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KRASFormer: a fully vision transformer-based framework for predicting KRAS gene mutations in histopathological images of colorectal cancer.
- Source :
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Biomedical physics & engineering express [Biomed Phys Eng Express] 2024 Jul 17; Vol. 10 (5). Date of Electronic Publication: 2024 Jul 17. - Publication Year :
- 2024
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Abstract
- Detecting the Kirsten Rat Sarcoma Virus ( KRAS ) gene mutation is significant for colorectal cancer (CRC) patients. The KRAS gene encodes a protein involved in the epidermal growth factor receptor (EGFR) signaling pathway, and mutations in this gene can negatively impact the use of monoclonal antibodies in anti-EGFR therapy and affect treatment decisions. Currently, commonly used methods like next-generation sequencing (NGS) identify KRAS mutations but are expensive, time-consuming, and may not be suitable for every cancer patient sample. To address these challenges, we have developed KRASFormer , a novel framework that predicts KRAS gene mutations from Haematoxylin and Eosin (H & E) stained WSIs that are widely available for most CRC patients. KRASFormer consists of two stages: the first stage filters out non-tumor regions and selects only tumour cells using a quality screening mechanism, and the second stage predicts the KRAS gene either wildtype' or mutant' using a Vision Transformer-based XCiT method. The XCiT employs cross-covariance attention to capture clinically meaningful long-range representations of textural patterns in tumour tissue and KRAS mutant cells. We evaluated the performance of the first stage using an independent CRC-5000 dataset, and the second stage included both The Cancer Genome Atlas colon and rectal cancer (TCGA-CRC-DX) and in-house cohorts. The results of our experiments showed that the XCiT outperformed existing state-of-the-art methods, achieving AUCs for ROC curves of 0.691 and 0.653 on TCGA-CRC-DX and in-house datasets, respectively. Our findings emphasize three key consequences: the potential of using H & E-stained tissue slide images for predicting KRAS gene mutations as a cost-effective and time-efficient means for guiding treatment choice with CRC patients; the increase in performance metrics of a Transformer-based model; and the value of the collaboration between pathologists and data scientists in deriving a morphologically meaningful model.<br /> (Creative Commons Attribution license.)
Details
- Language :
- English
- ISSN :
- 2057-1976
- Volume :
- 10
- Issue :
- 5
- Database :
- MEDLINE
- Journal :
- Biomedical physics & engineering express
- Publication Type :
- Academic Journal
- Accession number :
- 38925106
- Full Text :
- https://doi.org/10.1088/2057-1976/ad5bed