Different Training methods on Accuracy of Predicting Conlorectal Lesions and Selection of Interventions by trainees: multicenter, randomized, controlled trial
Ontology highlight
ABSTRACT:
DISEASE(S): Colorectal Tumors
PROVIDER: 2692245 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
Similar Datasets
Project description:Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesC?) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesC? to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored.
| S-EPMC3512156 | biostudies-literature
Project description:Interventions: Gold Standard:Clinical outcome;Index test:Study on endogenous biomarkers by targeted metabonomics
Primary outcome(s): SEN, SPE, ACC, AUC of ROC, PPV, NPV
Study Design: Sequential
| 2698756 | ecrin-mdr-crc
Project description:PURPOSE: Validated biomarkers predictive of response/resistance to anthracyclines in breast cancer are currently lacking. The neoadjuvant TOP trial, in which patients with estrogen receptor (ER)-negative tumors were treated with anthracycline (epirubicin) monotherapy, was specifically designed to evaluate the predictive value of topoisomerase IIα (TOP2A) and to develop a gene expression signature to identify those patients who do not benefit from anthracyclines. METHODS: The TOP trial included 149 patients, of which 141 were evaluable for response prediction analyses. The primary endpoint was pathological complete response (pCR). TOP2A and gene expression profiles were evaluated using pre-epirubicin biopsies. Gene expression data from ER-negative samples of the EORTC 10994/BIG 00-01 and MDACC 2003-0321 neoadjuvant trials were used for validation purposes. RESULTS: A pCR was obtained in 14% of the evaluable TOP patients. TOP2A amplification, but not protein overexpression, was significantly associated with pCR (p=0.001 and 0.22). We developed an “anthracycline-based score (A-Score)” that combines three signatures: a TOP2A gene signature and two previously published signatures related to tumor invasion and immune response. The A-Score was characterized by a high negative predictive value (NPV=0.98 [95% CI: 0.90-1.00]) overall, and in the HER2-negative and HER2-positive subpopulations. Its performance was independently confirmed in the anthracycline-based (FAC/FEC) arms of the two validation trials (BIG 00-01: 0.80 [0.61-0.92] and MDACC 2003-0321: 1.00 [0.80-1.00]). CONCLUSION: Given its high NPV, the A-Score could become, if further validated, a useful clinical tool to identify those patients who do not benefit from anthracyclines and could therefore be spared the non-negligible side effects.
2010-01-26 | GSE16446 | GEO
Project description:BACKGROUND: Genotype imputation is a cost efficient alternative to use of high density genotypes for implementing genomic selection. The objective of this study was to investigate variables affecting imputation accuracy from low density tagSNP (average distance between tagSNP from 100kb to 1Mb) sets in swine, selected using LD information, physical location, or accuracy for genotype imputation. We compared results of imputation accuracy based on several sets of low density tagSNP of varying densities and selected using three different methods. In addition, we assessed the effect of varying size and composition of the reference panel of haplotypes used for imputation. RESULTS: TagSNP density of at least 1 tagSNP per 340kb (~7000 tagSNP) selected using pairwise LD information was necessary to achieve average imputation accuracy higher than 0.95. A commercial low density (9K) tagSNP set for swine was developed concurrent to this study and an average accuracy of imputation of 0.951 based on these tagSNP was estimated. Construction of a haplotype reference panel was most efficient when these haplotypes were obtained from randomly sampled individuals. Increasing the size of the original reference haplotype panel (128 haplotypes sampled from 32 sire/dam/offspring trios phased in a previous study) led to an overall increase in imputation accuracy (IA = 0.97 with 512 haplotypes), but was especially useful in increasing imputation accuracy of SNP with MAF below 0.1 and for SNP located in the chromosomal extremes (within 5% of chromosome end). CONCLUSION: The new commercially available 9K tagSNP set can be used to obtain imputed genotypes with high accuracy, even when imputation is based on a comparably small panel of reference haplotypes (128 haplotypes). Average imputation accuracy can be further increased by adding haplotypes to the reference panel. In addition, our results show that randomly sampling individuals to genotype for the construction of a reference haplotype panel is more cost efficient than specifically sampling older animals or trios with no observed loss in imputation accuracy. We expect that the use of imputed genotypes in swine breeding will yield highly accurate predictions of GEBV, based on the observed accuracy and reported results in dairy cattle, where genomic evaluation of some individuals is based on genotypes imputed with the same accuracy as our Yorkshire population.
| S-EPMC3734000 | biostudies-literature
Project description:Interventions: Gold Standard:Clinical outcome;Index test:Prognostic prediction model for left and right colon cancer based on AI technology
Primary outcome(s): tumor stroma ratio;immunescore;SEN, SPE, ACC, AUC of ROC, PPV, NPV
Study Design: Sequential
| 2697591 | ecrin-mdr-crc
Project description:Interfacial thermal resistance (ITR) is a critical property for the performance of nanostructured devices where phonon mean free paths are larger than the characteristic length scales. The affordable, accurate and reliable prediction of ITR is essential for material selection in thermal management. In this work, the state-of-the-art machine learning methods were employed to realize this. Descriptor selection was conducted to build robust models and provide guidelines on determining the most important characteristics for targets. Firstly, decision tree (DT) was adopted to calculate the descriptor importances. And descriptor subsets with topX highest importances were chosen (topX-DT, X?=?20, 15, 10, 5) to build models. To verify the transferability of the descriptors picked by decision tree, models based on kernel ridge regression, Gaussian process regression and K-nearest neighbors were also evaluated. Afterwards, univariate selection (UV) was utilized to sort descriptors. Finally, the top5 common descriptors selected by DT and UV were used to build concise models. The performance of these refined models is comparable to models using all descriptors, which indicates the high accuracy and reliability of these selection methods. Our strategy results in concise machine learning models for a fast prediction of ITR for thermal management applications.
| S-EPMC7804206 | biostudies-literature
Project description:Interventions: Gold Standard:Clinical outcome;Index test:Therapeutic effect prediction model of first line treatment of bevacizumab combined with mFOLFOX6 in Ras mutant colorectal cancer patients with liver metastasis
Primary outcome(s): Objective response rate;SEN, SPE, ACC, AUC of ROC, PPV, NPV
Study Design: Sequential
| 2696954 | ecrin-mdr-crc
Project description:Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative procedure for a large number of diseases. However, the greatest barriers to the success of allo-HCT are relapse and graft-versus-host-disease (GVHD). Many studies have examined the reconstitution of the immune system after allo-HCT and searched for factors associated with clinical outcome. Serum biomarkers have also been studied to predict the incidence and prognosis of GVHD. However, the use of multiparametric immunophenotyping has been less extensively explored: studies usually focus on preselected and predefined cell phenotypes and so do not fully exploit the richness of flow cytometry data. Here we aimed to identify cell phenotypes present 30 days after allo-HCT that are associated with clinical outcomes in 37 patients participating in a trial relating to the prevention of GVHD, derived from 82 flow cytometry markers and 13 clinical variables. To do this we applied variable selection methods in a competing risks modeling framework, and identified specific subsets of T, B, and NK cells associated with relapse. Our study demonstrates the value of variable selection methods for mining rich, high dimensional clinical data and identifying potentially unexplored cell subpopulations of interest.
| S-EPMC7865009 | biostudies-literature
Project description:Physiological closed-loop controlled (PCLC) medical devices are complex systems integrating one or more medical devices with a patient's physiology through closed-loop control algorithms; introducing many failure modes and parameters that impact performance. These control algorithms should be tested through safety and efficacy trials to compare their performance to the standard of care and determine whether there is sufficient evidence of safety for their use in real care setting. With this aim, credible mathematical models have been constructed and used throughout the development and evaluation phases of a PCLC medical device to support the engineering design and improve safety aspects. Uncertainties about the fidelity of these models and ambiguities about the choice of measures for modeling performance need to be addressed before a reliable PCLC evaluation can be achieved. This research develops tools for evaluating the accuracy of physiological models and establishes fundamental measures for predictive capability assessment across different physiological models. As a case study, we built a refined physiological model of blood volume (BV) response by expanding an original model we developed in our prior work. Using experimental data collected from 16 sheep undergoing hemorrhage and fluid resuscitation, first, we compared the calibration performance of the two candidate physiological models, i.e., original and refined, using root-mean-squared error (RMSE), Akiake information criterion (AIC), and a new multi-dimensional approach utilizing normalized features extracted from the fitting error. Compared to the original model, the refined model demonstrated a significant improvement in calibration performance in terms of RMSE (9%, P = 0.03) and multi-dimensional measure (48%, P = 0.02), while a comparable AIC between the two models verified that the enhanced calibration performance in the refined model is not due to data over-fitting. Second, we compared the physiological predictive capability of the two models under three different scenarios: prediction of subject-specific steady-state BV response, subject-specific transient BV response to hemorrhage perturbation, and leave-one-out inter-subject BV response. Results indicated enhanced accuracy and predictive capability for the refined physiological model with significantly larger proportion of measurements that were within the prediction envelope in the transient and leave-one-out prediction scenarios (P < 0.02). All together, this study helps to identify and merge new methods for credibility assessment and physiological model selection, leading to a more efficient process for PCLC medical device evaluation.
| S-EPMC8087034 | biostudies-literature