Evidence map›Paper›PMID 39915321›Full record

ArticleEuropean radiology2025

Soft-tissue sarcoma: modified grading method improves the accuracy of preoperative MRI in predicting patient outcomes.

Xiangwen Li, Yiwen Hu, Yuxue Xie, Bijuan Huang, Shuang Chen, Hongyue Tao

Abstract read
PubMed Publisher
In one paragraph

Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

6 authors.

Xiangwen Li *Department of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, Shanghai, China.
Yiwen Hu *Department of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, Shanghai, China.
Yuxue XieDepartment of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, Shanghai, China.
Bijuan HuangDepartment of Radiology, Fuzhou Second Hospital, Fuzhou, China.
Shuang ChenDepartment of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, Shanghai, China.
Hongyue TaoDepartment of Radiology and Institute of Medical Functional and Molecular Imaging, Huashan Hospital, Fudan University, Shanghai, China. taohongyue@126.com.ORCID http://orcid.org/0000-0002-4774-9401

Funding

National Natural Science Foundation of China 82171911National Natural Science Foundation of China 82271968Shanghai Municipal Health Commission 20244Y0122
6 · The paper itself

Abstract

objectiveTo explore whether incorporating the Ki-67 expression into the modified grading method improves the accuracy of preoperative MRI features in predicting patient outcomes.

methodsThis retrospective study analyzed STS patients treated from February 2013 to August 2022. Preoperative MR images, FNCLCC grades, Ki-67 expressions, and follow-up information were recorded. Grade II patients with high Ki-67 expression and all Grade III sarcoma patients were defined as the high-grade group. The clinical usefulness of the four grading methods was also compared. MR images were evaluated to extract 17 oncologic, intratumoral, and peritumoral features. High-grade relevant MRI features were identified by multivariate binary logistic regression. Kaplan-Meier and Cox risk regression methods were performed for prognostic analysis of high-grade relevant MRI feature combinations.

resultsOverall 207 STS patients were included (mean age ± standard deviation, 59.5 ± 13.2 years). According to the modified grading method, 99 patients were classified in the high-grade group. The modified grading method was superior to the two traditional grading methods in predicting patient outcomes. According to multivariate analysis, a tumor size greater than 10 cm (odds ratio (OR), 5.44), a necrosis volume greater than 50% (OR, 2.71), peritumoral enhancement (OR, 3.06), and a multilobulated configuration (OR, 3.27) were associated with high-grade STS. At least three of the four MRI features were associated with worse overall and metastasis-free survival.

conclusionThe modified grading system demonstrated improved reliability in managing sarcoma patients. The combination of preoperative MRI features may predict high-grade sarcoma and, potentially, poor patient survival and metastasis. KEY POINTS: Question Does an improved grading method that considers Ki-67 expression and FNCLCC grading correlate with MRI features and improve prognostication of soft-tissue sarcoma (STS)? Findings MRI features, including tumor size, necrosis volume, peritumoral enhancement, and multilobulated configuration, are associated with high-grade STS, overall survival, and metastasis-free survival. Clinical relevance The modified grading method improves the accuracy of predicting patients' OS and MFS compared to conventional grading methods. Radiologists can suggest STS patients with potentially poor clinical outcomes and guide personalized treatment through high-grade associated MRI features.

Indexed as

Magnetic Resonance ImagingSarcomaSoft Tissue NeoplasmsAdultAgedFemaleHumansKi-67 AntigenMaleMiddle AgedNeoplasm GradingPredictive Value of TestsPreoperative CarePrognosisRetrospective StudiesKi-67 AntigenMagnetic resonance imagingSarcomaTumor grading

Identifiers

PMID39915321

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.