Differentiating Fat-Poor Angiomyolipoma from Renal Cell Carcinoma Using Contrast-Enhanced CT.

Jinglai Lin, Letong Zhang, Dengqiang Lin, Linpeng Yao, Kang Wang, Ying Xiong

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 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

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

6 authors.

Jinglai LinDepartment of Urology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China.ORCID 0009-0004-3310-601X

Letong ZhangClinical Medical College, Chengdu University, Chengdu 610086, China.

Dengqiang LinDepartment of Urology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China.ORCID 0000-0002-6732-6043

Linpeng YaoDepartment of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China.

Kang WangDigital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200000, China.

Ying XiongDepartment of Urology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fat-poor angiomyolipoma (fp-AML), a common benign renal mass, closely mimics renal cell carcinoma (RCC) on preoperative computed tomography (CT), frequently resulting in unnecessary surgical intervention. This multicenter retrospective study aimed to develop and externally validate an AI-assisted radiomics model based on triphasic contrast-enhanced CT to accurately distinguish fp-AML from RCC. A total of 655 eligible patients with sporadic solid renal lesions were enrolled and divided into a training cohort (

Indexed as

contrast-enhanced computed tomographydiagnosisfat-poor angiomyolipomaradiomicsrenal cell carcinoma

Identifiers

PMID42791840

DOI10.3390/bioengineering13090967

PMCPMC13603207

What OpenQuestion holds

Textmetadata

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.