Dataset Information

Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites.

ABSTRACT: Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number of annotators with varying, and often unspecified, levels of expertise. In this data descriptor, numerous clinician annotators manually generated segmentations for ROIs on computed tomography images across a variety of cancer sites (breast, sarcoma, head and neck, gynecologic, gastrointestinal; one patient per cancer site) for the Contouring Collaborative for Consensus in Radiation Oncology challenge. In total, over 200 annotators (experts and non-experts) contributed using a standardized annotation platform (ProKnow). Subsequently, we converted Digital Imaging and Communications in Medicine data into Neuroimaging Informatics Technology Initiative format with standardized nomenclature for ease of use. In addition, we generated consensus segmentations for experts and non-experts using the Simultaneous Truth and Performance Level Estimation method. These standardized, structured, and easily accessible data are a valuable resource for systematically studying variability in segmentation applications.

SUBMITTER: Wahid KA

PROVIDER: S-EPMC10033824 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites.

Wahid Kareem A KA Lin Diana D Sahin Onur O Cislo Michael M Nelms Benjamin E BE He Renjie R Naser Mohammed A MA Duke Simon S Sherer Michael V MV Christodouleas John P JP Mohamed Abdallah S R ASR Murphy James D JD Fuller Clifton D CD Gillespie Erin F EF

Scientific data 20230322 1

Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number ...[more]

PMID: 36949088

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Project description:Radiotherapy is integral to the care of a majority of patients with cancer. Despite differences in tumor responses to radiation (radioresponse), dose prescriptions are not currently tailored to individual patients. Recent large-scale cancer cell line databases hold the promise of unravelling the complex molecular arrangements underlying cellular response to radiation, which is critical for novel predictive biomarker discovery. Here, we present RadioGx, a computational platform for integrative analyses of radioresponse using radiogenomic databases. We fit the dose-response data within RadioGx to the linear-quadratic model. The imputed survival across a range of dose levels (AUC) was a robust radioresponse indicator that correlated with biological processes known to underpin the cellular response to radiation. Using AUC as a metric for further investigations, we found that radiation sensitivity was significantly associated with disruptive mutations in genes related to nonhomologous end joining. Next, by simulating the effects of different oxygen levels, we identified putative genes that may influence radioresponse specifically under hypoxic conditions. Furthermore, using transcriptomic data, we found evidence for tissue-specific determinants of radioresponse, suggesting that tumor type could influence the validity of putative predictive biomarkers of radioresponse. Finally, integrating radioresponse with drug response data, we found that drug classes impacting the cytoskeleton, DNA replication, and mitosis display similar therapeutic effects to ionizing radiation on cancer cell lines. In summary, RadioGx provides a unique computational toolbox for hypothesis generation to advance preclinical research for radiation oncology and precision medicine. SIGNIFICANCE: The RadioGx computational platform enables integrative analyses of cellular response to radiation with drug responses and genome-wide molecular data. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/79/24/6227/F1.large.jpg.See related commentary by Spratt and Speers, p. 6076.

Project description:BackgroundPhosphorylation is the most prevalent post-translational modification on eukaryotic proteins. Multisite phosphorylation enables a specific combination of phosphosites to determine the speed, specificity and duration of biological response. Until recent years, the lack of high quality data limited the possibility for analyzing the properties of phosphorylation at the proteome scale and in the context of a wide range of conditions. Thanks to advances of mass spectrometry technologies, thousands of phosphosites from in-vivo experiments were identified and archived in the public domain. Such resource is appropriate to derive an unbiased view on the phosphosites properties in eukaryotes and on their functional relevance.ResultsWe present statistically rigorous tests on the spatial and functional properties of a collection of approximately 70,000 reported phosphosites. We show that the distribution of phosphosites positioning along the protein tends to occur as dense clusters of Serine/Threonines (pS/pT) and between Serine/Threonines and Tyrosines, but generally not as much between Tyrosines (pY) only. This phenomenon is more ubiquitous than anticipated and is pertinent for most eukaryotic proteins: for proteins with > or = 2 phosphosites, 54% of all pS/pT sites are within 4 amino acids of another site. We found a strong tendency for clustered pS/pT to be activated by the same kinase. Large-scale analyses of phosphopeptides are thus consistent with a cooperative function within the cluster.ConclusionsWe present evidence supporting the notion that clusters of pS/pT but generally not pY should be considered as the elementary building blocks in phosphorylation regulation. Indeed, closely positioned sites tend to be activated by the same kinase, a signal that overrides the tendency of a protein to be activated by a single or only few kinases. Within these clusters, coordination and positional dependency is evident. We postulate that cellular regulation takes advantage of such design. Specifically, phosphosite clusters may increase the robustness of the effectiveness of phosphorylation-dependent response.ReviewersReviewed by Joel Bader, Frank Eisenhaber, Emmanuel Levy (nominated by Sarah Teichmann). For the full reviews, please go to the Reviewers' comments section.

Dataset Information

Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites.

Publications

Large scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites.

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