Evidence map›Paper›PMID 31011685›Full record

ArticleAdvances in radiation oncology

Improvements in CBCT Image Quality Using a Novel Iterative Reconstruction Algorithm: A Clinical Evaluation.

Stephen J Gardner, Weihua Mao, Chang Liu, Ibrahim Aref, Mohamed Elshaikh, Joon K Lee, Deepak Pradhan, Benjamin Movsas, Indrin J Chetty, Farzan Siddiqui

Abstract read
In one paragraph

Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
–field-weighted citation impact
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

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3 · Its place in the literature

Who cites it

41 citing papers in PubMed.

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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

10 authors.

Stephen J GardnerDepartment of Radiation Oncology, Josephine Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan.
Weihua MaoDepartment of Radiation Oncology, Josephine Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan.
Chang LiuDepartment of Radiation Oncology, Josephine Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan.
Ibrahim ArefDepartment of Radiation Oncology, Josephine Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan.
Mohamed ElshaikhDepartment of Radiation Oncology, Josephine Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan.
Joon K LeeDepartment of Radiation Oncology, Josephine Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan.
Deepak PradhanDepartment of Radiation Oncology, Josephine Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan.
Benjamin MovsasDepartment of Radiation Oncology, Josephine Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan.
Indrin J ChettyDepartment of Radiation Oncology, Josephine Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan.
Farzan SiddiquiDepartment of Radiation Oncology, Josephine Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to evaluate the clinical utility of a novel iterative cone beam computed tomography (CBCT) reconstruction algorithm for prostate and head and neck (HN) cancer. METHODS AND MATERIALS: A total of 10 patients with HN and 10 patients with prostate cancer were analyzed. For each patient, raw CBCT acquisition data were used to reconstruct images with a currently available algorithm (FDK_CBCT) and novel iterative algorithm (Iterative_CBCT). Quantitative contouring variation analysis was performed using structures delineated by several radiation oncologists. For prostate, observers contoured the prostate, proximal 2 cm seminal vesicles, bladder, and rectum. For HN, observers contoured the brain stem, spinal canal, right-left parotid glands, and right-left submandibular glands. Observer contours were combined to form a reference consensus contour using the simultaneous truth and performance level estimation method. All observer contours then were compared with the reference contour to calculate the Dice coefficient, Hausdorff distance, and mean contour distance (prostate contour only). Qualitative image quality analysis was performed using a 5-point scale ranging from 1 (much superior image quality for Iterative_CBCT) to 5 (much inferior image quality for Iterative_CBCT).

resultsThe Iterative_CBCT data sets resulted in a prostate contour Dice coefficient improvement of approximately 2.4% (

conclusionsObservers noted an improvement in image uniformity, noise level, and overall image quality for Iterative_CBCT data sets. In addition, expert observers displayed an improved ability to consistently delineate soft tissue structures, such as the prostate and parotid glands. Thus, the novel iterative reconstruction algorithm analyzed in this study is capable of improving the visualization for prostate and HN cancer image guided radiation therapy.

Identifiers

PMID31011685
PMCPMC6460237

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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.