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June 10, 2024Journal of Magnetic Resonance Imaging

Parallel CNN‐Deep Learning Clinical‐Imaging Signature for Assessing Pathologic Grade and Prognosis of Soft Tissue Sarcoma Patients

Jia Guo Yiming Li Hongwei Guo Dapeng Hao Jing‐xu Xu Chencui Huang Hua‐wei Han Feng Hou Shifeng Yang Jianling Cui He‐xiang Wang

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Abstract

Traditional biopsies pose risks and may not accurately reflect soft tissue sarcoma (STS) heterogeneity. MRI provides a noninvasive, comprehensive alternative.

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Cite This Study

Guo et al. (2024) studied this question.

synapsesocial.com/papers/68e6566db6db6435875e5404 https://doi.org/10.1002/jmri.29474

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Integrating <scp>nnU</scp> ‐Net Segmentation and Clinical–Radiomics for Multicenter <scp>MRI</scp> ‐Based Assessment of Soft Tissue Sarcoma Grade and Ki‐67 ExpressionOriginal Research · 2026 · 1 citations
2Research on application of multiparametric MRI to predict FNCLCC grading and ki67 expression in soft tissue sarcoma biopsy pathology: Based on a CT-MRI fusion image registration method2025
3Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma2024 · 3 citations
4Correlation of magnetic resonance imaging morphological features and diffusion-weighted imaging to soft tissue sarcoma gradingcross-sectional · 2025 · 1 citations
5Performance of deep learning-based segmentation of soft tissue sarcoma by MRI sequence, tumor type and locationOriginal Research · 2026 · 2 citations

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Beyond Clinical Examination: Utilizing MRI Surveillance to Detect Recurrence of Soft Tissue Sarcomas and Differentiate from Posttherapeutic Changes2024
Concordance of histopathological and radiological grading in soft tissue sarcomas2022
Application of Magnetic Resonance Imaging and Computerized Tomography as an Adjunct to the Surgical Management of Soft Tissue Sarcomas1987

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