Predicting Lung Cancers Using Epidemiological Data: A Generative-Discriminative Framework

Jinpeng Li; Yaling Tao; Ting Cai

doi:10.1109/JAS.2021.1003910

Volume 8 Issue 5

May 2021

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(5): 1067-1078

J. P. Li, Y. L. Tao, and T. Cai, "Predicting Lung Cancers Using Epidemiological Data: A Generative-Discriminative Framework," IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 1067-1078, May. 2021. doi: 10.1109/JAS.2021.1003910

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J. P. Li, Y. L. Tao, and T. Cai, "Predicting Lung Cancers Using Epidemiological Data: A Generative-Discriminative Framework," IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 1067-1078, May. 2021. doi: 10.1109/JAS.2021.1003910

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Predicting Lung Cancers Using Epidemiological Data: A Generative-Discriminative Framework

doi: 10.1109/JAS.2021.1003910

Jinpeng Li^{1, 2
,},
Yaling Tao^1
,,
Ting Cai^{1, 2
,
,}

1.
HwaMei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, China
2.
Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315010, China

Funds: This work was supported in part by Zhejiang Provincial Natural Science Foundation of China (LQ20F030013), Research Foundation of HwaMei Hospital, University of Chinese Academy of Sciences (2020HMZD22), Ningbo Public Service Technology Foundation (202002N3181), and Medical Scientific Research Foundation of Zhejiang Province (2021431314)

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Abstract

Abstract

Predictive models for assessing the risk of developing lung cancers can help identify high-risk individuals with the aim of recommending further screening and early intervention. To facilitate pre-hospital self-assessments, some studies have exploited predictive models trained on non-clinical data (e.g., smoking status and family history). The performance of these models is limited due to not considering clinical data (e.g., blood test and medical imaging results). Deep learning has shown the potential in processing complex data that combine both clinical and non-clinical information. However, predicting lung cancers remains difficult due to the severe lack of positive samples among follow-ups. To tackle this problem, this paper presents a generative-discriminative framework for improving the ability of deep learning models to generalize. According to the proposed framework, two nonlinear generative models, one based on the generative adversarial network and another on the variational autoencoder, are used to synthesize auxiliary positive samples for the training set. Then, several discriminative models, including a deep neural network (DNN), are used to assess the lung cancer risk based on a comprehensive list of risk factors. The framework was evaluated on over 55 000 subjects questioned between January 2014 and December 2017, with 699 subjects being clinically diagnosed with lung cancer between January 2014 and August 2019. According to the results, the best performing predictive model built using the proposed framework was based on DNN. It achieved an average sensitivity of 76.54% and an area under the curve of 69.24% in distinguishing between the cases of lung cancer and normal cases on test sets.
- Cancer prevention,
- discriminative model,
- generative model,
- lung cancer,
- machine learning

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References

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沈阳化工大学材料科学与工程学院沈阳 110142

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A deep learning model is constructed to assess the risk of developing lung cancers for the Chinese people aged between 40 and 74.
Basic information, diet habits, living conditions, psychology and emotion, medical history and family cancer history are used to facilitate the pre-hospital self-assessment.
A generative adversarial network is used to synthesize auxiliary positive samples, and a discriminative model is used to predict the lung cancer.
An average sensitivity of 76.54% is achieved in distinguishing between the cases of lung cancer and normal cases on test sets.

Predicting Lung Cancers Using Epidemiological Data: A Generative-Discriminative Framework

doi: 10.1109/JAS.2021.1003910

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