity (P = 0.04). The cause specific mortality in five reports of patients with breast cancer showed significant heterogeneity (P<0.0005) but the omission of one outlying study reduced heterogeneity (P = 0.19) and led to an HR = 0.87 (95% CI 0.69, 1.09). Heterogeneity between nine studies of prostate cancer was significant, but again, the omission of one study led to acceptable homogeneity (P = 0.26) and an overall HR = 0.89 (95% CI 0.79–0.99). Six single studies of other cancers suggested reductions in cause specific mortality by aspirin, and in five the effect is statistically significant. There were no significant differences between the pooled HRs for the three main cancers and after the omission of three reports already identified in sensitivity analyses heterogeneity was removed and revealed an overall HR of 0.83 (95% CI 0.76–0.90). A mutation of PIK3CA was present in about 20% of patients, and appeared to explain most of the reduction in colon cancer mortality by aspirin. Data were not adequate to examine the importance of this or any other marker in the effect of aspirin in the other cancers. On bleeding attributable to aspirin two reports stated that there had been no side effect or bleeding attributable to aspirin. Authors on the other reports were written to and 21 replied stating that no data on bleeding were available. Conclusions and Implications: The study highlights the need for randomised trials of aspirin treatment in a variety of cancers. While these are awaited there is an urgent need for evidence from observational studies of aspirin and the less common cancers, and for more evidence of the relevance of possible bio-markers of the aspirin effect on a wide variety of cancers. In the meantime it is urged that patients in whom a cancer is diagnosed should be given details of this research, together with its limitations, to enable each to make an informed decision as to whether or not to take low-dose aspirin. Systematic Review Protocol Number: CRD42015014145 [ABSTRACT FROM AUTHOR]

The presence of treatment-resistant cells is an important factor that limits the efficacy of cancer therapy, and the prospect of resistance is considered the major cause of the treatment strategy. Several recent studies have employed mathematical models to elucidate the dynamics of generating resistant cancer cells and attempted to predict the probability of emerging resistant cells. The purpose of this paper is to present numerical approach to compute the number of resistant cells and the emerging probability of resistance. Stochastic model was designed and developed a method to approximately but efficiently compute the number of resistant cells and the probability of resistance. To model the progression of cancer, a discrete-state, two-dimensional Markov process whose states are the total number of cells and the number of resistant cells was employed. Then exact analysis and approximate aggregation approaches were proposed to calculate the number of resistant cells and the probability of resistance when the cell population reaches detection size. To confirm the accuracy of computed results of approximation, relative errors between exact analysis and approximation were computed. The numerical values of our approximation method were very close to those of exact analysis calculated in the range of small detection size M = 500, 100, and 1500. Then computer simulation was performed to confirm the accuracy of computed results of approximation when the detection size was M = 10000,30000,50000,100000 and 1000000. All the numerical results of approximation fell between the upper level and the lower level of 95% confidential intervals and our method took less time to compute over a broad range of cell size. The effects of parameter change on emerging probabilities of resistance were also investigated by computed values using approximation method. The results showed that the number of divisions until the cell population reached the detection size is important for emerging the probability of resistance. The next step of numerical approach is to compute the emerging probabilities of resistance under drug administration and with multiple mutation. Another effective approximation would be necessary for the analysis of the latter case. [ABSTRACT FROM AUTHOR]

Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome. [ABSTRACT FROM AUTHOR]

Anxiety and depression can be heightened among individuals living with chronic diseases. Identifying these individuals is necessary for ensuring they are provided with adequate support. Traditional tools such as clinical interviews or symptom checklists are not always feasible to implement in practice. Robust single-item questions may be a useful alternative. This study aimed to measure agreement, sensitivity, specificity, positive predictive value and negative predictive value of a single-item question about anxiety and depression compared to the widely used Hospital Anxiety and Depression Scale (HADS). A cross-sectional survey of 2,811 people with cancer attending 19 treatment centres in Australia. Patients were approached in the waiting room prior to an outpatient clinic appointment and invited to complete a pen and paper survey. Participants completed the HADS as well as 2 single-items asking if they have felt anxious or depressed in the last week. The single-items for anxiety and depression each demonstrated moderate levels of sensitivity (0.78 for anxiety; 0.63 for depression) and specificity (0.75 for anxiety; 0.84 for depression) against the relevant HADS subscale. Positive predictive values were moderate (0.53 for anxiety and 0.52 for depression) while negative predictive values were high for both single-item questions (0.90 for anxiety and 0.89 for depression). The single-item measures of anxiety and depression may be useful to rule out individuals who do not require further psychological assessment or intervention for anxiety and depression. Further research is needed to explore whether these findings generalise to other chronic diseases. [ABSTRACT FROM AUTHOR]

Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient’s treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related symptoms in cancer patients are depression, anxiety, and sleep disturbance. In this paper, we elaborate on the efficiency of Support Vector Regression (SVR) and Non-linear Canonical Correlation Analysis by Neural Networks (n-CCA) to predict the severity of the aforementioned symptoms between two different time points during a cycle of chemotherapy (CTX). Our results demonstrate that these two methods produced equivalent results for all three symptoms. These types of predictive models can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions. [ABSTRACT FROM AUTHOR]

Background: Preclinical evidence suggests statins may have anti-tumor properties. Large observational studies are also consistent with improved survival and cancer-specific outcomes among cancer patients on statins. We sought to evaluate the randomized controlled trials of statins in addition to usual anti-cancer therapy. Methods: A systematic search of MEDLINE, Embase, CINAHL, Cochrane Library, Web of Science, Papers First and Clinicaltrials.gov was performed from inception through to July 4, 2017 to identify randomized clinical trials that investigated statin therapy in cancer patients. Our primary outcome was overall survival and our secondary outcome was progression-free survival. We calculated summary hazard ratio’s (HR) and 95% confidence intervals (CI) based on random-effects models using aggregate data. PROSPERO (CRD42017065503). Results: Ten studies with 1,881 individuals were included with 1,572 deaths and a median follow-up of 23 months. All trials included patients with advanced (stage 3 or higher) disease. There was minimal between-study statistical heterogeneity (I2 = 1.8%, for OS; I2 = 0%, for PFS). The pooled HR for overall survival in patients randomized to statins plus standard anti-cancer therapy versus standard therapy alone was 0.94 (95% CI, 0.85 to 1.04). In the 9 studies that reported progression-free survival (1,798 participants), the pooled HR for statin plus standard therapy versus standard therapy alone was 0.97 (95% CI, 0.87 to 1.07). Conclusions: In patients with advanced cancer and a prognosis <2 years, the addition of statins to standard anti-cancer therapy does not appear to improve overall survival or progression-free survival. Future research should assess if cancer patients with better prognosis benefit from longer-term statin therapy. [ABSTRACT FROM AUTHOR]

Background: Evidence is growing that low-dose aspirin used as an adjuvant treatment of cancer is associated with an increased survival and a reduction in metastatic spread. We therefore extended up to August 2017 an earlier systematic search and meta-analyses of published studies of low-dose aspirin taken by patients with a diagnosis of cancer. Methods: Searches were completed in Medline and Embase to August 2017 using a pre-defined search strategy to identify reports of relevant studies. References in all the selected papers were scanned. Two reviewers independently applied pre-determined eligibility criteria and extracted data on cause-specific cancer deaths, overall mortality and the occurrence of metastatic spread. Meta-analyses were then conducted for different cancers and heterogeneity and publication bias assessed. Sensitivity analyses and attempts to reduce heterogeneity were conducted. Results: Analyses of 29 studies reported since an earlier review up to April 2015 are presented in this report, and these are then pooled with the 42 studies in our earlier publication. Overall meta-analyses of the 71 studies are presented, based on a total of over 120 thousand patients taking aspirin. Ten of the studies also give evidence on the incidence of metastatic cancer spread. There are now twenty-nine observational studies describing colorectal cancer (CRC) and post-diagnostic aspirin. Pooling the estimates of reduction by aspirin which are reported as hazard ratios (HR), gives an overall HR for aspirin and CRC mortality 0.72 (95% CI 0.64–0.80). Fourteen observational studies have reported on aspirin and breast cancer mortality and pooling those that report the association with aspirin as a hazard ratio gives HR 0.69 (0.53–0.90). Sixteen studies report on aspirin and prostate cancer mortality and a pooled estimate yields an HR of 0.87 (95% CI 0.73–1.05). Data from 12 reports relating to other cancers are also listed. Ten studies give evidence of a reduction in metastatic spread; four give a pooled HR 0.31 (95% CI 0.18, 0.54) and five studies which reported odds ratio of metastatic spread give OR 0.79 (0.66 to 0.95). Conclusion: Being almost entirely from observational studies, the evidence of benefit from aspirin is limited. There is heterogeneity between studies and the results are subject to important biases, only some of which can be identified. Nevertheless, the evidence would seem to merit wide discussion regarding whether or not it is adequate to justify the recommendation of low-dose therapeutic aspirin, and if it is, for which cancers? [ABSTRACT FROM AUTHOR]

How can we predict the occurrence of acute kidney injury (AKI) in cancer patients based on machine learning with serum creatinine data? Given irregular and heterogeneous clinical data, how can we make the most of it for accurate AKI prediction? AKI is a common and significant complication in cancer patients, and correlates with substantial morbidity and mortality. Since no effective treatment for AKI still exists, it is important to take timely preventive measures. While several approaches have been proposed for predicting AKI, their scope and applicability are limited as they either assume regular data measured over a short hospital stay, or do not fully utilize heterogeneous data. In this paper, we provide an AKI prediction model with a greater applicability, which relaxes the constraints of existing approaches, and fully utilizes irregular and heterogeneous data for learning the model. In a cohort of 21,022 cancer patients who were registered into Korea Central Cancer Registry (KCCR) in Seoul National University Hospital between January 1, 2004 and December 31, 2013, our method achieves 0.7892 precision, 0.7506 recall, and 0.7576 F-measure in predicting whether a patient will develop AKI during the next 14 days. [ABSTRACT FROM AUTHOR]

In the scientific literature, spin refers to reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light. The presence of spin in biomedical research can negatively impact the development of further studies, clinical practice, and health policies. This systematic review aims to explore the nature and prevalence of spin in the biomedical literature. We searched MEDLINE, PreMEDLINE, Embase, Scopus, and hand searched reference lists for all reports that included the measurement of spin in the biomedical literature for at least 1 outcome. Two independent coders extracted data on the characteristics of reports and their included studies and all spin-related outcomes. Results were grouped inductively into themes by spin-related outcome and are presented as a narrative synthesis. We used meta-analyses to analyse the association of spin with industry sponsorship of research. We included 35 reports, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses. The nature of spin varied according to study design. The highest (but also greatest) variability in the prevalence of spin was present in trials. Some of the common practices used to spin results included detracting from statistically nonsignificant results and inappropriately using causal language. Source of funding was hypothesised by a few authors to be a factor associated with spin; however, results were inconclusive, possibly due to the heterogeneity of the included papers. Further research is needed to assess the impact of spin on readers’ decision-making. Editors and peer reviewers should be familiar with the prevalence and manifestations of spin in their area of research in order to ensure accurate interpretation and dissemination of research. [ABSTRACT FROM AUTHOR]

Background: Enhanced Recovery After Surgery (ERAS) protocol are well established in many surgical disciplines, leading to decrease in morbidity and length of hospital stay. These multi-modal protocols have been also introduced to oesophageal cancer surgery. This review aimed to evaluate current literature on ERAS in oesophageal cancer surgery and conduct a meta-analysis on primary and secondary outcomes. Methods: MEDLINE, Embase, Scopus and Cochrane Library were searched for eligible studies. We analyzed data up to May 2016. Eligible studies had to contain four described ERAS protocol elements. The primary outcome was overall morbidity. Secondary outcomes included length of hospital stay, specific complications, mortality and readmissions. Random effect meta-analyses were undertaken. Results: Initial search yielded 1,064 articles. Thorough evaluation resulted in 13 eligible articles which were analyzed. A total of 2,042 patients were included in the analysis (1,058 ERAS group and 984 treated with traditional protocols). Analysis of overall morbidity as well as complication rate did not show any significant reduction. Non-surgical complications and pulmonary complications were significantly lower in the ERAS group, RR = 0.71 95% CI 0.62–0.80, p < 0.00001 and RR = 0.75, 95% CI 0.60–0.94, p = 0.01, respectively. Meta-analysis on length of stay presented significant reduction Mean difference = -3.55, 95% CI -4.41 to -2.69, p for effect<0.00001. Conclusions: This systematic review with a meta-analysis on ERAS in oesophageal surgery indicates a reduction of non-surgical complications and no negative influence on overall morbidity. Moreover, a reduction in the length of hospital stay was presented. [ABSTRACT FROM AUTHOR]

Gene expression data analysis has paramount implications for gene treatments, cancer diagnosis and other domains. Clustering is an important and promising tool to analyze gene expression data. Gene expression data is often characterized by a large amount of genes but with limited samples, thus various projective clustering techniques and ensemble techniques have been suggested to combat with these challenges. However, it is rather challenging to synergy these two kinds of techniques together to avoid the curse of dimensionality problem and to boost the performance of gene expression data clustering. In this paper, we employ a projective clustering ensemble (PCE) to integrate the advantages of projective clustering and ensemble clustering, and to avoid the dilemma of combining multiple projective clusterings. Our experimental results on publicly available cancer gene expression data show PCE can improve the quality of clustering gene expression data by at least 4.5% (on average) than other related techniques, including dimensionality reduction based single clustering and ensemble approaches. The empirical study demonstrates that, to further boost the performance of clustering cancer gene expression data, it is necessary and promising to synergy projective clustering with ensemble clustering. PCE can serve as an effective alternative technique for clustering gene expression data. [ABSTRACT FROM AUTHOR]

One of fundamental challenges in cancer studies is that varying molecular characteristics of different tumor types may lead to resistance to certain drugs. As a result, the same drug can lead to significantly different results in different types of cancer thus emphasizing the need for individualized medicine. Individual prediction of drug response has great potential to aid in improving the clinical outcome and reduce the financial costs associated with prescribing chemotherapy drugs to which the patient’s tumor might be resistant. In this paper we develop a network based classifier (NBC) method for predicting sensitivity of cell lines to anticancer drugs from transcriptome data. In the literature, this strategy has been used for predicting cancer types. Here, we extend it to estimate sensitivity of cells from different tumor types to various anticancer drugs. Furthermore, we incorporate domain specific knowledge such as the use of apoptotic gene list and clinical dose information in our method to impart biological significance to the prediction. Our experimental results suggest that our network based classifier (NBC) method outperforms existing classifiers in estimating sensitivity of cell lines for different drugs. [ABSTRACT FROM AUTHOR]

Grade information has been considered in Yuan et al. (2007) wherein they proposed a Quasi-CRM method to incorporate the grade toxicity information in phase I trials. A potential problem with the Quasi-CRM model is that the choice of skeleton may dramatically vary the performance of the CRM model, which results in similar consequences for the Quasi-CRM model. In this paper, we propose a new model by utilizing bayesian model selection approach – Robust Quasi-CRM model – to tackle the above-mentioned pitfall with the Quasi-CRM model. The Robust Quasi-CRM model literally inherits the BMA-CRM model proposed by Yin and Yuan (2009) to consider a parallel of skeletons for Quasi-CRM. The superior performance of Robust Quasi-CRM model was demonstrated by extensive simulation studies. We conclude that the proposed method can be freely used in real practice. [ABSTRACT FROM AUTHOR]

Interleukin-12 is a pro-inflammatory cytokine which promotes Th1 and cytotoxic T lymphocyte activities, such as Interferon- secretion. For this reason Interleukin-12 could be a powerful therapeutic agent for cancer treatment. However, Interleukin-12 is also excessively toxic. Interleukin-27 is an immunoregulatory cytokine from the Interleukin-12 family, but it is not as toxic as Interleukin-12. In recent years, Interleukin-27 has been considered as a potential anti-tumor agent. Recent experiments in vitro and in vivo have shown that cancer cells transfected with IL-27 activate CD8+ T cells to promote the secretion of anti-tumor cytokines Interleukin-10, although, at the same time, IL-27 inhibits the secretion of Interferon- by CD8+ T cells. In the present paper we develop a mathematical model based on these experimental results. The model involves a dynamic network which includes tumor cells, CD8+ T cells and cytokines Interleukin-27, Interleukin-10 and Interferon-. Simulations of the model show how Interleukin-27 promotes CD8+ T cells to secrete Interleukin-10 to inhibit tumor growth. On the other hand Interleukin-27 inhibits the secretion of Interferon- by CD8+ T cells which somewhat diminishes the inhibition of tumor growth. Our numerical results are in qualitative agreement with experimental data. We use the model to design protocols of IL-27 injections for the treatment of cancer and find that, for some special types of cancer, with a fixed total amount of drug, within a certain range, continuous injection has better efficacy than intermittent injections in reducing the tumor load while the treatment is ongoing, although the decrease in tumor load is only temporary. [ABSTRACT FROM AUTHOR]

Context: Magnetic resonance imaging (MRI) is currently the most accurate imaging modality to assess local prostate cancer stage. Despite a growing body of evidence, incorporation of MRI images into decision-making process concerning surgical template of radical prostatectomy, is complex and still poorly understood. Objective: We sought to determine the value of MRI in preoperative planning before radical prostatectomy. Materials and methods: Systematic search through electronic PubMed, EMBASE, and Cochrane databases from 2000 up to April 2018 was performed. Only studies that used preoperative MRI in decision-making process regarding extension of resection in patients with prostate cancer, in whom radical prostatectomy was an initial form of treatment were included into analysis. Their quality was scored by Risk Of Bias In Non-Randomized Studies of Interventions system. Meta-analysis was performed to calculate the weighted summary proportion under the fixed or random effects model as appropriate and pooled effects were depicted on forest plots. Results: The results showed that the preoperative MRI led to the modification of initial surgical template in one third of cases (35%). This occurred increasingly with the rising prostate cancer-risk category: 28%, 33%, 52% in low-, intermediate- and high-risk group, respectively. Modification of neurovascular bundle-sparing surgery based on MRI appeared to have no impact on the positive surgical margin rate. The decision based on MRI was correct on average in 77% of cases and differed across prostate cancer-risk categories: 63%, 75% and 91% in low-, intermediate- and high-risk group, accordingly. Conclusions: In summary, MRI has a considerable impact on the decision-making process regarding the extent of resection during radical prostatectomy. Adaptation of MRI images by operating surgeons has at worst no significant impact on surgical margin status, however its ability to decrease the positive surgical margin rates remains unconfirmed. [ABSTRACT FROM AUTHOR]

Lung 4D computed tomography (4D-CT) plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results. [ABSTRACT FROM AUTHOR]

Background: LncRNA CCAT1 is significantly overexpressed in various types of cancers, suggesting that it might be associated with prognosis and clinicopathological features in patients with cancer. Methods: A comprehensive search was performed in Pubmed, Web of Science, OVID and CNKI databases. We also retrieved articles from other sources, such as retrieving from the reference lists of relevant articles. Eligible studies were included based on defined exclusion and inclusion criteria to perform a meta-analysis. STATA 14.0 was used to estimate pooled hazard ratios (HRs) with 95% confidence interval (95% CI), the heterogeneity among studies and publication bias to judge the prognostic value. Results: A total of 1587 patients from 11 eligible studies were included in the meta-analysis. The results showed that high expression level of CCAT1 was significantly associated with shorter overall survival in cancer patients (HR 2.335, 95% CI:1.551–3.517); in the subgroup analysis, region (China or UK), sample size (more or less than 100), type of cancer (digestive or non-digestive disease) and paper quality (score more or less than 7) did not alter the association between CCAT1 expression and cancer prognosis but preoperative treatment did. And CCAT1 expression was an independent prognostic marker for overall survival in patients with cancer (pooled HR 2.195, 95%CI:1.316–3.664) using Cox multivariate analyses. The clinicopathological parameters analysis further showed that increased expression level of CCAT1 was correlated with tumor size, lymph node metastasis, TNM stage, distant metastasis, microvascular invasion and capsular formation in relevant cancers. Conclusions: The meta-analysis results from present study suggested that increased expression level of CCAT1 was associated with poor prognosis and can serve as an independent biomarker. And the expression level of CCAT1 was associated with clinicopathological features in relevant cancers. [ABSTRACT FROM AUTHOR]

Background: Previous studies have revealed conflicting findings concerning the efficacy of radiotherapy (RT) and radiochemotherapy (RCT) in IE/IIE extranodal nasal-type natural killer/T cell lymphoma (ENKTL). In this study, we conducted a comprehensive meta-analysis to address this issue. Methods: We systematically searched PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), EmBase, BISOS, Clinical Trials and some Chinese databases for relevant studies, and 2 prospective and 15 retrospective studies involving a total of 1595 patients met our inclusion criteria. Results: The meta-analysis showed no significant differences in complete remission (CR) [odds ratio (OR) 0.85, 95% confidence interval (CI) 0.42–1.72, p = 0.65], 5-year overall survival (OS) [hazard ratio (HR) 1.11, 95% CI 0.85–1.45, p = 0.43] and 5-year progression free survival (PFS) (HR 1.07, 95% CI 0.75–1.53, p = 0.70) in patients who received RT versus RCT. Furthermore, the addition of CT decreased neither systemic failure (SL) (OR 0.75, 95% CI 0.47–1.21, p = 0.24) nor locoregional failure (LF) (OR 1.17, 95% CI 0.68–2.01, p = 0. 57). Conclusions: RCT did not have an obvious advantage over RT for treating IE/IIE ENKTL. [ABSTRACT FROM AUTHOR]

Background: Hydronephrosis is a common finding in patients with bladder cancer. The aim of the study was to appraise the prognostic value of preoperative hydronephrosis in bladder cancer patients undergoing radical cystectomy. Methods: We conducted a literature search using PubMed and Embase databases in Aug 2018. Summary hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using fixed-effect or random-effects models. The primary endpoint was overall survival (OS). Secondary endpoints were cancer-specific survival (CSS) and recurrence-free survival (RFS). Results: Overall, 13 studies published between 2008 and 2018 including 4,820 patients were selected for the meta-analysis. The age of bladder cancer patients ranged from 27 to 90.4 years, and the overall proportion of males was 72.5%. Preoperative hydronephrosis was reported in 27.4% of patients. The pooled HR was statistically significant for OS (HR, 1.36; 95% CI [1.20–1.55]) and CSS (HR, 1.64; 95% CI [1.33–2.02]), with no heterogeneity among the enrolled studies. Patients with bilateral hydronephrosis showed a poorer CSS compared to those with no hydronephrosis (HR 5.43, 95% CI [3.14–9.40]). However, there was no difference in CSS between no hydronephrosis and unilateral hydronephrosis groups (HR 1.35, 95% CI [0.84–2.14]). Despite a tendency towards poorer RFS (HR, 1.27; 95% CI [0.96–1.96]), the results demonstrated no significant association between presence of preoperative hydronephrosis and RFS after radical cystectomy. Conclusion: This meta-analysis indicates that preoperative hydronephrosis is significantly associated with poorer OS and CSS after radical cystectomy for patients with bladder cancer. Preoperative hydronephrosis has a stronger effect on CSS in patients with bilateral hydronephrosis. The presence of preoperative hydronephrosis not only predicts prognosis, but may also help to identify patients who benefit the most from neoadjuvant chemotherapy. [ABSTRACT FROM AUTHOR]

Background: The narrative surrounding the management of potentially resectable pancreatic cancer is complex. Surgical resection is the only potentially curative treatment. However resection rates are low, the risk of operative morbidity and mortality are high, and survival outcomes remain poor. The aim of this study was to create a prognostic Bayesian network that pre-operatively makes personalized predictions of post-resection survival time of 12months or less and also performs post-operative prognostic updating. Methods: A Bayesian network was created by synthesizing data from PubMed post-resection survival analysis studies through a two-stage weighting process. Input variables included: inflammatory markers, tumour factors, tumour markers, patient factors and, if applicable, response to neoadjuvant treatment for pre-operative predictions. Prognostic updating was performed by inclusion of post-operative input variables including: pathology results and adjuvant therapy. Results: 77 studies (n = 31,214) were used to create the Bayesian network, which was validated against a prospectively maintained tertiary referral centre database (n = 387). For pre-operative predictions an Area Under the Curve (AUC) of 0.7 (P value: 0.001; 95% CI 0.589–0.801) was achieved accepting up to 4 missing data-points in the dataset. For prognostic updating an AUC 0.8 (P value: 0.000; 95% CI:0.710–0.870) was achieved when validated against a dataset with up to 6 missing pre-operative, and 0 missing post-operative data-points. This dropped to AUC: 0.7 (P value: 0.000; 95% CI:0.667–0.818) when the post-operative validation dataset had up to 2 missing data-points. Conclusion: This Bayesian network is currently unique in the way it utilizes PubMed and patient level data to translate the existing empirical evidence surrounding potentially resectable pancreatic cancer to make personalized prognostic predictions. We believe such a tool is vital in facilitating better shared decision-making in clinical practice and could be further developed to offer a vehicle for delivering personalized precision medicine in the future. [ABSTRACT FROM AUTHOR]

Background: Randomized clinical trials compare participants receiving an experimental intervention to participants receiving standard of care (SOC). If one could predict the outcome for participants receiving SOC, a trial could be designed where all participants received the experimental intervention, with the observed outcome of the experimental group compared to the prediction for those individuals. Methods: We used the CancerMath calculator to predict outcomes for participants in two large clinical trials of adjuvant chemotherapy for breast cancer: NSABPB15 and CALGB9344. NSABPB15 was the training set, and we used the modified algorithm to predict outcomes for two groups from CALGB9344: one which received standard of care (SOC) chemotherapy and one which received paclitaxel in addition. We made a prediction for each individual CALGB9344 participant, assuming each received only SOC. Results: The predicted outcome for the group which received only SOC matched what was observed in the CALGB9344 trial. In contrast, the predicted outcome for the group also receiving paclitaxel was significantly worse than what was observed for this group. This matches the conclusion of CALGB9344 that adding paclitaxel to SOC improves survival. Conclusion: This project proves that a statistical model can predict the outcome of clinical trial participants treated with SOC. In some circumstances, a predictive model could be used instead of a control arm, allowing all participants to receive experimental treatment. Predictive models for cancer and other diseases could be constructed using the vast amount of outcomes data available to the federal government, and made available for public use. [ABSTRACT FROM AUTHOR]

Objective: We conducted this meta-analysis to assess the effectiveness of parathyroid gland autotransplantation in preserving parathyroid function during thyroid surgery for thyroid neoplasms. Methods: We conducted a search by using PubMed, Embase, and the Cochrane Library electronic databases for studies that were published up to January 2019. The reference lists of the retrieved articles were also reviewed. Two authors independently assessed the methodological quality and extracted the data. A random-effects model was used to calculate the overall combined risk estimates. Publication bias was evaluated with a funnel plot using Egger’s and Begg’s tests. Results: A total of 25 independent studies involving 10,531 participants were included in the meta-analysis. Compared with patients who did not undergo parathyroid gland autotransplantation, the overall pooled relative risks for patients who underwent parathyroid gland autotransplantation were 1.75 (95% CI: 1.51–2.02, p<0.001) for postoperative hypoparathyroidism, 1.72 (95% CI: 1.45–2.05, p<0.001) for protracted hypoparathyroidism, 1.06 (95% CI: 0.44–2.58, p = 0.894) and 0.71 (95% CI: 0.22–2.29, p = 0.561) for biochemical hypoparathyroidism and biochemical hypocalcemia at 6 months postoperatively, respectively, and 1.89 (95% CI: 1.33–2.69, p<0.001) and 0.22 (95% CI: 0.09–0.52, p = 0.001) for biochemical hypoparathyroidism and biochemical hypocalcemia at 12 months postoperatively, respectively. The pooled relative risks for patients who underwent one parathyroid gland autotransplantation and patients who underwent two or more parathyroid gland autotransplantations were 1.71 (95% CI: 1.25–2.35, p = 0.001) and 2.22 (95% CI: 1.43–3.45, p<0.001) for postoperative hypoparathyroidism, 1.09 (95% CI: 0.59–2.01, p = 0.781) and 0.55 (95% CI: 0.16–1.87, p = 0.341) for hypoparathyroidism at 6 months postoperatively compared with those of patients who did not undergo parathyroid gland autotransplantation. Conclusions: Parathyroid gland autotransplantation is significantly associated with increased risk of postoperative and protracted hypoparathyroidism, and the number of autoplastic parathyroid glands is positively correlated with the incidence of postoperative hypoparathyroidism. [ABSTRACT FROM AUTHOR]

Objective: To investigate morbidity for patients after the primary surgical management of cervical cancer in low and middle-income countries (LMIC). Methods: The Pubmed, Cochrane, the Cochrane Central Register of Controlled Trials, Embase, LILACS and CINAHL were searched for published studies from 1st Jan 2000 to 30th June 2017 reporting outcomes of surgical management of cervical cancer in LMIC. Random-effects meta-analytical models were used to calculate pooled estimates of surgical complications including blood transfusions, ureteric, bladder, bowel, vascular and nerve injury, fistulae and thromboembolic events. Secondary outcomes included five-year progression free (PFS) and overall survival (OS). Findings: Data were available for 46 studies, including 10,847 patients from 11 middle income countries. Pooled estimates were: blood transfusion 29% (95%CI 0.19–0.41, P = 0.00, I2 = 97.81), nerve injury 1% (95%CI 0.00–0.03, I2 77.80, P = 0.00), bowel injury, 0.5% (95%CI 0.01–0.01, I2 = 0.00, P = 0.77), bladder injury 1% (95%CI 0.01–0.02, P = 0.10, I2 = 32.2), ureteric injury 1% (95%CI 0.01–0.01, I2 0.00, P = 0.64), vascular injury 2% (95% CI 0.01–0.03, I2 60.22, P = 0.00), fistula 2% (95%CI 0.01–0.03, I2 = 77.32, P = 0.00,), pulmonary embolism 0.4% (95%CI 0.00–0.01, I2 26.69, P = 0.25), and infection 8% (95%CI 0.04–0.12, I2 95.72, P = 0.00). 5-year PFS was 83% for laparotomy, 84% for laparoscopy and OS was 85% for laparotomy cases and 80% for laparoscopy. Conclusion: This is the first systematic review and meta-analysis of surgical morbidity in cervical cancer in LMIC, which highlights the limitations of the current data and provides a benchmark for future health services research and policy implementation. [ABSTRACT FROM AUTHOR]

Glioblastomas are the most common primary brain tumours. They are known for their highly aggressive growth and invasion, leading to short survival times. Treatments for glioblastomas commonly involve a combination of surgical intervention, chemotherapy, and external beam radiation therapy (XRT). Previous works have not only successfully modelled the natural growth of glioblastomas in vivo, but also show potential for the prediction of response to radiation prior to treatment. This suggests that the efficacy of XRT can be optimized before treatment in order to yield longer survival times. However, while current efforts focus on optimal scheduling of radiotherapy treatment, they do not include a similarly sophisticated spatial optimization. In an effort to improve XRT, we present a method for the spatial optimization of radiation profiles. We expand upon previous results in the general problem and examine the more physically reasonable cases of 1-step and 2-step radiation profiles during the first and second XRT fractions. The results show that by including spatial optimization in XRT, while retaining a constant prescribed total dose amount, we are able to increase the total cell kill from the clinically-applied uniform case. [ABSTRACT FROM AUTHOR]

Background: Recent literature has suggested that tumor sidedness in colorectal cancer (CRC) is an independent prognostic and potentially predictive marker of survival. The aims of the study were to determine the prognostic significance of tumor sidedness in colorectal cancer patients undergoing primary tumor resection and to assess associated tumor biology. Methods: A total of 3281 consecutive patients who underwent surgical resection of their primary CRC from January 1998 to December 2012 were analyzed for association with tumor biologic factors and with overall survival. Metastatic patients were excluded from analysis. Results: Left sided CRCs were associated with a number of additional key prognostic markers including BRAFV600E wildtype status (P<0.001), mismatch repair proficiency (p<0.001), absence of peritumoral lymphocytic response (p = 0.001), high lymphocyte-to-monocyte ratio (p<0.001) and low neutrophil-to-lymphocyte ratio (p<0.001). In primary analysis with 3067 patients, there was no statistical difference in sidedness in the univariate analysis (p = 0.291). Three further subgroup analyses were performed. In the first subgroup, only stage III patients were analyzed. In the second, patients with mismatch repair deficiency were removed. In the third, additional clinicopathologic variables known to be independently prognostic were added into analysis. In all three subgroup analyses tumor sidedness was not an independent prognostic marker. Conclusions: Tumor sidedness was not an independent prognostic marker of CRC. However, right sided CRCs were associated with several key independent prognostic markers supporting a hypothesis that tumor sidedness is a surrogate for other biomarkers. [ABSTRACT FROM AUTHOR]

Objectives: Adjuvant paclitaxel and trastuzumab has been shown to be an effective regimen with low risk of cancer recurrence and treatment-related toxicities in early-stage node-negative, HER2-positive breast cancer. We investigated the cost-effectiveness of this regimen. Methods: A Markov-based microsimulation model with six health states is used to simulate four adjuvant therapy options for women with early-stage node-negative, HER2-positive breast cancer at different age groups. The four treatment arms are 1) adjuvant paclitaxel and trastuzumab (TH), 2) doxorubicin, cyclophosphamide, paclitaxel and trastuzumab (ACTH), 3) docetaxel, carboplatin and trastuzumab (TCH), and 4) no adjuvant trastuzumab (NT). Data from randomized trials were used to estimate treatment efficacy. Societal perspective was used in this cost-effectiveness analysis. Costs were measured in 2016 US dollars (US$) and quality-adjusted life-years (QALYs) was used for health outcomes. Sensitivity analyses were performed to evaluate the impact of uncertainty in parameter estimation. Results: We found that 40-year-old women undergoing TH treatment would have an average of 16.17 QALYs for the cost of $178,650 when lifetime horizon is used. Compared to NT, TH has incremental cost-effectiveness ratios ranged from $10,584 (ages 40–49) to $84,981 (age 80+) per additional QALYs. The sensitivity analysis showed that TH is cheaper and leads to higher QALYs compared to both ACTH and TCH for all age groups and time horizons. Conclusions: TH is cost-effective for all age groups in the base case scenario and in the sensitivity analysis. In order to reduce the parameter uncertainty, clinical trials with longer follow-up times are needed. [ABSTRACT FROM AUTHOR]

The random allocation of patients to treatments is a crucial step in the design and conduct of a randomized controlled trial. For this purpose, a variety of randomization procedures is available. In the case of imperfect blinding, the extent to which a randomization procedure forces balanced group sizes throughout the allocation process affects the predictability of allocations. As a result, some randomization procedures perform superior with respect to selection bias, whereas others are less susceptible to chronological bias. The choice of a suitable randomization procedure therefore depends on the expected risk for selection and chronological bias within the particular study in question. To enable a sound comparison of different randomization procedures, we introduce a model for the combined effect of selection and chronological bias in randomized studies with a survival outcome. We present an evaluation method to quantify the influence of bias on the test decision of the log-rank test in a randomized parallel group trial with a survival outcome. The effect of selection and chronological bias and the dependence on the study setting are illustrated in a sensitivity analysis. We conclude with a case study to showcase the application of our model for comparing different randomization procedures in consideration of the expected type I error probability. [ABSTRACT FROM AUTHOR]

Background: Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scan acquired during treatment. Methods: The dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called ‘cohort differences model’ approach. Moreover, the potential prognostic performances for overall survival of radiomic features extracted from CT or FDG-PET, or relative or absolute differences between the scans at the two time points, were assessed using the LASSO regression method. Furthermore, the performances of five different classifiers were evaluated for all image sets. Results: The individual cohorts substantially differed in terms of patient characteristics. Moreover, the cohort differences model indicated statistically significant differences between the cohorts. Neither LASSO nor any of the tested classifiers resulted in a clinical relevant prognostic model that could be validated on the available datasets. Conclusion: The results imply that the study might have been influenced by a limited sample size, heterogeneous patient characteristics, and inconsistent imaging parameters. No prognostic performance of FDG-PET or CT based radiomics models can be reported. This study highlights the necessity of extensive evaluations of cohorts and of validation datasets, especially in retrospective multi-centric datasets. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) represents a major cause for cancer death and every third patient develops liver metastases (CRLM). Several factors including number and size of metastases and primary tumour lymph-node status have been linked to survival. The primary tumour location along the colo-rectum continuum (sidedness) was analysed in first-line chemotherapy trials, where right-sided CRCs showed decreased survival. This association has not yet been clearly established in patients undergoing resection for CRLM. Methods: Clinicopathological differences in CRLM resections according to sidedness in two Austrian centres (2003–2016) are described and survival is compared through Kaplan-Meier and multivariable analysis. A risk-score is presented with time-dependent receiver operating curve analysis and international validation in two major hepatobiliary centres. Furthermore, a systematic meta-analysis of studies on primary tumour location and survival after CRLM resection was performed. Results: 259 patients underwent hepatectomy. Right-sided CRC patients (n = 59) more often had positive primary tumour lymph-nodes (76.3%/61.3%;p = 0.043) and RAS-mutations (60%/34.9%;p = 0.036). The median overall and disease-free survival was 33.5 and 9.1 months in right-sided versus 55.5 (p = 0.051) and 12.1 months (p = 0.078) in left-sided patients. In multivariable analysis nodal-status (HR 1.52), right-sidedness (HR 1.53), extrahepatic disease (HR 1.71) and bilobar hepatic involvement (HR 1.41) were significantly associated with overall survival. Sidedness was not independently associated with disease-free survival (HR 1.33; p = 0.099). A clinical risk score including right-sidedness, nodal-positivity and extrahepatic involvement significantly predicted overall (p = 0.005) and disease-free survival (p = 0.027), which was confirmed by international validation in 527 patients (p = 0.001 and p = 0.011). Meta-analysis including 10 studies (n = 4312) showed a significant association of right-sidedness with overall survival after resection (HR 1.55;p<0.001). There was no significant association with disease-free survival (HR 1.22;p = 0.077), except when rectal-cancers were excluded (HR 1.39;p = 0.006). Conclusions: Patients with liver metastases from right-sided CRC experience worse survival after hepatic resection. Sidedness is a simple yet effective factor to predict outcome. [ABSTRACT FROM AUTHOR]

Drug screening studies typically involve assaying the sensitivity of a range of cancer cell lines across an array of anti-cancer therapeutics. Alongside these sensitivity measurements high dimensional molecular characterizations of the cell lines are typically available, including gene expression, copy number variation and genomic mutations. We propose a sparse multitask regression model which learns discriminative latent characteristics that predict drug sensitivity and are associated with specific molecular features. We use ideas from Bayesian nonparametrics to automatically infer the appropriate number of these latent characteristics. The resulting analysis couples high predictive performance with interpretability since each latent characteristic involves a typically small set of drugs, cell lines and genomic features. Our model uncovers a number of drug-gene sensitivity associations missed by single gene analyses. We functionally validate one such novel association: that increased expression of the cell-cycle regulator C/EBPδ decreases sensitivity to the histone deacetylase (HDAC) inhibitor panobinostat. [ABSTRACT FROM AUTHOR]

Panniculitis and vitiligo-like lesions have been recently identified as rare cutaneous side effects of the combination of BRAF and MEK inhibitors, a standard of care in metastatic and locally advanced BRAF V600 mutated melanoma. An immune-mediated mechanism has been advocated in the pathogenesis of these skin lesions. Herein we retrospectively reviewed our institutional experience with the aim to explore the association between the occurrence of panniculitis and vitiligo-like lesions during combination therapy with dabrafenib (D) and trametinib (T) and outcome of advanced melanoma patients. Among 52 consecutive BRAF V600 mutated melanoma patients submitted to DT in our center, 12 (23%) developed immune related skin lesions (IRSLs): 8 panniculitis and 4 vitiligo. Patients with IRSLs diagnosis obtained a better disease response (83% versus 25%) (p = 0.001) than their counterpart and had a longer progression free survival and overall survival. The association of IRSLs and lower risk of disease progression (HR 0.19; CI 95% 0.04–0.90; p = 0.043) was confirmed after adjusting for major prognostic factors in multivariate analysis. IRSLs might represent an easy predictive surrogate marker for treatment response and favourable outcome in melanoma patients submitted to DT combination therapy. [ABSTRACT FROM AUTHOR]

Current methods to quantify T-cell clonal expansion only account for variance due to random sampling from a highly diverse repertoire space. We propose a beta-binomial model to incorporate time-dependent variance into the assessment of differentially abundant T-cell clones, identified by unique T Cell Receptor (TCR) β-chain rearrangements, and show that this model improves specificity for detecting clinically relevant clonal expansion. Using blood samples from ten healthy donors, we modeled the variance of T-cell clones within each subject over time and calibrated the dispersion parameters of the beta distribution to fit this variance. As a validation, we compared pre- versus post-treatment blood samples from urothelial cancer patients treated with atezolizumab, where clonal expansion (quantified by the earlier binomial model) was previously reported to correlate with benefit. The beta-binomial model significantly reduced the false-positive rate for detecting differentially abundant clones over time compared to the earlier binomial method. In the urothelial cancer cohort, the beta-binomial model enriched for tumor infiltrating lymphocytes among the clones detected as expanding in the peripheral blood in response to therapy compared to the binomial model and improved the overall correlation with clinical benefit. Incorporating time-dependent variance into the statistical framework for measuring differentially abundant T-cell clones improves the model's specificity for T-cells that correlate more strongly with the disease and treatment setting of-interest. Reducing background-level clonal expansion, therefore, improves the quality of clonal expansion as a biomarker for assessing the T cell immune response and correlations with clinical measures. [ABSTRACT FROM AUTHOR]

Objective: A Clinical Decision Support System (CDSS) that can amass Electronic Health Record (EHR) and other patient data holds promise to provide accurate classification and guide treatment choices. Our objective is to develop the ecision Support System for Making ersonalized ssessments and Recommendations Concerning Breast ancer Patients (DPAC), which is a CDSS learned from data that recommends the optimal treatment decisions based on a patient’s features. Method: We developed a Bayesian network architecture called Causal Modeling with Internal Layers (CAMIL), and an algorithm called Treatment Feature Interactions (TFI), which learns from data the interactions needed in a CAMIL model. Using the TFI algorithm, we learned interactions for six treatments from the LSDS-5YDM dataset. We created a CAMIL model using these interactions, resulting in a DPAC which recommends treatments towards preventing 5-year breast cancer metastasis. Results: In a 5-fold cross-validation analysis, we compared the probability of being metastasis free in 5 years for patients who made decisions recommended by DPAC to those who did not. These probabilities are (the probability for those making the decisions appears first): chemotherapy (.938, .872); breast/chest wall radiation (.939, .902); nodal field radiation (.940, .784); antihormone (.941, .906); HER2 inhibitors (.934, .880); neadjuvant therapy (.931, .837). In an application of DPAC to the independent METABRIC dataset, the probabilities for chemotherapy were (.845, .788). Discussion: Patients who took the advice of DPAC had, as a group, notably better outcomes than those who did not. We conclude that DPAC is effective at amassing and analyzing data towards treatment recommendations. Some of the findings in DPAC are controversial. For example, DPAC says that chemotherapy increases the chances of metastasis for many node negative patients. This controversy shows the importance of developing a conclusive version of DPAC to ensure we provide patients with the best patient-specific treatment recommendations. [ABSTRACT FROM AUTHOR]

Background: Neoadjuvant therapy has emerged as an alternative treatment strategy for potentially resectable pancreatic cancer. In the absence of large randomized controlled trials offering a direct comparison, this study aims to use Markov decision analysis to compare efficacy of traditional surgery first (SF) and neoadjuvant treatment (NAT) pathways for potentially resectable pancreatic cancer. Methods: An advanced Markov decision analysis model was constructed to compare SF and NAT pathways for potentially resectable pancreatic cancer. Transition probabilities were calculated from randomized control and Phase II/III trials after comprehensive literature search. Utility outcomes were measured in overall and quality-adjusted life months (QALMs) on an intention-to-treat basis as the primary outcome. Markov cohort analysis of treatment received was the secondary outcome. Model uncertainties were tested with one and two-way deterministic and probabilistic Monte Carlo sensitivity analysis. Results: SF gave 23.72 months (18.51 QALMs) versus 20.22 months (16.26 QALMs). Markov Cohort Analysis showed that where all treatment modalities were received NAT gave 35.05 months (29.87 QALMs) versus 30.96 months (24.86QALMs) for R0 resection and 34.08 months (29.87 QALMs) versus 25.85 months (20.72 QALMs) for R1 resection. One-way deterministic sensitivity analysis showed that NAT was superior if the resection rate was greater than 51.04% or below 75.68% in SF pathway. Two-way sensitivity analysis showed that pathway superiority depended on obtaining multimodal treatment in either pathway. Conclusion: Whilst NAT is a viable alternative to traditional SF approach, superior pathway selection depends on the individual patient’s likelihood of receiving multimodal treatment in either pathway. [ABSTRACT FROM AUTHOR]

Tumour heterogeneity plays a large role in the response of tumour tissues to radiation therapy. Inherent biological, physical, and even dose deposition heterogeneity all play a role in the resultant observed response. We here implement the use of Haralick textural analysis to quantify the observed glycogen production response, as observed via Raman spectroscopic mapping, of tumours irradiated within a murine model. While an array of over 20 Haralick features have been proposed, we here concentrate on five of the most prominent features: homogeneity, local homogeneity, contrast, entropy, and correlation. We show that these Haralick features can be used to quantify the inherent heterogeneity of the Raman spectroscopic maps of tumour response to radiation. Furthermore, our results indicate that Haralick-calculated textural features show a statistically significant dose dependent variation in response heterogeneity, specifically, in glycogen production in tumours irradiated with clinically relevant doses of ionizing radiation. These results indicate that Haralick textural analysis provides a quantitative methodology for understanding the response of murine tumours to radiation therapy. Future work in this area can, for example, utilize the Haralick textural features for understanding the heterogeneity of radiation response as measured by biopsied patient tumour samples, which remains the standard of patient tumour investigation. [ABSTRACT FROM AUTHOR]

Objective: The objective is to present a proof-of-concept of a semi-automatic method to reduce hippocampus segmentation time on magnetic resonance images (MRI). Materials and methods: FAst Segmentation Through SURface Fairing (FASTSURF) is based on a surface fairing technique which reconstructs the hippocampus from sparse delineations. To validate FASTSURF, simulations were performed in which sparse delineations extracted from full manual segmentations served as input. On three different datasets with different diagnostic groups, FASTSURF hippocampi were compared to the original segmentations using Jaccard overlap indices and percentage volume differences (PVD). In one data set for which back-to-back scans were available, unbiased estimates of overlap and PVD were obtained. Using longitudinal scans, we compared hippocampal atrophy rates measured by manual, FASTSURF and two automatic segmentations (FreeSurfer and FSL-FIRST). Results: With only seven input contours, FASTSURF yielded mean Jaccard indices ranging from 72(±4.3)% to 83(±2.6)% and PVDs ranging from 0.02(±2.40)% to 3.2(±3.40)% across the three datasets. Slightly poorer results were obtained for the unbiased analysis, but the performance was still considerably better than both tested automatic methods with only five contours. Conclusions: FASTSURF segmentations have high accuracy and require only a fraction of the delineation effort of fully manual segmentation. Atrophy rate quantification based on completely manual segmentation is well reproduced by FASTSURF. Therefore, FASTSURF is a promising tool to be implemented in clinical workflow, provided a future prospective validation confirms our findings. [ABSTRACT FROM AUTHOR]

The current research study is concerned with the automated differentiation between histopathological slides from colon tissues with respect to four classes (healthy tissue and cancerous of grades 1, 2 or 3) through an optimized ensemble of predictors. Six distinct classifiers with prediction accuracies ranging from 87% to 95% are considered for the task. The proposed method of combining them takes into account the probabilities of the individual classifiers for each sample to be assigned to any of the four classes, optimizes weights for each technique by differential evolution and attains an accuracy that is significantly better than the individual results. Moreover, a degree of confidence is defined that would allow the pathologists to separate the data into two distinct sets, one that is correctly classified with a high level of confidence and the rest that would need their further attention. The tandem is also validated on other benchmark data sets. The proposed methodology proves to be efficient in improving the classification accuracy of each algorithm taken separately and performs reasonably well on other data sets, even with default weights. In addition, by establishing a degree of confidence the method becomes more viable for use by actual practitioners. [ABSTRACT FROM AUTHOR]

Objective: The standard treatment for patients with advanced/metastatic soft tissue sarcomas (ASTS) is systemic chemotherapy with doxorubicin. A previous meta-analysis of 8 randomized controlled trials (RCTs) demonstrated the superiority of single-agent doxorubicin over doxorubicin-based combination chemotherapy for ASTS. However, meta-analyses of all RCTs that compare doxorubicin to other single-agent or combination regimens as first-line treatments for ASTS are lacking. We conducted a systematic review and meta-analysis to evaluate the efficacy and toxicity of current primary treatments for ASTS. Methods: Eligible studies were RCTs of first-line chemotherapies for ASTS comparing doxorubicin alone to other single agents or to combination therapies (experimental arm). Data from studies reporting hazard ratios (HR) and 95% confidence intervals (CI) for overall survival (OS) and progression-free survival (PFS) were pooled. Other time-to-event endpoints were extracted from the studies based on Kaplan-Meier estimates, and pooled odds ratios (OR) and 95% CI were calculated. Results: Twenty-seven eligible RCTs comprising 6156 patients were identified. Overall, the 1-year OS (OR 0.88, 95% CI 0.79–0.99, P = 0.03) was significantly improved in the experimental arm over the doxorubicin-only arm; however, there was no significant difference in 2-year OS (OR 0.87, 95% CI 0.73–1.03, P = 0.11) or OS (HR 0.97, 95% CI 0.91–1.03, P = 0.28) between the two groups. PFS and other time-to-event endpoints were not significantly different between the two treatment arms. While incidences of overall severe adverse events were not significantly different (OR 1.20, 95% CI 0.88–1.65, P = 0.26), severe nausea/vomiting was significantly more frequent in the experimental arm (OR 1.90, 95% CI 1.27–2.83, P = 0.002). Conclusion: The efficacies of doxorubicin-only and experimental arm regimens were similar, although toxicities were more frequent in the experimental arms. Hence, doxorubicin monotherapy remains suitable as a standard first-line regimen for ASTS. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate the dosimetric accuracy of respiratory gated volumetric modulated arc therapy (VMAT) for lung stereotactic body radiation therapy (SBRT) under simulation conditions similar to the actual clinical situation using patient-specific lung phantoms and realistic target movements. Methods: Six heterogeneous lung phantoms were fabricated using a 3D-printer (3DISON, ROKIT, Seoul, Korea) to be dosimetrically equivalent to actual target regions of lung SBRT cases treated via gated VMAT. They were designed to move realistically via a motion device (QUASAR, Modus Medical Devices, Canada). Using the lung phantoms and a homogeneous phantom (model 500–3315, Modus Medical Devices), film dosimetry was performed with and without respiratory gating for VMAT delivery (TrueBeam STx; Varian Medical Systems, Palo Alto, CA, USA). The measured results were analyzed with the gamma passing rates (GPRs) of 2%/1 mm criteria, by comparing with the calculated dose via the AXB and AAA algorithms of the Eclipse Treatment Planning System (version 10.0.28; Varian Medical Systems). Results: GPRs were greater than the acceptance criteria 80% for all film measurements with the stationary and homogeneous phantoms in conventional QAs. Regardless of the heterogeneity of phantoms, there were no significant differences (p > 0.05) in GPRs obtained with and without target motions; the statistical significance (p = 0.031) was presented between both algorithms under the utilization of heterogeneous phantoms. Conclusions: Dosimetric verification with heterogeneous patient-specific lung phantoms could be successfully implemented as the evaluation method for gated VMAT delivery. In addition, it could be dosimetrically confirmed that the AXB algorithm improved the dose calculation accuracy under patient-specific simulations using 3D printed lung phantoms. [ABSTRACT FROM AUTHOR]

Despite canine B-cell Lymphoma (BCL) representing the most common haematological tumour, epigenetic events driving development and progression are scarcely known. Recently, canine Diffuse Large BCL (DLBCL) DNA methylome by genome-wide CpG microarray has identified genes and pathways associated to pathogenesis. To validate data previously obtained by array analysis, the CLBL-1 cell line was used and the HOXD10, FGFR2, ITIH5 and RASAL3 genes were selected. CLBL-1 cells were treated with two hypomethylating drugs (HDs; IC50, 50% inhibitory concentration), i.e. azacytidine and decitabine (DEC), either alone or in combination with three histone deacetylase inhibitors (HDACis; IC20), i.e. valproic acid, trichostatin and vorinostat. Following the incubation with both HDs, an overall decrease of promoter methylation was highlighted, thus confirming target genes hypermethylation. The highest mRNA restoration was observed following the exposure to HDs combined with HDACis, and mostly with valproic acid. Contrasting results were only obtained for RASAL3. An in vivo confirmation was finally attempted treating Nod-Scid mice engrafted with CLBL-1 cells with DEC. Although DEC did not arrest tumour growth, target genes promoter methylation was significantly reduced in DEC-treated mice vs controls. Overall, this work demonstrates that CLBL-1 cell line represents a reliable in vitro model to validate the methylation-dependent silencing of key genes for BCL; moreover, it may be useful for xenograft models in mice, despite its aggressive behaviour. In future, functional studies will be performed to deepen the role of selected genes on BCL pathogenesis and progression, and their methylation-dependent mechanism of regulation. [ABSTRACT FROM AUTHOR]

Background: Ovarian cancer is the leading cause of death by gynecologic cancers in the Western world. The aim of the study was to identify microRNAs (miRNAs) associated with prognosis and/or resistance to chemotherapy among patients with epithelial ovarian cancer. Methods: Using information from the Pelvic Mass Study we identified a cohort of women with epithelial ovarian cancer. Tumor tissues were then collected and analyzed by global miRNA microarrays. MiRNA profiling was then linked to survival and time to progression using Cox proportional-hazards regression models. Logistic regression models were used for the analysis of resistance to chemotherapy. Our results were validated using external datasets retrieved from the NCBI Gene Expression Omnibus database. Results: A total of 197 patients with epithelial ovarian cancer were included for miRNA microarray analysis. In multivariate analyses we identified a number of miRNAs significantly correlated with overall survival (miR-1183 (HR: 1.42, 95% CI:1.17–1.74, p = 0.0005), miR-126-3p (HR: 1.38, 95% CI:1.11–1.71, p = 0.0036), time to progression (miR-139-3p (HR: 1.48, 95% CI: 1.13–1.94, p = 0.0047), miR-802 (HR: 0.48, 95% CI: 0.29–0.78, p = 0.0035)), progression free survival (miR-23a-5p (HR:1.32, 95% CI:1.09–1.61, p = 0.004), miR-23a-3p (HR:1.70, 95% CI:1.15–2.51, p = 0.0074), miR-802 (HR: 0.48, 95% CI: 0.29–0.80, p = 0.0048)), and resistance to chemotherapy (miR-1234 (HR: 0.26, 95% CI: 0.11–0.64, p = 0.003)). A few miRNAs identified in our training cohort, were validated in external cohorts with similar results. Conclusion: Eight miRNAs were identified as significant predictors of overall survival, progression free survival, time to progression, and chemotherapy resistance. A number of these miRNAs were significantly validated using external datasets. Inter-platform and inter-laboratory variations may have influence on the ability to compare and reproduce miRNA results. The use of miRNAs as potential markers of relapse and survival in ovarian cancer warrants further investigation. [ABSTRACT FROM AUTHOR]

Background: Published evidence on treatment costs of breast cancer varies widely in methodology and a global systematic review is lacking. Objectives: This study aimed to conduct a systematic review to compare treatment costs of breast cancer by stage at diagnosis across countries at different levels of socio-economic development, and to identify key methodological differences in costing approaches. Data sources: MEDLINE, EMBASE, and NHS Economic Evaluation Database (NHS EED) before April 2018. Eligibility criteria: Studies were eligible if they reported treatment costs of breast cancer by stage at diagnosis using patient level data, in any language. Study appraisal and synthesis methods: Study characteristics and treatment costs by stage were summarised. Study quality was assessed using the Drummond Checklist, and detailed methodological differences were further compared. Results: Twenty studies were included, 15 from high-income countries and five from low- and middle-income countries. Eleven studies used the FIGO staging system, and the mean treatment costs of breast cancer at Stage II, III and IV were 32%, 95%, and 109% higher than Stage I. Five studies categorised stage as in situ, local, regional and distant. The mean treatment costs of regional and distant breast cancer were 41% and 165% higher than local breast cancer. Overall, the quality of studies ranged from 50% (lowest quality) to 84% (highest). Most studies used regression frameworks but the choice of regression model was rarely justified. Few studies described key methodological issues including skewness, zero values, censored data, missing data, and the inclusion of control groups to estimate disease-attributable costs. Conclusions: Treatment costs of breast cancer generally increased with the advancement of the disease stage at diagnosis. Methodological issues should be better handled and properly described in future costing studies. [ABSTRACT FROM AUTHOR]

Background: Peritumoral inflammation—a response mainly involving polimorphonuclear neutrophils—has traditionally been thought protumoral in its effects. In recent years, however, a number of studies have indicated that it may play an important antitumoral role. This discrepancy has been difficult to explain. Methods and findings: This work describes a tool for simulating tumor growth that obeys the universal model of tumor growth dynamics, and shows through its use that low intensity peritumoral inflammation exerts a protumoral effect, while high intensity inflammation exerts a potent antitumoral effect. Indeed, the simulation results obtained indicate that a sufficiently strong antitumoral effect can reverse tumor growth, as has been suggested several times in the clinical literature. Conclusions: The present result indicate that an ‘immunological threshold’ must exist, marking the boundary between states in which peritumoral inflammation is either harmful or beneficial. These findings lend support to the idea that stimulating intense peritumoral inflammation could be used as a treatment against solid tumors. [ABSTRACT FROM AUTHOR]