Evidence map›Paper›PMID 28718888›Full record

ArticleMedical physics2017

Assessing lung function using contrast-enhanced dual-energy computed tomography for potential applications in radiation therapy.

Andréanne Lapointe, Houda Bahig, Danis Blais, Hugo Bouchard, Édith Filion, Jean-François Carrier, Stéphane Bedwani

Abstract read
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In one paragraph

Article in Medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 14% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Andréanne LapointeDépartement de physique, Université de Montréal, Pavillon Roger-Gaudry (D-428), 2900 boulevard Édouard-Montpetit, Montréal, Québec, H3T 1J4, Canada.
Houda BahigDépartement de radio-oncologie, Centre hospitalier de l'Université de Montréal (CHUM), 1560 rue Sherbrooke est, Montréal, Québec, H2L 4M1, Canada.
Danis BlaisDépartement de radio-oncologie, Centre hospitalier de l'Université de Montréal (CHUM), 1560 rue Sherbrooke est, Montréal, Québec, H2L 4M1, Canada.
Hugo BouchardDépartement de physique, Université de Montréal, Pavillon Roger-Gaudry (D-428), 2900 boulevard Édouard-Montpetit, Montréal, Québec, H3T 1J4, Canada.
Édith FilionDépartement de radio-oncologie, Centre hospitalier de l'Université de Montréal (CHUM), 1560 rue Sherbrooke est, Montréal, Québec, H2L 4M1, Canada.
Jean-François CarrierDépartement de physique, Université de Montréal, Pavillon Roger-Gaudry (D-428), 2900 boulevard Édouard-Montpetit, Montréal, Québec, H3T 1J4, Canada.
Stéphane BedwaniDépartement de physique, Université de Montréal, Pavillon Roger-Gaudry (D-428), 2900 boulevard Édouard-Montpetit, Montréal, Québec, H3T 1J4, Canada.
Centre Hospitalier de l’Université de Montréal · CAUniversité de Montréal · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThere is an increasing interest in the evaluation of lung function from physiological images in radiation therapy treatment planning to reduce the extent of postradiation toxicities. The purpose of this work was to retrieve reliable functional information from contrast-enhanced dual-energy computed tomography (DECT) for new applications in radiation therapy. The functional information obtained by DECT is also compared with other methods using single-energy CT (SECT) and single-photon emission computed tomography (SPECT) with CT. The differential function between left and right lung, as well as between lobes is computed for all methods.

methodsFive lung cancer patients were retrospectively selected for this study; each underwent a SPECT/CT scan and a contrast-injected DECT scan, using 100 and 140 Sn kVp. The DECT images are postprocessed into iodine concentration maps, which are further used to determine the perfused blood volume. These maps are calculated in two steps: (a) a DECT stoichiometric calibration adapted to the presence of iodine and followed by (b) a two-material decomposition technique. The functional information from SECT is assumed proportional to the HU numbers from a mixed CT image. The functional data from SPECT/CT are considered proportional to the number of counts. A radiation oncologist segmented the entire lung volume into five lobes on both mixed CT images and low-dose CT images from SPECT/CT to allow a regional comparison. The differential function for each subvolume is computed relative to the entire lung volume.

resultsThe differential function per lobe derived from SPECT/CT correlates strongly with DECT (Pearson's coefficient r = 0.91) and moderately with SECT (r = 0.46). The differential function for the left lung shows a mean difference of 7% between SPECT/CT and DECT; and 17% between SPECT/CT and SECT. The presence of nonfunctional areas, such as localized emphysema or a lung tumor, is reflected by an intensity drop in the iodine concentration maps. Functional dose volume histograms (fDVH) are also generated for two patients as a proof of concept.

conclusionThe extraction of iodine concentration maps from a contrast-enhanced DECT scan is achieved to compute the differential function for each lung subvolume and good agreement is found in respect to SPECT/CT. One promising avenue in radiation therapy is to include such functional information during treatment planning dose optimization to spare functional lung tissues.

Indexed as

Contrast MediaRadiotherapy, Image-GuidedTomography, X-Ray ComputedHumansLung NeoplasmsRetrospective StudiesSingle Photon Emission Computed Tomography Computed TomographyContrast Mediadual-energy computed tomographylung differential functionperfusion scintigraphyradiation therapy

Identifiers

PMID28718888
OpenAlexW2737691002

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.