Evidence map›Paper›PMID 39611806›Full record

ArticleJournal of applied clinical medical physics2025

Evaluation and comparison of synthetic computed tomography algorithms with 3T MRI for prostate radiotherapy: AI-based versus bulk density method.

Sakari S Karhula, Piia Karppinen, Henna Hietala, Juha Nikkinen

Abstract readComparative StudyEvaluation Study
In one paragraph

Article in Journal of applied clinical medical physics, 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

4 authors.

Sakari S KarhulaDepartment of Oncology and Radiotherapy, Oulu University Hospital, Oulu, Finland.ORCID https://orcid.org/0000-0003-3401-4198
Piia KarppinenDepartment of Oncology and Radiotherapy, Oulu University Hospital, Oulu, Finland.
Henna HietalaDepartment of Oncology and Radiotherapy, Oulu University Hospital, Oulu, Finland.
Juha NikkinenDepartment of Oncology and Radiotherapy, Oulu University Hospital, Oulu, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSynthetic computed tomography (sCT)-algorithms, which generate computed tomography images from magnetic resonance imaging data, are becoming part of the clinical radiotherapy workflow. The aim of this retrospective study was to evaluate and compare commercial bulk-density-method (BM)-based and AI (artificial intelligence)-based-algorithms using 3T magnetic resonance imaging (MRI) with patient data as part of the local clinical commissioning process.

methods44 prostate radiotherapy patients were subjected to MRI and treatment planning CT (TPCT) scans. From the MRI images, sCT images with two different sCT algorithms were generated. The sCT images were evaluated by visual inspection of artifacts. Both sCT methods were compared to TPCT, with Dice similarity score(DSC) of bone and body contours, DVH parameters for CTV, bladder and rectum, and gamma-analysis. Accuracy for treatment alignment using sCT images was also tested. Various limits were used to define whether the differences between sCT methods to TPCT were clinically relevant (DVH parameters <2%, gamma-analysis passing rates 90%, 95%, and 98%, and the DSC 0.98 for body and 0.7 for bone).

resultsOur results show that, differences in CTV-dose coverage values were <2% in most of the patients with both sCT algorithms when compared to reference dose coverage. While AI-sCT had mean dose coverage difference <0,5% and BM-sCT <1%. Gamma-analysis showed that the AI-sCT mean passing rates were 95.4%, 98.6%, and 99.4% with 1mm1%, 2mm2%, and 3mm3% criteria, respectively. Similarly for BM-sCT the mean passing rates were 93.4%, 98.2%, and 99.2%. For the treatment alignment accuracy, the mean difference in magnitude of the translational shifts was 1.43 mm for BM-sCT and 1.57 mm for AI-sCT. Even though AI-sCT showed statistically better correspondence to TPCT, the differences were not clinically relevant with any of the limits. Visual evaluation showed artifacts in the AI-sCT especially in the bowel area and fiducial markers were not generated with either of the sCT algorithms.

conclusionsIn conclusion, sCT-algorithms were clinically usable on prostate treatments using 3T MR-only workflow. While AI-sCT showed better correspondence to TPCT than BM-sCT, it generated characteristic artifacts. As sCT algorithms perform well, we still recommend testing the sCT-algorithms with retrospective analyses from patient data prior to implementing sCT into the routine workflow to better understand the specific limitations and capabilities of these algorithms.

Indexed as

AlgorithmsArtificial IntelligenceImage Processing, Computer-AssistedMagnetic Resonance ImagingProstatic NeoplasmsRadiotherapy Planning, Computer-AssistedTomography, X-Ray ComputedHumansMaleOrgans at RiskRadiotherapy DosageRadiotherapy, Intensity-ModulatedRetrospective Studiesartificial intelligenceclinical commissioningcomputed tomographydosimetrymagnetic resonance imagingprostate cancerradiotherapysynthetic computed tomography

Identifiers

PMID39611806
PMCPMC11905239

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.