Evidence map›Paper›PMID 40794221›Full record

ArticleInsights into imaging2025

Bone reporting and data system on CT (Bone-RADS-CT): a validation study by four readers on 328 cases from three local and two public databases.

Yue Xing, Defang Ding, Shun Dai, Yangfan Hu, Xianwei Liu, Liangjing Lyu, Guangcheng Zhang, Shiqi Mao, Qian Yin, Junjie Lu and 6 more

Abstract read
In one paragraph

Article in Insights into imaging, 2025. 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. RADS classification systems for bone tumors: current status and where do we go from here?Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    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

16 authors.

Yue Xing *Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Defang Ding *Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shun Dai *Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yangfan HuDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xianwei LiuDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Liangjing LyuDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Guangcheng ZhangDepartment of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shiqi MaoDepartment of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Qian YinDepartment of Pathology, Renhe Hospital, Shanghai, China.
Junjie LuDepartment of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA.
Jiarui YangDepartment of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.
Yang SongMR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, China.
Huan ZhangDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Chengzhou LiDepartment of Nuclear Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. lichengzhou005@163.com.
Weiwu YaoDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. yaoweiwuhuan@163.com.
Jingyu ZhongDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. wal_zjy@163.com.ORCID http://orcid.org/0000-0002-9817-2294

Funding

Laboratory Open Fund of Key Technology and Materials in Minimally Invasive Spine Surgery 2024JZWC-YBA07Laboratory Open Fund of Key Technology and Materials in Minimally Invasive Spine Surgery 2024JZWC-ZDA03National Natural Science Foundation of China 82271934National Natural Science Foundation of China 82302183National Natural Science Foundation of China 82471935Research Found of Health Commission of Changing District, Shanghai Municipality 2023QN01Research Found of Health Commission of Shanghai Municipality 20244Y0214Research Found of Science and Technology Commission of Changing District, Shanghai Municipality CNKW2024Y07Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRGG202101Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRKYRC-XX202204Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRYJ2021JC06Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRYXJH18Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine TRYXJH28Yangfan Project of Science and Technology Commission of Shanghai Municipality 22YF1442400
6 · The paper itself

Abstract

objectiveTo evaluate the reproducibility and effectiveness of the bone reporting and data system on CT (Bone-RADS-CT) for incidental solitary bone lesions in adults. MATERIALS AND

methodsWe retrospectively included 328 CT cases from three local and two public databases, respectively. All the cases were histopathologically or clinically confirmed bone lesions, "do not touch" lesions with typical appearance, and remained stable for at least 2 years. Each lesion with gender, age, and clinical history was categorized according to the Bone-RADS algorithm by two musculoskeletal radiologists and two non-musculoskeletal radiologists. The Bone-RADS categories were as follows: Bone-RADS-1, likely benign, leave alone; Bone-RADS-2, incomplete assessed on imaging, perform different imaging modality; Bone-RADS-3, intermediate, perform follow-up imaging; Bone-RADS-4, suspicious for malignancy or need for treatment, biopsy and/or oncologic referral. Inter-reader agreement was evaluated. The diagnostic performance of the Bone-RADS-CT for distinguishing positive cases (intermediate or malignant lesions or osteomyelitis) from negative cases (benign lesions), were measured, using histopathology results, clinical diagnosis, or follow-up as a standard reference.

resultsThere were 223 positive cases and 105 negative cases, respectively. The overall inter-reader agreement between two musculoskeletal and two non-musculoskeletal radiologists were both moderate (weighted kappa 0.553 and 0.403). The diagnostic performance for identifying intermediate or malignant lesions or osteomyelitis ranged according to radiologists with sensitivities of 88.8% to 94.6%, specificities of 42.9% to 71.1%, and accuracies of 78.0% to 86.6%.

conclusionBone-RADS-CT is effective for identifying bone lesions that need further treatment, but is only moderately reliable for readers of different specialties and experience. CRITICAL RELEVANCE STATEMENT: Bone-RADS-CT has been demonstrated to be a reliable algorithm for non-musculoskeletal radiologists and an effective tool for identifying the "need for treatment" incidental solitary bone lesions in adults, but still needs improvement in the rating method and category definition. KEY POINTS: Bone-RADS-CT has been demonstrated to be reliable and accurate when rated by musculoskeletal radiologists. Bone-RADS-CT achieved moderate agreement for musculoskeletal and non-musculoskeletal radiologists. Bone-RADS-CT presented high sensitivities but low specificities for identifying "need for treatment" bone lesions.

Indexed as

Bone neoplasmsClinical decision-makingMultidetector computed tomographyReproducibility of results

Identifiers

PMID40794221
PMCPMC12344047

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.