Evidence map›Paper›PMID 40444332›Full record

ArticlePhysics in medicine and biology2025

Deep learning-based applicator selection between Syed and T&O in high-dose-rate brachytherapy for locally advanced cervical cancer: a retrospective study.

Runyu Jiang, Malvern Madondo, Xiaoman Zhang, Yuan Shao, Mohammadamin Moradi, James J Sohn, Tianming Wu, Xiaofeng Yang, Yasmin Hasan, Zhen Tian

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026
    Review
  2. 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

10 authors.

Runyu JiangDepartment of Radiation & Cellular Oncology, University of Chicago, Chicago, IL, United States of America.ORCID 0000-0002-2097-764X
Malvern MadondoDepartment of Radiation & Cellular Oncology, University of Chicago, Chicago, IL, United States of America.ORCID 0000-0002-9191-0127
Xiaoman ZhangSchool of Public Health, University of Illinois Chicago, Chicago, IL, United States of America.ORCID 0009-0004-3886-2359
Yuan ShaoSchool of Public Health, University of Illinois Chicago, Chicago, IL, United States of America.
Mohammadamin MoradiDepartment of Radiation & Cellular Oncology, University of Chicago, Chicago, IL, United States of America.
James J SohnDepartment of Radiation & Cellular Oncology, University of Chicago, Chicago, IL, United States of America.ORCID 0000-0001-7209-4104
Tianming WuDepartment of Radiation & Cellular Oncology, University of Chicago, Chicago, IL, United States of America.
Xiaofeng YangDepartment of Radiation Oncology, Emory University, Atlanta, GA, United States of America.ORCID 0000-0001-9023-5855
Yasmin HasanDepartment of Radiation & Cellular Oncology, University of Chicago, Chicago, IL, United States of America.
Zhen TianDepartment of Radiation & Cellular Oncology, University of Chicago, Chicago, IL, United States of America.ORCID 0000-0002-4098-5882

Funding

Real-time Volumetric Imaging for Motion Management and Dose Delivery VerificationR01CA272991 · NCI · EMORY UNIVERSITY · PI Zhen Tian, Xiaofeng Yang · 2023 to 2026
$2.3M
Artificial Intelligence Driven Automatic Treatment Planning of Stereotactic Radiosurgery for the Management of Multiple Brain MetastasesR37CA272755 · NCI · UNIVERSITY OF CHICAGO · PI Zhen Tian · 2022 to 2026
$1.8M
NCI NIH HHS R01 CA272991NCI NIH HHS R37 CA272755
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

BrachytherapyDeep LearningRadiation DosageUterine Cervical NeoplasmsFemaleHumansRadiotherapy DosageRetrospective Studiesapplicator selectiondecision-makingdeep learninghigh-dose-rate brachytherapylocally advanced cervical cancer

Identifiers

PMID40444332
PMCPMC12142457

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.