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A segmentation-based sequence residual attention model for KRAS gene mutation status prediction in colorectal cancer

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Abstract

In the computer-aided diagnosis of colorectal cancer, accurate determination of kratom sarcoma (KRAS) gene mutation status is crucial to provide better treatment for patients. In recent years, deep learning methods have excelled in computer vision. However, in the KRAS gene mutation status prediction, deep learning methods are usually designed for only classification tasks, ignoring the potential facilitation of segmentation tasks for classification tasks. In this paper, we propose a Segmentation-based Sequence Residual Attention Model (SSRAM) that facilitates the execution of the classification task by transferring the captured helpful information and the generated lesion masks in the segmentation task to the classification task, which in turn predicts the KRAS gene mutation status of the patient. This model consists of a Pixel Gated Segmentation Network (PG-SN) and a Channel Guided Classification Network (CG-CN). Specifically, PG-SN captures the segmentation features of lesions at different levels and generates lesion masks to provide CG-CN with prior guidance. After obtaining the precise localisation information of lesions provided by PG-SN, CG-CN shares encoders and decoders with PG-SN. That enables CG-CN to acquire the necessary high-level semantic features to enrich the classification features for accurate KRAS gene mutation status prediction. Meanwhile, to better optimise our SSRAM, we design a new boundary loss and use it jointly with Combo loss in PG-SN to solve the problem that the lesion boundary pixels are difficult to be classified correctly. We evaluate the proposed SSRAM on the T2-weighted MRI datasets and achieve an accuracy of 87.5% and an AUC of 94.74% in the KRAS gene mutation status prediction, which is superior to the performance of current non-invasive methods for predicting KRAS gene mutation status in colorectal cancer. The results suggest that our SSRAM, which accomplishes the classification task by segmentation task facilitates classification task, can effectively improve the performance and effectiveness of the classification task, thereby better helping physicians diagnose the KRAS gene mutation status of patients. The code is publicly available.

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Authors and Affiliations

  1. College of Information and Computer, Taiyuan University of Technology, Taiyuan, China

    Lin Zhao, Kai Song, Meiling Cai, Yan Qiang & Juanjuan Zhao

  2. College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, China

    Yulan Ma

  3. College of Software, Taiyuan University of Technology, Taiyuan, China

    Jingyu Sun

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  1. Lin Zhao
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Correspondence to Juanjuan Zhao.

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Zhao, L., Song, K., Ma, Y. et al. A segmentation-based sequence residual attention model for KRAS gene mutation status prediction in colorectal cancer. Appl Intell 53, 10232–10254 (2023). https://doi.org/10.1007/s10489-022-04011-3

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