Evidence map›Paper›PMID 27072838›Full record

ArticleInternational journal of computer assisted radiology and surgery2016

Knowledge-driven decision support for assessing dose distributions in radiation therapy of head and neck cancer.

Ruchi R Deshpande, John DeMarco, James W Sayre, Brent J Liu

Abstract readEvaluation Study
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Knowledge-based radiation treatment planning: A data-driven method survey.Journal of applied clinical medical physics · 2021
    Review
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Ruchi R DeshpandeImage Processing and Informatics Laboratory, Denney Research Center (DRB) 264 1042 Downey Way, University of Southern California, Los Angeles, CA, 90089-1111, USA. ruchi.deshpande@gmail.com.
John DeMarcoDepartment of Radiation Oncology, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA.
James W SayreDepartment of Biostatistics and Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA.
Brent J LiuImage Processing and Informatics Laboratory, Denney Research Center (DRB) 264 1042 Downey Way, University of Southern California, Los Angeles, CA, 90089-1111, USA.
University of Southern California · USCedars-Sinai Medical Center · USUniversity of California, Los Angeles · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeClinical data that are generated through routine radiation therapy procedures can be leveraged as a source of knowledge to provide evidence-based decision support for future patients. Treatment planning in radiation therapy often relies on trial-and-error iterations, experience, judgment calls and general guidelines. The authors present a knowledge-driven decision support system that assists clinicians by reducing some of the uncertainties associated with treatment planning and provides quantified empirical estimates to help minimize the radiation dose to healthy critical structures surrounding the tumor.

methodsA database of retrospective DICOM RT data fuels a decision support engine, which assists clinicians in selecting dose constraints and assessing dose distributions. The first step is to quantify the spatial relationships between the tumor and surrounding critical structures through features that account for distance, volume, overlap, location, shape and orientation. These features are used to identify database cases that are anatomically similar to the new patient. The dose profiles of these database cases can help clinicians to estimate an acceptable dose distribution for the new case, based on empirical evidence. Since database diversity is essential for good system performance, an infrastructure for multi-institutional collaboration was also conceptualized in order to pave the way for data sharing of protected health information.

resultsA set of 127 retrospective test cases was collected from a single institution in order to conduct a leave-one-out evaluation of the decision support module. In 72 % of these retrospective test cases, patients with similar tumor anatomy were also found to exhibit similar radiation dose distributions. This demonstrates the system's ability to successfully extract retrospective database cases that can estimate the new patient's dose distribution.

conclusionThe radiation therapy treatment planning decision support system presented here can assist clinicians in determining good dose constraints and assessing dose distributions by using knowledge gained from retrospective treatment plans.

Indexed as

Databases, FactualDecision Support TechniquesHead and Neck NeoplasmsHumansRadiotherapy DosageRadiotherapy Planning, Computer-AssistedRetrospective StudiesDecision supportHead and neck cancerImaging informaticsRadiation therapy

Identifiers

PMID27072838
OpenAlexW2340337762

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.