Evidence map›Paper›PMID 34942395›Full record

ArticleComputers in biology and medicine2022

Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy.

Xiaokun Liang, Maxime Bassenne, Dimitre H Hristov, Md Tauhidul Islam, Wei Zhao, Mengyu Jia, Zhicheng Zhang, Michael Gensheimer, Beth Beadle, Quynh Le and 1 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Review
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

11 authors at 2 institutions in 1 country.

Xiaokun LiangDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. Electronic address: xiaokun@stanford.edu.
Maxime BassenneDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. Electronic address: bassenne@stanford.edu.
Dimitre H HristovDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. Electronic address: dhristov@stanford.edu.
Md Tauhidul IslamDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. Electronic address: tauhid@stanford.edu.
Wei ZhaoDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. Electronic address: zhaow85@stanford.edu.
Mengyu JiaDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. Electronic address: jeremy18@stanford.edu.
Zhicheng ZhangDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. Electronic address: zzc623@stanford.edu.
Michael GensheimerDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. Electronic address: mgens@stanford.edu.
Beth BeadleDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. Electronic address: bbeadle@stanford.edu.
Quynh LeDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. Electronic address: qle@stanford.edu.
Lei XingDepartment of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA. Electronic address: lei@stanford.edu.
Stanford Medicine · USStanford University · US

Funding

Dual Modality X-ray Luminescence CT for in vivo Cancer ImagingR01CA227713 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2019 to 2023
$2.9M
DASSIM-RT and Compressed Sensing-Based Inverse PlanningR01CA176553 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2014 to 2018
$2.4M
Leveraging deep learning for markerless motion management in radiation therapyR01CA256890 · NCI · STANFORD UNIVERSITY · PI XING, LEI · 2021 to 2025
$2.1M
NCI NIH HHS R01 CA176553NCI NIH HHS R01 CA227713NCI NIH HHS R01 CA256890
6 · The paper itself

Abstract

purposeTo develop a deep unsupervised learning method with control volume (CV) mapping from patient positioning daily CT (dCT) to planning computed tomography (pCT) for precise patient positioning.

methodsWe propose an unsupervised learning framework, which maps CVs from dCT to pCT to automatically generate the couch shifts, including translation and rotation dimensions. The network inputs are dCT, pCT and CV positions in the pCT. The output is the transformation parameter of the dCT used to setup the head and neck cancer (HNC) patients. The network is trained to maximize image similarity between the CV in the pCT and the CV in the dCT. A total of 554 CT scans from 158 HNC patients were used for the evaluation of the proposed model. At different points in time, each patient had many CT scans. Couch shifts are calculated for the testing by averaging the translation and rotation from the CVs. The ground-truth of the shifts come from bone landmarks determined by an experienced radiation oncologist.

resultsThe system positioning errors of translation and rotation are less than 0.47 mm and 0.17°, respectively. The random positioning errors of translation and rotation are less than 1.13 mm and 0.29°, respectively. The proposed method enhanced the proportion of cases registered within a preset tolerance (2.0 mm/1.0°) from 66.67% to 90.91% as compared to standard registrations.

conclusionsWe proposed a deep unsupervised learning architecture for patient positioning with inclusion of CVs mapping, which weights the CVs regions differently to mitigate any potential adverse influence of image artifacts on the registration. Our experimental results show that the proposed method achieved efficient and effective HNC patient positioning.

Indexed as

Head and Neck NeoplasmsRadiotherapy, Image-GuidedCone-Beam Computed TomographyHumansRadiotherapy Planning, Computer-AssistedTomography, X-Ray ComputedHead and neckImage-guided radiation therapyImage registrationPatient positioningUnsupervised learning

Identifiers

PMID34942395
PMCPMC8810749
OpenAlexW4200625794

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.