A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors

Overview

Journal Sci Data

Date 2025 Jan 17

PMID 39820508

Authors

Shunhan Yao

Yuanxiang Huang

Xiaoyu Wang

Yiwen Zhang

Ian Costa Paixao

Zhikang Wang

Charla Lu Chai

Hongtao Wang

Dinggui Lu

Geoffrey I Webb

Shanshan Li

Yuming Guo

Qingfeng Chen

Jiangning Song

Affiliations

Soon will be listed here.

Abstract

Primary malignant bone tumors are the third highest cause of cancer-related mortality among patients under the age of 20. X-ray scan is the primary tool for detecting bone tumors. However, due to the varying morphologies of bone tumors, it is challenging for radiologists to make a definitive diagnosis based on radiographs. With the recent advancement in deep learning algorithms, there is a surge of interest in computer-aided diagnosis of primary bone tumors. Nonetheless, the development in this field has been hindered by the lack of publicly available X-ray datasets for bone tumors. To tackle this challenge, we established the Bone Tumor X-ray Radiograph dataset (termed BTXRD) in collaboration with multiple medical institutes and hospitals. The BTXRD dataset comprises 3,746 bone images (1,879 normal and 1,867 tumor), with clinical information and global labels available for each image, and distinct mask and annotated bounding box for each tumor instance. This publicly available dataset can support the development and evaluation of deep learning algorithms for the diagnosis of primary bone tumors.

References

Park C, Oh S, Kim K, Jang M, Kim I, Lee Y . Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation. PLoS One. 2022; 17(2):e0264140. PMC: 8870496. DOI: 10.1371/journal.pone.0264140. View

Breden S, Hinterwimmer F, Consalvo S, Neumann J, Knebel C, von Eisenhart-Rothe R . Deep Learning-Based Detection of Bone Tumors around the Knee in X-rays of Children. J Clin Med. 2023; 12(18). PMC: 10531620. DOI: 10.3390/jcm12185960. View

Anttila T, Aspinen S, Pierides G, Haapamaki V, Laitinen M, Ryhanen J . Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool-A Feasibility Study. J Clin Med. 2023; 12(22). PMC: 10672653. DOI: 10.3390/jcm12227129. View

Do N, Jung S, Yang H, Kim S . Multi-Level Seg-Unet Model with Global and Patch-Based X-ray Images for Knee Bone Tumor Detection. Diagnostics (Basel). 2021; 11(4). PMC: 8070216. DOI: 10.3390/diagnostics11040691. View

Maier-Hein L, Eisenmann M, Sarikaya D, Marz K, Collins T, Malpani A . Surgical data science - from concepts toward clinical translation. Med Image Anal. 2021; 76:102306. PMC: 9135051. DOI: 10.1016/j.media.2021.102306. View

Chen P, Wu T, Wang P, Chang D, Liu K, Wu M . Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study. Radiology. 2022; 306(1):172-182. DOI: 10.1148/radiol.220152. View

Liu R, Pan D, Xu Y, Zeng H, He Z, Lin J . A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors. Eur Radiol. 2021; 32(2):1371-1383. DOI: 10.1007/s00330-021-08195-z. View

Li J, Li S, Li X, Miao S, Dong C, Gao C . Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model. Eur Radiol. 2022; 33(6):4237-4248. DOI: 10.1007/s00330-022-09289-y. View

Abedeen I, Rahman M, Prottyasha F, Ahmed T, Chowdhury T, Shatabda S . FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs. Sci Data. 2023; 10(1):521. PMC: 10404222. DOI: 10.1038/s41597-023-02432-4. View

10.

Yu D, Hu J, Feng Z, Song M, Zhu H . Deep learning based diagnosis for cysts and tumors of jaw with massive healthy samples. Sci Rep. 2022; 12(1):1855. PMC: 8814152. DOI: 10.1038/s41598-022-05913-5. View

11.

Shao J, Lin H, Ding L, Li B, Xu D, Sun Y . Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study. Insights Imaging. 2024; 15(1):35. PMC: 10847082. DOI: 10.1186/s13244-024-01610-1. View

12.

Meng Y, Yang Y, Hu M, Zhang Z, Zhou X . Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application. Semin Cancer Biol. 2023; 95:75-87. DOI: 10.1016/j.semcancer.2023.07.003. View

13.

Hinterwimmer F, Smits Serena R, Wilhelm N, Breden S, Consalvo S, Seidl F . Recommender-based bone tumour classification with radiographs-a link to the past. Eur Radiol. 2024; 34(10):6629-6638. PMC: 11399296. DOI: 10.1007/s00330-024-10672-0. View

14.

Xu Z, Niu K, Tang S, Song T, Rong Y, Guo W . Bone tumor necrosis rate detection in few-shot X-rays based on deep learning. Comput Med Imaging Graph. 2022; 102:102141. DOI: 10.1016/j.compmedimag.2022.102141. View

15.

Ying H, Liu X, Zhang M, Ren Y, Zhen S, Wang X . A multicenter clinical AI system study for detection and diagnosis of focal liver lesions. Nat Commun. 2024; 15(1):1131. PMC: 10850133. DOI: 10.1038/s41467-024-45325-9. View

16.

Consalvo S, Hinterwimmer F, Neumann J, Steinborn M, Salzmann M, Seidl F . Two-Phase Deep Learning Algorithm for Detection and Differentiation of Ewing Sarcoma and Acute Osteomyelitis in Paediatric Radiographs. Anticancer Res. 2022; 42(9):4371-4380. DOI: 10.21873/anticanres.15937. View

17.

Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A . Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol. 2021; 19(2):132-146. PMC: 9034765. DOI: 10.1038/s41571-021-00560-7. View

18.

Gianferante D, Mirabello L, Savage S . Germline and somatic genetics of osteosarcoma - connecting aetiology, biology and therapy. Nat Rev Endocrinol. 2017; 13(8):480-491. DOI: 10.1038/nrendo.2017.16. View

19.

Lacroix M, Aouad T, Feydy J, Biau D, Larousserie F, Fournier L . Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications. Diagn Interv Imaging. 2022; 104(1):18-23. DOI: 10.1016/j.diii.2022.10.004. View

20.

von Schacky C, Wilhelm N, Schafer V, Leonhardt Y, Gassert F, Foreman S . Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs. Radiology. 2021; 301(2):398-406. DOI: 10.1148/radiol.2021204531. View