Evidence map›Paper›PMID 33370345›Full record

Observational studyPloS one2020

Prediction of radiation pneumonitis using dose-volume histogram parameters with high attenuation in two types of cancer: A retrospective study.

Yasuki Uchida, Takuya Tsugawa, Sachiko Tanaka-Mizuno, Kazuo Noma, Ken Aoki, Kentaro Fukunaga, Hiroaki Nakagawa, Daisuke Kinose, Masafumi Yamaguchi, Makoto Osawa and 3 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 32% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

13 authors at 2 institutions in 1 country.

Yasuki UchidaDivision of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.ORCID 0000-0002-1999-4709
Takuya TsugawaDepartment of Radiology, Shiga University of Medical Science, Otsu, Shiga, Japan.
Sachiko Tanaka-MizunoDepartment of Medical Statistics, Shiga University of Medical Science, Otsu, Shiga, Japan.
Kazuo NomaDepartment of Radiology, Shiga University of Medical Science Hospital, Otsu, Shiga, Japan.
Ken AokiDepartment of Radiology, Shiga University of Medical Science, Otsu, Shiga, Japan.
Kentaro FukunagaDivision of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.ORCID 0000-0002-6261-0344
Hiroaki NakagawaDivision of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.
Daisuke KinoseDivision of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.
Masafumi YamaguchiDivision of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.
Makoto OsawaDivision of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.
Taishi NagaoDivision of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.
Emiko OgawaDivision of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.
Yasutaka NakanoDivision of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.
Shiga University of Medical Science · JPShiga University of Medical Science Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The constraint values of dose-volume histogram (DVH) parameters for radiation pneumonitis (RP) prediction have not been uniform in previous studies. We compared the differences between conventional DVH parameters and DVH parameters with high attenuation volume (HAV) in CT imaging in both esophageal cancer and lung cancer patients to determine the most suitable DVH parameters in predicting RP onset. Seventy-seven and 72 patients who underwent radiation therapy for lung cancer and esophageal cancer, respectively, were retrospectively assessed. RP was valued according to the Common Terminology Criteria for Adverse Events. We quantified HAV with quantitative computed tomography analysis. We compared conventional DVH parameters and DVH parameters with HAV in both groups of patients. Then, the thresholds of DVH parameters that predicted symptomatic RP and the differences in threshold of DVH parameters between lung cancer and esophageal cancer patient groups were compared. The predictive performance of DVH parameters for symptomatic RP was compared using the area under the receiver operating characteristic curve. Mean lung dose, HAV30% (the proportion of the lung with HAV receiving ≥30 Gy), and HAV20% were the top three parameters in lung cancer, while HAV10%, HAV5%, and V10 (the percentage of lung volume receiving 10 Gy or more) were the top three in esophageal cancer. By comparing the differences in the threshold for parameters predicting RP between the two cancers, we saw that HAV30% retained the same value in both cancers. DVH parameters with HAV showed narrow differences in the threshold between the two cancer patient groups compared to conventional DVH parameters. DVH parameters with HAV may have higher commonality than conventional DVH parameters in both patient groups tested.

Indexed as

Esophageal NeoplasmsLung NeoplasmsRadiation PneumonitisTomography, X-Ray ComputedAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedPredictive Value of TestsRadiotherapy, ConformalRetrospective Studies

Identifiers

PMID33370345
PMCPMC7769248
OpenAlexW3113640105

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.