Identifying relevant studies for systematic reviews.

OBJECTIVE--To examine the sensitivity and precision of Medline searching for randomised clinical trials. DESIGN--Comparison of results of Medline searches to a "gold standard" of known randomised clinical trials in ophthalmology published in 1988; systematic review (meta-analysis) of results of similar, but separate, studies from many fields of medicine. POPULATIONS--Randomised clinical trials published in 1988 in journals indexed in Medline, and those not indexed in Medline and identified by hand search, comprised the gold standard. Gold standards for the other studies combined in the meta-analysis were based on: randomised clinical trials published in any journal, whether indexed in Medline or not; those published in any journal indexed in Medline; or those published in a selected group of journals indexed in Medline. MAIN OUTCOME MEASURE--Sensitivity (proportion of the total number of known randomised clinical trials identified by the search) and precision (proportion of publications retrieved by Medline that were actually randomised clinical trials) were calculated for each study and combined to obtain weighted means. Searches producing the "best" sensitivity were used for sensitivity and precision estimates when multiple searches were performed. RESULTS--The sensitivity of searching for ophthalmology randomised clinical trials published in 1988 was 82%, when the gold standard was for any journal, 87% for any journal indexed in Medline, and 88% for selected journals indexed in Medline. Weighted means for sensitivity across all studies were 51%, 77%, and 63%, respectively. The weighted mean for precision was 8% (median 32.5%). Most searchers seemed not to use freetext subject terms and truncation of those terms. CONCLUSION--Although the indexing terms available for searching Medline for randomised clinical trials have improved, sensitivity still remains unsatisfactory. A mechanism is needed to "''register" known trials, preferably by retrospective tagging of Medline entries, and incorporating trials published before 1966 and in journals not indexed by Medline into the system.     

e-LiSe--an online tool for finding needles in the '(Medline) haystack'

Summary: Using literature databases one can find not only knownand true relations between processes but also less studied,non-obvious associations. The main problem with discoveringsuch type of relevant biological information is ‘selection’.The ability to distinguish between a true correlation (e.g.between different types of biological processes) and randomchance that this correlation is statistically significant iscrucial for any bio-medical research, literature mining beingno exception. This problem is especially visible when searchingfor information which has not been studied and described inmany publications. Therefore, a novel bio-linguistic statisticalmethod is required, capable of ‘selecting’ truecorrelations, even when they are low-frequency associations.In this article, we present such statistical approach basedon Z-score and implemented in a web-based application ‘e-LiSe’. Availability: The software is available at http://miron.ibb.waw.pl/elise/ Contact: piotr{at}ibb.waw.pl Supplementary information: Supplementary materials are availableat http://miron.ibb.waw.pl/elise/supplementary/ Associate Editor: Alfonso Valencia     

Guidelines for performing systematic reviews in sports science

Most of the reviews carried out in sports science have used the general items suggested by Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). Due to the specific requirements of each knowledge area, several modifications of the PRISMA are necessary to optimize the process of the systematic reviews and, in consequence, the quality of the conclusions provided in this type of study. Therefore, this work aimed to adapt PRISMA to provide specific guidelines to carry out systematic reviews in sports science. The methodology criteria (search strategy, databases, and eligibility) and the results section (flow diagrams and study contents) were adapted based on previous studies, and several new considerations were added to design the new guidelines. We compiled 28 items suggested by sports science researchers and included two new items: (i) population/problem (i.e., age, level, and country) and (ii) the entire training process, which is monitored and compared between groups (e.g., total training load). To maximize the benefit of this document, we encourage people to read it in conjunction with the PRISMA statement. The main differences between PRISMA and the PRISMA adapted to sports science were related to registration, search strategy, flow diagrams, and results. Application of the new guidelines could improve the information provided to readers and make it easier to generalize and compare the results in sports science.     

The search for novel diagnostic and prognostic biomarkers in cholangiocarcinoma

The poor prognosis of cholangiocarcinoma (CCA) is in part due to late diagnosis, which is currently achieved by a combination of clinical, radiological and histological approaches. Available biomarkers determined in serum and biopsy samples to assist in CCA diagnosis are not sufficiently sensitive and specific. Therefore, the identification of new biomarkers, preferably those obtained by minimally invasive methods, such as liquid biopsy, is important. The development of innovative technologies has permitted to identify a significant number of genetic, epigenetic, proteomic and metabolomic CCA features with potential clinical usefulness in early diagnosis, prognosis or prediction of treatment response. Potential new candidates must be rigorously evaluated prior to entering routine clinical application. Unfortunately, to date, no such biomarker has achieved validation for these purposes. This review is an up-to-date of currently used biomarkers and the candidates with promising characteristics that could be included in the clinical practice in the next future. This article is part of a Special Issue entitled: Cholangiocytes in Health and Disease edited by Jesus Banales, Marco Marzioni, Nicholas LaRusso and Peter Jansen.     

Evolutionary algorithms for finding optimal gene sets in microarray prediction

MOTIVATION: Microarray data has been shown recently to be efficacious in distinguishing closely related cell types that often appear in different forms of cancer, but is not yet practical clinically. However, the data might be used to construct a minimal set of marker genes that could then be used clinically by making antibody assays to diagnose a specific type of cancer. Here a replication algorithm is used for this purpose. It evolves an ensemble of predictors, all using different combinations of genes to generate a set of optimal predictors. RESULTS: We apply this method to the leukemia data of the Whitehead/MIT group that attempts to differentially diagnose two kinds of leukemia, and also to data of Khan et al. to distinguish four different kinds of childhood cancers. In the latter case we were able to reduce the number of genes needed from 96 to less than 15, while at the same time being able to classify all of their test data perfectly. We also apply this method to two other cases, Diffuse large B-cell lymphoma data (Shipp et al., 2002), and data of Ramaswamy et al. on multiclass diagnosis of 14 common tumor types. AVAILABILITY: http://stravinsky.ucsc.edu/josh/gesses/.     

MicroRNA-21 as a diagnostic and prognostic biomarker of lung cancer: a systematic review and meta-analysis

Background: The relationship between microRNA-21 (miRNA-21) and pathogenesis of lung cancer is a considerable focus of research interest. However, to our knowledge, no in-depth meta-analyses based on existing evidence to ascertain the value of miRNA-21 in diagnosis and clinical prognosis of lung cancer have been documented.Methods: We comprehensively searched all the literature pertaining to ‘miRNA-21’ and ‘lung cancer’ from four databases from the period of inception of each database until May 2020. Using specific inclusion and exclusion criteria, the literature for inclusion was identified and the necessary data extracted.Results: In total, 46 articles were included in the meta-analysis, among which 31 focused on diagnostic value and 15 on prognostic value. Combined sensitivity (SEN) of miRNA-21 in diagnosis of lung cancer was 0.77 (95% confidence interval (CI): 0.72–0.81), specificity (SPE) was 0.86 (95% CI: 0.80–0.90), diagnostic odds ratio (DOR) was (95% CI: 12–33), and area under the SROC curve (AUC) was 0.87 (95% CI: 0.84–0.90). No significant correlations were observed between abnormal expression of miRNA-21 and gender, smoking habits, pathological type and clinical stage of lung cancer (P>0.05). In terms of overall survival (OS), univariate analysis (hazards ratio (HR) = 1.49, 95% CI: 1.22–1.82) revealed high expression of miRNA-21 as an influencing factor for lung cancer. MiRNA-21 was confirmed as an independent risk factor for poor prognosis in multivariate analysis (HR = 1.65, 95% CI: 1.24–2.19).Conclusion: MiRNA-21 has potential clinical value in the diagnosis and prognosis of lung cancer and may serve as an effective diagnostic marker and therapeutic target in the future.     

Grade-specific diagnostic and prognostic biomarkers in breast cancer

An integrative approach is presented to identify grade-specific biomarkers for breast cancer. Grade-specific molecular interaction networks were constructed with differentially expressed genes (DEGs) of cancer grade 1, 2, and 3. We observed that the molecular network of grade3 is predominantly associated with cancer-specific processes. Among the top ten connected DEGs in the grade3, the increase in the expression of UBE2C and CCNB2 genes was statistically significant across different grades. Along with UBE2C and CCNB2 genes, the CDK1, KIF2C, NDC80, and CCNB2 genes are also profoundly expressed in different grades and reduce the patient's survival. Gene set enrichment analysis of these six genes reconfirms their role in metastatic phenotype. Moreover, the coexpression network shows a strong association of these six genes promotes cancer specific biological processes and possibly drives cancer from lower to a higher grade. Collectively the identified genes can act as potential biomarkers for breast cancer diagnosis and prognosis.     

A computer program for selection of variables in diagnostic and prognostic problems

The computer program INDEP-SELECT has been developed for selection of an optimal subset from a set of possibly informative diagnostic or prognostic variables. But the program is equally useful for other discriminant analysis or pattern recognition problems involving variable selection. The approach is probabilistic; i.e., diagnostic probabilities are assigned to a patient on the basis of the values observed on the diagnostic variables.The statistical model used is largely based on the assumption of independency between the variables, but one model-parameter, the so-called ‘global association factor’, is added in order to take dependency into account. The stepwise forward selection strategy of adding in each selection step a new variable to the set of already selected variables, is used. The user may choose between a number of selection criteria. Such a criterion is used in order to decide in each selection step which variable should be added.All criteria are based on measures of diagnostic or prognostic performance. INDEP-SELECT is able to handle a large number of variables, also with missing data, and a large number of patients. The program is written in ANS Standard FORTRAN, and takes relatively little computation time.     

Homological radiomics analysis for prognostic prediction in lung cancer patients

PurposeThis study explored a novel homological analysis method for prognostic prediction in lung cancer patients.Materials and methodsThe potential of homology-based radiomic features (HFs) was investigated by comparing HFs to conventional wavelet-based radiomic features (WFs) and combined radiomic features consisting of HFs and WFs (HWFs), using training (n = 135) and validation (n = 70) datasets, and Kaplan–Meier analysis. A total of 13,824 HFs were derived through homology-based texture analysis using Betti numbers, which represent the topologically invariant morphological characteristics of lung cancer. The prognostic potential of HFs was evaluated using statistically significant differences (p-values, log-rank test) to compare the survival curves of high- and low-risk patients. Those patients were stratified into high- and low-risk groups using the medians of the radiomic scores of signatures constructed with an elastic-net-regularized Cox proportional hazard model. Furthermore, deep learning (DL) based on AlexNet was utilized to compare HFs by stratifying patients into the two groups using a network that was pre-trained with over one million natural images from an ImageNet database.ResultsFor the training dataset, the p-values between the two survival curves were 6.7 × 10⁻⁶ (HF), 5.9 × 10⁻³ (WF), 7.4 × 10⁻⁶ (HWF), and 1.1 × 10⁻³ (DL). The p-values for the validation dataset were 3.4 × 10⁻⁵ (HF), 6.7 × 10⁻¹ (WF), 1.7 × 10⁻⁷ (HWF), and 1.2 × 10⁻¹ (DL).ConclusionThis study demonstrates the excellent potential of HFs for prognostic prediction in lung cancer patients.