Evidence map›Paper›PMID 40458868›Full record

ArticleHead & neck2025

Deep Learning Pipeline for Automated Assessment of Distances Between Tonsillar Tumors and the Internal Carotid Artery.

Aseem Jain, Ameen Amanian, Nimesh Nagururu, Francis X Creighton, Eitan Prisman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Head & neck, 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

5 authors.

Aseem JainDepartment of Otolaryngology-Head and Neck Surgery, University of Iowa, Iowa City, Iowa, USA.ORCID 0000-0003-2105-4940
Ameen AmanianDivision of Otolaryngology-Head and Neck Surgery, University of British Columbia, Vancouver, British Columbia, Canada.ORCID 0000-0002-4418-8215
Nimesh NagururuDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins Medical Institute, Baltimore, Maryland, USA.
Francis X CreightonDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins Medical Institute, Baltimore, Maryland, USA.
Eitan PrismanDivision of Otolaryngology-Head and Neck Surgery, University of British Columbia, Vancouver, British Columbia, Canada.

Funding

Cooperative Control Robotics and Computer Vision: Development of Semi-Autonomous Temporal Bone and Skull Base SurgeryK08DC019708 · NIDCD · JOHNS HOPKINS UNIVERSITY · PI CREIGHTON, FRANCIS XAVIER · 2021 to 2025
$959k
NIDCD NIH HHS K08 DC019708
6 · The paper itself

Abstract

backgroundEvaluating the minimum distance (dTICA) between the internal carotid artery (ICA) and tonsillar tumors (TT) on imaging is essential for preoperative planning; we propose a tool to automatically extract dTICA.

methodsCT scans of 96 patients with TT were selected from the cancer imaging archive. nnU-Net, a deep learning framework, was implemented to automatically segment both the TT and ICA from these scans. Dice similarity coefficient (DSC) and average hausdorff distance (AHD) were used to evaluate the performance of the nnU-Net. Thereafter, an automated tool was built to calculate the magnitude of dTICA from these segmentations.

resultsThe average DSC and AHD were 0.67, 2.44 mm, and 0.83, 0.49 mm for the TT and ICA, respectively. The mean dTICA was 6.66 mm and statistically varied by tumor T stage (p = 0.00456).

conclusionThe proposed pipeline can accurately and automatically capture dTICA, potentially assisting clinicians in preoperative evaluation.

Indexed as

Carotid Artery, InternalDeep LearningTonsillar NeoplasmsAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesTomography, X-Ray Computeddeep learninginternal carotid arterymedical imagingtonsillar carcinomatransoral robotic surgery

Identifiers

PMID40458868
PMCPMC12434563

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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.