DKFZ-HIPO
Ontology highlight
ABSTRACT:
PROVIDER: EGAO00000000713 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Organisation EGAO00000000298
| EGAO00000000298 | EGA
Project description:Organisation EGAO00000001381
| EGAO00000001381 | EGA
Project description:Organisation EGAO00000001147
| EGAO00000001147 | EGA
Project description:PurposeThis study aimed to compare the inverse planning simulated annealing (IPSA) stochastic algorithm with the hybrid inverse planning and optimization (HIPO) algorithm for interstitial tongue high-dose-rate (HDR) brachytherapy.MethodsTwenty patients who received radiotherapy for tongue cancer using interstitial HDR brachytherapy were retrospectively selected for this study. Oncentra Brachy v. 4.3 was used for IPSA and HIPO planning. Four to eight fixed catheter configurations were determined according to the target shape. During the optimization process, predetermined constrain values were used for each IPSA and HIPO plan. The dosimetric parameters and dwell time were analyzed to evaluate the performances of the plans.ResultsThe total dwell time using IPSA was 4 seconds longer than that of HIPO. The number of active positions per catheter for the IPSA plans were approximately 2.5 fewer than those of the HIPO plans. The dose-volumetric parameters related to the clinical target volume with IPSA were lower than those with HIPO. In terms of the dose-volumetric parameters related to normal tissue, HIPO tended to associate with slightly higher values than IPSA, without statistical significance. After GrO, the target coverages were satisfied to clinical goal for all patients. The total dwell times was approximately increased by 10%.ConclusionsThe IPSA and HIPO dose optimization algorithms generate similar dosimetric results. In terms of the dwell time, HIPO appears to be more beneficial.
| S-EPMC6171910 | biostudies-literature
Project description:Genome-wide association studies have shown that pleiotropy is a common phenomenon that can potentially be exploited for enhanced detection of susceptibility loci. We propose heritability informed power optimization (HIPO) for conducting powerful pleiotropic analysis using summary-level association statistics. We find optimal linear combinations of association coefficients across traits that are expected to maximize non-centrality parameter for the underlying test statistics, taking into account estimates of heritability, sample size variations and overlaps across the traits. Simulation studies show that the proposed method has correct type I error, robust to population stratification and leads to desired genome-wide enrichment of association signals. Application of the proposed method to publicly available data for three groups of genetically related traits, lipids (N = 188,577), psychiatric diseases (Ncase = 33,332, Ncontrol = 27,888) and social science traits (N ranging between 161,460 to 298,420 across individual traits) increased the number of genome-wide significant loci by 12%, 200% and 50%, respectively, compared to those found by analysis of individual traits. Evidence of replication is present for many of these loci in subsequent larger studies for individual traits. HIPO can potentially be extended to high-dimensional phenotypes as a way of dimension reduction to maximize power for subsequent genetic association testing.
| S-EPMC6192650 | biostudies-literature
Project description:Diagnosing bone and soft tissue neoplasms remains challenging because of the large number of subtypes, many of which lack diagnostic biomarkers. DNA methylation profiles have proven to be a reliable basis for the classification of brain tumours and, following this success, a DNA methylation-based sarcoma classification tool from the Deutsches Krebsforschungszentrum (DKFZ) in Heidelberg has been developed. In this study, we assessed the performance of their classifier on DNA methylation profiles of an independent data set of 986 bone and soft tissue tumours and controls. We found that the 'DKFZ Sarcoma Classifier' was able to produce a diagnostic prediction for 55% of the 986 samples, with 83% of these predictions concordant with the histological diagnosis. On limiting the validation to the 820 cases with histological diagnoses for which the DKFZ Classifier was trained, 61% of cases received a prediction, and the histological diagnosis was concordant with the predicted methylation class in 88% of these cases, findings comparable to those reported in the DKFZ Classifier paper. The classifier performed best when diagnosing mesenchymal chondrosarcomas (CHSs, 88% sensitivity), chordomas (85% sensitivity), and fibrous dysplasia (83% sensitivity). Amongst the subtypes least often classified correctly were clear cell CHSs (14% sensitivity), malignant peripheral nerve sheath tumours (27% sensitivity), and pleomorphic liposarcomas (29% sensitivity). The classifier predictions resulted in revision of the histological diagnosis in six of our cases. We observed that, although a higher tumour purity resulted in a greater likelihood of a prediction being made, it did not correlate with classifier accuracy. Our results show that the DKFZ Classifier represents a powerful research tool for exploring the pathogenesis of sarcoma; with refinement, it has the potential to be a valuable diagnostic tool.
| S-EPMC8185366 | biostudies-literature