Vivek Kumar Singh, Yasmine Makhlouf, Md Mostafa Kamal Sarker, Stephanie Craig, Juvenal Baena, Christine Greene, Lee Mason, Jacqueline A James, Manuel Salto-Tellez, Paul O'Reilly, Perry Maxwell
{"title":"KRASFormer: a fully vision transformer-based framework for predicting<i>KRAS</i>gene mutations in histopathological images of colorectal cancer.","authors":"Vivek Kumar Singh, Yasmine Makhlouf, Md Mostafa Kamal Sarker, Stephanie Craig, Juvenal Baena, Christine Greene, Lee Mason, Jacqueline A James, Manuel Salto-Tellez, Paul O'Reilly, Perry Maxwell","doi":"10.1088/2057-1976/ad5bed","DOIUrl":null,"url":null,"abstract":"<p><p>Detecting the Kirsten Rat Sarcoma Virus (<i>KRAS</i>) gene mutation is significant for colorectal cancer (CRC) patients. The<i>KRAS</i>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<i>KRAS</i>mutations but are expensive, time-consuming, and may not be suitable for every cancer patient sample. To address these challenges, we have developed<i>KRASFormer</i>, a novel framework that predicts<i>KRAS</i>gene mutations from Haematoxylin and Eosin (H & E) stained WSIs that are widely available for most CRC patients.<i>KRASFormer</i>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<i>KRAS</i>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<i>KRAS</i>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<i>KRAS</i>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.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Physics & Engineering Express","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2057-1976/ad5bed","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Detecting the Kirsten Rat Sarcoma Virus (KRAS) gene mutation is significant for colorectal cancer (CRC) patients. TheKRASgene 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) identifyKRASmutations but are expensive, time-consuming, and may not be suitable for every cancer patient sample. To address these challenges, we have developedKRASFormer, a novel framework that predictsKRASgene mutations from Haematoxylin and Eosin (H & E) stained WSIs that are widely available for most CRC patients.KRASFormerconsists 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 theKRASgene 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 andKRASmutant 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 predictingKRASgene 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.
期刊介绍:
BPEX is an inclusive, international, multidisciplinary journal devoted to publishing new research on any application of physics and/or engineering in medicine and/or biology. Characterized by a broad geographical coverage and a fast-track peer-review process, relevant topics include all aspects of biophysics, medical physics and biomedical engineering. Papers that are almost entirely clinical or biological in their focus are not suitable. The journal has an emphasis on publishing interdisciplinary work and bringing research fields together, encompassing experimental, theoretical and computational work.