Enhancing Spinal Metastasis Detection and Feature Evaluation on Computed Tomography Scans Using Deep‐Learning Systems
Spinal metastases can result in pathological fractures, which reduce survival time and quality of life. Physician experience significantly influences the detection of spinal metastases and the evaluation of associated features. This study aims to develop a deep‐learning system (DLS) for automatic de...
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400956 |
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| author | Zhiyu Wang Guangyu Yao Shengyuan Xu Yifeng Gu Yujie Chang Jing Sun Jingyi Guo Shiqi Peng Bolin Lai Xiaoyun Zhang Chunbin Wang Haiying Jiang Surong Chen Yanfeng Wang Ya Zhang Yuehua Li Hui Zhao |
| author_facet | Zhiyu Wang Guangyu Yao Shengyuan Xu Yifeng Gu Yujie Chang Jing Sun Jingyi Guo Shiqi Peng Bolin Lai Xiaoyun Zhang Chunbin Wang Haiying Jiang Surong Chen Yanfeng Wang Ya Zhang Yuehua Li Hui Zhao |
| author_sort | Zhiyu Wang |
| collection | DOAJ |
| description | Spinal metastases can result in pathological fractures, which reduce survival time and quality of life. Physician experience significantly influences the detection of spinal metastases and the evaluation of associated features. This study aims to develop a deep‐learning system (DLS) for automatic detection of spinal metastasis and feature evaluation using computed tomography and to determine the impact of the DLS on physician performance in the detection and assessment of spinal metastasis. DLS assistance in a multireader, multicase test study results in higher sensitivity and specificity in spinal metastasis detection and feature evaluation (all p < 0.001). Additionally, resident physicians show a more significant improvement in sensitivity and specificity compared with attending or chief physicians in spinal metastasis detection and most feature evaluation (p < 0.01). In a cohort test study, resident oncologists assisted by the DLS achieve significantly higher sensitivity and specificity compared with those without assistance (all p < 0.01), except for the sensitivity of vertebral body collapse evaluation (p > 0.01). DLS assistance may improve physicians’ performance in the detection and evaluation of spinal metastases, particularly that of resident oncologists. |
| format | Article |
| id | doaj-art-e69b8ac7bea14ea7a2c1533de3095b0a |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-e69b8ac7bea14ea7a2c1533de3095b0a2025-08-21T11:05:47ZengWileyAdvanced Intelligent Systems2640-45672025-08-0178n/an/a10.1002/aisy.202400956Enhancing Spinal Metastasis Detection and Feature Evaluation on Computed Tomography Scans Using Deep‐Learning SystemsZhiyu Wang0Guangyu Yao1Shengyuan Xu2Yifeng Gu3Yujie Chang4Jing Sun5Jingyi Guo6Shiqi Peng7Bolin Lai8Xiaoyun Zhang9Chunbin Wang10Haiying Jiang11Surong Chen12Yanfeng Wang13Ya Zhang14Yuehua Li15Hui Zhao16Clinical Cancer Center for Metastatic Bone Diseases Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine 600 Yishan Rd. Shanghai 200233 ChinaDepartment of Cancer Medical Center Peking Union Medical College Hospital Chinese Academy of Medical Science & Peking Union Medical College Beijing 100005 ChinaMailman School of Public Health Columbia University New York 10027 NY USADepartment of Radiology Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine 600 Yishan Rd. Shanghai 200233 ChinaClinical Cancer Center for Metastatic Bone Diseases Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine 600 Yishan Rd. Shanghai 200233 ChinaClinical Cancer Center for Metastatic Bone Diseases Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine 600 Yishan Rd. Shanghai 200233 ChinaClinical Research Center Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai 2002337 ChinaCooperative Medianet Innovation Center Shanghai Jiao Tong University 800 Dongchuan Rd. Shanghai 201100 ChinaCooperative Medianet Innovation Center Shanghai Jiao Tong University 800 Dongchuan Rd. Shanghai 201100 ChinaCooperative Medianet Innovation Center Shanghai Jiao Tong University 800 Dongchuan Rd. Shanghai 201100 ChinaDepartment of Internal Oncology Yancheng Third People's Hospital Yanchen 224001 ChinaDepartment of Internal Oncology Xuzhou Third People's Hospital Xuzhou 221009 ChinaDepartment of Internal Oncology Yancheng First People's Hospital Yanchen 224000 ChinaCooperative Medianet Innovation Center Shanghai Jiao Tong University 800 Dongchuan Rd. Shanghai 201100 ChinaCooperative Medianet Innovation Center Shanghai Jiao Tong University 800 Dongchuan Rd. Shanghai 201100 ChinaDepartment of Radiology Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine 600 Yishan Rd. Shanghai 200233 ChinaClinical Cancer Center for Metastatic Bone Diseases Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine 600 Yishan Rd. Shanghai 200233 ChinaSpinal metastases can result in pathological fractures, which reduce survival time and quality of life. Physician experience significantly influences the detection of spinal metastases and the evaluation of associated features. This study aims to develop a deep‐learning system (DLS) for automatic detection of spinal metastasis and feature evaluation using computed tomography and to determine the impact of the DLS on physician performance in the detection and assessment of spinal metastasis. DLS assistance in a multireader, multicase test study results in higher sensitivity and specificity in spinal metastasis detection and feature evaluation (all p < 0.001). Additionally, resident physicians show a more significant improvement in sensitivity and specificity compared with attending or chief physicians in spinal metastasis detection and most feature evaluation (p < 0.01). In a cohort test study, resident oncologists assisted by the DLS achieve significantly higher sensitivity and specificity compared with those without assistance (all p < 0.01), except for the sensitivity of vertebral body collapse evaluation (p > 0.01). DLS assistance may improve physicians’ performance in the detection and evaluation of spinal metastases, particularly that of resident oncologists.https://doi.org/10.1002/aisy.202400956computed tomographies (CTs)deep‐learning systemsfeature evaluationsmultireaders multicasesspinal metastasis detections |
| spellingShingle | Zhiyu Wang Guangyu Yao Shengyuan Xu Yifeng Gu Yujie Chang Jing Sun Jingyi Guo Shiqi Peng Bolin Lai Xiaoyun Zhang Chunbin Wang Haiying Jiang Surong Chen Yanfeng Wang Ya Zhang Yuehua Li Hui Zhao Enhancing Spinal Metastasis Detection and Feature Evaluation on Computed Tomography Scans Using Deep‐Learning Systems Advanced Intelligent Systems computed tomographies (CTs) deep‐learning systems feature evaluations multireaders multicases spinal metastasis detections |
| title | Enhancing Spinal Metastasis Detection and Feature Evaluation on Computed Tomography Scans Using Deep‐Learning Systems |
| title_full | Enhancing Spinal Metastasis Detection and Feature Evaluation on Computed Tomography Scans Using Deep‐Learning Systems |
| title_fullStr | Enhancing Spinal Metastasis Detection and Feature Evaluation on Computed Tomography Scans Using Deep‐Learning Systems |
| title_full_unstemmed | Enhancing Spinal Metastasis Detection and Feature Evaluation on Computed Tomography Scans Using Deep‐Learning Systems |
| title_short | Enhancing Spinal Metastasis Detection and Feature Evaluation on Computed Tomography Scans Using Deep‐Learning Systems |
| title_sort | enhancing spinal metastasis detection and feature evaluation on computed tomography scans using deep learning systems |
| topic | computed tomographies (CTs) deep‐learning systems feature evaluations multireaders multicases spinal metastasis detections |
| url | https://doi.org/10.1002/aisy.202400956 |
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