Evidence map›Paper›PMID 26083082›Full record

ArticlePhysics in medicine and biology2015

Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy.

Tyler Bradshaw, Rau Fu, Stephen Bowen, Jun Zhu, Lisa Forrest, Robert Jeraj

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.6field-weighted citation impact, top 29% 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

7 citing papers in PubMed, 12 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Tyler BradshawDepartment of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI 53705-2275, USA.
Rau FuDepartment of Statistic, University of Wisconsin, Madison, WI 53705-2275, USA.
Stephen BowenDepartments of Radiology and Radiation Oncology, University of Washington, Seattle, WA 53705-2275, USA.
Jun ZhuDepartment of Statistic, University of Wisconsin, Madison, WI 53705-2275, USA.
Lisa ForrestDepartment of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin, Madison, WI 53705-2275, USA.
Robert JerajDepartment of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI 53705-2275, USA.
University of Wisconsin–Madison · US

Funding

Dose painting based on hypoxia and proliferative responseR01CA136927 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI JERAJ, ROBERT · 2009 to 2013
$1.4M
NCI NIH HHS R01 CA136927
6 · The paper itself

Abstract

Dose painting relies on the ability of functional imaging to identify resistant tumor subvolumes to be targeted for additional boosting. This work assessed the ability of FDG, FLT, and Cu-ATSM PET imaging to predict the locations of residual FDG PET in canine tumors following radiotherapy. Nineteen canines with spontaneous sinonasal tumors underwent PET/CT imaging with radiotracers FDG, FLT, and Cu-ATSM prior to hypofractionated radiotherapy. Therapy consisted of 10 fractions of 4.2 Gy to the sinonasal cavity with or without an integrated boost of 0.8 Gy to the GTV. Patients had an additional FLT PET/CT scan after fraction 2, a Cu-ATSM PET/CT scan after fraction 3, and follow-up FDG PET/CT scans after radiotherapy. Following image registration, simple and multiple linear and logistic voxel regressions were performed to assess how well pre- and mid-treatment PET imaging predicted post-treatment FDG uptake. R(2) and pseudo R(2) were used to assess the goodness of fits. For simple linear regression models, regression coefficients for all pre- and mid-treatment PET images were significantly positive across the population (P < 0.05). However, there was large variability among patients in goodness of fits: R(2) ranged from 0.00 to 0.85, with a median of 0.12. Results for logistic regression models were similar. Multiple linear regression models resulted in better fits (median R(2) = 0.31), but there was still large variability between patients in R(2). The R(2) from regression models for different predictor variables were highly correlated across patients (R ≈ 0.8), indicating tumors that were poorly predicted with one tracer were also poorly predicted by other tracers. In conclusion, the high inter-patient variability in goodness of fits indicates that PET was able to predict locations of residual tumor in some patients, but not others. This suggests not all patients would be good candidates for dose painting based on a single biological target.

Indexed as

AnimalsCoordination ComplexesDideoxynucleosidesDog DiseasesDogsFluorodeoxyglucose F18Multimodal ImagingNeoplasm Recurrence, LocalNose NeoplasmsOrganometallic CompoundsParanasal Sinus NeoplasmsPositron-Emission TomographyRadiopharmaceuticalsThiosemicarbazonesTomography, X-Ray ComputedCoordination Complexescopper (II) diacetyl-di(N(4)-methylthiosemicarbazone)DideoxynucleosidesFluorodeoxyglucose F18Organometallic CompoundsRadiopharmaceuticalsThiosemicarbazones

Identifiers

PMID26083082
PMCPMC6415760
OpenAlexW2157706665

What OpenQuestion holds

Textmetadata
LicenceTDM
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.