Evidence map›Paper›PMID 42057840›Full record

ArticleWorld journal of radiology2026

Letter to the Editor: Magnetic resonance imaging-based deep learning radiomics for preoperative risk stratification in pediatric hepatoblastoma.

Ujjayita Chowdhury, Atharva A Mahajan, Muthu Subash Kavitha, Ramya Lakshmi Rajendran, Prakash Gangadaran, Byeong-Cheol Ahn

Abstract readLetterComment
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of radiology, 2026. 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

6 authors.

Ujjayita ChowdhuryCancer Research Institute, Advanced Centre for Treatment Research and Education in Cancer, Navi Mumbai 410210, Maharashtra, India.
Atharva A MahajanCancer Research Institute, Advanced Centre for Treatment Research and Education in Cancer, Navi Mumbai 410210, Maharashtra, India.
Muthu Subash KavithaSchool of Information and Data Sciences, Nagasaki University, Nagasaki 852-8521, Japan.
Ramya Lakshmi RajendranBK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, Department of Biomedical Sciences, School of Medicine, Kyungpook National University, Daegu 41944, South Korea.
Prakash GangadaranDepartment of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu 41944, South Korea.
Byeong-Cheol AhnBK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, Department of Biomedical Sciences, School of Medicine, Kyungpook National University, Daegu 41944, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This letter to the editor discusses a recent multi-institutional study that developed a noninvasive deep learning-based radiomics score derived from preoperative magnetic resonance imaging (MRI) to predict event-free survival in pediatric hepatoblastoma. The original study by Yang and Li published in

Indexed as

Alpha-fetoproteinDeep learningEvent-free survivalMagnetic resonance imaging radiomicsNomogramPediatric hepatoblastomaPreoperative risk stratificationPretreatment extension of disease stage

Identifiers

PMID42057840
PMCPMC13122067

What OpenQuestion holds

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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.