Evidence map›Paper›PMID 39398213›Full record

ArticleArXiv2024

A Review of Artificial Intelligence in Brachytherapy.

Jingchu Chen, Richard L J Qiu, Tonghe Wang, Shadab Momin, Xiaofeng Yang

Abstract readPreprint
In one paragraph

Article in ArXiv, 2024. 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

5 · Who and what money

Authors and funding

5 authors.

Jingchu ChenDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308.
Richard L J QiuDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308.
Tonghe WangDepartment of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065.
Shadab MominDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308.
Xiaofeng YangDepartment of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Michael Jason de la Cruz · 1985 to 2026
$347.4M
Real-time Volumetric Imaging for Motion Management and Dose Delivery VerificationR01CA272991 · NCI · EMORY UNIVERSITY · PI Zhen Tian, Xiaofeng Yang · 2023 to 2026
$2.3M
Multiparametric MRI-guided Prostate HDR Brachytherapy with Focal Tumor BoostR01CA215718 · NCI · EMORY UNIVERSITY · PI YANG, XIAOFENG · 2018 to 2022
$1.8M
Intelligent and Personalized Online Adaptive Proton TherapyR01DE033512 · NIDCR · UNIVERSITY OF CHICAGO · PI Zhen Tian, Xiaofeng Yang · 2024 to 2026
$1.8M
NCI NIH HHS P30 CA008748NCI NIH HHS R01 CA215718NCI NIH HHS R01 CA272991NIDCR NIH HHS R01 DE033512
6 · The paper itself

Abstract

Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in facilitating various aspects of brachytherapy. We analyze AI's role in making brachytherapy treatments more personalized, efficient, and effective. The applications are systematically categorized into seven categories: imaging, preplanning, treatment planning, applicator reconstruction, quality assurance, outcome prediction, and real-time monitoring. Each major category is further subdivided based on cancer type or specific tasks, with detailed summaries of models, data sizes, and results presented in corresponding tables. This review offers insights into the current advancements, challenges, and the impact of AI on treatment paradigms, encouraging further research to expand its clinical utility.

Indexed as

AIbrachytherapyHDRLDRmachine learning

Identifiers

PMID39398213
PMCPMC11469420

What Socratic 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 Socratic graph.