Evidence map›Paper›PMID 40897833›Full record

ArticleJournal of imaging informatics in medicine2026

Fusion of Deep Transfer Learning and Radiomics in MRI-Based Prediction of Post-Surgical Recurrence in Soft Tissue Sarcoma.

Yujian Wang, Tongyu Wang, Fei Zheng, Wenhan Hao, Qi Hao, Wenjia Zhang, Ping Yin, Nan Hong

Abstract read
In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Yujian WangDepartment of Radiology, Peking University People's Hospital, No.11, Xizhimen South Street, Xicheng District, Beijing, 100044, China.
Tongyu WangDepartment of Radiology, The Affiliated Hospital of Qingdao University, 266003, Qingdao, China.
Fei ZhengDepartment of Radiology, Peking University People's Hospital, No.11, Xizhimen South Street, Xicheng District, Beijing, 100044, China.
Wenhan HaoDepartment of Radiology, Peking University People's Hospital, No.11, Xizhimen South Street, Xicheng District, Beijing, 100044, China.
Qi HaoDepartment of Radiology, Peking University People's Hospital, No.11, Xizhimen South Street, Xicheng District, Beijing, 100044, China.
Wenjia ZhangDepartment of Radiology, Peking University People's Hospital, No.11, Xizhimen South Street, Xicheng District, Beijing, 100044, China.
Ping YinDepartment of Radiology, Peking University People's Hospital, No.11, Xizhimen South Street, Xicheng District, Beijing, 100044, China.
Nan HongDepartment of Radiology, Peking University People's Hospital, No.11, Xizhimen South Street, Xicheng District, Beijing, 100044, China. hongnan1968@163.com.

Funding

Innovative Research Group Project of the National Natural Science Foundation of China 82471950Natural Science Foundation of Beijing Municipality L242061
6 · The paper itself

Abstract

Soft  tissue sarcomas (STS) are heterogeneous malignancies with high recurrence rates (33-39%) post-surgery, necessitating improved prognostic tools. This study proposes a fusion model integrating deep transfer learning and radiomics from MRI to predict postoperative STS recurrence. Axial T2-weighted fat-suppressed imaging (T

Indexed as

Deep LearningImage Interpretation, Computer-AssistedMagnetic Resonance ImagingNeoplasm Recurrence, LocalRadiomicsSarcomaAdultFemaleHumansMalePredictive Learning ModelsPredictive Value of TestsRetrospective StudiesTransfer Machine LearningDeep learningMRIRadiomicsRecurrenceSoft tissue sarcoma

Identifiers

PMID40897833
PMCPMC13230404

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.