Evidence map›Paper›PMID 41862709›Full record

ReviewSkeletal radiology2026

Soft tissue reporting and data system (soft tissue-RADS): framework for radiologists specializing in imaging of musculoskeletal tumors and tumor-like lesions.

Avneesh Chhabra, Mark D Murphey, Mahad Rehman, Majid Chalian, Ty Subhawong, Shivani Ahlawat, Kambiz Motamedi, Jim S Wu, Behrang Amini, Gitanjali Bajaj and 8 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Skeletal radiology, 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

18 authors.

Avneesh ChhabraProfessor Radiology and Orthopedic Surgery, UTSW, Dallas, TX, USA. avneesh.chhabra@utsouthwestern.edu.ORCID http://orcid.org/0000-0002-0718-3802
Mark D MurpheyUniformed Services University of the Health Sciences, Bethesda, MD, USA.
Mahad RehmanUTSW, Dallas, TX, USA.
Majid ChalianMusculoskeletal Imaging and Intervention, Department of Radiology, University of Washington, Seattle, WA, USA.
Ty SubhawongMusculoskeletal Imaging, Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, USA.
Shivani AhlawatRussell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA.
Kambiz MotamediProfessor of Radiology and Orthopedic Surgery, Musculoskeletal Imaging and Intervention, Department of Radiology, University of California, Los Angeles, CA, USA.
Jim S WuMusculoskeletal Imaging and Intervention, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
Behrang AminiDepartment of Musculoskeletal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Gitanjali BajajUniversity of Arkansas for Medical Sciences, Little Rock, AR, USA.
Jonathan SametDepartment of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, USA.
Shyam RamachandranTexas A&M School of Medicine, College Station, TX, USA.
Madison HavranekMSK Radiology Division, UTSW, Dallas, TX, USA.
Yin XiDepartment of Radiology, UTSW, Dallas, TX, USA.
Julie Fanburg-SmithDepartment of Pathology, University of Virginia, Charlottesville, VA, USA.
Edwin ChoyDivision of Hematology Oncology, Mass General Brigham Cancer Institute, Boston, MA, USA.
Carol D MorrisOrthopedic Surgery Service, Department of Orthopedic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Hillary W GarnerMusculoskeletal Imaging, Department of Radiology, Mayo Clinic, Jacksonville, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soft tissue-RADS scoring system for musculoskeletal (MSK) tumor and tumor-like lesions is recently published. The system includes diagnostic MRI flowcharts and algorithms for lipomatous masses, cyst-like or high-water content masses, and indeterminate-solid appearing masses with consistent and standardized management recommendations. MSK radiology and cancer imaging training commonly provides the essential skills needed to render appropriate prospective diagnosis for many non-neoplastic tumor-like lesions and various commonly encountered sarcomas. Such trained readers may benefit from additional advanced algorithms to accurately identify and categorize different benign tumors and sarcomas and correctly input them in the soft tissue-RADS scoring system. These algorithms are built from a comprehensive literature search to establish an evidence-based composite and are supported by the expert opinions of the multidisciplinary ACR-soft tissue-RADS team. In this article we provide (1) a literature review of multimodality imaging, immunopathology, and clinical findings of soft tissue tumors and tumor-like lesions, presented in 7 tables (condensed from the 12 tables published in the WHO classification of soft tissue tumors) with respective table summaries; (2) a suggested modality-based lexicon; and (3) advanced diagnostic algorithms for trained readers based on published evidence and expert opinion that feed into the soft tissue-RADS scoring system (categories 1-6). The purpose of this work is to provide comprehensive guidance on soft tissue-RADS scoring to help streamline care for soft tissue tumors and tumor-like lesions and optimize patient outcomes.

Indexed as

Bone NeoplasmsMagnetic Resonance ImagingMuscle NeoplasmsRadiology Information SystemsSarcomaSoft Tissue NeoplasmsAlgorithmsDiagnosis, DifferentialHumansMRISarcomaSoft-tissue RADSSoft tissue tumor-like lesionsSoft tissue tumors

Identifiers

PMID41862709

What OpenQuestion holds

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Registered trials

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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.