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Article

Survival Prediction of Lung Cancer Using Small-Size Clinical Data with a Multiple Task Variational Autoencoder

1
Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea
2
Department of Internal Medicine, Chonnam National University Medical School and Hwasun Hospital, Jeonnam 58128, Korea
3
Department of Nuclear Medicine, Chonnam National University Medical School and Hwasun Hospital, Jeonnam 58128, Korea
*
Authors to whom correspondence should be addressed.
Academic Editors: Jian Sun and Chilukuri K. Mohan
Electronics 2021, 10(12), 1396; https://doi.org/10.3390/electronics10121396
Received: 23 March 2021 / Revised: 4 June 2021 / Accepted: 8 June 2021 / Published: 10 June 2021
Due to the increase of lung cancer globally, and particularly in Korea, survival analysis for this type of cancer has gained prominence in recent years. For this task, mathematical and traditional machine learning approaches are commonly used by medical doctors. While the deep learning approach has had proven success in computer vision tasks, natural language processing and other AI techniques are also adopted for this task. Due to the privacy issues and management process, data in medicine are difficult to collect, which leads to a paucity of samples. The small number of samples makes it difficult to use deep learning and renders this approach unusable. In this investigation, we propose a network architecture that combines a variational autoencoder (VAE) with the typical DNN architecture to solve the survival analysis task. With a training size of n = 4107, MVAESA achieves a C-index of 0.722 while CoxCC, CoxPH, and CoxTime achieved scores of 0.713, 0.703, and 0.710, respectively. With a small training size of n = 379, MVAESA achieves a C-index of 0.707, compared with 0.689, 0.688 and 0.690 for CoxCC, CoxPH, and CoxTime, respectively. The results show that the combination of a VAE with a target task makes the network more stable and that the network could be trained using a small-sized sample. View Full-Text
Keywords: survival analysis; lung cancer; variational autoencoder; multiple tasks; prognosis survival analysis; lung cancer; variational autoencoder; multiple tasks; prognosis
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MDPI and ACS Style

Vo, T.-H.; Lee, G.-S.; Yang, H.-J.; Oh, I.-J.; Kim, S.-H.; Kang, S.-R. Survival Prediction of Lung Cancer Using Small-Size Clinical Data with a Multiple Task Variational Autoencoder. Electronics 2021, 10, 1396. https://doi.org/10.3390/electronics10121396

AMA Style

Vo T-H, Lee G-S, Yang H-J, Oh I-J, Kim S-H, Kang S-R. Survival Prediction of Lung Cancer Using Small-Size Clinical Data with a Multiple Task Variational Autoencoder. Electronics. 2021; 10(12):1396. https://doi.org/10.3390/electronics10121396

Chicago/Turabian Style

Vo, Thanh-Hung; Lee, Guee-Sang; Yang, Hyung-Jeong; Oh, In-Jae; Kim, Soo-Hyung; Kang, Sae-Ryung. 2021. "Survival Prediction of Lung Cancer Using Small-Size Clinical Data with a Multiple Task Variational Autoencoder" Electronics 10, no. 12: 1396. https://doi.org/10.3390/electronics10121396

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