Evidence map›Paper›PMID 41250211›Full record

Observational studyRadiation oncology (London, England)2025

Autostent: a semi-automated approach to designing customized 3D-printed oral radiation stents for patients with head and neck cancer.

Anshuman Agrawal, Rance B Tino, Mohamed Zaid, Millicent Roach, Lianchun Xiao, Mark S Chambers, Anna Lee, Eugene J Koay

Abstract readObservational Study
In one paragraph

Observational study in Radiation oncology (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Anshuman AgrawalDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 97, Houston, TX, 77030, USA.
Rance B TinoDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 97, Houston, TX, 77030, USA. rbtino@mdanderson.org.
Mohamed ZaidDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 97, Houston, TX, 77030, USA.
Millicent RoachDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 97, Houston, TX, 77030, USA.
Lianchun XiaoDivision of VP-Research, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Avenue, Houston, TX, 77030, USA.
Mark S ChambersDivision of Surgery, Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Anna LeeDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 97, Houston, TX, 77030, USA.
Eugene J KoayDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 97, Houston, TX, 77030, USA. EKoay@mdanderson.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOral stents may reduce toxicities during radiation therapy for head and neck cancer (HNC). Customized 3D-printed oral stents offer faster production and achieve comparable patient-reported outcomes to conventionally fabricated stents. However, their design process remains time-consuming, lacks standardization, and relies heavily on skilled technicians. We hypothesized that semi-automating the design process for 3D-printed, mouth-opening, tongue-depressing (MOTD) stents could standardize the design workflow and decrease design time.

methodsUsing oral stent design principles established over decades by oral oncologists, we created a customized computer program (Autostent) using MATLAB to semi-automate the design process of MOTD stents. We subsequently compared Autostent to a previously described method that utilized non-automated computer-aided design. Three users designed stents for four patients with HNC enrolled in a prospective observational study. These patients were selected based on their varying dental anatomies, and each user repeatedly designed an MOTD stent for each patient three times, employing both the non-automated and semi-automated methods. Both methods were compared in terms of design time and stent volume.

resultsSemi-automation reduced the average design time by 23.6 min (51.2%, p = 0.001), regardless of user, dental anatomy, or trial number. Additionally, semi-automation decreased the average stent volume by 4.33 mL (12.9%, p = 0.016, univariate analysis). Although this reduction was not statistically significant when considering other experimental variables (p = 0.40, multivariate analysis), semi-automation did lower the variability in stent volume among users (the overall standard error of the mean decreased by 40%).

conclusionOur semi-automated workflow for designing and fabricating customized, 3D-printed MOTD stents significantly improves efficiency and reduces variability in the design. While these results indicate greater consistency compared to manual methods, further development is warranted to achieve full automation and to optimize clinical integration.

Indexed as

Computer-Aided DesignHead and Neck NeoplasmsPrinting, Three-DimensionalProsthesis DesignStentsAutomationHumansProspective StudiesRadiotherapy Planning, Computer-Assisted3D printingAutomationDigital manufacturingHead and neck cancerIntraoral scanningOral stentRadiation therapySoftwareStereolithography

Identifiers

PMID41250211
PMCPMC12625475

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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.