Manuscript received December 1, 2022; revised March 20, 2023; accepted April 30, 2023.

Abstract—Non-small Cell Lung Cancer (NSCLC) is one of the malignant tumors with the highest morbidity and mortality. The postoperative recurrence rate in patients with NSCLC is high, which directly endangers the lives of patients. In recent years, many studies have used Computed Tomography (CT) images to predict NSCLC recurrence. Although this approach is inexpensive, it has low prediction accuracy. Gene expression data can achieve high accuracy. However, gene acquisition is expensive and invasive, and cannot meet the recurrence prediction requirements of all patients. In this study, a low-cost, high-accuracy residual multilayer perceptrons-based genotype-guided recurrence (ResMLP_GGR) prediction method is proposed that uses a gene estimation model to guide recurrence prediction. First, a gene estimation model is proposed to construct a mapping function of mixed features (handcrafted and deep features) and gene data to estimate the genetic information of tumor heterogeneity. Then, from gene estimation data obtained using a regression model, representations related to recurrence are learned to realize NSCLC recurrence prediction. In the testing phase, NSCLC recurrence prediction can be achieved with only CT images. The experimental results show that the proposed method has few parameters, strong generalization ability, and is suitable for small datasets. Compared with state-of-the-art methods, the proposed method significantly improves recurrence prediction accuracy by 3.39% with only 1% of parameters.

Keywords—deep learning, non-small cell lung cancer, prediction of recurrence, radiogenomics, residual multilayer perceptrons

Cite: Yang Ai, Yinhao Li, Yen-Wei Chen, Panyanat Aonpong, and Xianhua Han, "ResMLP_GGR: Residual Multilayer Perceptrons- Based Genotype-Guided Recurrence Prediction of Non-small Cell Lung Cancer," Journal of Image and Graphics, Vol. 11, No. 2, pp. 185-194, June 2023.

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