PubMed Health⌕ Search

PubMed · 15984690

Dose sculpting with generalized equivalent uniform dose.

Abstract

With intensity-modulated radiotherapy (IMRT), a variety of user-defined dose distribution can be produced using inverse planning. The generalized equivalent uniform dose (gEUD) has been used in IMRT optimization as an alternative objective function to the conventional dose-volume-based criteria. The purpose of this study was to investigate the effectiveness of gEUD optimization to fine tune the dose distributions of IMRT plans. We analyzed the effect of gEUD-based optimization parameters on plan quality. The objective was to determine whether dose distribution to selected structures could be improved using gEUD optimization without adversely altering the doses delivered to other structures, as in sculpting. We hypothesized that by carefully defining gEUD parameters (EUD0 and n) based on the current dose distributions, the optimization system could be instructed to search for alternative solutions in the neighborhood, and we could maintain the dose distributions for structures already satisfactory and improve dose for structures that need enhancement. We started with an already acceptable IMRT plan optimized with any objective function. The dose distribution was analyzed first. For structures that dose should not be changed, a higher value of n was used and EUD0 was set slightly higher/lower than the EUD value at the current dose distribution for critical structures/targets. For structures that needed improvement in dose, a higher to medium value of n was used, and EUD0 was set to the EUD value or slightly lower/higher for the critical structure/target at the current dose distribution. We evaluated this method in one clinical case each of head and neck, lung and prostate cancer. Dose volume histograms, isodose distributions, and relevant tolerance doses for critical structures were used for the assessment. We found that by adjusting gEUD optimization parameters, the dose distribution could be improved with only a few iterations. A larger value of n could lead to faster convergence and a medium value of n could result in a search in a broader area. Such improvement could also be achieved by optimization based on other criteria, but the gEUD-based method has the advantage of efficiency and flexibility. Therefore, gEUD-based optimization can be used as a tool to improve IMRT plans by adjusting the planning parameters, thereby making dose sculpting feasible.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Qiuwen Wu, David Djajaputra, Helen H Liu, Lei Dong, Radhe Mohan, Yan Wu. 2005. Dose sculpting with generalized equivalent uniform dose.. https://doi.org/10.1118/1.1897464

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗