Comparison and optimization of deep learning-based radiosensitivity prediction models using gene expression profiling in National Cancer Institute-60 cancer cell line

• Published in: Nuclear engineering and technology Vol. 54; no. 8; pp. 3027 - 3033
• Main Authors: Kim, Euidam, Chung, Yoonsun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2022; Elsevier; 한국원자력학회
• Subjects: Deep learning; Gene expression; Model comparison; Prediction; Radiosensitivity; Survival fraction at 2Gy; 원자력공학; Deep learning; Model comparison; Radiosensitivity; Gene expression; Survival fraction at 2Gy; Prediction
• ISSN: 1738-5733; 2234-358X
• DOI: 10.1016/j.net.2022.03.019

In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.