Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics
Abstract Background To develop and validate a risk prediction nomogram based on a deep learning convolutional neural networks (CNN) model and epidemiological characteristics for lung cancer screening in patients with small pulmonary nodules (SPN). Methods This study included three data sets. First,...
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Wiley
2021
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oai:doaj.org-article:310cf8f699e347778a36609f02e60ae32021-12-02T02:34:55ZPredictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics1759-77141759-770610.1111/1759-7714.14140https://doaj.org/article/310cf8f699e347778a36609f02e60ae32021-12-01T00:00:00Zhttps://doi.org/10.1111/1759-7714.14140https://doaj.org/toc/1759-7706https://doaj.org/toc/1759-7714Abstract Background To develop and validate a risk prediction nomogram based on a deep learning convolutional neural networks (CNN) model and epidemiological characteristics for lung cancer screening in patients with small pulmonary nodules (SPN). Methods This study included three data sets. First, a CNN model was developed and tested on data set 1. Then, a hybrid prediction model was developed on data set 2 by multivariable binary logistic regression analysis. We combined the CNN model score and the selected epidemiological risk factors, and a risk prediction nomogram was presented. An independent multicenter cohort was used for model external validation. The performance of the nomogram was assessed with respect to its calibration and discrimination. Results The final hybrid model included the CNN model score and the screened risk factors included age, gender, smoking status and family history of cancer. The nomogram showed good discrimination and calibration with an area under the curve (AUC) of 91.6% (95% CI: 89.4%–93.5%), compare with the CNN model, the improvement was significance. The performance of the nomogram still showed good discrimination and good calibration in the multicenter validation cohort, with an AUC of 88.3% (95% CI: 83.1%–92.3%). Conclusions Our study showed that epidemiological characteristics should be considered in lung cancer screening, which can significantly improve the efficiency of the artificial intelligence (AI) model alone. We combined the CNN model score with Asian lung cancer epidemiological characteristics to develop a new nomogram to facilitate and accurately perform individualized lung cancer screening, especially for Asians.Dahai LiuXiao SunAo LiuLun LiShaoke LiJinmiao LiXiaojun LiuYu YangZhe WuXiaoliang LengYang WoZhangfeng HuangWenhao SuWenxing DuTianxiang YuanWenjie JiaoWileyarticleartificial intelligenceAsians lung cancer screeningconvolutional neural networksepidemiological characteristicsnomogramNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENThoracic Cancer, Vol 12, Iss 23, Pp 3130-3140 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
artificial intelligence Asians lung cancer screening convolutional neural networks epidemiological characteristics nomogram Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 |
| spellingShingle |
artificial intelligence Asians lung cancer screening convolutional neural networks epidemiological characteristics nomogram Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Dahai Liu Xiao Sun Ao Liu Lun Li Shaoke Li Jinmiao Li Xiaojun Liu Yu Yang Zhe Wu Xiaoliang Leng Yang Wo Zhangfeng Huang Wenhao Su Wenxing Du Tianxiang Yuan Wenjie Jiao Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics |
| description |
Abstract Background To develop and validate a risk prediction nomogram based on a deep learning convolutional neural networks (CNN) model and epidemiological characteristics for lung cancer screening in patients with small pulmonary nodules (SPN). Methods This study included three data sets. First, a CNN model was developed and tested on data set 1. Then, a hybrid prediction model was developed on data set 2 by multivariable binary logistic regression analysis. We combined the CNN model score and the selected epidemiological risk factors, and a risk prediction nomogram was presented. An independent multicenter cohort was used for model external validation. The performance of the nomogram was assessed with respect to its calibration and discrimination. Results The final hybrid model included the CNN model score and the screened risk factors included age, gender, smoking status and family history of cancer. The nomogram showed good discrimination and calibration with an area under the curve (AUC) of 91.6% (95% CI: 89.4%–93.5%), compare with the CNN model, the improvement was significance. The performance of the nomogram still showed good discrimination and good calibration in the multicenter validation cohort, with an AUC of 88.3% (95% CI: 83.1%–92.3%). Conclusions Our study showed that epidemiological characteristics should be considered in lung cancer screening, which can significantly improve the efficiency of the artificial intelligence (AI) model alone. We combined the CNN model score with Asian lung cancer epidemiological characteristics to develop a new nomogram to facilitate and accurately perform individualized lung cancer screening, especially for Asians. |
| format |
article |
| author |
Dahai Liu Xiao Sun Ao Liu Lun Li Shaoke Li Jinmiao Li Xiaojun Liu Yu Yang Zhe Wu Xiaoliang Leng Yang Wo Zhangfeng Huang Wenhao Su Wenxing Du Tianxiang Yuan Wenjie Jiao |
| author_facet |
Dahai Liu Xiao Sun Ao Liu Lun Li Shaoke Li Jinmiao Li Xiaojun Liu Yu Yang Zhe Wu Xiaoliang Leng Yang Wo Zhangfeng Huang Wenhao Su Wenxing Du Tianxiang Yuan Wenjie Jiao |
| author_sort |
Dahai Liu |
| title |
Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics |
| title_short |
Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics |
| title_full |
Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics |
| title_fullStr |
Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics |
| title_full_unstemmed |
Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics |
| title_sort |
predictive value of a novel asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics |
| publisher |
Wiley |
| publishDate |
2021 |
| url |
https://doaj.org/article/310cf8f699e347778a36609f02e60ae3 |
| work_keys_str_mv |
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