PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 10070796

An objective function for radiation treatment optimization based on local biological measures.

M Alber·F Nüsslin
View original source ↗Export metadata ↓

Abstract

The implementation of biological optimization of radiation treatment plans is impeded by both computational and modelling problems. We derive an objective function from basic model assumptions which includes the normal tissue constraints as interior penalty functions. For organs that are composed of parallel subunits, a mean response model is proposed which leads to constraints similar to dose-volume constraints. This objective function is convex in the case when no parallel organs lie in the treatment volume. Otherwise, an argument is given to show that a number of local minima may exist which are near degenerate to the global minimum. Thus, together with the measure quality of the objective function, highly efficient gradient algorithms can be used. The number of essential biological model parameters could be reduced to a minimum. However, if the optimization constraints are given as TCP/NTCP values, Lagrange multiplier updates have to be performed by invoking comprehensive biological models.

Explore related subjects

HumansModels, BiologicalNeoplasmsPoisson DistributionRadiation DosageRadiotherapy DosageRadiotherapy Planning, Computer-AssistedScattering, RadiationComparative StudyJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M Alber, F Nüsslin. 1999. An objective function for radiation treatment optimization based on local biological measures.. https://doi.org/10.1088/0031-9155%2F44%2F2%2F014

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

KEEP EXPLORING

Related citations

PubMed2027

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗
PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗
PubMed2027

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans↗