Evidence map›Paper›PMID 40831453›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2025

High-dose-rate Brachytherapy Planning with Dendrite Cross-Attention UNet.

Sourav Saini, Yawen Wei, Jingzhao Rong, Xiaofeng Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 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

4 authors.

Sourav SainiDept. of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT 06519, USA.
Yawen WeiDept. of Biostatistics (Health Informatics and Data Science), Yale School of Public Health, Yale University, New Haven, CT 06519, USA.
Jingzhao RongDept. of Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT 06519, USA.
Xiaofeng LiuDept. of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT 06519, USA.

Funding

Deep Learning for Individualized Treatment Effect Inference with Multimodal DepictionR21EB034911 · NIBIB · YALE UNIVERSITY · PI Xiaofeng Liu · 2024 to 2026
$653k
NIBIB NIH HHS R21 EB034911
6 · The paper itself

Abstract

Treatment of cervical cancer commonly involves high-dose-rate brachytherapy (HDR-BT), a procedure that requires precise and efficient planning to achieve the best patient outcomes. Historically, the HDR-BT planning process has been labor-intensive and largely dependent on the expertise of the clinician, resulting in potential inconsistencies in the quality of treatment. To overcome this issue, we propose an innovative method that employs advanced deep-learning models to improve HDR-BT planning. This paper presents the

Indexed as

BrachytherapyCervical CancerDeep LearningDose PredictionTreatment Planning

Identifiers

PMID40831453
PMCPMC12360165

What OpenQuestion holds

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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.