Artificial intelligence in skeletal metastasis imaging

被引:11
作者
Dong, Xiying [1 ,2 ,3 ,4 ]
Chen, Guilin [1 ,2 ,6 ]
Zhu, Yuanpeng [1 ,2 ,6 ]
Ma, Boyuan [8 ]
Ban, Xiaojuan [8 ]
Wu, Nan [1 ,2 ,7 ]
Ming, Yue [5 ]
机构
[1] Peking Union Med Coll & Chinese Acad Med Sci, Peking Union Med Coll Hosp, Dept Orthoped Surg, State Key Lab Complex Severe & Rare Dis, Beijing 100730, Peoples R China
[2] Chinese Acad Med Sci, Key Lab Big Data Spinal Deform, Beijing 100730, Peoples R China
[3] Peking Union Med Coll & Chinese Acad Med Sci, Peking Union Med Coll Hosp, Beijing, Peoples R China
[4] Chinese Acad Med Sci & Peking Union Med Coll, Canc Hosp, Dept Urol, Natl Clin Res Ctr Canc,Natl Canc Ctr, Beijing 100021, Peoples R China
[5] Chinese Acad Med Sci & Peking Union Med Coll, PET CT Ctr, Natl Canc Ctr, Natl Clin Res Ctr Canc,Dept Nucl Med,Canc Hosp, Beijing 100021, Peoples R China
[6] Peking Union Med Coll, Grad Sch, Beijing 100730, Peoples R China
[7] Beijing Key Lab Genet Res Skeletal Deform, Beijing 100730, Peoples R China
[8] Univ Sci & Technol Beijing, Sch Intelligence Sci & Technol, Beijing, Peoples R China
关键词
Artificial intelligence; Deep learning; Bone metastasis; Medical imaging; BONE METASTASES; CLINICAL-FEATURES; PROSTATE-CANCER; DISEASE; PATHOPHYSIOLOGY; RADIOMICS; SURVIVAL;
D O I
10.1016/j.csbj.2023.11.007
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
In the field of metastatic skeletal oncology imaging, the role of artificial intelligence (AI) is becoming more prominent. Bone metastasis typically indicates the terminal stage of various malignant neoplasms. Once identified, it necessitates a comprehensive revision of the initial treatment regime, and palliative care is often the only resort. Given the gravity of the condition, the diagnosis of bone metastasis should be approached with utmost caution. AI techniques are being evaluated for their efficacy in a range of tasks within medical imaging, including object detection, disease classification, region segmentation, and prognosis prediction in medical imaging. These methods offer a standardized solution to the frequently subjective challenge of image interpretation.This subjectivity is most desirable in bone metastasis imaging. This review describes the basic imaging modalities of bone metastasis imaging, along with the recent developments and current applications of AI in the respective imaging studies. These concrete examples emphasize the importance of using computer-aided systems in the clinical setting. The review culminates with an examination of the current limitations and prospects of AI in the realm of bone metastasis imaging. To establish the credibility of AI in this domain, further research efforts are required to enhance the reproducibility and attain robust level of empirical support.
引用
收藏
页码:157 / 164
页数:8
相关论文
共 61 条
[1]   Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study [J].
Acar, Emine ;
Leblebici, Asim ;
Ellidokuz, Berat Ender ;
Basbinar, Yasemin ;
Kaya, Gamze Capa .
BRITISH JOURNAL OF RADIOLOGY, 2019, 92 (1101)
[2]   Machine learning and deep learning methods that use omics data for metastasis prediction [J].
Albaradei, Somayah ;
Thafar, Maha ;
Alsaedi, Asim ;
Van Neste, Christophe ;
Gojobori, Takashi ;
Essack, Magbubah ;
Gao, Xin .
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2021, 19 :5008-5018
[3]   The function of the vertebral veins and their role in the spread of metastases [J].
Batson, OV .
ANNALS OF SURGERY, 1940, 112 :138-149
[4]   Automated quantification of PET/CT skeletal tumor burden in prostate cancer using artificial intelligence: The PET index [J].
Belal, Sarah Lindgren ;
Larsson, Mans ;
Holm, Jorun ;
Buch-Olsen, Karen Middelbo ;
Soerensen, Jens ;
Bjartell, Anders ;
Edenbrandt, Lars ;
Tragardh, Elin .
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2023, 50 (05) :1510-1520
[5]   Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases [J].
Belal, Sarah Lindgren ;
Sadik, May ;
Kaboteh, Reza ;
Enqvist, Olof ;
Ulen, Johannes ;
Poulsen, Mads H. ;
Simonsen, Jane ;
Hoilund-Carlsen, Poul F. ;
Edenbrandt, Lars ;
Tragardh, Elin .
EUROPEAN JOURNAL OF RADIOLOGY, 2019, 113 :89-95
[6]   3D skeletal uptake of 18F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer [J].
Belal, Sarah Lindgren ;
Sadik, May ;
Kaboteh, Reza ;
Hasani, Nezar ;
Enqvist, Olof ;
Svarm, Linus ;
Kahl, Fredrik ;
Simonsen, Jane ;
Poulsen, Mads H. ;
Ohlsson, Mattias ;
Hoilund-Carlsen, Poul F. ;
Edenbrandt, Lars ;
Tragardh, Elin .
EJNMMI RESEARCH, 2017, 7
[7]  
Castelvecchi D, 2016, NATURE, V537, P20, DOI [10.1038/538020a, 10.1038/538020a]
[8]  
Cecchini MG., 2005, EAU Update Series, V3, P214, DOI [10.1016/j.euus. 2005.09.006, 10.1016/j.euus.2005.09.006, DOI 10.1016/J.EUUS.2005.09.006]
[9]  
Cicek Ozgun, 2016, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. 19th International Conference. Proceedings: LNCS 9901, P424, DOI 10.1007/978-3-319-46723-8_49
[10]   Metastatic bone disease: clinical features, pathophysiology and treatment strategies [J].
Coleman, RE .
CANCER TREATMENT REVIEWS, 2001, 27 (03) :165-176