Evidence map›Paper›PMID 41171407›Full record

ArticleAbdominal radiology (New York)2026

Artificial intelligence-based segmentation of small renal masses: a multi-center, multi-scanner, multi-sequence study.

Mengqiu Cui, Zilong Zeng, Silu Chen, He Wang, Jiahui Jiang, Yang Cao, Xiaohui Ding, Wei Xu, Tengda Zhao, Jian Zhao and 8 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 2026. 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. Article
  2. Review
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

18 authors.

Mengqiu CuiDepartment of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.
Zilong ZengState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Silu ChenDepartment of Radiology, Seventh Medical Center, Chinese PLA General Hospital, Beijing, China.
He WangRadiology Department, Peking University First Hospital, Beijing, China.
Jiahui JiangDepartment of Radiology, Beijing Friendship Hospital, Beijing, China.
Yang CaoRadiology Department, Peking University First Hospital, Beijing, China.
Xiaohui DingDepartment of Pathology, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
Wei XuDepartment of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.
Tengda ZhaoState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Jian ZhaoDepartment of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.
Xu BaiDepartment of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.
Huanhuan KangDepartment of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.
Yuwei HaoDepartment of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.
Lin LiDepartment of Innovative Medical Research, Chinese PLA General Hospital, Beijing, China.
Dawei YangDepartment of Radiology, Beijing Friendship Hospital, Beijing, China.
Huiyi YeDepartment of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.
Yong HeState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Haiyi WangDepartment of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China. wanghaiyi301@outlook.com.

Funding

National Natural Science Foundation of China U24A20755, 82271951
6 · The paper itself

Abstract

objectivesThis study aims to develop an artificial intelligence (AI)-based automated segmentation method for small renal masses (SRMs) using multi-center, multi-scanner, multi-sequence MRI data.

methodsMR images from 988 pathologically confirmed SRM patients from three different centers were retrospectively included. Segmentation networks were independently developed for each MRI sequence using deep learning techniques. A GE dataset of 733 patients from Center 1 was used for training and validation. A GE test set, consisting of internal (99 from Center 1) and external test sets (81 from Center 2 and 3), was created for evaluation. Furthermore, a non-GE generalization set, consisting of 75 patients from Center 2 and 3, was used to assess the generalization ability. The method's performance was evaluated in terms of detection rate and segmentation accuracy (Dice similarity coefficient [DSC]). Subgroup analysis and multiple linear regression were used for further exploration.

resultsOur method demonstrated promising results in the detection and segmentation of SRMs. All patients in the GE test set were correctly detected in at least one sequence. Our model achieved a median DSC of 0.769-0.855 across five MRI sequences and demonstrated reasonable generalization to non-GE scanners (median DSC range: 0.523-0.785).

conclusionsThe implementation of automated segmentation achieved encouraging outcomes in both correct-detection rates and segmentation accuracy across a diverse cohort spanning multiple centers and scanners, suggesting its potential as a key component of future diagnostic pipelines for SRMs.

Indexed as

Artificial IntelligenceImage Interpretation, Computer-AssistedKidney NeoplasmsMagnetic Resonance ImagingAdultAgedDeep LearningFemaleHumansMaleMiddle AgedRetrospective StudiesDeep learningMulti-centerMulti-sequence MRISegmentationSmall renal mass

Identifiers

PMID41171407

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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.