Noninvasive Preoperative Stratification of Renal Tumors for Aggressiveness and Prognosis.

Jinglai Lin, Linpeng Yao, Jianbo Gao, Ning Xu, Ying Xiong, Qi Bai, Kang Wang, Qi Sun

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

8 authors.

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

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

Jianbo GaoDepartment of Urology, The People's Hospital of Lincang, Lincang 677000, China.

Ning XuDepartment of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China.

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

Qi BaiDepartment of Urology, Zhongshan Hospital, Fudan University, Shanghai 200000, China.

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

Qi SunXiamen Clinical Research Center for Cancer Therapy, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate preoperative discrimination between indolent and aggressive renal lesions is essential for personalized risk-stratified management and improves the clinical outcomes of renal tumor patients. This study aimed to establish and externally validate a non-invasive computed tomography (CT)-derived radiomic model for identifying aggressive renal tumors and predicting long-term postoperative survival. A total of 3407 patients with pathologically confirmed renal tumors from multiple centers were retrospectively enrolled and divided into three independent cohorts: a training cohort (

Indexed as

aggressivenesscomputed tomographyprognosisradiomicsrenal tumors

Identifiers

PMID42791956

DOI10.3390/bioengineering13091084

PMCPMC13603619

What Socratic 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 Socratic graph.