Breast cancer pre-clinical screening using infrared thermography and artificial intelligence : a prospective, multicentre, diagnostic accuracy cohort study
Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc..
BACKGROUND: Given the limited access to breast cancer (BC) screening, the authors developed and validated a mobile phone-artificial intelligence-based infrared thermography (AI-IRT) system for BC screening.
MATERIALS AND METHODS: This large prospective clinical trial assessed the diagnostic performance of the AI-IRT system. The authors constructed two datasets and two models, performed internal and external validation, and compared the diagnostic accuracy of the AI models and clinicians. Dataset A included 2100 patients recruited from 19 medical centres in nine regions of China. Dataset B was used for independent external validation and included 102 patients recruited from Langfang People's Hospital.
RESULTS: The area under the receiver operating characteristic curve of the binary model for identifying low-risk and intermediate/high-risk patients was 0.9487 (95% CI: 0.9231-0.9744) internally and 0.9120 (95% CI: 0.8460-0.9790) externally. The accuracy of the binary model was higher than that of human readers (0.8627 vs. 0.8088, respectively). In addition, the binary model was better than the multinomial model and used different diagnostic thresholds based on BC risk to achieve specific goals.
CONCLUSIONS: The accuracy of AI-IRT was high across populations with different demographic characteristics and less reliant on manual interpretations, demonstrating that this model can improve pre-clinical screening and increase screening rates.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:109 |
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| Enthalten in: |
International journal of surgery (London, England) - 109(2023), 10 vom: 01. Okt., Seite 3021-3031 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wang, Xuefei [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 27.10.2023 Date Revised 12.12.2023 published: Electronic ClinicalTrials.gov: NCT04761211 Citation Status MEDLINE |
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| doi: |
10.1097/JS9.0000000000000594 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM361759010 |
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| 100 | 1 | |a Wang, Xuefei |e verfasserin |4 aut | |
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| 500 | |a Date Completed 27.10.2023 | ||
| 500 | |a Date Revised 12.12.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a ClinicalTrials.gov: NCT04761211 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. | ||
| 520 | |a BACKGROUND: Given the limited access to breast cancer (BC) screening, the authors developed and validated a mobile phone-artificial intelligence-based infrared thermography (AI-IRT) system for BC screening | ||
| 520 | |a MATERIALS AND METHODS: This large prospective clinical trial assessed the diagnostic performance of the AI-IRT system. The authors constructed two datasets and two models, performed internal and external validation, and compared the diagnostic accuracy of the AI models and clinicians. Dataset A included 2100 patients recruited from 19 medical centres in nine regions of China. Dataset B was used for independent external validation and included 102 patients recruited from Langfang People's Hospital | ||
| 520 | |a RESULTS: The area under the receiver operating characteristic curve of the binary model for identifying low-risk and intermediate/high-risk patients was 0.9487 (95% CI: 0.9231-0.9744) internally and 0.9120 (95% CI: 0.8460-0.9790) externally. The accuracy of the binary model was higher than that of human readers (0.8627 vs. 0.8088, respectively). In addition, the binary model was better than the multinomial model and used different diagnostic thresholds based on BC risk to achieve specific goals | ||
| 520 | |a CONCLUSIONS: The accuracy of AI-IRT was high across populations with different demographic characteristics and less reliant on manual interpretations, demonstrating that this model can improve pre-clinical screening and increase screening rates | ||
| 650 | 4 | |a Clinical Trial | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Multicenter Study | |
| 700 | 1 | |a Chou, Kuanyu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Guochao |e verfasserin |4 aut | |
| 700 | 1 | |a Zuo, Zhichao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Yidong |e verfasserin |4 aut | |
| 700 | 1 | |a Mao, Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Songjie |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiaohui |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xuejing |e verfasserin |4 aut | |
| 700 | 1 | |a Zhong, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Qin, Xue |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Hailin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiaojie |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Yao |e verfasserin |4 aut | |
| 700 | 1 | |a Yi, Qianchuan |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Cunli |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Dongdong |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Mengwen |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Xiaoying |e verfasserin |4 aut | |
| 700 | 1 | |a Tao, Jiangtao |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Qiang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhai, Jidong |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Likun |e verfasserin |4 aut | |
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