Evidence map›Paper›PMID 40629162›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2025

Assessment of T2-weighted MRI-derived synthetic CT for the detection of suspected lumbar facet arthritis: a comparative analysis with conventional CT.

Gan Cao, Haiqiao Wang, Shanghuang Xie, Die Cai, Jiandong Guo, Jichao Zhu, Keliang Ye, Yi Wang, Jun Xia

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2025. 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
–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

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. Article
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

9 authors.

Gan Cao *Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China.
Haiqiao Wang *Smart Medical Imaging, Learning and Engineering (SMILE) Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China.
Shanghuang XieDepartment of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China.
Die CaiDepartment of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China.
Jiandong GuoShenzhen Hospital of Guangzhou University of Chinese Medicine (Futian), Shenzhen, China.
Jichao ZhuDepartment of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China.
Keliang YeFuyong People's Hospital, Shenzhen, China.
Yi Wang *Smart Medical Imaging, Learning and Engineering (SMILE) Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China. onewang@szu.edu.cn.
Jun Xia *Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China. xiajun@email.szu.edu.cn.

Funding

the National Natural Science Foundation of China 82171913
6 · The paper itself

Abstract

purposeWe evaluated sCT generated from T2-weighted imaging (T2WI) using deep learning techniques to detect structural lesions in lumbar facet arthritis, with conventional CT as the reference standard.

methodsThis single-center retrospective study included 40 patients who had lumbar MRI and CT with in 1 week (September 2020 to August 2021). A Pix2Pix-GAN framework generated CT images from MRI data, and image quality was assessed using structural similarity index (SSIM), mean absolute error (MAE), peak signal-to-noise ratio (PSNR), nd Dice similarity coefficient (DSC). Two senior radiologists evaluated 15 anatomical landmarks. Sensitivity, specificity, and accuracy for detecting bone erosion, osteosclerosis, and joint space alterations were analyzed for sCT, T2-weighted MRI, and conventional CT.

resultsForty participants (21 men, 19 women) were enrolled, with a mean age of 39 ± 16.9 years. sCT showed strong agreement with conventional CT, with SSIM values of 0.888 for axial and 0.889 for sagittal views. PSNR and MAE values were 24.56 dB and 0.031 for axial and 23.75 dB and 0.038 for sagittal views, respectively. DSC values were 0.935 for axial and 0.876 for sagittal views. sCT showed excellent intra- and inter-reader reliability intraclass correlation coefficients (0.953-0.995 and 0.839-0.983, respectively). sCT had higher sensitivity (57.9% vs. 5.3%), specificity (98.8% vs. 84.6%), and accuracy (93.0% vs. 73.3%) for bone erosion than T2-weighted MRI and outperformed it for osteosclerosis and joint space changes.

conclusionssCT outperformed conventional T2-weighted MRI in detecting structural lesions indicative of lumbar facet arthritis, with conventional CT as the reference standard.

Indexed as

ArthritisLumbar VertebraeMagnetic Resonance ImagingTomography, X-Ray ComputedZygapophyseal JointAdultAgedDeep LearningFemaleHumansMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityComputed tomographyGenerative adversarial networksImage qualityLumbar facet arthritisMagnetic resonance imaging

Identifiers

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.