Evidence map›Paper›PMID 36203028›Full record

ArticleJournal of neuro-oncology2022

Proposing a quantitative MRI-based linear measurement framework for response assessment following stereotactic body radiation therapy in patients with spinal metastasis.

Pejman Jabehdar Maralani, Hanbo Chen, Bahareh Moazen, Mahtab Mojtahed Zadeh, Fateme Salehi, Aimee Chan, Liang K Zeng, Ahmed Abugharib, Chia-Lin Tseng, Zain Husain and 7 more

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Article in Journal of neuro-oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.1field-weighted citation impact, top 23% 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

6 citing papers in PubMed, 5 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

17 authors at 3 institutions in 2 countries.

Pejman Jabehdar MaralaniDepartment of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, AG270c, Toronto, ON, M4N 3M5, Canada. pejman.maralani@sunnybrook.ca.ORCID http://orcid.org/0000-0001-9975-4379
Hanbo ChenDepartment of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada.
Bahareh MoazenDepartment of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, AG270c, Toronto, ON, M4N 3M5, Canada.
Mahtab Mojtahed ZadehDepartment of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, AG270c, Toronto, ON, M4N 3M5, Canada.
Fateme SalehiDepartment of Radiology, McMaster University, Hamilton, Canada.
Aimee ChanDepartment of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, AG270c, Toronto, ON, M4N 3M5, Canada.ORCID http://orcid.org/0000-0002-1978-2853
Liang K ZengDepartment of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada.
Ahmed AbugharibDepartment of Clinical Oncology, Sohag University Hospital, Sohag, Egypt.
Chia-Lin TsengDepartment of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada.ORCID http://orcid.org/0000-0002-3700-0886
Zain HusainDepartment of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada.
Sten MyrehaugDepartment of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada.ORCID http://orcid.org/0000-0002-5765-1969
Hany SolimanDepartment of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada.ORCID http://orcid.org/0000-0003-2824-2100
Jay DetskyDepartment of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada.ORCID http://orcid.org/0000-0002-0117-6839
Chinthaka HeynDepartment of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, AG270c, Toronto, ON, M4N 3M5, Canada.ORCID http://orcid.org/0000-0002-3226-5199
Mark RuschinDepartment of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada.
Jeremie LaroucheDivision of Orthopedic Surgery, Department of Surgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.ORCID http://orcid.org/0000-0001-9636-2273
Arjun SahgalDepartment of Radiation Oncology, Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada.ORCID http://orcid.org/0000-0001-7991-8970
Sunnybrook Health Science Centre · CAMcMaster University · CASohag University · EG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo provide evidence towards a quantitative response assessment framework incorporating MRI-based linear measurements for spinal metastasis that predicts outcome following stereotactic body radiation therapy (SBRT).

methodsAdult patients with de novo spinal metastases treated with SBRT between 2008 and 2018 were retrospectively assessed. The metastatic lesions involving the pedicles, articular processes, lamina, transverse process, spinous process and vertebral body at leach level were measured separately using linear measurements on pre- and all post-SBRT MRIs. The outcome was segment-specific progression (SSP) using SPINO guidelines which was dated to the first clinical documentation of progression, or the date of the associated MRI if imaging was the reason for progression. Random forest analysis for variable selection and recursive partitioning analysis for SSP probability prediction were used.

resultsFive Hundred Ninety-three spinal levels (323 patients) from 4081 MRIs were evaluated. The appearance of new T1 hypointensity and increase in Bilsky grade had an odds ratio (OR) of 33.5 and 15.5 for SSP, respectively. Compared to baseline, an increase of > 3 mm in any lesion dimension, combined with a 1.67-fold increase in area, had an OR of 4.6 for SSP. The sensitivity, specificity, positive predictive value, negative predictive value, balanced accuracy and area under the curve of the training model were 96.7%, 89.6%, 28.6%, 99.8%, 93.2% and 0.905 and of the test model were 91.3%, 89.3%, 27.1% 99.6%, 90.3% and 0.933, respectively.

conclusionWith further refinement and validation in prospective multicentre studies, MRI-based linear measurements can help predict response assessment in SBRT-treated spinal metastases.

Indexed as

RadiosurgerySpinal NeoplasmsAdultHumansMagnetic Resonance ImagingProspective StudiesRetrospective StudiesMRIQuantitativeResponse assessmentSpineStereotactic body radiation therapy

Identifiers

PMID36203028
OpenAlexW4302773510

What OpenQuestion holds

Textmetadata
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Registered trials

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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.