Evidence map›Paper›PMID 42183164›Full record

ArticleAmerican journal of nuclear medicine and molecular imaging2026

Calibration and prediction of results after failed injection in SPECT renal dynamic imaging.

Jianping Zhang, Jiaqi Zhang, Miaomiao Zhang, Ruxin Lei, Kai Zhang, Xingru Pang, Xiaoxue Tian, Luxi Yang, Zhen Cao, Jiangyan Liu and 1 more

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Article in American journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

11 authors.

Jianping ZhangDepartment of Nuclear Medicine, The Second Hospital and Clinical Medical School, Lanzhou University Lanzhou 730030, Gansu, China.
Jiaqi ZhangDepartment of Medical Imaging, The No. 2 People's Hospital of Lanzhou Lanzhou 730030, Gansu, China.
Miaomiao ZhangThe Second Clinical Medical School of Lanzhou University Lanzhou 730030, Gansu, China.
Ruxin LeiThe Second Clinical Medical School of Lanzhou University Lanzhou 730030, Gansu, China.
Kai ZhangDepartment of Nuclear Medicine, The Second Hospital and Clinical Medical School, Lanzhou University Lanzhou 730030, Gansu, China.
Xingru PangDepartment of Nuclear Medicine, The Second Hospital and Clinical Medical School, Lanzhou University Lanzhou 730030, Gansu, China.
Xiaoxue TianDepartment of Nuclear Medicine, The Second Hospital and Clinical Medical School, Lanzhou University Lanzhou 730030, Gansu, China.
Luxi YangKey Laboratory of Digestive System Tumors of Gansu Province, The Second Hospital and Clinical Medical School, Lanzhou University Lanzhou 730030, Gansu, China.
Zhen CaoSiemens Healthineers Ltd. Shanghai 200120, China.
Jiangyan LiuDepartment of Nuclear Medicine, The Second Hospital and Clinical Medical School, Lanzhou University Lanzhou 730030, Gansu, China.
Jicheng LiDepartment of Nuclear Medicine, The Second Hospital and Clinical Medical School, Lanzhou University Lanzhou 730030, Gansu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To predict bilateral renal curves and glomerular filtration rate (GFR) following unsuccessful tracer injection in SPECT renal dynamic imaging using a Transformer model, thereby obviating the need for repeat examinations. We retrospectively studied patients who underwent repeat imaging post-extravasation (2015-2025). Patient height, weight, serum creatinine, urea, uric acid, and renal dynamic curves were used as inputs to develop a Transformer deep learning model for calibrating curves and predicting GFR. Following injection extravasation during SPECT renal dynamic imaging, the proposed model achieved a high overall alignment with the ground truth curves (Median R

Indexed as

calibration and predictionGFRRenal dynamic imagingtransformer model

Identifiers

PMID42183164
PMCPMC13191563

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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.